Molecular Mechanisms of Palmitic Acid Augmentation in COVID-19 Pathologies

Joshi, C.N.; Jadeja, Viren; Zhou, Heping

doi:10.3390/ijms22137127

Cited by 10 publications

(6 citation statements)

References 71 publications

(84 reference statements)

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“…Additionally, some lightgreen module hub-high traffic genes, including GLUL ( 268 ), ACSL1 ( 269 ), TRIB1 ( 236 ), TUBA1B ( 270 ), PLEK ( 271 , 272 ), HSPA5 ( 273 , 274 ), and IRAK3 ( 275 ) may contribute to the pathogenesis of COVID-19. For example, SARS-CoV-2 spikes can drive the infection process of host cells by binding to cell-surface heat shock protein A5 ( HSPA5 ) receptor ( 273 , 276 ).…”

Section: Discussionmentioning

confidence: 99%

“…Other hub-high traffic genes in this module, such as BTAF1 ( 216 ), EZH2 ( 346 ), NPM1 ( 347 ), TPT1 ( 151 , 269 , 348 ), LDHA ( 217 , 268 ), PTPRC ( 189 ), SOS1 ( 349 , 350 ), DDX5 ( 351 , 352 ), BCL10 ( 162 , 353 ), EEF1B2 ( 354 ), CALM1 ( 344 ), EIF4A2 , EIF4B ( 336 , 355 , 356 ), EEF1A1 ( 269 , 354 ), HNRNPA1 ( 228 , 347 ), and IFNG ( 8 , 357 , 358 ), have important roles in development/inhibition of COVID-19. Overexpression of SARS-CoV-2 ORF6 caused HNRNPA1 nuclear accumulation, subverting the host mRNA export system and reducing nucleus size ( 359 ).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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BackgroundThe 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.MethodsRNA-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.ResultsBased 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.ConclusionThis 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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…The proinflammatory cytokines and chemokines, such as IL‐6, IL‐8, tumour necrosis factor (TNF), IL‐17, IL‐12, IL‐1, IP‐10, MCP‐1, and CCL5, were generally increased with higher BMI. 27 , 28 , 36 , 40 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 Furthermore, high levels of IL‐10, an anti‐inflammatory cytokine, appear to be a hallmark of hyperinflammation during severe SARS‐CoV‐2 infection, 57 , 58 and some studies included in this review observed that IL‐10 levels predict poor outcomes in COVID‐19 patients with obesity. 32 , 51 In an attempt to reduce the high COVID‐19 mortality rates due to a cytokine storm, intensive research is underway examining the use of anti‐inflammatory drugs.…”

Section: Resultsmentioning

confidence: 87%

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

et al. 2022

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Summary 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.

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“…36 Increase in the level of palmitic acid has been correlated with inflammatory mediation and increased apoptotic events, being associated with worse outcomes in COVID-19 patients. 37 Higher levels of 4hydroxy-2-nonenal-glutathione in deceased patients may indicate enhanced levels of reactive oxygen species (ROS). Sulfate (SO 3 − ) and iduronic acid (IdoA) may partially result from the glycosaminoglycan dermatan sulfate degradation, which has been associated with the SARS-CoV-2 infection 38 both through the virus' direct effect on tissues and indirectly through inflammatory processes.…”

Section: ■ Resultsmentioning

confidence: 99%

“…Thus, higher levels of this metabolite can be associated with oxidative damage. , Furthermore, SARS-CoV-2 is able to recruit biliverdin at the level of the spike protein via allostery, dampening the binding with neutralizing antibodies and leading to the worsening of the clinical condition of COVID-19 patients . Increase in the level of palmitic acid has been correlated with inflammatory mediation and increased apoptotic events, being associated with worse outcomes in COVID-19 patients . Higher levels of 4-hydroxy-2-nonenal-glutathione in deceased patients may indicate enhanced levels of reactive oxygen species (ROS).…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2023

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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.

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Molecular Mechanisms of Palmitic Acid Augmentation in COVID-19 Pathologies

Cited by 10 publications

References 71 publications

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

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

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

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

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