Dataset Information

Differential gene expression profiling reveals potential biomarkers and pharmacological compounds against SARS-CoV-2: Insights from machine learning and bioinformatics approaches.

ABSTRACT: The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has created an urgent global situation. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability and disease comorbidities. The pandemic continues to spread worldwide, despite intense efforts to develop multiple vaccines and therapeutic options against COVID-19. However, the precise role of SARS-CoV-2 in the pathophysiology of the nasopharyngeal tract (NT) is still unfathomable. This study utilized machine learning approaches to analyze 22 RNA-seq data from COVID-19 patients (n = 8), recovered individuals (n = 7), and healthy individuals (n = 7) to find disease-related differentially expressed genes (DEGs). We compared dysregulated DEGs to detect critical pathways and gene ontology (GO) connected to COVID-19 comorbidities. We found 1960 and 153 DEG signatures in COVID-19 patients and recovered individuals compared to healthy controls. In COVID-19 patients, the DEG-miRNA, and DEG-transcription factors (TFs) interactions network analysis revealed that E2F1, MAX, EGR1, YY1, and SRF were the highly expressed TFs, whereas hsa-miR-19b, hsa-miR-495, hsa-miR-340, hsa-miR-101, and hsa-miR-19a were the overexpressed miRNAs. Three chemical agents (Valproic Acid, Alfatoxin B1, and Cyclosporine) were abundant in COVID-19 patients and recovered individuals. Mental retardation, mental deficit, intellectual disability, muscle hypotonia, micrognathism, and cleft palate were the significant diseases associated with COVID-19 by sharing DEGs. Finally, the detected DEGs mediated by TFs and miRNA expression indicated that SARS-CoV-2 infection might contribute to various comorbidities. Our results provide the common DEGs between COVID-19 patients and recovered humans, which suggests some crucial insights into the complex interplay between COVID-19 progression and the recovery stage, and offer some suggestions on therapeutic target identification in COVID-19 caused by the SARS-CoV-2.

SUBMITTER: Hoque MN

PROVIDER: S-EPMC9429819 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Differential gene expression profiling reveals potential biomarkers and pharmacological compounds against SARS-CoV-2: Insights from machine learning and bioinformatics approaches.

Hoque M Nazmul MN Sarkar Md Murshed Hasan MMH Khan Md Arif MA Hossain Md Arju MA Hasan Md Imran MI Rahman Md Habibur MH Habib Md Ahashan MA Akter Shahina S Banu Tanjina Akhtar TA Goswami Barna B Jahan Iffat I Nafisa Tasnim T Molla Md Maruf Ahmed MMA Soliman Mahmoud E ME Araf Yusha Y Khan M Salim MS Zheng Chunfu C Islam Tofazzal T

Frontiers in immunology 20220817

The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has created an urgent global situation. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability and disease comorbidities. The pandemic continues to spread worldwide, despite intense efforts to develop multiple vaccines and therapeutic options against C ...[more]

PMID: 36059456

Similar Datasets

Project description:BackgroundRecently, numerous novel bioactive fungal metabolites have been identified that possess broad therapeutic activities including anti-inflammatory, antibiotic, antioxidant, and antitumor. The fungal mycochemicals as well as extracts have increased the interest of the scientific community in drug discovery research through a combination approach such as, molecular metabolic, pharmacological and computational techniques. Therefore, the natural fungus Aspergillus ficuum (A. ficuum) (FCBP-DNA-1266) was selected for metabolic and pharmacological profiling in this study.ResultsThe metabolic profile of A. ficuum was explored for the first time and revealed the presence of bioactive compounds such as choline sulfate, noruron, hydroxyvittatine, aurasperone D, cetrimonium, kurilensoside, heneicosane, nonadecane and eicosane. Similarly, a pharmacological screen of A. ficuum was performed for the first time in in vivo and in vitro models. Interestingly, both the ethyl acetate and n-hexane fractions of A. ficuum were found to be more active against Bacillus subtilis among five tested bacteria with their zone of inhibition (ZOI) values of 21.00 mm ±1.00 and 23.00 mm ±1.00, at a concentration of 150 μgmL-1 respectively. Similarly, a significant decrease (P<0.001) and (P<0.01) in paw edema was observed in A. ficuum-treated animals at doses of 50 and 150 mgkg-1, respectively, reflecting its potent anti-inflammatory effect. Furthermore, the docking results supported the antibacterial and anti-inflammatory effects of A. ficuum. In addition, the crude extract demonstrated no acute toxicity and the highest percent radical scavenging was recorded for both n-hexane and ethyl acetate extracts.ConclusionThe metabolic profile of A. ficuum indicated the presence of biological relevant compounds. A. ficuum extract exhibited potent antibacterial and anti-inflammatory effects supported by docking results. Furthermore, A. ficuum extract demonstrated the highest percentage of radical scavenging activity along with no acute toxicity.

Project description:Sepsis, characterized as a systemic inflammatory response triggered by pathogen invasion, represents a continuum that may progress from mild systemic infection to severe sepsis, potentially culminating in septic shock and multiple organ dysfunction syndrome. A pivotal element in the pathogenesis and progression of sepsis involves the significant disruption of oncological metabolic networks, where cells within the pathological milieu exhibit metabolic functions that diverge from their healthy counterparts. Among these, purine metabolism plays a crucial role in nucleic acid synthesis. However, the contribution of Purine Metabolism Genes (PMGs) to the defense mechanisms against sepsis remains inadequately explored. Leveraging bioinformatics, this study aimed to identify and substantiate potential PMGs implicated in sepsis. The approach encompassed a differential expression analysis across a pool of 75 candidate PMGs. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were employed to assess the biological significance and pathways associated with these genes. Additionally, Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) methodologies were implemented to identify key hub genes and evaluate the diagnostic potential of nine selected PMGs in sepsis identification. The study also examined the correlation between these hub PMGs and related genes, with validation conducted through expression level analysis using the GSE13904 and GSE65682 datasets. The study identified twelve PMGs correlated with sepsis, namely AK9, ENTPD3, NUDT16, GMPR2, PKM, RRM2B, POLR2J, POLE3, ADCY3, ADCY4, ADSSL1, and AMPD1. Functional analysis revealed their involvement in critical processes such as purine nucleotide and ribose phosphate metabolism. The diagnostic capability of these PMGs to effectively differentiate sepsis cases underscored their potential as biomarkers. This research elucidates twelve PMGs associated with sepsis, providing valuable insights into novel biomarkers for this condition and facilitating the monitoring of its progression. These findings highlight the significance of purine metabolism in sepsis pathogenesis and open avenues for further investigation into therapeutic targets.

Project description:Sepsis, characterized as a systemic inflammatory response triggered by the invasion of pathogens, represents a continuum that may escalate from mild systemic infection to severe sepsis, potentially resulting in septic shock and multiple organ dysfunction syndrome. Advancements in lipidomics and metabolomics have unveiled the complex role of fatty acid metabolism (FAM) in both healthy and pathological states. Leveraging bioinformatics, this investigation aimed to identify and substantiate potential FAM-related genes (FAMGs) implicated in sepsis. The approach encompassed a differential expression analysis across a pool of 36 candidate FAMGs. GSEA and GSVA were employed to assess the biological significance and pathways associated with these genes. Furthermore, Lasso regression and SVM-RFE methodologies were implemented to determine key hub genes and assess the diagnostic prowess of nine selected FAMGs in sepsis identification. The study also investigated the correlation between these hub FAMGs. Validation was conducted through expression-level analysis using the GSE13904 and GSE65682 datasets. The study identified 13 sepsis-associated FAMGs, including ABCD2, ACSL3, ACSM1, ACSS1, ACSS2, ACOX1, ALDH9A1, ACACA, ACACB, FASN, OLAH, PPT1, and ELOVL4. As demonstrated by functional enrichment analysis results, these genes played key roles in several critical biological pathways, such as the Peroxisome, PPAR signaling pathway, and Insulin signaling pathway, all of which are intricately linked to metabolic regulation and inflammatory responses. The diagnostic potential of these FAMGs was further highlighted. In short, the expression patterns of these FAMGs c effectively distinguished sepsis cases from non-septic controls, which suggested that they may be promising biomarkers for early sepsis detection. This discovery not only enhanced our understanding of the molecular mechanisms underpinning sepsis but also paved the way for developing novel diagnostic tools and therapeutic strategies targeting metabolic dysregulation in septic patients. This research sheds light on 13 FAMGs associated with sepsis, providing valuable insights into novel biomarkers for this condition and facilitating the monitoring of its progression. These findings underscore the significance of purine metabolism in sepsis pathogenesis and open avenues for further investigation into therapeutic targets.

Dataset Information

Differential gene expression profiling reveals potential biomarkers and pharmacological compounds against SARS-CoV-2: Insights from machine learning and bioinformatics approaches.

Publications

Differential gene expression profiling reveals potential biomarkers and pharmacological compounds against SARS-CoV-2: Insights from machine learning and bioinformatics approaches.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets