Palmitoylethanolamide (PEA) Inhibits SARS-CoV-2 Entry by Interacting with S Protein and ACE-2 Receptor.

Lipids play a crucial role in the entry and egress of viruses, regardless of whether they are naked or enveloped. Recent evidence shows that lipid involvement in viral infection goes much further. During replication, many viruses rearrange internal lipid membranes to create niches where they replicate and assemble. Because of the close connection between lipids and inflammation, the derangement of lipid metabolism also results in the production of inflammatory stimuli. Due to its pivotal function in the viral life cycle, lipid metabolism has become an area of intense research to understand how viruses seize lipids and to design antiviral drugs targeting lipid pathways. Palmitoylethanolamide (PEA) is a lipid-derived peroxisome proliferator-activated receptor-α (PPAR-α) agonist that also counteracts SARS-CoV-2 entry and its replication. Our work highlights for the first time the antiviral potency of PEA against SARS-CoV-2, exerting its activity by two different mechanisms. First, its binding to the SARS-CoV-2 S protein causes a drop in viral infection of ~70%. We show that this activity is specific for SARS-CoV-2, as it does not prevent infection by VSV or HSV-2, other enveloped viruses that use different glycoproteins and entry receptors to mediate their entry. Second, we show that in infected Huh-7 cells, treatment with PEA dismantles lipid droplets, preventing the usage of these vesicular bodies by SARS-CoV-2 as a source of energy and protection against innate cellular defenses. This is not surprising since PEA activates PPAR-α, a transcription factor that, once activated, generates a cascade of events that leads to the disruption of fatty acid droplets, thereby bringing about lipid droplet degradation through ß-oxidation. In conclusion, the present work demonstrates a novel mechanism of action for PEA as a direct and indirect antiviral agent against SARS-CoV-2. This evidence reinforces the notion that treatment with this compound might significantly impact the course of COVID-19. Indeed, considering that the protective effects of PEA in COVID-19 are the current objectives of two clinical trials (NCT04619706 and NCT04568876) and given the relative lack of toxicity of PEA in humans, further preclinical and clinical tests will be needed to fully consider PEA as a promising adjuvant therapy in the current COVID-19 pandemic or against emerging RNA viruses that share the same route of replication as coronaviruses.

Ymr210wp leads to the accumulation of phospholipids and steryl esters in yeast.

Ymr210w was identified as a MAG (Monoacylglycerol) lipase. The accumulation of the phospholipids in the ymr210wΔ was not clearly understood. It was expressed in S. cerevisiae using pYES2/CT vector and His-tag purified recombinant protein confirmed TAG lipase activity. To further evaluate the role of YMR210w, ester hydrolase activity was also confirmed with pNP-acetate, pNP-butyrate and pNP - palmitate. GC-MS lipid profiling of ymr210wΔ showed an increase in the 15:0 Pentadecanoic acid by 76% among the total lipids. Phospholipid, Erucic acid 22:1 (Δ13) showed 43% increase while steryl esters showed significant changes with 16:0 hexadecanoic acid augmentations by 80% and 18:0 Octadecanoic acid by 165% when compared to wild type (WT). Increase in the steryl ester and TAG content supports the accumulation of lipid bodies in ymr210wΔ strain when compared with WT cells.

A growth inhibitory model with SO(x) influenced effective growth rate for estimation of algal biomass concentration under flue gas atmosphere.

A theoretical model for the prediction of biomass concentration under rice husk flue gas emission has been developed. The growth inhibitory model (GIM) considers the CO2 mass transfer rate, the critical SOx concentration and its role in pH-based inter-conversion of bicarbonate. The calibration and subsequent validation of the growth profile of Nannochloropsis limnetica at 2% and 10% (v/v) CO2 showed that the predicted values were consistent with the measured values, with r(2) being 0.96 and 0.98, respectively, and p<0.001 in both cases. The constants used in the GIM for the prediction of biomass have been justified using sensitivity analysis. GIM applicability was defined as ±30% of the calibrated flow rate (3.0 L min(-1)). This growth model can be applied to predict algal growth in photo-bioreactors treated with flue gas in the generation of biomass feed stock for biofuel production.