Proteomics and metabonomics analyses of Covid-19 complications in patients with pulmonary fibrosis

Yang, Jianrong; Chen, Chunxia; Chen, Wan; Huang, Luying; Fu, Zhao; Ye, Kun; Lv, Liwen; Nong, Zhihuang; Zhou, Xing; Lu, Wensheng; Zhong, Ming

doi:10.1038/s41598-021-94256-8

Cited by 13 publications

(11 citation statements)

References 52 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Significant functional terms such as “B cell receptor signaling pathway”, “B cell activation (GO:0042113)”, and “regulation of B cell proliferation (GO:0030888)” demonstrated that the grey60 module was closely related to the humoral adaptive immunity. In agreement with our results, similar transcriptomic and systemic studies of COVID-19 reported enrichment of B cell receptor signaling pathway, B cell activation, and B cell proliferation pathways during SARS-CoV-2 infection ( 7 , 244 – 246 ). Furthermore, B cell depleted patients infected with SARS-CoV-2 had prolonged disease ( 247 ).…”

Section: Discussionsupporting

confidence: 93%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 93%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…This makes it an interesting metabolite to monitor for progression to pulmonary fibrosis. Generally, arginine and its derivatives are part of the urea cycle metabolic pathway, by which its dysfunction is known to correlate with lung disease ( 63 ). This could also explain our MSEA analysis, which displayed the urea cycle to be one of the top enriched pathways observed in our study.…”

Section: Discussionmentioning

confidence: 99%

N6-Acetyl-L-Lysine and p-Cresol as Key Metabolites in the Pathogenesis of COVID-19 in Obese Patients

et al. 2022

View full text Add to dashboard Cite

Despite the growing number of the vaccinated population, COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains a global health burden. Obesity, a metabolic syndrome affecting one-third of the population, has proven to be a major risk factor for COVID-19 severe complications. Several studies have identified metabolic signatures and disrupted metabolic pathways associated with COVID-19, however there are no reports evaluating the role of obesity in the COVID-19 metabolic regulation. In this study we highlight the involvement of obesity metabolically in affecting SARS-CoV-2 infection and the consequent health complications, mainly cardiovascular disease. We measured one hundred and forty-four (144) metabolites using ultra high-performance liquid chromatography-quadrupole time of flight mass spectrometry (UHPLC-QTOF-MS) to identify metabolic changes in response to SARS-CoV-2 infection, in lean and obese COVID-19 positive (n=82) and COVID-19 negative (n=24) patients. The identified metabolites are found to be mainly correlating with glucose, energy and steroid metabolisms. Further data analysis indicated twelve (12) significantly yet differentially abundant metabolites associated with viral infection and health complications, in COVID-19 obese patients. Two of the detected metabolites, n6-acetyl-l-lysine and p-cresol, are detected only among the COVID-19 cohort, exhibiting significantly higher levels in COVID-19 obese patients when compared to COVID-19 lean patients. These metabolites have important roles in viral entry and could explain the increased susceptibility of obese patients. On the same note, a set of six metabolites associated with antiviral and anti-inflammatory functions displayed significantly lower abundance in COVID-19 obese patients. In conclusion, this report highlights the plasma metabolome of COVID-19 obese patients as a metabolic feature and signature to help improve clinical outcomes. We propose n6-acetyl-l-lysine and p-cresol as potential metabolic markers which warrant further investigations to better understand their involvement in different metabolic pathways in COVID-19.

show abstract

“…This has increased research interest in proteomic and metabolomic methods to investigate pathways linked to energy production and amino acid metabolism in patients with SARS-CoV-2 infections ( 110 ). Metabolomic analyses in COVID-19 patients with and without pulmonary fibrosis revealed that pathways including the peroxisome proliferator-activated receptor (PPAR), D-arginine and D-ornithine metabolism, inflammatory tryptophan metabolic pathway (TRP), and alpha-linolenic acid metabolism were significantly increased in fibrotic lungs, thus suggesting that PPAR signaling is one of the main pathways involved in the formation and development of lung fibrosis in COVID-19 ( 9 ). A proteomic and metabolomic analysis identified hypoxanthine and betaine as predictors of ICU stay, and early ICU admission, elevated creatinine, and D-dimer were found to be associated with these pathways ( 8 ).…”

Section: Future Perspectives: Metabolomic and Proteomic Biomarkers An...mentioning

confidence: 99%

Laboratory Biomarkers for Diagnosis and Prognosis in COVID-19

et al. 2022

View full text Add to dashboard Cite

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) causes a wide spectrum of clinical manifestations, with progression to multiorgan failure in the most severe cases. Several biomarkers can be altered in coronavirus disease 2019 (COVID-19), and they can be associated with diagnosis, prognosis, and outcomes. The most used biomarkers in COVID-19 include several proinflammatory cytokines, neuron-specific enolase (NSE), lactate dehydrogenase (LDH), aspartate transaminase (AST), neutrophil count, neutrophils-to-lymphocytes ratio, troponins, creatine kinase (MB), myoglobin, D-dimer, brain natriuretic peptide (BNP), and its N-terminal pro-hormone (NT-proBNP). Some of these biomarkers can be readily used to predict disease severity, hospitalization, intensive care unit (ICU) admission, and mortality, while others, such as metabolomic and proteomic analysis, have not yet translated to clinical practice. This narrative review aims to identify laboratory biomarkers that have shown significant diagnostic and prognostic value for risk stratification in COVID-19 and discuss the possible clinical application of novel analytic strategies, like metabolomics and proteomics. Future research should focus on identifying a limited but essential number of laboratory biomarkers to easily predict prognosis and outcome in severe COVID-19.

show abstract

Proteomics and metabonomics analyses of Covid-19 complications in patients with pulmonary fibrosis

Cited by 13 publications

References 52 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

N6-Acetyl-L-Lysine and p-Cresol as Key Metabolites in the Pathogenesis of COVID-19 in Obese Patients

Laboratory Biomarkers for Diagnosis and Prognosis in COVID-19

Contact Info

Product

Resources

About