scholarly journals MicroRNA-based regulation of genomics and transcriptomics of inflammatory cytokines in COVID-19

2021 ◽  
Author(s):  
Manoj Khokhar ◽  
Sojit Tomo ◽  
Purvi Purohit
Keyword(s):  
Transcription Factors ◽  
Inflammatory Response ◽  
In Silico ◽  
Wide Spectrum ◽  
Interaction Network ◽  
In Silico Analysis ◽  
Enrichment Analysis ◽  
Microarray Dataset ◽  
Therapeutic Strategies ◽  
Protein Protein Interaction

Background: Coronavirus disease 2019 is characterized by the elevation of a wide spectrum of inflammatory mediators which are associated with poor disease outcomes. We aimed at an in-silico analysis of regulatory microRNA and their transcription factors (TF) for these inflammatory genes that may help to devise potential therapeutic strategies in the future. Methods: The cytokine regulating immune-expressed genes (CRIEG) were sorted from literature and GEO microarray dataset and their co-differentially expressed miRNA and transcription factors were predicted from publicly available databases. Enrichment analysis was done through mienturnet, MiEAA, and Gene Ontology, and pathways predicted by KEGG and Reactome pathways. The functional and regulatory features were analyzed and visualized through Cytoscape. Results: Sixteen CRIEG were observed to have a significant protein-protein interaction network. The ontological analysis revealed significantly enriched pathways for biological processes, molecular functions, and cellular components. The search performed in the miRNA database yielded 10 miRNAs that are significantly involved in the regulation of these genes and their transcription factors. Conclusion: An In-Silico representation of a network involving miRNAs, CRIEGs, and TF which take part in the inflammatory response in COVID-19 has been elucidated. These regulatory factors may have potentially critical roles in the inflammatory response in COVID-19 and may be explored further for the development of targeted therapeutic strategies and mechanistic validation.

scholarly journals An In-Silico Analysis of Differentially Expressed Genes and mi-RNA Regulating Them in HPV Positive and HPV Negative Head and Neck Cancers (H&NC).

2021 ◽  
Author(s):  
Jyoti Shekhawat ◽  
Ashita Gadwal ◽  
Manoj Khokhar ◽  
Kavya Gauba ◽  
Shruti Gupta ◽  
...  
Keyword(s):  
Transcription Factors ◽  
In Silico ◽  
In Silico Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Potential Candidate ◽  
Hub Genes ◽  
Microarray Gene Expression ◽  
Significant Gene

Abstract HPV contributes to the pathogenesis of H&NC. H&NC patients are diagnosed at an advanced stage and are more disposed to lymph node metastasis. There is conflicting evidence with regard to distant metastasis in H&NC. Identification of potential candidate genes which can be used as predictors of prognosis in HPV positive/negative H&NCs are needed. We initiated the study using publicly available microarray gene expression datasets from the GEO. Functional enrichment analysis of significant genes was done using the DAVID. The PPI network was constructed in the STRING and visualized using Cytoscape. The CytoHubb plugin was utilized to identify the key genes in this complicated network. The MCODE plugin was applied to screen out significant gene modules. Hub genes identified were analysed for overall survival (OS) in GEPIA. Analysis of the miRNA and transcription factors targeting the hub gene with significance was done using miRNet. GEO datasets GSE39366, GSE40774, GSE117973 were used in this study. We identified 67 overlapping DEGs which were significant. Gene Ontology identified key terms as ‘cellular component’, ‘biological process’, and ‘molecular function’ respectively. Cytoscape and MCODE revealed two significant modules. The common hub genes identified using cytohubb plugin. CAV1 has been reported to be significantly associated with OS in H&NC patients. 3 targeting miRNA and 54 transcription factors were identified using miRNet. In-silico survival and expression analyses revealed CAV1 as a candidate gene that may be used to predict the course of disease progression in HPV positive and HPV negative H&NC patients. Further invitro studies validating CAV1 and its three target miRNAs as predictors of prognosis in H&NC are highly warranted.

scholarly journals In silico Structural Characterization of Plasmodium falciparum helicase, PfBrr2

2018 ◽  
Vol 15 (3) ◽  
pp. 517-527
Author(s):  
Ritu Saxena ◽  
Prakash Chandra Mishra
Keyword(s):  
Plasmodium Falciparum ◽  
In Silico ◽  
Drug Targets ◽  
3D Structure ◽  
Interaction Network ◽  
In Silico Analysis ◽  
Docking Studies ◽  
Dependent Manner ◽  
Protein Protein Interaction ◽  
Ligand Interactions

Plasmodium falciparum is a causative agent of one of the most devastating disease, cerebral malaria. Absence of suitable vaccine and the emergence of multi drug resistant parasites hinder prevention of malaria disease worldwide. One of the most reliable approaches to control this disease is to develop antimalarial against drug targets which are specific for ubiquitous and necessary enzymes such as helicases. Helicases work in ATP dependent manner and help in unwinding of nucleic acids during replication, transcription and repair mechanism. In this study, in silico analysis and homology modeling method were used to characterize the physicochemical properties and 3D structure of PfBrr2 helicase. Suitable structure of different domains was validated using in silico tools and used for docking studies to understand protein-ligand interactions. Protein-protein interaction network of PfBrr2 was investigated to understand its function inside the parasite.

Bioinformatics Analysis Reveals Functions of MicroRNAs in Rice Under the Drought Stress

2021 ◽  
Vol 15 (8) ◽  
pp. 927-936 ◽  
Author(s):  
Yan Peng ◽  
Yuewu Liu ◽  
Xinbo Chen
Keyword(s):  
Drought Stress ◽  
Target Genes ◽  
Hot Spot ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Protein Protein Interaction ◽  
Promising Tool ◽  
Differentially Expressed Mirnas ◽  
Aba Biosynthesis

Background: Drought is one of the most damaging and widespread abiotic stresses that can severely limit the rice production. MicroRNAs (miRNAs) act as a promising tool for improving the drought tolerance of rice and have become a hot spot in recent years. Objective: In order to further extend the understanding of miRNAs, the functions of miRNAs in rice under drought stress are analyzed by bioinformatics. Method: In this study, we integrated miRNAs and genes transcriptome data of rice under the drought stress. Some bioinformatics methods were used to reveal the functions of miRNAs in rice under drought stress. These methods included target genes identification, differentially expressed miRNAs screening, enrichment analysis of DEGs, network constructions for miRNA-target and target-target proteins interaction. Results: (1) A total of 229 miRNAs with differential expression in rice under the drought stress, corresponding to 73 rice miRNAs families, were identified. (2) 1035 differentially expressed genes (DEGs) were identified, which included 357 up-regulated genes, 542 down-regulated genes and 136 up/down-regulated genes. (3) The network of regulatory relationships between 73 rice miRNAs families and 1035 DEGs was constructed. (4) 25 UP_KEYWORDS terms of DEGs, 125 GO terms and 7 pathways were obtained. (5) The protein-protein interaction network of 1035 DEGs was constructed. Conclusion: (1) MiRNA-regulated targets in rice might mainly involve in a series of basic biological processes and pathways under drought conditions. (2) MiRNAs in rice might play critical roles in Lignin degradation and ABA biosynthesis. (3) MiRNAs in rice might play an important role in drought signal perceiving and transduction.

In silico analysis and prediction of transcription factors of the proteins interacting with astrocyte elevated gene-1

2021 ◽  
Vol 92 ◽  
pp. 107478
Author(s):  
Sushmitha Sriramulu ◽  
Suman K. Nandy ◽  
Harsha Ganesan ◽  
Antara Banerjee ◽  
Surajit Pathak
Keyword(s):  
Transcription Factors ◽  
In Silico ◽  
In Silico Analysis ◽  
Silico Analysis

scholarly journals Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suthanthiram Backiyarani ◽  
Rajendran Sasikala ◽  
Simeon Sharmiladevi ◽  
Subbaraya Uma
Keyword(s):  
Candidate Genes ◽  
Protein Interaction ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
The Difference

AbstractBanana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.

scholarly journals Characterization and risk association of polymorphisms in Aurora kinases A, B and C with genetic susceptibility to gastric cancer development

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Aner Mesic ◽  
Marija Rogar ◽  
Petra Hudler ◽  
Nurija Bilalovic ◽  
Izet Eminovic ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Transcription Factors ◽  
In Silico ◽  
In Silico Analysis ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Mitotic Kinases ◽  
Genes Encoding ◽  
Silico Analysis ◽  
Control Study

Abstract Background Single nucleotide polymorphisms (SNPs) in genes encoding mitotic kinases could influence development and progression of gastric cancer (GC). Methods Case-control study of nine SNPs in mitotic genes was conducted using qPCR. The study included 116 GC patients and 203 controls. In silico analysis was performed to evaluate the effects of polymorphisms on transcription factors binding sites. Results The AURKA rs1047972 genotypes (CT vs. CC: OR, 1.96; 95% CI, 1.05–3.65; p = 0.033; CC + TT vs. CT: OR, 1.94; 95% CI, 1.04–3.60; p = 0.036) and rs911160 (CC vs. GG: OR, 5.56; 95% CI, 1.24–24.81; p = 0.025; GG + CG vs. CC: OR, 5.26; 95% CI, 1.19–23.22; p = 0.028), were associated with increased GC risk, whereas certain rs8173 genotypes (CG vs. CC: OR, 0.60; 95% CI, 0.36–0.99; p = 0.049; GG vs. CC: OR, 0.38; 95% CI, 0.18–0.79; p = 0.010; CC + CG vs. GG: OR, 0.49; 95% CI, 0.25–0.98; p = 0.043) were protective. Association with increased GC risk was demonstrated for AURKB rs2241909 (GG + AG vs. AA: OR, 1.61; 95% CI, 1.01–2.56; p = 0.041) and rs2289590 (AC vs. AA: OR, 2.41; 95% CI, 1.47–3.98; p = 0.001; CC vs. AA: OR, 6.77; 95% CI, 2.24–20.47; p = 0.001; AA+AC vs. CC: OR, 4.23; 95% CI, 1.44–12.40; p = 0.009). Furthermore, AURKC rs11084490 (GG + CG vs. CC: OR, 1.71; 95% CI, 1.04–2.81; p = 0.033) was associated with increased GC risk. A combined analysis of five SNPs, associated with an increased GC risk, detected polymorphism profiles where all the combinations contribute to the higher GC risk, with an OR increased 1.51-fold for the rs1047972(CT)/rs11084490(CG + GG) to 2.29-fold for the rs1047972(CT)/rs911160(CC) combinations. In silico analysis for rs911160 and rs2289590 demonstrated that different transcription factors preferentially bind to polymorphic sites, indicating that AURKA and AURKB could be regulated differently depending on the presence of particular allele. Conclusions Our results revealed that AURKA (rs1047972 and rs911160), AURKB (rs2241909 and rs2289590) and AURKC (rs11084490) are associated with a higher risk of GC susceptibility. Our findings also showed that the combined effect of these SNPs may influence GC risk, thus indicating the significance of assessing multiple polymorphisms, jointly. The study was conducted on a less numerous but ethnically homogeneous Bosnian population, therefore further investigations in larger and multiethnic groups and the assessment of functional impact of the results are needed to strengthen the findings.

scholarly journals Elucidating the Mechanisms of Action of Lianhua Qingwen decoction for the Treatment of COVID-19 via Systems Pharmacology Approaches

2021 ◽  
Author(s):  
Xiting Wang ◽  
Tao Lu
Keyword(s):  
Molecular Docking ◽  
Blood Circulation ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Detection Algorithm ◽  
Network Pharmacology ◽  
Systems Pharmacology ◽  
Protein Protein Interaction ◽  
Further Development ◽  
Protein Protein Interaction Network

Abstract Due to the severity of the COVID-19 epidemic, to identify a proper treatment for COVID-19 is of great significance. Traditional Chinese Medicine (TCM) has shown its great potential in the prevention and treatment of COVID-19. One of TCM decoction, Lianhua Qingwen decoction displayed promising treating efficacy. Nevertheless, the underlying molecular mechanism has not been explored for further development and treatment. Through systems pharmacology and network pharmacology approaches, we explored the potential mechanisms of Lianhua Qingwen treating COVID-19 and acting ingredients of Lianhua Qingwen decoction for COVID-19 treatment. Through this way, we generated an ingredients-targets database. We also used molecular docking to screen possible active ingredients. Also, we applied the protein-protein interaction network and detection algorithm to identify relevant protein groupings of Lianhua Qingwen. Totally, 605 ingredients and 1,089 targets were obtained. Molecular Docking analyses revealed that 35 components may be the promising acting ingredients, 7 of which were underlined according to the comprehensive analysis. Our enrichment analysis of the 7 highlighted ingredients showed relevant significant pathways that could be highly related to their potential mechanisms, e.g. oxidative stress response, inflammation, and blood circulation. In summary, this study suggests the promising mechanism of the Lianhua Qingwen decoction for COVID-19 treatment. Further experimental and clinical verifications are still needed.

scholarly journals Literature based discovery of alternative TCM medicine for adverse reactions to depression drugs

2020 ◽  
Vol 21 (S5) ◽  
Author(s):  
Qing Xie ◽  
Kyoung Min Yang ◽  
Go Eun Heo ◽  
Min Song
Keyword(s):  
Side Effects ◽  
Herbal Medicines ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
New Drugs ◽  
Protein Protein Interaction ◽  
Target Drug ◽  
Target Disease ◽  
Literature Based Discovery

Abstract Background In recent years, Traditional Chinese Medicine (TCM) and alternative medicine have been widely used along with western drugs as a complementary form of treatment. In this study, we first use the scientific literature to identify western drugs with obvious side effects. Then, we find TCM alternatives for these western drugs to ameliorate their side effects. Results We used depression as a case study. To evaluate our method, we showed the relation between herb-ingredients-target-disease for representative alternative herbs of western drugs. Further, a protein-protein interaction network of western drugs and alternative herbs was produced, and we performed enrichment analysis of the targets of the active ingredients of the herbs and examined the enrichment of Gene Ontology terms for Biological Process, Cellular Component, and Molecular Function and KEGG Pathway levels, to show how these targets affect different levels of gene expression. Conclusion Our proposed method is able to select herbs that are highly relevant to the target indication (depression) and are able to treat the side effects caused by the target drug. The compounds from our selected alternative herbal medicines can therefore be complementary to the western drugs and ameliorate their side effects, which may help in the development of new drugs.

scholarly journals Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis

2020 ◽  
Vol 21 (3) ◽  
pp. 1053 ◽  
Author(s):  
Alejandro Cabrera-Andrade ◽  
Andrés López-Cortés ◽  
Gabriela Jaramillo-Koupermann ◽  
César Paz-y-Miño ◽  
Yunierkis Pérez-Castillo ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Bone Cancer ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Pathogenic Genes ◽  
Total List ◽  
Consensus Strategy ◽  
Primary Bone ◽  
Molecular Etiology

Osteosarcoma is the most common subtype of primary bone cancer, affecting mostly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has still not been determined with precision. Therefore, we applied a consensus strategy with the use of several bioinformatics tools to prioritize genes involved in its pathogenesis. Subsequently, we assessed the physical interactions of the previously selected genes and applied a communality analysis to this protein–protein interaction network. The consensus strategy prioritized a total list of 553 genes. Our enrichment analysis validates several studies that describe the signaling pathways PI3K/AKT and MAPK/ERK as pathogenic. The gene ontology described TP53 as a principal signal transducer that chiefly mediates processes associated with cell cycle and DNA damage response It is interesting to note that the communality analysis clusters several members involved in metastasis events, such as MMP2 and MMP9, and genes associated with DNA repair complexes, like ATM, ATR, CHEK1, and RAD51. In this study, we have identified well-known pathogenic genes for osteosarcoma and prioritized genes that need to be further explored.

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