Dataset Information

Multi-Omics Approach in the Identification of Potential Therapeutic Biomolecule for COVID-19.

ABSTRACT: COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has a disastrous effect on mankind due to the contagious and rapid nature of its spread. Although vaccines for SARS-CoV-2 have been successfully developed, the proven, effective, and specific therapeutic molecules are yet to be identified for the treatment. The repurposing of existing drugs and recognition of new medicines are continuously in progress. Efforts are being made to single out plant-based novel therapeutic compounds. As a result, some of these biomolecules are in their testing phase. During these efforts, the whole-genome sequencing of SARS-CoV-2 has given the direction to explore the omics systems and approaches to overcome this unprecedented health challenge globally. Genome, proteome, and metagenome sequence analyses have helped identify virus nature, thereby assisting in understanding the molecular mechanism, structural understanding, and disease propagation. The multi-omics approaches offer various tools and strategies for identifying potential therapeutic biomolecules for COVID-19 and exploring the plants producing biomolecules that can be used as biopharmaceutical products. This review explores the available multi-omics approaches and their scope to investigate the therapeutic promises of plant-based biomolecules in treating SARS-CoV-2 infection.

SUBMITTER: Singh R

PROVIDER: S-EPMC8149611 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multi-Omics Approach in the Identification of Potential Therapeutic Biomolecule for COVID-19.

Singh Rachana R Singh Pradhyumna Kumar PK Kumar Rajnish R Kabir Md Tanvir MT Kamal Mohammad Amjad MA Rauf Abdur A Albadrani Ghadeer M GM Sayed Amany A AA Mousa Shaker A SA Abdel-Daim Mohamed M MM Uddin Md Sahab MS

Frontiers in pharmacology 20210512

COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has a disastrous effect on mankind due to the contagious and rapid nature of its spread. Although vaccines for SARS-CoV-2 have been successfully developed, the proven, effective, and specific therapeutic molecules are yet to be identified for the treatment. The repurposing of existing drugs and recognition of new medicines are continuously in progress. Efforts are being made to single out plant-based novel the ...[more]

PMID: 34054532

Similar Datasets

Project description:BackgroundMuscle atrophy caused by denervation is common in neuromuscular diseases, leading to loss of muscle mass and function. However, a comprehensive understanding of the overall molecular network changes during muscle denervation atrophy is still deficient, hindering the development of effective treatments.MethodIn this study, a sciatic nerve transection model was employed in male C57BL/6 J mice to induce muscle denervation atrophy. Gastrocnemius muscles were harvested at 3 days, 2 weeks, and 4 weeks post-denervation for transcriptomic and proteomic analysis. An integrative multi-omics approach was utilized to identify key genes essential for disease progression. Targeted proteomics using PRM was then employed to validate the differential expression of central genes. Combine single-nucleus sequencing results to observe the expression levels of PRM-validated genes in different cell types within muscle tissue.Through upstream regulatory analysis, NRF2 was identified as a potential therapeutic target. The therapeutic potential of the NRF2-targeting drug Omaveloxolone was evaluated in the mouse model.ResultThis research examined the temporal alterations in transcripts and proteins during muscle atrophy subsequent to denervation. A comprehensive analysis identified 54,534 transcripts and 3,218 proteins, of which 23,282 transcripts and 1,852 proteins exhibited statistically significant changes at 3 days, 2 weeks, and 4 weeks post-denervation. Utilizing multi-omics approaches, 30 hubgenes were selected, and PRM validation confirmed significant expression variances in 23 genes. The findings highlighted the involvement of mitochondrial dysfunction, oxidative stress, and metabolic disturbances in the pathogenesis of muscle atrophy, with a pronounced impact on type II muscle fibers, particularly type IIb fibers. The potential therapeutic benefits of Omaveloxolone in mitigating oxidative stress and preserving mitochondrial morphology were confirmed, thereby presenting novel strategies for addressing muscle atrophy induced by denervation. GSEA analysis results show that Autophagy, glutathione metabolism, and PPAR signaling pathways are significantly upregulated, while inflammation-related and neurodegenerative disease-related pathways are significantly inhibited in the Omaveloxolone group.GSR expression and the GSH/GSSG ratio were significantly higher in the Omaveloxolone group compared to the control group, while MuSK expression was significantly lower than in the control group.ConclusionIn our study, we revealed the crucial role of oxidative stress, glucose metabolism, and mitochondrial dysfunction in denervation-induced muscle atrophy, identifying NRF2 as a potential therapeutic target. Omaveloxolone was shown to stabilize mitochondrial function, enhance antioxidant capacity, and protect neuromuscular junctions, thereby offering promising therapeutic potential for treating denervation-induced muscle atrophy.

Project description:ObjectiveTo identify potential therapeutic targets and evaluate the safety profiles for Idiopathic Pulmonary Fibrosis (IPF) using a comprehensive multi-omics approach.MethodWe integrated genomic and transcriptomic data to identify therapeutic targets for IPF. First, we conducted a transcriptome-wide association study (TWAS) using the Omnibus Transcriptome Test using Expression Reference Summary data (OTTERS) framework, combining plasma expression quantitative trait loci (eQTL) data with IPF Genome-Wide Association Studies (GWAS) summary statistics from the Global Biobank (discovery) and Finngen (duplication). We then applied Mendelian randomization (MR) to explore causal relationships. RNA-seq co-expression analysis (bulk, single-cell and spatial transcriptomics) was used to identify critical genes, followed by molecular docking to evaluate their druggability. Finally, phenome-wide MR (PheW-MR) using GWAS data from 679 diseases in the UK Biobank assessed the potential adverse effects of the identified genes.ResultWe identified 696 genes associated with IPF in the discovery dataset and 986 genes in the duplication dataset, with 126 overlapping genes through TWAS. MR analysis revealed 29 causal genes in the discovery dataset, with 13 linked to increased and 16 to decreased IPF risk. Summary data-based MR (SMR) confirmed six essential genes: ANO9, BRCA1, CCDC200, EZH1, FAM13A, and SFR1. Bulk RNA-seq showed FAM13A upregulation and SFR1 and EZH1 downregulation in IPF. Single-cell RNA-seq revealed gene expression changes across cell types. Molecular docking identified binding solid affinities for essential genes with respiratory drugs, and PheW-MR highlighted potential side effects.ConclusionWe identified six key genes-ANO9, BRCA1, CCDC200, EZH1, FAM13A, and SFR1-as potential drug targets for IPF. Molecular docking revealed strong drug affinities, while PheW-MR analysis highlighted therapeutic potential and associated risks. These findings offer new insights for IPF treatment and further investigation of potential side effects.

Project description:BackgroundCOVID-19 disease is characterized by a spectrum of disease phases (mild, moderate, and severe). Each disease phase is marked by changes in omics profiles with corresponding changes in the expression of features (biosignatures). However, integrative analysis of multiple omics data from different experiments across studies to investigate biosignatures at various disease phases is limited. Exploring an integrative multi-omics profile analysis through a network approach could be used to determine biosignatures associated with specific disease phases and enable the examination of the relationships between the biosignatures.AimTo identify and characterize biosignatures underlying various COVID-19 disease phases in an integrative multi-omics data analysis.MethodWe leveraged a multi-omics network-based approach to integrate transcriptomics, metabolomics, proteomics, and lipidomics data. The World Health Organization Ordinal Scale WHO Ordinal Scale was used as a disease severity reference to harmonize COVID-19 patient metadata across two studies with independent data. A unified COVID-19 knowledge graph was constructed by assembling a disease-specific interactome from the literature and databases. Disease-state specific omics-graphs were constructed by integrating multi-omics data with the unified COVID-19 knowledge graph. We expanded on the network layers of multiXrank, a random walk with restart on multilayer network algorithm, to explore disease state omics-specific graphs and perform enrichment analysis.ResultsNetwork analysis revealed the biosignatures involved in inducing chemokines and inflammatory responses as hubs in the severe and moderate disease phases. We observed distinct biosignatures between severe and moderate disease phases as compared to mild-moderate and mild-severe disease phases. Mild COVID-19 cases were characterized by a unique biosignature comprising C-C Motif Chemokine Ligand 4 (CCL4), and Interferon Regulatory Factor 1 (IRF1). Hepatocyte Growth Factor (HGF), Matrix Metallopeptidase 12 (MMP12), Interleukin 10 (IL10), Nuclear Factor Kappa B Subunit 1 (NFKB1), and suberoylcarnitine form hubs in the omics network that characterizes the moderate disease state. The severe cases were marked by biosignatures such as Signal Transducer and Activator of Transcription 1 (STAT1), Superoxide Dismutase 2 (SOD2), HGF, taurine, lysophosphatidylcholine, diacylglycerol, triglycerides, and sphingomyelin that characterize the disease state.ConclusionThis study identified both biosignatures of different omics types enriched in disease-related pathways and their associated interactions (such as protein-protein, protein-transcript, protein-metabolite, transcript-metabolite, and lipid-lipid interactions) that are unique to mild, moderate, and severe COVID-19 disease states. These biosignatures include molecular features that underlie the observed clinical heterogeneity of COVID-19 and emphasize the need for disease-phase-specific treatment strategies. The approach implemented here can be used to find associations between transcripts, proteins, lipids, and metabolites in other diseases.

Dataset Information

Multi-Omics Approach in the Identification of Potential Therapeutic Biomolecule for COVID-19.

Publications

Multi-Omics Approach in the Identification of Potential Therapeutic Biomolecule for COVID-19.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets