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Among reproductive health problems, idiopathic infertility affects married couples. The current diagnosis of male infertility focuses on the concentration, motility, and morphology of sperm in the ejaculate. Since the molecular mechanism of idiopathic infertility is unknown, identification of Differentially Expressed Genes (DEGs) among the control and idiopathic infertile male can shed light on diagnosis and treatment. Here, we analyzed the dataset GSE65683 to identify DEGs in idiopathic human sperm in three groups of patients: (i) Timed Intercourse (TIC); (ii) Intrauterine Insemination (IUI); and (iii) Assisted Reproductive Technology (ART). The enrichment analysis was carried out using DAVID (Database for Annotation, Visualization and Integrated Discovery) and GeneCodis for the DEGs. Protein-Protein Interaction (PPI) network of these DEGs were constructed using the STRING database. The network parameters such as degree and betweenness were calculated to select the important hubs. In total, 118 DEGs in TIC, 446 in IUI, and 188 in ART were identified. PPI network was constructed and identified critical top hub genes such as ACTB, BTBD6, EIF2S3, EIF3A, EIF4E, POLR2L, RPL4, RPL7, RPS11, RPL13, RPS15, RPL23, RPL27, RPL9, RPLP0 and UBA52 that may play an essential role in idiopathic male infertility. Thus, the identified hub genes may provide an insight into the molecular mechanism and contribute to discovering novel therapeutic targets and developing new strategies for idiopathic male infertility.

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The Zika Virus (ZIKV) infection is a serious, public health concern with no vaccines or antiviral treatments. This study aims to identify the differentially expressed long non-coding RNAs (lncRNAs) in ZIKV infected human-induced neuroprogenitor cells (hiNPCs). Though lncRNA is well-known for its role in gene regulation, its role in ZIKV infection remains unclear. Thus, taking advantage of publicly available transcriptome data, BioProject PRJNA551246 was analysed. Performed the gene ontology and pathway analysis of differentially expressed lncRNAs were functionally interpreted based on the neighbouring protein-coding genes (100 kb upstream and downstream of each lncRNAs). The study revealed 19 novels and 237 differentially expressed lncRNAs in ZIKV infected hiNPCs. They are found to be significantly enriched in type I interferon signalling pathway, negative regulation of viral genome replication, defense response to the virus, pathways involved in Influenza A and Herpes simplex infection, tumor necrosis factor signalling pathway, and apoptosis. In ZIKV, associated microcephaly type I interferon act as potential modulating factors. Type-I interferon inhibits ZIKV replication in many human cell types. The results support future studies on understanding the structure and function of the novel lncRNAs and experimental approaches to determine the role of the lncRNAs in ZIKV induced infection. Supplementary Information: The online version contains supplementary material available at 10.1007/s13337-022-00771-1.

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Ebola Virus (EBOV) is one of the deadliest pathogenic virus which causes hemorrhagic fever. Though many Ebola-human interaction studies and databases are already reported, the unavailability of an adequate model and lack of publically accessible resources requires a comprehensive study to curate the Ebola-Human-Drug interactions. In total, 270 human proteins interacted with EBOV are collected from published experimental evidence. Then the protein-protein interaction networks are generated as EBOV-human and EBOV-Human-Drugs interaction. These results can help the researcher to find the effective repurposed drug for EBOV treatment. Further, the illustration of gene enrichment and pathway analysis would provide knowledge and insight of EBOV-human interaction describes the importance of the study. Investigating the networks may help to identify a suitable human-based drug target for ebola research community. The inclusion of an emerging concept, a human-based drug targeted therapy plays a very significant role in drug repurposing which reduces the time and effort is the highlight of the current research. An integrated database namely, Ebolabase has been developed and linked with other repositories such as Epitopes, Structures, Literature, Genomics and Proteomics. All generated networks are made to be viewed in a customized manner and the required data can be downloaded freely. The Ebolabase is available at http://ebola.bicpu.edu.in.

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The Ebola virus is a human aggressive pathogen causes Ebola virus disease that threatens public health, for which there is no Food Drug Administration approved medication. Drug repurposing is an alternative method to find the novel indications of known drugs to treat the disease effectively at low cost. The present work focused on understanding the host-virus interaction as well as host virus drug interaction to identify the disease pathways and host-directed drug targets. Thus, existing direct physical Ebola-human protein-protein interaction (PPI) was collected from various publicly available databases and also literature through manual curation. Further, the functional and pathway enrichment analysis for the proteins were performed using database for annotation, visualization, and integrated discovery and the enriched gene ontology biological process terms includes chromatin assembly or disassembly, nucleosome organization, nucleosome assembly. Also, the enriched Kyoto Encyclopedia of Genes and Genome pathway terms includes systemic lupus erythematosus, alcoholism, and viral carcinogenesis. From the PPI network, important large histone clusters and tubulin were observed. Further, the host-virus and host-virus-drug interaction network has been generated and found that 182 drugs are associated with 45 host genes. The obtained drugs and their interacting targets could be considered for Ebola treatment.

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The study aimed to explore the molecular mechanism underlying triple-negative breast cancer (TNBC) and to identify their potential diagnostic/prognostic biomarkers. The differentially expressed lncRNAs (DElncRNAs) were identified by meta-analysis and machine learning feature selection methods. The dysregulated lncRNA-miRNA-mRNA network was constructed based on the competing endogenous RNA (ceRNA) hypothesis. A total of 26 DElncRNAs were identified with a meta-analysis approach of which 18 DElncRNAs attained high accuracy in training and test dataset by Support Vector Machine-Recursive Feature Elimination (SVM-RFE) which could act as diagnostic biomarkers. Among the identified DElncRNAs, LINC01315 and CTA-384D8.35 could act as prognostic biomarkers. Finally, two important sub-modules from lncRNA-miRNA-mRNA network were identified which consists of DElncRNAs (LINC01087, LINC01315, and SOX9-AS1) interacting with co-expressed DEmRNAs and DEmiRNAs. Thus, the study indicated the importance of DElncRNAs and highlighted the efficacy as potential biomarkers in TNBC.

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Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer with poor clinical outcomes and lack of approved targeted therapy. Dysregulated microRNAs (miRNAs) have been considered a promising biomarker, which plays an important role in the tumorigenesis of human cancer. Due to the increase in miRNA profiling datasets of TNBC, a proper analysis is required for studying. Therefore, this study used meta-analysis to amalgamate ten miRNA profiling studies of TNBC. By the robust rank aggregation method, metasignatures of six miRNAs (4 upregulated: hsa-miR-135b-5p, hsa-miR-18a-5p, hsa-miR-9-5p and hsa-miR-522-3p; 2 downregulated: hsa-miR-190b and hsa-miR-449a) were obtained. The gene ontology analysis revealed that target genes regulated by miRNAs were associated with processes like the regulation of transcription, DNA dependent, and signal transduction. Also, it is noted from the pathway analysis that signaling and cancer pathways were associated with the progression of TNBC. A Naïve Bayes-based classifier built with miRNA signatures discriminates TNBC and non-TNBC samples in test data set with high diagnostic sensitivity and specificity. From the analysis carried out by the study, it is suggested that the identified miRNAs are of great importance in improving the diagnostics and therapeutics for TNBC.

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Triple-negative breast cancer (TNBC) has attracted more attention compared with other breast cancer subtypes due to its aggressive nature, poor prognosis, and chemotherapy remains the mainstay of treatment with no other approved targeted therapy. Therefore, the study aimed to discover more promising therapeutic targets and investigating new insights of biological mechanism of TNBC. Six microarray data sets consisting of 463 non-TNBC and 405 TNBC samples were mined from Gene Expression Omnibus. The data sets were integrated by meta-analysis and identified 1075 differentially expressed genes. Protein-protein interaction network was constructed which consists of 486 nodes and 1932 edges, where 29 hub genes were obtained with high topological measures. Further, 16 features (hub genes), 12 upregulated (AURKB, CCNB2, CDC20, DDX18, EGFR, ENO1, MYC, NUP88, PLK1, PML, POLR2F, and SKP2) and four downregulated ( CCND1, GLI3, SKP1, and TGFB3) were selected through machine learning correlation based feature selection method on training data set. A naïve Bayes based classifier built using the expression profiles of 16 features (hub genes) accurately and reliably classify TNBC from non-TNBC samples in the validation test data set with a receiver operating curve of 0.93 to 0.98. Subsequently, Gene Ontology analysis revealed that the hub genes were enriched in mitotic cell cycle processes and Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that they were enriched in cell cycle pathways. Thus, the identified key hub genes and pathways highlighted in the study would enhance the understanding of molecular mechanism of TNBC which may serve as potential therapeutic target.

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Zika virus (ZIKV), a single-strand RNA flavivirus, is transmitted primarily through Aedes aegypti. The recent outbreaks in America and unexpected association between ZIKV infection and birth defects have triggered the global attention. This vouches to understand the molecular mechanisms of ZIKV infection to develop effective drug therapy. A systems-level understanding of biological process affected by ZIKV infection in fetal brain sample led us to identify the candidate genes for pharmaceutical intervention and potential biomarkers for diagnosis. To identify the key genes, transcriptomics data (RNA-Seq) with GSE93385 of ZIKV (Strain: MR766) infected human fetal neural stem cell are analyzed. In total, 1,084 differentially expressed genes (DEGs) are identified, that is, 471 upregulated and 613 downregulated genes. Further analysis such as the gene ontology term suggested that the downregulated genes are mostly enriched in defense response to virus, receptor binding, laminin binding, extracellular matrix, endoplasmic reticulum, and for upregulated DEGs: translation initiation, RNA binding, cytosol, and nucleosome are enriched. And through pathway analysis, systemic lupus erythematosus (SLE) is found to be the most enriched pathway. Protein-protein interaction (PPI) network is constructed to find the hub genes using STRING database. The seven key genes namely cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), histone cluster 1 H2B family member K, (HIST1H2BK) histone cluster 1 H2B family member O (HIST1H2BO), and histone cluster 1 H2B family member B (HIST1H2BB), polo-like kinase 1 (PLK1), and cell division cycle 20 (CDC20) with highest degree are found to be hub genes using Centiscape, a Cytoscape plugin. The modules of PPI network using Molecular Complex Detection plugin are found significant in structural constituent of ribosome, defense response to virus, nucleosome, SLE, extracellular region, and regulation of gene silencing. Thus, identified key hub genes and pathways shed light on molecular mechanism that may contribute to the discovery of novel therapeutic targets and development of new strategies for the intervention of ZIKV disease.

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Re-emergence of ZIKV has caused infections in more than 1.5 million people. The molecular mechanism and pathogenesis of ZIKV is not well explored due to unavailability of adequate model and lack of publically accessible resources to provide information of ZIKV-Human protein interactome map till today. This study made an attempt to curate the ZIKV-Human interaction proteins from published literatures and RNA-Seq data. 11 direct interaction, 12 associated genes are retrieved from literatures and 3742 Differentially Expressed Genes (DEGs) are obtained from RNA-Seq analysis. The genes have been analyzed to construct the ZIKV-Human Interactome Map. The importance of the study has been illustrated by the enrichment analysis and observed that direct interaction and associated genes are enriched in viral entry into host cell. Also, ZIKV infection modulates 32% signal and 27% immune system pathways. The integrated database, ZikaBase has been developed to help the virology research community and accessible at https://test5.bicpu.edu.in.

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