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  1. Article ; Online: Assessment of the genes and molecular mechanisms of B cells activation through systems biology approaches.

    Fatehi, Razieh

    Human antibodies

    2019  Volume 28, Issue 1, Page(s) 83–87

    Abstract: Two classes of T helper lymphocytes, Th1 and Th2, have different roles in B cell activation based on specific cytokines. To understand the difference of molecular mechanisms of B cell activation, the microarray dataset of B cells co-cultured with type 1 ... ...

    Abstract Two classes of T helper lymphocytes, Th1 and Th2, have different roles in B cell activation based on specific cytokines. To understand the difference of molecular mechanisms of B cell activation, the microarray dataset of B cells co-cultured with type 1 and 2 T helper, Be1 and Be2, were investigated. After quality assessment, using the GEO2R tool, the GSE84948 dataset was re-analyzed. Genes with adjusted p-value ⩽ 0.05 were assumed as differentially expressed (DE). The protein-protein interaction (PPI) networks were constructed using CluePedia plugin of Cytoscape, and analyzed by NetworkAnalyzer tool and MCODE plugin. Using ClueGO plugin of Cytoscape software, gene ontology (GO) analysis was performed. The comparison of Be1 and Be2 cells with naive B cells revealed 8742 and 8748 DE genes, respectively. The topology analysis of PPI networks predicted central genes. Among these, Jak3, Actrt3, and Pik3cb genes were determined as crucial genes in Be1 network. Prkx, Smarca4, and Jak2 genes were defined as critical genes in Be2 PPI network. GO analysis with PPI networks genes resulted in the promotion of immune system activation. In conclusion, we explored holistic methods for molecular assay of the difference between B cell activation mechanisms with Th1 and Th2.
    MeSH term(s) B-Lymphocytes/immunology ; Humans ; Lymphocyte Activation/genetics ; Lymphocyte Activation/immunology ; Protein Interaction Maps/genetics ; Protein Interaction Maps/immunology ; Systems Biology ; Th1 Cells/immunology ; Th2 Cells/immunology
    Language English
    Publishing date 2019-09-15
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1383468-x
    ISSN 1875-869X ; 1093-2607
    ISSN (online) 1875-869X
    ISSN 1093-2607
    DOI 10.3233/HAB-190393
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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  2. Article ; Online: Time-series bioinformatics analysis of SARS-CoV-infected cells to identify the biological processes associated with severe acute respiratory syndrome.

    Fatehi, Razieh / Khosravian, Farinaz / Salehi, Mansoor / Kazemi, Mohammad

    Human antibodies

    2023  Volume 31, Issue 4, Page(s) 81–88

    Abstract: Background: The COVID-19 pandemic, caused by the new virus of the coronavirus family, SARS-CoV-2, could lead to acute respiratory syndrome. The molecular mechanisms related to this disorder are still debatable.: Methods: In this study to understand ... ...

    Abstract Background: The COVID-19 pandemic, caused by the new virus of the coronavirus family, SARS-CoV-2, could lead to acute respiratory syndrome. The molecular mechanisms related to this disorder are still debatable.
    Methods: In this study to understand the pathogenicity mechanism of SARS-CoV-2, using the bioinformatics approaches, we investigated the expression of involved genes, their regulatory, and main signaling pathways during the time on days 1, 2, 3, and 4 of SARS-CoV infected cells.
    Results: Here, our investigation shows the complex changes in gene expression on days 2 and 3 post-infection. The functional analysis showed that especially related to immune response, response to other organisms, and defense response. IL6-AS1 is the predicted long non-coding RNA and is a key regulator during infection. In this study, for the first time has been reported the role of IL6-AS1. Also, the correlation of differential expression genes with the level of immune infiltration was shown in the relationship of Natural killer cells and T cell CD 4+ with DE genes.
    Conclusion: In the current study, identification of the altered expression pattern of genes in SARS-CoV-infected cells in time course also can help identify and link the molecular mechanisms and explore the holistic view of infection of SARS-CoV-2.
    MeSH term(s) Humans ; Pandemics ; Interleukin-6/genetics ; COVID-19/genetics ; SARS-CoV-2 ; Computational Biology ; Biological Phenomena
    Chemical Substances Interleukin-6
    Language English
    Publishing date 2023-12-24
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1383468-x
    ISSN 1875-869X ; 1093-2607
    ISSN (online) 1875-869X
    ISSN 1093-2607
    DOI 10.3233/HAB-230012
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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  3. Article: Identification of Significant Genes and Pathways Associated with Tenascin-C in Cancer Progression by Bioinformatics Analysis.

    Rahimmanesh, Ilnaz / Fatehi, Razieh / Khanahmad, Hossein

    Advanced biomedical research

    2022  Volume 11, Page(s) 17

    Abstract: Background: Tenascin-C (TNC) is a large glycoprotein of the extracellular matrix which associated with poor clinical outcomes in several malignancies. TNC over-expression is repeatedly observed in several cancer tissues and promotes several processes in ...

    Abstract Background: Tenascin-C (TNC) is a large glycoprotein of the extracellular matrix which associated with poor clinical outcomes in several malignancies. TNC over-expression is repeatedly observed in several cancer tissues and promotes several processes in tumor progression. Until quite recently, more needs to be known about the potential mechanisms of TNC as a key player in cancer progression and metastasis.
    Materials and methods: In the present study, we performed a bioinformatics analysis of breast and colorectal cancer expression microarray data to survey TNC role and function with holistic view. Gene expression profiles were analyzed to identify differentially expressed genes (DEGs) between normal samples and cancer biopsy samples. The protein-protein interaction (PPI) networks of the DEGs with CluePedia plugin of Cytoscape software were constructed. Furthermore, after PPI network construction, gene-regulatory networks analysis was performed to predict long noncoding RNAs and microRNAs associated with TNC and cluster analysis was performed. Using the Clue gene ontology (GO) plugin of Cytoscape software, the GO and pathway enrichment analysis were performed.
    Results: PPI and DEGs-miRNA-lncRNA regulatory networks showed TNC is a significant node in a huge network, and one of the main gene with high centrality parameters. Furthermore, from the regulatory level perspective, TNC could be significantly impressed by miR-335-5p. GO analysis results showed that TNC was significantly enriched in cancer-related biological processes.
    Conclusions: It is important to identify the TNC underlying molecular mechanisms in cancer progression, which may be clinically useful for tumor-targeting strategies. Bioinformatics analysis provides an insight into the significant roles that TNC plays in cancer progression scenarios.
    Language English
    Publishing date 2022-02-28
    Publishing country India
    Document type Journal Article
    ZDB-ID 2672524-1
    ISSN 2277-9175
    ISSN 2277-9175
    DOI 10.4103/abr.abr_201_20
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Systems biology approaches toward autosomal dominant polycystic kidney disease (ADPKD).

    Rahimmanesh, Ilnaz / Fatehi, Razieh

    Clinical and translational medicine

    2020  Volume 9, Issue 1, Page(s) 1

    Abstract: Background: Autosomal dominant polycystic kidney disease (ADPKD), a common of monogenetic disorder caused by the polycystic kidney disease-1 (PKD1) or PKD2 genes deficiency. In this study, we have re-analyzed a microarray dataset to generate a holistic ... ...

    Abstract Background: Autosomal dominant polycystic kidney disease (ADPKD), a common of monogenetic disorder caused by the polycystic kidney disease-1 (PKD1) or PKD2 genes deficiency. In this study, we have re-analyzed a microarray dataset to generate a holistic view of this disease.
    Methodology: GSE7869, an expression profiling dataset was downloaded from the Gene Expression Omnibus (GEO) database. After quality control assessment, using GEO2R tool of GEO, genes with adjusted p-value ≤ 0.05 were determined as differentially expressed (DE). The expression profiles from ADPKD samples in different sizes were compared. Using CluePedia plugin of Cytoscape software, the protein-protein interaction (PPI) networks were constructed and analyzed by Cytoscape NetworkAnalyzer tool and MCODE application. Pathway enrichment analysis of clustered genes by MCODE with the high centrality parameters in PPI networks was performed using Cytoscape ClueGO plugin. Moreover, by Enrichr database, microRNAs (miRNAs) and transcription factors (TFs) targeted DE genes were identified.
    Results: In this study to explore the molecular pathogenesis of kidney in ADPKD, mRNA expression profiles of cysts from patients in different sizes were re-analyzed. The comparisons were performed between normal with minimally cystic tissue (MCT) samples, MCTs with small cysts, and small cysts with large cysts. 512, 7024, and 655 DE genes were determined, respectively. The top central genes, e.g. END1, EGFR, and FOXO1 were identified with topology and clustering analysis. DE genes that were significantly enriched in PPI networks are critical genes and their roles in ADPKD remain to be assessed in future experimental studies beside miRNAs and TFs predicted. Furthermore, the functional analysis resulted in which most of them are expected to be associated with ADPKD pathogenesis, such as signal pathways that involved in cell growth, inflammation, and cell polarity.
    Conclusion: We have here explored systematic approaches for molecular mechanisms assay of ADPKD as a monogenic disease, which may also be used for other monogenetic diseases beside complex diseases to provide suitable therapeutic targets.
    Language English
    Publishing date 2020-01-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2697013-2
    ISSN 2001-1326
    ISSN 2001-1326
    DOI 10.1186/s40169-019-0254-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Big data to knowledge: common pitfalls in transcriptomics data analysis and representation.

    Abedi, Maryam / Fatehi, Razieh / Moradzadeh, Kobra / Gheisari, Yousof

    RNA biology

    2019  Volume 16, Issue 11, Page(s) 1531–1533

    Abstract: The omics technologies provide an invaluable opportunity to employ a global view towards human diseases. However, the appropriate translation of big data to knowledge remains a major challenge. In this study, we have performed quality control assessments ...

    Abstract The omics technologies provide an invaluable opportunity to employ a global view towards human diseases. However, the appropriate translation of big data to knowledge remains a major challenge. In this study, we have performed quality control assessments for 91 transcriptomics datasets deposited in gene expression omnibus database and also have evaluated the publications derived from these datasets. This survey shows that drawbacks in the analyses and reports of transcriptomics studies are more common than one may assume. This report is concluded with some suggestions for researchers and reviewers to enhance the minimal requirements for gene expression data generation, analysis and report.
    MeSH term(s) Animals ; Big Data ; Data Accuracy ; Data Analysis ; Databases, Genetic/standards ; Gene Expression Profiling/methods ; Humans ; Mice ; Quality Control
    Language English
    Publishing date 2019-08-12
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2159587-2
    ISSN 1555-8584 ; 1555-8584
    ISSN (online) 1555-8584
    ISSN 1555-8584
    DOI 10.1080/15476286.2019.1652525
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Identification of miRNA-mRNA Network in Autism Spectrum Disorder Using a Bioinformatics Method.

    Noroozi, Rezvan / Dinger, Marcel E / Fatehi, Razieh / Taheri, Mohammad / Ghafouri-Fard, Soudeh

    Journal of molecular neuroscience : MN

    2020  Volume 71, Issue 4, Page(s) 761–766

    Abstract: Autism spectrum disorder (ASD) includes a heterogeneous group of disorders with different contributing genetics and epigenetics factors. Aberrant expression of miRNAs has been detected in ASD children compared with normally developed children. Due to the ...

    Abstract Autism spectrum disorder (ASD) includes a heterogeneous group of disorders with different contributing genetics and epigenetics factors. Aberrant expression of miRNAs has been detected in ASD children compared with normally developed children. Due to the heterogeneity of this disorder, there is no consensus on ASD-associated miRNAs; thus, it is necessary to develop a model for comprehensive assessment of the role of miRNAs in ASD. We interrogated the PubMed, Google Scholar, and Web of Science databases until the end of 2019 to identify ASD-associated miRNAs. In addition, mRNA-coding genes that contribute to the pathogenesis of ASD were downloaded from the SFARI GENE ( https://gene.sfari.org/ ). The obtained 201 miRNAs and 478 target mRNAs were imported into the Cytoscape software suite to construct a miRNA-mRNA network. A protein-protein interaction network was constructed for target mRNAs using the CluPedia program in Cytoscape. Using this approach, we detected five modules that were associated with neurexins and neuroligins, glutamatergic synapse, cell adhesion molecules, NOTCH, MECP2 and circadian clock pathways, L1CAM interactions, and neurotransmitter release cycle. Taken together, functional analysis of these genes led to determination of critical pathways related to CNS disorders. Thus, the suggested approach in the current study resulted in the identification of the most relevant pathways in the pathogenesis of ASD that can be used as biomarkers or therapeutic targets.
    MeSH term(s) Autistic Disorder/genetics ; Autistic Disorder/metabolism ; Computational Biology ; Gene Regulatory Networks ; Humans ; MicroRNAs/genetics ; MicroRNAs/metabolism ; RNA, Messenger/genetics ; RNA, Messenger/metabolism ; Transcriptome
    Chemical Substances MicroRNAs ; RNA, Messenger
    Language English
    Publishing date 2020-09-02
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1043392-2
    ISSN 1559-1166 ; 0895-8696
    ISSN (online) 1559-1166
    ISSN 0895-8696
    DOI 10.1007/s12031-020-01695-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 3006: Show issues Location:
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  7. Article: Heterozygosity analysis of polycystic kidney disease 1 gene microsatellite markers for linkage analysis of autosomal dominant polycystic kidney disease type 1 in the Iranian population.

    Fatehi, Razieh / Khosravi, Sharifeh / Abedi, Maryam / Salehi, Rasoul / Gheisari, Yousof

    Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences

    2017  Volume 22, Page(s) 102

    Abstract: Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic cause of end-stage renal disease. Although imaging techniques are a means of accurate diagnosis when the cysts appear in the third or fourth decades of the ... ...

    Abstract Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic cause of end-stage renal disease. Although imaging techniques are a means of accurate diagnosis when the cysts appear in the third or fourth decades of the patient's life, they are of little value for early diagnosis. Genetic tests are required for preimplantation genetic diagnosis, decision-making for kidney donation to an affected relative. Although mutation of the polycystic kidney disease (
    Materials and methods: In this study, we assessed the informativeness of the
    Results: We found that the HET and PIC values for the D16S475 marker are 0.92 and 0.91, respectively. These two values are 0.82 and 0.80 for D16S291 and 0.50 and 0.47 for D16S3252, respectively.
    Conclusion: Based on this data, D16S475 and D16S291 are highly and D16S3252 is moderately informative for indirect genetic diagnosis of
    Language English
    Publishing date 2017-09-26
    Publishing country India
    Document type Journal Article
    ZDB-ID 2513029-8
    ISSN 1735-7136 ; 1735-1995
    ISSN (online) 1735-7136
    ISSN 1735-1995
    DOI 10.4103/jrms.JRMS_136_17
    Database MEDical Literature Analysis and Retrieval System OnLINE

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