SOD1 is a Possible Predictor of COVID-19 Progression as Revealed by Plasma Proteomics

Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology

PURPOSE

Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations.

METHODS

To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins.

RESULTS

We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences.

CONCLUSION

Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

Meta-analysis of the serum/plasma proteome identifies significant associations between COVID-19 with Alzheimer's/Parkinson's diseases

In recent years, we have seen the widespread devastations and serious health complications manifested by COVID-19 globally. Although we have effectively controlled the pandemic, uncertainties persist regarding its potential long-term effects, including prolonged neurological issues. To gain comprehensive insights, we conducted a meta-analysis of mass spectrometry-based proteomics data retrieved from different studies with a total of 538 COVID-19 patients and 523 healthy controls. The meta-analysis revealed that top-enriched pathways were associated with neurological disorders, including Alzheimer's (AD) and Parkinson's disease (PD). Further analysis confirmed a direct correlation in the expression patterns of 24 proteins involved in Alzheimer's and 23 proteins in Parkinson's disease with COVID-19. Protein-protein interaction network and cluster analysis identified SNCA as a hub protein, a known biomarker for Parkinson's disease, in both AD and PD. To the best of our knowledge, this is the first meta-analysis study providing proteomic profiling evidence linking COVID-19 to neurological complications.

Probing long COVID through a proteomic lens: a comprehensive two-year longitudinal cohort study of hospitalised survivors

BACKGROUND

As a debilitating condition that can impact a whole spectrum of people and involve multi-organ systems, long COVID has aroused the most attention than ever. However, mechanisms of long COVID are not clearly understood, and underlying biomarkers that can affect the long-term consequences of COVID-19 are paramount to be identified.

METHODS

Participants for the current study were from a cohort study of COVID-19 survivors discharged from hospital between Jan 7, and May 29, 2020. We profiled the proteomic of plasma samples from hospitalised COVID-19 survivors at 6-month, 1-year, and 2-year after symptom onset and age and sex matched healthy controls. Fold-change of >2 or <0.5, and false-discovery rate adjusted P value of 0.05 were used to filter differentially expressed proteins (DEPs). In-genuity pathway analysis was performed to explore the down-stream effects in the dataset of significantly up- or down-regulated proteins. Proteins were integrated with long-term consequences of COVID-19 survivors to explore potential biomarkers of long COVID.

FINDINGS

The proteomic of 709 plasma samples from 181 COVID-19 survivors and 181 matched healthy controls was profiled. In both COVID-19 and control group, 114 (63%) were male. The results indicated four major recovery modes of biological processes. Pathways related to cell-matrix interactions and cytoskeletal remodeling and hypertrophic cardiomyopathy and dilated cardiomyopathy pathways recovered relatively earlier which was before 1-year after infection. Majority of immune response pathways, complement and coagulation cascade, and cholesterol metabolism returned to similar status of matched healthy controls later but before 2-year after infection. Fc receptor signaling pathway still did not return to status similar to healthy controls at 2-year follow-up. Pathways related to neuron generation and differentiation showed persistent suppression across 2-year after infection. Among 98 DEPs from the above pathways, evidence was found for association of 11 proteins with lung function recovery, with the associations consistent at two consecutive or all three follow-ups. These proteins were mainly enriched in complement and coagulation (COMP, PLG, SERPINE1, SRGN, COL1A1, FLNA, and APOE) and hypertrophic/dilated cardiomyopathy (TPM2, TPM1, and AGT) pathways. Two DEPs (APOA4 and LRP1) involved in both neuron and cholesterol pathways showed associations with smell disorder.

INTERPRETATION

The study findings provided molecular insights into potential mechanism of long COVID, and put forward biomarkers for more precise intervention to reduce burden of long COVID.

FUNDING

National Natural Science Foundation of China; Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences; Clinical Research Operating Fund of Central High Level Hospitals; the Talent Program of the Chinese Academy of Medical Science; Training Program of the Big Science Strategy Plan; Ministry of Science and Technology of the People's Republic of China; New Cornerstone Science Foundation; Peking Union Medical College Education Foundation; Research Funds from Health@InnoHK Program.

Hepatic Anti-Oxidative Genes CAT and GPX4 Are Epigenetically Modulated by RORγ/NRF2 in Alphacoronavirus-Exposed Piglets

As a member of alpha-coronaviruses, PEDV could lead to severe diarrhea and dehydration in newborn piglets. Given that lipid peroxides in the liver are key mediators of cell proliferation and death, the role and regulation of endogenous lipid peroxide metabolism in response to coronavirus infection need to be illuminated. The enzymatic activities of SOD, CAT, mitochondrial complex-I, complex-III, and complex-V, along with the glutathione and ATP contents, were significantly decreased in the liver of PEDV piglets. In contrast, the lipid peroxidation biomarkers, malondialdehyde, and ROS were markedly elevated. Moreover, we found that the peroxisome metabolism was inhibited by the PEDV infection using transcriptome analysis. These down-regulated anti-oxidative genes, including GPX4, CAT, SOD1, SOD2, GCLC, and SLC7A11, were further validated by qRT-PCR and immunoblotting. Because the nuclear receptor RORγ-driven MVA pathway is critical for LPO, we provided new evidence that RORγ also controlled the genes CAT and GPX4 involved in peroxisome metabolism in the PEDV piglets. We found that RORγ directly binds to these two genes using ChIP-seq and ChIP-qPCR analysis, where PEDV strongly repressed the binding enrichments. The occupancies of histone active marks such as H3K9/27ac and H3K4me1/2, together with active co-factor p300 and polymerase II at the locus of CAT and GPX4, were significantly decreased. Importantly, PEDV infection disrupted the physical association between RORγ and NRF2, facilitating the down-regulation of the CAT and GPX4 genes at the transcriptional levels. RORγ is a potential factor in modulating the CAT and GPX4 gene expressions in the liver of PEDV piglets by interacting with NRF2 and histone modifications.

Shared miRNA landscapes of COVID-19 and neurodegeneration confirm neuroinflammation as an important overlapping feature

Introduction

Development and worsening of most common neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis, have been associated with COVID-19 However, the mechanisms associated with neurological symptoms in COVID-19 patients and neurodegenerative sequelae are not clear. The interplay between gene expression and metabolite production in CNS is driven by miRNAs. These small non-coding molecules are dysregulated in most common neurodegenerative diseases and COVID-19.

Methods

We have performed a thorough literature screening and database mining to search for shared miRNA landscapes of SARS-CoV-2 infection and neurodegeneration. Differentially expressed miRNAs in COVID-19 patients were searched using PubMed, while differentially expressed miRNAs in patients with five most common neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, and multiple sclerosis) were searched using the Human microRNA Disease Database. Target genes of the overlapping miRNAs, identified with the miRTarBase, were used for the pathway enrichment analysis performed with Kyoto Encyclopedia of Genes and Genomes and Reactome.

Results

In total, 98 common miRNAs were found. Additionally, two of them (hsa-miR-34a and hsa-miR-132) were highlighted as promising biomarkers of neurodegeneration, as they are dysregulated in all five most common neurodegenerative diseases and COVID-19. Additionally, hsa-miR-155 was upregulated in four COVID-19 studies and found to be dysregulated in neurodegeneration processes as well. Screening for miRNA targets identified 746 unique genes with strong evidence for interaction. Target enrichment analysis highlighted most significant KEGG and Reactome pathways being involved in signaling, cancer, transcription and infection. However, the more specific identified pathways confirmed neuroinflammation as being the most important shared feature.

Discussion

Our pathway based approach has identified overlapping miRNAs in COVID-19 and neurodegenerative diseases that may have a valuable potential for neurodegeneration prediction in COVID-19 patients. Additionally, identified miRNAs can be further explored as potential drug targets or agents to modify signaling in shared pathways. Graphical AbstractShared miRNA molecules among the five investigated neurodegenerative diseases and COVID-19 were identified. The two overlapping miRNAs, hsa-miR-34a and has-miR-132, present potential biomarkers of neurodegenerative sequelae after COVID-19. Furthermore, 98 common miRNAs between all five neurodegenerative diseases together and COVID-19 were identified. A KEGG and Reactome pathway enrichment analyses was performed on the list of shared miRNA target genes and finally top 20 pathways were evaluated for their potential for identification of new drug targets. A common feature of identified overlapping miRNAs and pathways is neuroinflammation. AD, Alzheimer's disease; ALS, amyotrophic lateral sclerosis; COVID-19, coronavirus disease 2019; HD, Huntington's disease; KEGG, Kyoto Encyclopedia of Genes and Genomes; MS, multiple sclerosis; PD, Parkinson's disease.

Plasma proteomics identify potential severity biomarkers from COVID-19 associated network

PURPOSE

Coronavirus disease 2019 (COVID-19) continues to threaten public health globally. Severe acute respiratory coronavirus type 2 (SARS-CoV-2) infection-dependent alterations in the host cell signaling network may unveil potential target proteins and pathways for therapeutic strategies. In this study, we aim to define early severity biomarkers and monitor altered pathways in the course of SARS-CoV-2 infection.

EXPERIMENTAL DESIGN

We systematically analyzed plasma proteomes of COVID-19 patients from Turkey by using mass spectrometry. Different severity grades (moderate, severe, and critical) and periods of disease (early, inflammatory, and recovery) are monitored. Significant alterations in protein expressions are used to reconstruct the COVID-19 associated network that was further extended to connect viral and host proteins.

RESULTS

Across all COVID-19 patients, 111 differentially expressed proteins were found, of which 28 proteins were unique to our study mainly enriching in immunoglobulin production. By monitoring different severity grades and periods of disease, CLEC3B, MST1, and ITIH2 were identified as potential early predictors of COVID-19 severity. Most importantly, we extended the COVID-19 associated network with viral proteins and showed the connectedness of viral proteins with human proteins. The most connected viral protein ORF8, which has a role in immune evasion, targets many host proteins tightly connected to the deregulated human plasma proteins.

CONCLUSIONS AND CLINICAL RELEVANCE

Plasma proteomes from critical patients are intrinsically clustered in a distinct group than severe and moderate patients. Importantly, we did not recover any grouping based on the infection period, suggesting their distinct proteome even in the recovery phase. The new potential early severity markers can be further studied for their value in the clinics to monitor COVID-19 prognosis. Beyond the list of plasma proteins, our disease-associated network unravels altered pathways, and the possible therapeutic targets in SARS-CoV-2 infection by connecting human and viral proteins. Follow-up studies on the disease associated network that we propose here will be useful to determine molecular details of viral perturbation and to address how the infection affects human physiology.

COVID-19 plasma proteome reveals novel temporal and cell-specific signatures for disease severity and high-precision disease management

Coronavirus disease 2019 (COVID-19) is a systemic inflammatory condition with high mortality that may benefit from personalized medicine and high-precision approaches. COVID-19 patient plasma was analysed with targeted proteomics of 1161 proteins. Patients were monitored from Days 1 to 10 of their intensive care unit (ICU) stay. Age- and gender-matched COVID-19-negative sepsis ICU patients and healthy subjects were examined as controls. Proteomic data were resolved using both cell-specific annotation and deep-analysis for functional enrichment. COVID-19 caused extensive remodelling of the plasma microenvironment associated with a relative immunosuppressive milieu between ICU Days 3-7, and characterized by extensive organ damage. COVID-19 resulted in (1) reduced antigen presentation and B/T-cell function, (2) increased repurposed neutrophils and M1-type macrophages, (3) relatively immature or disrupted endothelia and fibroblasts with a defined secretome, and (4) reactive myeloid lines. Extracellular matrix changes identified in COVID-19 plasma could represent impaired immune cell homing and programmed cell death. The major functional modules disrupted in COVID-19 were exaggerated in patients with fatal outcome. Taken together, these findings provide systems-level insight into the mechanisms of COVID-19 inflammation and identify potential prognostic biomarkers. Therapeutic strategies could be tailored to the immune response of severely ill patients.

Network Pharmacology and Bioinformatics Analysis Identifies Potential Therapeutic Targets of Paxlovid Against LUAD/COVID-19

Background

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a pandemic in many countries around the world. The virus is highly contagious and has a high fatality rate. Lung adenocarcinoma (LUAD) patients may have higher susceptibility and mortality to COVID-19. While Paxlovid is the first oral drug approved by the U.S. Food and Drug Administration (FDA) for COVID-19, its specific drug mechanism for lung cancer patients infected with COVID-19 remains to be further studied.

Methods

COVID-19 related genes were obtained from NCBI, GeneCards, and KEGG, and then the transcriptome data for LUAD was downloaded from TCGA. The drug targets of Paxlovid were revealed through BATMAN-TCM, DrugBank, SwissTargetPrediction, and TargetNet. The genes related to susceptibility to COVID-19 in LUAD patients were obtained through differential analysis. The interaction of LUAD/COVID-19 related genes was evaluated and displayed by STRING, and a COX risk regression model was established to screen and evaluate the correlation between genes and clinical characteristics. The Venn diagram was drawn to select the candidate targets of Paxlovid against LUAD/COVID-19, and the functional analysis of the target genes was performed using KEGG and GO enrichment analysis. Finally, Cytoscape was used to screen and visualize the Hub Gene, and Autodock was used for molecular docking between the drug and the target.

Result

Bioinformatics analysis was performed by combining COVID-19-related genes with the gene expression and clinical data of LUAD, including analysis of prognosis-related genes, survival rate, and hub genes screened out by the prognosis model. The key targets of Paxlovid against LUAD/COVID-19 were obtained through network pharmacology, the most important targets include IL6, IL12B, LBP. Furthermore, pathway analysis showed that Paxlovid modulates the IL-17 signaling pathway, the cytokine-cytokine receptor interaction, during LUAD/COVID-19 treatment.

Conclusions

Based on bioinformatics and network pharmacology, the prognostic signature of LUAD/COVID-19 patients was screened. And identified the potential therapeutic targets and molecular pathways of Paxlovid Paxlovid in the treatment of LUAD/COVID. As promising features, prognostic signatures and therapeutic targets shed light on improving the personalized management of patients with LUAD.