Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach

Frontiers in Immunology, Год журнала: 2022, Номер 13

Опубликована: Авг. 31, 2022

Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with high mortality rate. In clinical practice, we have noted that many critically ill or patients COVID-19 present typical sepsis-related manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. addition, it been demonstrated some pathological similarities sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted neutrophil dysfunction. Considering the parallels between non-SARS-CoV-2 induced sepsis (hereafter referred to sepsis), aim of this study was analyze underlying molecular mechanisms these two diseases bioinformatics systems biology approach, providing new insights into pathogenesis development treatments. Specifically, gene expression profiles were obtained from Gene Expression Omnibus (GEO) database compared extract common differentially expressed genes (DEGs). Subsequently, DEGs used investigate genetic links sepsis. Based on enrichment analysis DEGs, pathways closely related inflammatory response observed, Cytokine-cytokine receptor interaction pathway NF-kappa B signaling pathway. protein-protein networks regulatory constructed, results showed ITGAM may be potential key biomarker base analysis. Furthermore, diagnostic model risk prediction nomogram for constructed using machine learning methods. Finally, therapeutic agents, progesterone emetine, screened through drug-protein docking simulations. We hope provide strategies future research treatment elucidating

Язык: Английский

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Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies

PLoS ONE, Год журнала: 2022, Номер 17(5), С. e0268967 - e0268967

Опубликована: Май 26, 2022

Integrated bioinformatics and statistical approaches are now playing the vital role in identifying potential molecular biomarkers more accurately presence of huge number alternatives for disease diagnosis, prognosis therapies by reducing time cost compared to wet-lab based experimental procedures. Breast cancer (BC) is one leading causes related deaths women worldwide. Several dry-lab studies have identified different sets BC. But they did not compare their results each other so much either computationally or experimentally. In this study, an attempt was made propose a set that might be effective BC therapies, using integrated approaches. At first, we 190 differentially expressed genes (DEGs) between control samples LIMMA approach. Then 13 DEGs ( AKR1C1 , IRF9 OAS1 OAS3 SLCO2A1 NT5E NQO1 ANGPT1 FN1 ATF6B HPGD BCL11A TP53INP1 ) as key (KGs) protein-protein interaction (PPI) network analysis. investigated pathogenetic processes highlighting KGs GO terms KEGG pathway enrichment Moreover, disclosed transcriptional post-transcriptional regulatory factors analysis with transcription (TFs) micro-RNAs. Both supervised unsupervised learning’s including multivariate survival confirmed strong prognostic power proposed KGs. Finally, suggested KGs-guided seven candidate drugs (NVP-BHG712, Nilotinib, GSK2126458, YM201636, TG-02, CX-5461, AP-24534) published cross-validation state-of-the-art top-ranked independent receptor proteins. Thus, our findings played breast therapies.

Язык: Английский

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Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis

Journal of Translational Medicine, Год журнала: 2023, Номер 21(1)

Опубликована: Ноя. 1, 2023

Long COVID is a debilitating chronic condition that has affected over 100 million people globally. It characterized by diverse array of symptoms, including fatigue, cognitive dysfunction and respiratory problems. Studies have so far largely failed to identify genetic associations, the mechanisms behind disease, or any common pathophysiology with other conditions such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) present similar symptoms.We used combinatorial analysis approach combinations variants significantly associated development long examine biological underpinning its various symptoms. We compared two subpopulations patients from Sano Genetics' GOLD study cohort, focusing on severe dominant phenotypes. evaluated signatures previously identified in an ME/CFS population against this understand similarities disorders may be triggered prior viral infection. Finally, we also output known associations diseases, range metabolic neurological disorders, overlap pathophysiological mechanisms.Combinatorial 73 genes were highly at least one populations included analysis. Of these, 9 acute COVID-19, 14 differentially expressed transcriptomic patients. A pathway enrichment revealed pathways most mainly aligned cardiometabolic diseases. Expanded genotype suggests specific SNX9 genotypes are significant contributor risk protection infection, but gene-disease relationship context dependent mediated interactions KLF15 RYR3. Comparison uniquely Severe Fatigue Dominant differences between enriched each subgroup. The unique immune myeloid differentiation macrophage foam cells. Genes subgroup MAPK/JNK signaling. ME/CFS, several involved circadian rhythm regulation insulin regulation. Overall, 39 SNPs can linked recent patient UK Biobank. Among COVID, 42 potentially tractable for novel drug discovery approaches, 13 these already targeted drugs clinical pipelines. From example, TLR4 antagonists repurposing candidates potential protect term impairment pathology caused SARS-CoV-2. currently evaluating targets use treating and/or ME/CFS.This demonstrates power analytics stratifying heterogeneous complex diseases do not simple monogenic etiologies. These results build upon findings analyses COVID-19 expect access additional independent, larger datasets will further improve disease insights validate treatment options COVID.

Язык: Английский

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In-silico study for the identification of potential destabilizers between the spike protein of SARS-CoV-2 and human ACE-2

Informatics in Medicine Unlocked, Год журнала: 2023, Номер 40, С. 101278 - 101278

Опубликована: Янв. 1, 2023

The emergence of the new SARS-CoV-2 virus, which causes disease known as COVID-19, has generated a pandemic that plunged world into health crisis. infection process is triggered by direct binding receptor-binding domain (RBD) spike (S) protein to angiotensin-converting enzyme 2 (ACE2) host cell. In present study, virtual screening techniques such molecular docking, dynamics, calculation free energy using GBSA method, prediction drug similarity, pharmacokinetic, and toxicological properties various ligands interacting with RBD-ACE2 complex were applied. radotinib, hinokiflavone, ginkgetin identified potential destabilizers interaction, could produce their pharmacological effect at an allosteric site ACE2, affinity values −10.2 ± 0.1, −9.8 0.0, −9.4 0.0 kcal/mol, indicating strong receptor affinity. hinokiflavone showed highest conformational stability rigidity dynamic simulation also obtained best three molecules, −215.86 kcal/mol.

Язык: Английский

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Bioinformatics and system biology approach to identify the influences among COVID-19, ARDS and sepsis

Frontiers in Immunology, Год журнала: 2023, Номер 14

Опубликована: Май 16, 2023

Background Severe coronavirus disease 2019 (COVID -19) has led to severe pneumonia or acute respiratory distress syndrome (ARDS) worldwide. we have noted that many critically ill patients with COVID-19 present typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. The molecular mechanisms underlie COVID-19, ARDS sepsis are not well understood. objectives of this study were analyze potential identify drugs for the treatment using bioinformatics a systems biology approach. Methods Three RNA-seq datasets (GSE171110, GSE76293 GSE137342) from Gene Expression Omnibus (GEO) employed detect mutual differentially expressed genes (DEGs) functional enrichment, pathway analysis, candidate analysis. Results We obtained 110 common DEGs among sepsis. ARG1, FCGR1A, MPO, TLR5 most influential hub genes. infection immune-related pathways functions main these three diseases. FOXC1, YY1, GATA2, FOXL, STAT1 STAT3 important TFs COVID-19. mir-335-5p, miR-335-5p hsa-mir-26a-5p associated Finally, retrieved DSigDB database indicate drug molecules drug-targets interaction. Conclusion performed analysis under ontology terms found some associations Transcription factors–genes interaction, protein–drug interactions, DEGs-miRNAs coregulatory network also identified on datasets. believe in may contribute effective

Язык: Английский

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Concurrent Preimplantation Genetic Testing and Competence Assessment of Human Embryos by Transcriptome Sequencing

Advanced Science, Год журнала: 2024, Номер 11(32)

Опубликована: Июнь 20, 2024

Abstract Preimplantation genetic testing (PGT) can minimize the risk of birth defects. However, accuracy and applicability routine PGT is confounded by uneven genome coverage high allele drop‐out rate from existing single‐cell whole amplification methods. Here, a method to diagnose mutations concurrently evaluate embryo competence leveraging abundant mRNA transcript copies present in trophectoderm cells developed. The feasibility confirmed with 19 donated blastocysts. Next, applied 82 embryos 26 families monogenic defects for simultaneous mutation detection assessment. direct up 95%, which significantly higher than DNA‐based method. Meanwhile, this approach correctly predicted seven out eight (87.5%) that failed implant. Of six are implant successfully, four met such expectations (66.7%). Notably, superior at conditions challenging when using PGT, as detecting pathogenic genes de novo rate, multiple pseudogenes, or an abnormal expansion CAG trinucleotide repeats. Taken together, study establishes RNA‐based also informative assessing implantation competence.

Язык: Английский

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Identification of Drug Targets and Agents Associated with Hepatocellular Carcinoma through Integrated Bioinformatics Analysis

Current Cancer Drug Targets, Год журнала: 2023, Номер 23(7), С. 547 - 563

Опубликована: Фев. 14, 2023

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death globally. The mechanisms underlying development HCC are mostly unknown till now.The main goal this study was to identify potential drug target proteins and agents for treatment HCC.The publicly available three independent mRNA expression profile datasets were downloaded from NCBI-GEO database explore common differentially expressed genes (cDEGs) between control samples using Statistical LIMMA approach. Hub-cDEGs as targets highlighting their functions, pathways, regulators identified by integrated bioinformatics tools databases. Finally, Hub-cDEGs-guided top-ranked molecular docking HCC.We 160 DEGs in which ten cDEGs (CDKN3, TK1, NCAPG, CDCA5, RACGAP1, AURKA, PRC1, UBE2T, MELK, ASPM) selected Hub-cDEGs. GO functional KEGG pathway enrichment analysis revealed some crucial cancer-stimulating biological processes, cellular components, signaling pathways. interaction network TF five miRNAs key transcriptional post-transcriptional HubcDEGs. Then, we detected proposed guided anti-HCC molecules (Dactinomycin, Vincristine, Sirolimus) that also highly supported already published HCC-causing Hub-DEGs mediated receptors.The findings would be useful resources diagnosis, prognosis, therapies HCC.

Язык: Английский

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Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections

PLoS ONE, Год журнала: 2023, Номер 18(3), С. e0281981 - e0281981

Опубликована: Март 13, 2023

The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite success vaccination efforts in reducing spread virus, situation remains largely uncontrolled due random mutation RNA sequence acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants effective drugs. Disease-causing gene-mediated proteins are usually used as receptors explore drug molecules. In this study, we analyzed two RNA-Seq one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted co-expression network robust rank aggregation approaches, revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 IL6 host genomic biomarkers. Gene Ontology pathway enrichment analyses HubGs significantly enriched some crucial biological processes, molecular functions, cellular components signaling pathways that associated with mechanisms infections. Regulatory analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 MYC) miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p hsa-miR-20a-5p) key transcriptional post-transcriptional regulators HubGs. Then, conducted docking determine potential candidates could interact HubGs-mediated receptors. This resulted identification ten agents, Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole Danoprevir. Finally, investigated binding stability three molecules Tegobuvir Proscillaridin proposed (AURKA, OAS1) using 100 ns MD-based MM-PBSA simulations observed their stable performance. Therefore, findings study might be useful resources for diagnosis therapies

Язык: Английский

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Identification of Hub of the Hub-Genes From Different Individual Studies for Early Diagnosis, Prognosis, and Therapies of Breast Cancer

Bioinformatics and Biology Insights, Год журнала: 2024, Номер 18

Опубликована: Янв. 1, 2024

Breast cancer (BC) is a complex disease, which causes of high mortality rate in women. Early diagnosis and therapeutic improvements may reduce the rate. There were more than 74 individual studies that have suggested BC-causing hub-genes (HubGs) literature. However, we observed their HubG sets are not so consistent with each other. It be happened due to regional environmental variations sample units. Therefore, it was required explore hub (hHubG) might representative for early therapies BC different country regions environments. In this study, selected top-ranked 10 HubGs (

Язык: Английский

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Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis

Vaccines, Год журнала: 2022, Номер 10(5), С. 771 - 771

Опубликована: Май 12, 2022

Non-small-cell lung cancer (NSCLC) is considered as one of the malignant cancers that causes premature death. The present study aimed to identify a few potential novel genes highlighting their functions, pathways, and regulators for diagnosis, prognosis, therapies NSCLC by using integrated bioinformatics approaches. At first, we picked out 1943 DEGs between control samples statistical LIMMA approach. Then selected 11 (CDK1, EGFR, FYN, UBC, MYC, CCNB1, FOS, RHOB, CDC6, CDC20, CHEK1) hub-DEGs (potential key genes) protein-protein interaction network analysis DEGs. regulatory commonly revealed four transcription factors (FOXC1, GATA2, YY1, NFIC) five miRNAs (miR-335-5p, miR-26b-5p, miR-92a-3p, miR-155-5p, miR-16-5p) transcriptional post-transcriptional well hub-DEGs. We also disclosed pathogenetic processes investigating biological processes, molecular function, cellular components, KEGG pathways multivariate survival probability curves based on expression in SurvExpress web-tool database showed significant differences low- high-risk groups, which indicates strong prognostic power Then, explored top-ranked 5-hub-DEGs-guided repurposable drugs Connectivity Map (CMap) database. Out drugs, validated six FDA-approved launched (Dinaciclib, Afatinib, Icotinib, Bosutinib, Dasatinib, TWS-119) docking with respective target proteins treatment against NSCLC. detected therapeutic targets require further attention experimental studies establish them biomarkers precision medicine treatment.

Язык: Английский

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