Complement System Activation Is a Plasma Biomarker Signature during Malaria in Pregnancy.

Malaria in pregnancy (MiP) is a public health problem in malaria-endemic areas, contributing to detrimental outcomes for both mother and fetus. Primigravida and second-time mothers are most affected by severe anemia complications and babies with low birth weight compared to multigravida women. Infected erythrocytes (IE) reach the placenta, activating the immune response by placental monocyte infiltration and inflammation. However, specific markers of MiP result in poor outcomes, such as low birth weight, and intrauterine growth restriction for babies and maternal anemia in women infected with Plasmodium falciparum are limited. In this study, we identified the plasma proteome signature of a mouse model infected with Plasmodium berghei ANKA and pregnant women infected with Plasmodium falciparum infection using quantitative mass spectrometry-based proteomics. A total of 279 and 249 proteins were quantified in murine and human plasma samples, of which 28% and 30% were regulated proteins, respectively. Most of the regulated proteins in both organisms are involved in complement system activation during malaria in pregnancy. CBA anaphylatoxin assay confirmed the complement system activation by the increase in C3a and C4a anaphylatoxins in the infected plasma compared to non-infected plasma. Moreover, correlation analysis showed the association between complement system activation and reduced head circumference in newborns from Pf-infected mothers. The data obtained in this study highlight the correlation between the complement system and immune and newborn outcomes resulting from malaria in pregnancy.

Integrative Analysis of the Ethanol Tolerance of Saccharomyces cerevisiae.

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.

LncRNAs of Saccharomyces cerevisiae bypass the cell cycle arrest imposed by ethanol stress.

Ethanol alters many subsystems of Saccharomyces cerevisiae, including the cell cycle. Two ethanol-responsive lncRNAs in yeast interact with cell cycle proteins, and here, we investigated the role of these RNAs in cell cycle. Our network dynamic modeling showed that higher and lower ethanol-tolerant strains undergo cell cycle arrest in mitosis and G1 phases, respectively, during ethanol stress. The higher population rebound of the lower ethanol-tolerant phenotype after stress relief responds to the late phase arrest. We found that the lncRNA lnc9136 of SEY6210 (a lower ethanol-tolerant strain) induces cells to skip mitosis arrest. Simulating an overexpression of lnc9136 and analyzing CRISPR-Cas9 mutants lacking this lncRNA suggest that lnc9136 induces a regular cell cycle even under ethanol stress, indirectly regulating Swe1p and Clb1/2 by binding to Gin4p and Hsl1p. Notably, lnc10883 of BY4742 (a higher ethanol-tolerant strain) does not prevent G1 arrest in this strain under ethanol stress. However, lnc19883 circumvents DNA and spindle damage checkpoints, maintaining a functional cell cycle by interacting with Mec1p or Bub1p even in the presence of DNA/spindle damage. Overall, we present the first evidence of direct roles for lncRNAs in regulating yeast cell cycle proteins, the dynamics of this system in different ethanol-tolerant phenotypes, and a new yeast cell cycle model.

Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19.

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.