A high-throughput lipidomics and transcriptomic approach reveals novel compounds from sugarcane linked with promising therapeutic potential against COVID-19

Sugarcane (Saccharum ssp., Poaceae) provides enormous metabolites such as sugars, lipid, and other dietary metabolites to humans. Among them, lipids are important metabolites that perform various functions and have promising pharmacological value. However, in sugarcane, few studies are focusing on lipidomics and few lipid compounds were reported, and their pharmacological values are not explored yet. The transcriptomic and widely targeted lipidomics approach quantified 134 lipid compounds from the rind of six sugarcane genotypes. These lipid compounds include 57 fatty acids, 30 lysophosphatidylcholines, 23 glycerol esters, 21 lysophosphatidylethanolamines, 2 phosphatidylcholines, and 1 sphingolipid. Among them, 119 compounds were first time reported in sugarcane rind. Seventeen lipids compounds including 12 fatty acids, 2 glycerol lipids, LysoPC 16:0, LysoPE 16:0, and choline alfoscerate were abundantly found in the rind of sugarcane genotypes. From metabolic and transcriptomic results, we have developed a comprehensive lipid metabolic pathway and highlighted key genes that are differentially expressed in sugarcane. Several genes associated with α-linolenic acid and linoleic acid biosynthesis pathways were highly expressed in the rind of the ROC22 genotype. ROC22 has a high level of α-linolenic acid (an essential fatty acid) followed by ROC16. Moreover, we have explored pharmacological values of lipid compounds and found that the 2-linoleoylglycerol and gingerglycolipid C have strong binding interactions with 3CLpro of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) and these compounds can be utilized against SARS-CoV-2 as therapeutic agents. The transcriptome, metabolome, and bioinformatics analysis suggests that the sugarcane cultivars have a diversity of lipid compounds having promising therapeutic potential, and exploring the lipid metabolism will help to know more compounds that have promising cosmetic and pharmacological value.

Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer and coronary disease pathways (preprint)

Identification of the plasma proteomic changes of Coronavirus disease 2019 (COVID-19) is essential to understanding the pathophysiology of the disease and developing predictive models and novel therapeutics. We performed plasma deep proteomic profiling from 332 COVID-19 patients and 150 controls and pursued replication in an independent cohort (297 cases and 76 controls) to find potential biomarkers and causal proteins for three COVID-19 outcomes (infection, ventilation, and death). We identified and replicated 1,449 proteins associated with any of the three outcomes (841 for infection, 833 for ventilation, and 253 for death) that can be query on a web portal (https://covid.proteomics.wustl.edu/). Using those proteins and machine learning approached we created and validated specific prediction models for ventilation (AUC>0.91), death (AUC>0.95) and either outcome (AUC>0.80). These proteins were also enriched in specific biological processes, including immune and cytokine signaling (FDR < 3.72x10-14), Alzheimer's disease (FDR < 5.46x10-10) and coronary artery disease (FDR < 4.64x10-2). Mendelian randomization using pQTL as instrumental variants nominated BCAT2 and GOLM1 as a causal proteins for COVID-19. Causal gene network analyses identified 141 highly connected key proteins, of which 35 have known drug targets with FDA-approved compounds. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes (ventilation and death), reveal their relationship to Alzheimer's disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.

The Correlation Between Brain Performance Capacity and COVID-19: A Cross-sectional Survey and Canonical Correlation Analysis (preprint)

ObjectiveTo generate a concept of brain performance capacity (BPC) with sleep, fatigue and mental workload as evaluation indicators and to analyze the correlation between BPC and the impact of COVID-19. MethodsA cluster sampling method was adopted to randomly select 259 civil air crew members. The measurements of sleep quality, fatigue and mental workload (MWL) were assessed using the Pittsburgh Sleep Quality Index (PSQI), Multidimensional Fatigue Inventory (MFI-20) and NASA Task Load Index. The impact of COVID-19 included 7 dimensions scored on a Likert scale. Canonical correlation analysis (CCA) was conducted to examine the relationship between BPC and COVID-19. ResultsA total of 259 air crew members participated in the survey. Participants average PSQI score was 7.826 (SD = 3.796), with 49.8% reporting incidents of insomnia, mostly of a minor degree. Participants MFI was an average 56.112 (SD = 10.040), with 100% reporting some incidence of fatigue, mainly severe. The weighted mental workload (MWL) score was an average of 43.084 (SD = 17.543), with reports of mostly a mid-level degree. There was a significant relationship between BPC and COVID-19, with a canonical correlation coefficient of 0.507 (P=0.000), an eigenvalue of 0.364 and a contribution rate of 69.1%. All components of the BPC variable set: PSQI, MFI and MWL contributed greatly to BPC, with absolute canonical loadings of 0.790, 0.606 and 0.667, respectively; the same was true for the COVID-19 variable set, with absolute canonical loadings ranging from 0.608 to 0.951. ConclusionMultiple indicators to measure BPC and the interrelationship of BPC and COVID-19 should be used in future research to gain a comprehensive understanding of anti-epidemic measures to ensure victory in the battle against the spread of the disease.