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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.

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

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 Human susceptibility to obesity is mainly genetic, yet the underlying evolutionary drivers causing variation from person to person are not clear. One theory rationalizes that populations that have adapted to warmer climates have reduced their metabolic rates, thereby increasing their propensity to store energy. We uncover here the function of a gene that supports this theory. THADA is one of the genes most strongly selected during evolution as humans settled in different climates. We report here that THADA knockout flies are obese, hyperphagic, have reduced energy production, and are sensitive to the cold. THADA binds the sarco/ER Ca2+ ATPase (SERCA) and acts on it as an uncoupler. Reducing SERCA activity in THADA mutant flies rescues their obesity, pinpointing SERCA as a key effector of THADA function. In sum, this identifies THADA as a regulator of the balance between energy consumption and energy storage, which was selected during human evolution., Highlights • Drosophila knockouts of the conserved gene THADA are obese and hyperphagic • THADA knockouts produce less heat and are cold sensitive • THADA binds SERCA and uncouples its ATP hydrolysis from Ca2+ pumping • Reducing SERCA activity rescues the THADA loss-of-function phenotypes, One theory for variable human susceptibility to obesity is altered metabolic rates due to adaptation to warmer climates. Moraru et al. examine the function of THADA, a positively selected gene in human evolution associated with type 2 diabetes, in Drosophila and show that THADA modulates, via calcium signaling, energy storage and thermogenesis balance.

While quantitative proteomics is a key technology in biological research, the routine industry and diagnostics application is so far still limited by a moderate throughput, data consistency and robustness. In part, the restrictions emerge in the proteomics dependency on nanolitre/minute flow rate chromatography that enables a high sensitivity, but is difficult to handle on large sample series, and on the stochastic nature in data-dependent acquisition strategies. We here establish and benchmark a label-free, quantitative proteomics platform that uses microlitre/minute flow rate chromatography in combination with data-independent SWATH acquisition. Being able to largely compensate for the loss of sensitivity by exploiting the analytical capacities of microflow chromatography, we show that microLC-SWATH-MS is able to precisely quantify up to 4000 proteins in an hour or less, enables the consistent processing of sample series in high-throughput, and gains quantification precisions comparable to targeted proteomic assays. MicroLC-SWATH-MS can hence routinely process hundreds to thousands of samples to systematically create precise, label free quantitative proteomes.