SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted laser desorption ionization mass spectrometry analysis. Sample preparation, MS, and data analysis parameters were optimized to achieve an overall accuracy of 92%, sensitivity of 93%, and specificity of 92% in dataset without feature selection. We identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of SDS–PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins, both already described as biomarkers for viral infections in the acute phase. Unbiased discrimination of high- and low-risk COVID-19 patients using a technology that is currently in clinical use may have a prompt application in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.

Ethanol (EtOH) is a substantial stressor for Saccharomyces cerevisiae. Data integration from strains with different phenotypes, including EtOH stress-responsive lncRNAs, are still not available. We covered these issues seeking systems modifications that drive the divergences between higher (HT) and lower (LT) EtOH tolerant strains under their highest stress conditions. We showed that these phenotypes are neither related to high viability nor faster population rebound after stress relief. LncRNAs work on many stress-responsive systems in a strain-specific manner promoting the EtOH tolerance. Cells use membraneless RNA/protein storage and degradation systems to endure the stress harming, and lncRNAs jointly promote EtOH tolerance. CTA1 and longevity are primer systems promoting phenotype-specific gene expression. The lower cell viability and growth under stress is a byproduct of sphingolipids and inositol phosphorylceramide dampening, acerbated in HTs by sphinganine, ERG9, and squalene overloads; LTs diminish this harm by accumulating inositol 1-phosphate. The diauxic shift drives an EtOH buffering by promoting an energy burst under stress, mainly in HTs. Analysis of mutants showed genes and lncRNAs in three strains critical for their EtOH tolerance. Finally, longevity, peroxisome, energy and lipid metabolisms, RNA/protein degradation and storage systems are the main pathways driving the EtOH tolerance phenotypes.

Ethanol (EtOH) alters many cellular processes in yeast. An integrated view of different EtOH-tolerant phenotypes and their long noncoding RNAs (lncRNAs) is not yet available. Here, large-scale data integration showed the core EtOH-responsive pathways, lncRNAs, and triggers of higher (HT) and lower (LT) EtOH-tolerant phenotypes. LncRNAs act in a strain-specific manner in the EtOH stress response. Network and omics analyses revealed that cells prepare for stress relief by favoring activation of life-essential systems. Therefore, longevity, peroxisomal, energy, lipid, and RNA/protein metabolisms are the core processes that drive EtOH tolerance. By integrating omics, network analysis, and several other experiments, we showed how the HT and LT phenotypes may arise: (1) the divergence occurs after cell signaling reaches the longevity and peroxisomal pathways, with CTA1 and ROS playing key roles; (2) signals reaching essential ribosomal and RNA pathways via SUI2 enhance the divergence; (3) specific lipid metabolism pathways also act on phenotype-specific profiles; (4) HTs take greater advantage of degradation and membraneless structures to cope with EtOH stress; and (5) our EtOH stress-buffering model suggests that diauxic shift drives EtOH buffering through an energy burst, mainly in HTs. Finally, critical genes, pathways, and the first models including lncRNAs to describe nuances of EtOH tolerance are reported here.