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Funder: United Arab Emirates University (UAEU); doi: https://doi.org/10.13039/501100006013, 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.

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

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.

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.

Regeneration after injury occurs in axons that lie in the peripheral nervous system but fails in the central nervous system, thereby limiting functional recovery. Differences in axonal signalling in response to injury that might underpin this differential regenerative ability are poorly characterized. Combining axoplasmic proteomics from peripheral sciatic or central projecting dorsal root ganglion (DRG) axons with cell body RNA-seq, we uncover injury-dependent signalling pathways that are uniquely represented in peripheral versus central projecting sciatic DRG axons. We identify AMPK as a crucial regulator of axonal regenerative signalling that is specifically downregulated in injured peripheral, but not central, axons. We find that AMPK in DRG interacts with the 26S proteasome and its CaMKIIα-dependent regulatory subunit PSMC5 to promote AMPKα proteasomal degradation following sciatic axotomy. Conditional deletion of AMPKα1 promotes multiple regenerative signalling pathways after central axonal injury and stimulates robust axonal growth across the spinal cord injury site, suggesting inhibition of AMPK as a therapeutic strategy to enhance regeneration following spinal cord injury.

SummaryThe COVID-19 pandemic is an unprecedented global challenge. Highly variable in its presentation, spread and clinical outcome, novel point-of-care diagnostic classifiers are urgently required. Here, we describe a set of COVID-19 clinical classifiers discovered using a newly designed low-cost high-throughput mass spectrometry-based platform. Introducing a new sample preparation pipeline coupled with short-gradient high-flow liquid chromatography and mass spectrometry, our methodology facilitates clinical implementation and increases sample throughput and quantification precision. Providing a rapid assessment of serum or plasma samples at scale, we report 27 biomarkers that distinguish mild and severe forms of COVID-19, of which some may have potential as therapeutic targets. These proteins highlight the role of complement factors, the coagulation system, inflammation modulators as well as pro-inflammatory signalling upstream and downstream of Interleukin 6. Application of novel methodologies hence transforms proteomics from a research tool into a rapid-response, clinically actionable technology adaptable to infectious outbreaks.Highlights-A completely redesigned clinical proteomics platform increases throughput and precision while reducing costs.-27 biomarkers are differentially expressed between WHO severity grades for COVID-19.-The study highlights potential therapeutic targets that include complement factors, the coagulation system, inflammation modulators as well as pro-inflammatory signalling both upstream and downstream of interleukin 6.

Summary 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 standardisation and high-flow liquid chromatography to facilitate implementation in clinical laboratories. Our low-cost workflow quantifies 180 proteomes per day per mass spectrometer, 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 upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, our platform supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets., Graphical Abstract, Highlights - A standardized, ultra-highthroughput clinical platform for serum and plasma proteomics - Platform enables high precision quantification of 180 patient samples/day at low cost - 27 biomarkers are differentially expressed between WHO severity grades for COVID-19 - Biomarkers include proteins not previously associated with COVID-19 infection, Messner and Demichev et al. present a standardized, low cost, ultra-highthroughput platform for serum and plasma proteomics designed for clinical use and apply it to a cohort of hospitalized COVID-19 patients. They identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19.

Viral infections are often accompanied by the induction of autophagy as an intrinsic cellular defense mechanism. Herpesviruses have developed strategies to evade autophagic degradation and to manipulate autophagy of the host cells to their benefit. Here we addressed the role of macroautophagy/autophagy in human cytomegalovirus replication and for particle morphogenesis. We found that proteins of the autophagy machinery localize to cytoplasmic viral assembly compartments and enveloped virions in the cytoplasm. Surprisingly, the autophagy receptor SQSTM1/p62 was also found to colocalize with HCMV capsids in the nucleus of infected cells. This finding indicates that the autophagy machinery interacts with HCMV already at the early nuclear stages of particle morphogenesis. The membrane-bound form of LC3 and several autophagy receptors were packaged into extracellular HCMV virions. This suggested that autophagic membranes were included during secondary envelopment of HCMV virions. To further address the importance of autophagy in HCMV infection, we generated an HCMV mutant that expressed a dominant-negative version of the protease ATG4B (BAD-ATG4B(C74A)). The proteolytic activity of ATG4B is required for LC3 cleavage, priming it for membrane conjugation. Surprisingly, both genome replication and virus release were enhanced in cells infected with BAD-ATG4B(C74A), compared to control strains. These results show that autophagy operates as an antiviral process during HCMV infection but is dispensable for secondary HCMV particle envelopment. Abbreviations: ATG: autophagy-related; BAC: bacterial artificial chromosome; BECN1: beclin 1; CPE: cytopathic effect; cVACs: cytoplasmic viral assembly compartments; d.p.i.: days post-infection; DB: dense body; EBV: Epstein-Barr virus; galK: galactokinase; HCMV: human cytomegalovirus; HFF: human foreskin fibroblasts; IE: immediate-early; IRS: internal repeat short; LC3: MAP1LC3A/B; m.o.i.; multiplicity of infection; MCP: major capsid protein; Pp: phosphoprotein; sCP/UL48a: smallest capsid protein; TRS: terminal repeat short; UL: unique long; US: unique short

International audience; Recently discovered new chemical entities in RNA modifications have involved surprising functional groups that enlarge the chemical space of RNA. Using LC-MS, we found over 100 signals of RNA constituents that contained a ribose moiety in tRNAs from E. coli. Feeding experiments with variegated stable isotope labeled compounds identified 37 compounds that are new structures of RNA modifications. One structure was elucidated by deuterium exchange and high-resolution mass spectrometry. The structure of msms2 i6 A (2-methylthiomethylenethio-N6-isopentenyl-adenosine) was confirmed by methione-D3 feeding experiments and by synthesis of the nucleobase. The msms2 i6 A contains a thioacetal, shown in vitro to be biosynthetically derived from ms2 i6 A by the radical-SAM enzyme MiaB. This enzyme performs thiomethylation, forming ms2 i6 A from i6 A in a first turnover. The new thioacetal is formed by a second turnover. Along with the pool of 36 new modifications, this work describes a new layer of RNA modification chemistry.

Phytoplasmas are pathogenic bacteria within the class of Mollicutes, which are associated with more than 1000 plant diseases. In this study, we applied quantitative mass spectrometry to analyse affected pathways of the model plant tobacco (Nicotiana occidentalis) upon Candidatus Phytoplasma mali strain AT infection. Using tissue obtained from leaf midribs, 1466 plant-assigned proteins were identified. For 1019 of these proteins, we could reproducibly quantify the expression changes of infected versus noninfected plants, of which 157 proteins were up- and 173 proteins were downregulated. Differential expression took place in a number of pathways, among others strong downregulation of porphyrin and chlorophyll metabolism and upregulation of alpha-linolenic acid metabolism, which was consistent with observed increased levels of jasmonic acid, a key signal molecule of plant defence. Our data shed light on the molecular networks that are involved in defence of plants against phytoplasma infection and provide a resource for further studies.

Over the last decades polyetiological metabolic diseases such as obesity and type 2 diabetes have emerged as a global epidemic. Efficient strategies for prevention and treatment include dietary intervention and the development of validated nutraceuticals. Safe extracts of edible plants provide a resource of structurally diverse molecules that can effectively interfere with multifactorial diseases. In this study, we describe the application of ethanolic lemon balm (Melissa officinalis) leaves extract for the treatment of insulin-resistance and dyslipidemia in mice. We show that lemon balm extract (LBE) activates the peroxisome proliferator-activated receptors (PPARs), which have key roles in the regulation of whole body glucose and lipid metabolism. Application of LBE (0.6 mg/mL) to human primary adipocytes resulted in specific peroxisome proliferator-activated receptor target gene expression. LBE treatment of insulin-resistant high-fat diet-fed C57BL/6 mice (200 mg/kg/day) for 6 weeks considerably reduced hyperglycemia and insulin resistance, plasma triacylglycerol, nonesterified fatty acids and LDL/VLDL cholesterol levels. Taken together, ethanolic lemon balm extract can potentially be used to prevent or concomitantly treat type 2 diabetes and associated disorders such as dyslipidemia and hypercholesterolemia.

AIMS/HYPOTHESIS: The nuclear receptor peroxisome proliferator-activated receptor gamma (PPARgamma) is an important gene regulator in glucose and lipid metabolism. Unfortunately, PPARgamma-activating drugs of the thiazolidinedione class provoke adverse side effects. As recently shown, amorfrutin A1 is a natural glucose-lowering compound that selectively modulates PPARgamma. In this study we aimed to characterise, in vitro, a large spectrum of the amorfrutins and similar molecules, which we isolated from various plants. We further studied in vivo the glucose-lowering effects of the so far undescribed amorfrutin B, which featured the most striking PPARgamma-binding and pharmacological properties of this family of plant metabolites. METHODS: Amorfrutins were investigated in vitro by binding and cofactor recruitment assays and by transcriptional activation assays in primary human adipocytes and murine preosteoblasts, as well as in vivo using insulin-resistant high-fat-diet-fed C57BL/6 mice treated for 27 days with 100 mg kg(-1) day(-1) amorfrutin B. RESULTS: Amorfrutin B showed low nanomolar binding affinity to PPARgamma, and micromolar binding to the isotypes PPARalpha and PPARbeta/delta. Amorfrutin B selectively modulated PPARgamma activity at low nanomolar concentrations. In insulin-resistant mice, amorfrutin B considerably improved insulin sensitivity, glucose tolerance and blood lipid variables after several days of treatment. Amorfrutin B treatment did not induce weight gain and furthermore showed liver-protecting properties. Additionally, amorfrutins had no adverse effects on osteoblastogenesis and fluid retention. CONCLUSIONS/INTERPRETATION: The application of plant-derived amorfrutins or synthetic analogues thereof constitutes a promising approach to prevent or treat complex metabolic diseases such as insulin resistance or type 2 diabetes.

Bacteria are a convenient source of intrinsic marker proteins, which can be detected efficiently by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The patterns of protein masses observed can be used for accurate classification and identification of bacteria. Key to the reliability of the method is a robust and standardized procedure for sample preparations, including bacterial culturing, chemical treatment for bacterial cell wall disruption and for protein extraction, and mass spectrometry analysis. The protocol is an excellent alternative to classical microbiological classification and identification procedures, requiring minimal sample preparation efforts and costs. Without cell culturing, the protocol takes in general

The slowing down or stalling of replication forks is commonly known as replication stress and arises from multiple causes such as DNA lesions, nucleotide depletion, RNA-DNA hybrids, and oncogene activation. The ataxia telangiectasia and Rad3-related kinase (ATR) plays an essential role in the cellular response to replication stress and inhibition of ATR has emerged as therapeutic strategy for the treatment of cancers that exhibit high levels of replication stress. However, the cellular signaling induced by replication stress and the substrate spectrum of ATR has not been systematically investigated. In this study, we employed quantitative MS-based proteomics to define the cellular signaling after nucleotide depletion-induced replication stress and replication fork collapse following ATR inhibition. We demonstrate that replication stress results in increased phosphorylation of a subset of proteins, many of which are involved in RNA splicing and transcription and have previously not been associated with the cellular replication stress response. Furthermore, our data reveal the ATR-dependent phosphorylation following replication stress and discover novel putative ATR target sites on MCM6, TOPBP1, RAD51AP1, and PSMD4. We establish that ATR inhibition rewires cellular signaling networks induced by replication stress and leads to the activation of the ATM-driven double-strand break repair signaling.

Cellular communication is a fundamental process in biology. The interaction of adipocytes with macrophages is a key event in the development of common diseases such as type 2 diabetes. We applied an established bilayer cell co-culture system and comprehensive mass spectrometry analysis to detect proteome-wide the paracrine interaction of murine adipocytes and macrophages. Altogether, we identified 4486 proteins with at least two unique peptides of which 2392 proteins were informative for 3T3-L1 adipocytes and 2957 proteins for RAW 264.7 macrophages. Further, we observed over 12,000 phosphorylation sites of which we could assign 3,200 informative phosphopeptides with a single phosphosite for adipocytes and 4,514 for macrophages. Using protein set enrichment and phosphosite analyses, we deciphered regulatory protein pathways involved in cellular stress and inflammation, which can contribute to metabolic impairment of cells including insulin resistance and other disorders. The generated data sets provide a holistic, molecular pathway-centric view on the interplay of adipocytes and macrophages in disease processes and a resource for further studies.

Natural products are a rich resource for the development of chemical probes, functional foods (nutraceuticals), and pharmaceuticals. The remarkable structural diversity of natural product or biology-oriented compound libraries in particular argues for their use in applications such as drug screenings. Enzymes that can posttranslationally modify proteins or nucleic acids are attractive drug targets and include kinases and phosphatases, methyltransferases and acetyltransferases, and deacetylases. Recently, histone-modifying enzymes like the deacetylase sirtuin 1 (SIRT1) were suggested as drug targets for treating a variety of age-related disorders such as neuropathogenic diseases, metabolic diseases, and cancer. Compound screenings for SIRT1 modulators have revealed promising enzymatic activators such as the natural product resveratrol and the synthetic compound SRT1720. However, these findings are still highly controversial due to reported assay artifacts. The employed screening assays were based on fluorescence-labeled peptide substrates and resulted in the purported but artificial enzymatic activation of SIRT1. These findings have misled many researchers over the years. Besides the generation of artifacts as observed in SIRT1 assays, there is a second general drawback of fluorescencebased assays which is broadly underestimated: Autofluorescence of compound libraries and in particular natural product libraries interferes with widely applied optical analyses such as time-resolved fluorescence resonance energy transfer (TRFRET). By contrast, mass spectrometry (MS) represents an attractive alternative as substrates are detected directly. Peptides are ionized and detected according to their mass to charge ratios (m/z). For example, deacetylation of a substrate peptide can be directly detected as a 42 Da mass peak shift (Figure 1).

Given worldwide increases in the incidence of obesity and type 2 diabetes, new strategies for preventing and treating metabolic diseases are needed. The nuclear receptor PPARγ (peroxisome proliferator-activated receptor gamma) plays a central role in lipid and glucose metabolism; however, current PPARγ-targeting drugs are characterized by undesirable side effects. Natural products from edible biomaterial provide a structurally diverse resource to alleviate complex disorders via tailored nutritional intervention. We identified a family of natural products, the amorfrutins, from edible parts of two legumes, Glycyrrhiza foetida and Amorpha fruticosa , as structurally new and powerful antidiabetics with unprecedented effects for a dietary molecule. Amorfrutins bind to and activate PPARγ, which results in selective gene expression and physiological profiles markedly different from activation by current synthetic PPARγ drugs. In diet-induced obese and db/db mice, amorfrutin treatment strongly improves insulin resistance and other metabolic and inflammatory parameters without concomitant increase of fat storage or other unwanted side effects such as hepatoxicity. These results show that selective PPARγ-activation by diet-derived ligands may constitute a promising approach to combat metabolic disease.

Background To date, telomere research in fungi has mainly focused on Saccharomyces cerevisiae and Schizosaccharomyces pombe, despite the fact that both yeasts have degenerated telomeric repeats in contrast to the canonical TTAGGG motif found in vertebrates and also several other fungi. Results Using label-free quantitative proteomics, we here investigate the telosome of Neurospora crassa, a fungus with canonical telomeric repeats. We show that at least six of the candidates detected in our screen are direct TTAGGG-repeat binding proteins. While three of the direct interactors (NCU03416 [ncTbf1], NCU01991 [ncTbf2] and NCU02182 [ncTay1]) feature the known myb/homeobox DNA interaction domain also found in the vertebrate telomeric factors, we additionally show that a zinc-finger protein (NCU07846) and two proteins without any annotated DNA-binding domain (NCU02644 and NCU05718) are also direct double-strand TTAGGG binders. We further find two single-strand binders (NCU02404 [ncGbp2] and NCU07735 [ncTcg1]). Conclusion By quantitative label-free interactomics we identify TTAGGG-binding proteins in Neurospora crassa, suggesting candidates for telomeric factors that are supported by phylogenomic comparison with yeast species. Intriguingly, homologs in yeast species with degenerated telomeric repeats are also TTAGGG-binding proteins, e.g. in S. cerevisiae Tbf1 recognizes the TTAGGG motif found in its subtelomeres. However, there is also a subset of proteins that is not conserved. While a rudimentary core TTAGGG-recognition machinery may be conserved across yeast species, our data suggests Neurospora as an emerging model organism with unique features. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2158-0) contains supplementary material, which is available to authorized users.