Molecular Mechanisms of Palmitic Acid Augmentation in COVID-19 Pathologies

Investigating the anti-atherosclerotic effects and potential mechanism of Dalbergia odorifera in ApoE-deficient mice using network pharmacology combined with metabolomics

Dalbergia odorifera (DO) is a precious rosewood species in Southern Asia, and its heartwood is used in China as an official plant for invigorating blood circulation and eliminating stasis. This study aims to evaluate the efficacy of DO on atherosclerosis (AS), and further explore its active components and potential mechanisms. The apolipoprotein-E (ApoE)-deficient mice fed a high-fat diet were used as model animals, and the pathological changes in mice with or without DO treatment were compared to evaluate the pharmacodynamics of DO on AS. The mechanisms were preliminarily expounded by combining with metabolomics and network pharmacology. Moreover, the bioactive components and targets were assessed by cell experiments and molecular docking, respectively. Our findings suggested that DO significantly modulated blood lipid levels and alleviated intimal hyperplasia in atherosclerotic-lesioned mice, and the mechanisms may involve the regulation of 18 metabolites that changed during the progression of AS, thus affecting 3 major metabolic pathways and 3 major signaling pathways. Moreover, the interactions between 16 compounds with anti-proliferative effect and hub targets in the 3 signaling pathways were verified using molecular docking. Collectively, our findings preliminarily support the therapeutic effect of DO in atherosclerosis, meanwhile explore the active constituents and potential pharmacological mechanisms, which is conducive to its reasonable exploitation and utilization.

The Impact of the Serum Extraction Protocol on Metabolomic Profiling Using UPLC-MS/MS and FTIR Spectroscopy

Biofluid metabolomics is a very appealing tool to increase the knowledge associated with pathophysiological mechanisms leading to better and new therapies and biomarkers for disease diagnosis and prognosis. However, due to the complex process of metabolome analysis, including the metabolome isolation method and the platform used to analyze it, there are diverse factors that affect metabolomics output. In the present work, the impact of two protocols to extract the serum metabolome, one using methanol and another using a mixture of methanol, acetonitrile, and water, was evaluated. The metabolome was analyzed by ultraperformance liquid chromatography associated with tandem mass spectrometry (UPLC-MS/MS), based on reverse-phase and hydrophobic chromatographic separations, and Fourier transform infrared (FTIR) spectroscopy. The two extraction protocols of the metabolome were compared over the analytical platforms (UPLC-MS/MS and FTIR spectroscopy) concerning the number of features, the type of features, common features, and the reproducibility of extraction replicas and analytical replicas. The ability of the extraction protocols to predict the survivability of critically ill patients hospitalized at an intensive care unit was also evaluated. The FTIR spectroscopy platform was compared to the UPLC-MS/MS platform and, despite not identifying metabolites and consequently not contributing as much as UPLC-MS/MS in terms of information concerning metabolic information, it enabled the comparison of the two extraction protocols as well as the development of very good predictive models of patient's survivability, such as the UPLC-MS/MS platform. Furthermore, FTIR spectroscopy is based on much simpler procedures and is rapid, economic, and applicable in the high-throughput mode, i.e., enabling the simultaneous analysis of hundreds of samples in the microliter range in a couple of hours. Therefore, FTIR spectroscopy represents a very interesting complementary technique not only to optimize processes as the metabolome isolation but also for obtaining biomarkers such as those for disease prognosis.

TLR7 Activation in M-CSF-Dependent Monocyte-Derived Human Macrophages Potentiates Inflammatory Responses and Prompts Neutrophil Recruitment

Toll-like receptor 7 (TLR7) is an endosomal pathogen-associated molecular pattern (PAMP) receptor that senses single-stranded RNA (ssRNA) and whose engagement results in the production of type I IFN and pro-inflammatory cytokines upon viral exposure. Recent genetic studies have established that a dysfunctional TLR7-initiated signaling is directly linked to the development of inflammatory responses. We present evidence that TLR7 is preferentially expressed by monocyte-derived macrophages generated in the presence of M-CSF (M-MØ). We now show that TLR7 activation in M-MØ triggers a weak MAPK, NFκB, and STAT1 activation and results in low production of type I IFN. Of note, TLR7 engagement reprograms MAFB+ M-MØ towards a pro-inflammatory transcriptional profile characterized by the expression of neutrophil-attracting chemokines (CXCL1-3, CXCL5, CXCL8), whose expression is dependent on the transcription factors MAFB and AhR. Moreover, TLR7-activated M-MØ display enhanced pro-inflammatory responses and a stronger production of neutrophil-attracting chemokines upon secondary stimulation. As aberrant TLR7 signaling and enhanced pulmonary neutrophil/lymphocyte ratio associate with impaired resolution of virus-induced inflammatory responses, these results suggest that targeting macrophage TLR7 might be a therapeutic strategy for viral infections where monocyte-derived macrophages exhibit a pathogenic role.

Indian food habit & food ingredients may have a role in lowering the severity & high death rate from COVID-19 in Indians: findings from the first nutrigenomic analysis

Background & objectives

During the COVID-19 pandemic, the death rate was reportedly 5-8 fold lower in India which is densely populated as compared to less populated western countries. The aim of this study was to investigate whether dietary habits were associated with the variations in COVID-19 severity and deaths between western and Indian population at the nutrigenomics level.

Methods

In this study nutrigenomics approach was applied. Blood transcriptome of severe COVID-19 patients from three western countries (showing high fatality) and two datasets from Indian patients were used. Gene set enrichment analyses were performed for pathways, metabolites, nutrients, etc., and compared for western and Indian samples to identify the food- and nutrient-related factors, which may be associated with COVID-19 severity. Data on the daily consumption of twelve key food components across four countries were collected and a correlation between nutrigenomics analyses and per capita daily dietary intake was investigated.

Results

Distinct dietary habits of Indians were observed, which may be associated with low death rate from COVID-19. Increased consumption of red meat, dairy products and processed foods by western populations may increase the severity and death rate by activating cytokine storm-related pathways, intussusceptive angiogenesis, hypercapnia and enhancing blood glucose levels due to high contents of sphingolipids, palmitic acid and byproducts such as CO2 and lipopolysaccharide (LPS). Palmitic acid also induces ACE2 expression and increases the infection rate. Coffee and alcohol that are highly consumed in western countries may increase the severity and death rates from COVID-19 by deregulating blood iron, zinc and triglyceride levels. The components of Indian diets maintain high iron and zinc concentrations in blood and rich fibre in their foods may prevent CO2 and LPS-mediated COVID-19 severity. Regular consumption of tea by Indians maintains high high-density lipoprotein (HDL) and low triglyceride in blood as catechins in tea act as natural atorvastatin. Importantly, regular consumption of turmeric in daily food by Indians maintains strong immunity and curcumin in turmeric may prevent pathways and mechanisms associated with SARS-CoV-2 infection and COVID-19 severity and lowered the death rate.

Interpretation & conclusions

Our results suggest that Indian food components suppress cytokine storm and various other severity related pathways of COVID-19 and may have a role in lowering severity and death rates from COVID-19 in India as compared to western populations. However, large multi-centered case-control studies are required to support our current findings.

Physical Activity Increases Immunity to COVID-19 Infection

Coronavirus are truly one of the maximum critical fantastic-stranded non-segmented RNA viruses, named after the approximately 126-nm-diameter envelope around the nucleic acid-protein complicated. The virus causes significant harm to human fitness, including direct injury to the respiratory system, immune system compromise, worsening of the underlying clinical conditions, and eventually systemic failure and death. Exercise affects the immune system's antiviral mechanisms. Modest exercise, done before or after infection, improves morbidity and mortality to the contamination, according to animal investigations using influenza and simplex virus in the respiratory tract. Moreover, preclinical research has demonstrated that overtraining has a negative impact on the body's response to viral infections. Follow-up research has shed some light on the mechanisms underlying these discoveries. Through the activation of muscle protein synthesis, physical activity (PA) and exercise are essential for maintaining muscle mass. On the other hand, a lack of muscle contractile activity throughout the country of no exercise, particularly in elderly people, is a major contributor to anabolic rigidity and muscle atrophy.

COVID-19, obesity, and immune response 2 years after the pandemic: A timeline of scientific advances

In the 2 years since the COVID-19 pandemic was officially declared, science has made considerable strides in understanding the disease's pathophysiology, pharmacological treatments, immune response, and vaccination, but there is still much room for further advances, especially in comprehending its relationship with obesity. Science has not yet described the mechanisms that explain how obesity is directly associated with a poor prognosis. This paper gathers all published studies over the past 2 years that have described immune response, obesity, and COVID-19, a historical and chronological record for researchers and the general public alike. In summary, these studies describe how the cytokine/adipokine levels and inflammatory markers, such as the C-reactive protein, are associated with a higher body mass index in COVID-19-positive patients, suggesting that the inflammatory background and immune dysregulation in individuals with obesity may be expressed in the results and that adiposity may influence the immune response. The timeline presented here is a compilation of the results of 2 years of scientific inquiry, describing how the science has progressed, the principal findings, and the challenges ahead regarding SARS-CoV-2, COVID-19, and emerging variants, especially in patients with obesity.

Collective Knowledge Used to Unveil Cardiovascular Injury Emerged during COVID-19

Two years have passed since the unprecedented breakout of the global pandemic of the coronavirus disease COVID-19, which began at the end of 2019 [...].

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Background

The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.

Methods

RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.

Results

Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.

Conclusion

This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.