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      Analyzing molecular signatures in preeclampsia and fetal growth restriction: Identifying key genes, pathways, and therapeutic targets for preterm birth

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          Abstract

          Background:

          Intrauterine growth restriction (IUGR) and preeclampsia (PE) are intricately linked with specific maternal health conditions, exhibit shared placental abnormalities, and play pivotal roles in precipitating preterm birth (PTB) incidences. However, the molecular mechanism underlying the association between PE and IUGR has not been determined. Therefore, we aimed to analyze the data of females with PE and those with PE + IUGR to identify the key gene(s), their molecular pathways, and potential therapeutic interactions.

          Methods:

          In this study, a comprehensive relationship analysis of both PE and PE + IUGR was conducted using RNA sequence datasets. Using two datasets (GSE148241 and GSE114691), differential gene expression analysis via DESeq2 through R-programming was performed. Gene set enrichment analysis was performed using ClusterProfiler, protein‒protein interaction (PPI) networks were constructed, and cluster analyses were conducted using String and MCODE in Cytoscape. Functional enrichment analyses of the resulting subnetworks were performed using ClueGO software. The hub genes were identified under both conditions using the CytoHubba method. Finally, the most common hub protein was docked against a library of bioactive flavonoids and PTB drugs using the PyRx AutoDock tool, followed by molecular dynamic (MD) simulation analysis. Pharmacokinetic analysis was performed to determine the ADMET properties of the compounds using pkCSM.

          Results:

          We identified eight hub genes highly expressed in the case of PE, namely, PTGS2, ENG, KIT, MME, CGA, GAPDH, GPX3, and P4HA1, and the network of the PE + IUGR gene set demonstrated that nine hub genes were overexpressed, namely, PTGS2, FGF7, FGF10, IL10, SPP1, MPO, THBS1, CYBB, and PF4. PTGS2 was the most common hub gene found under both conditions (PE and PEIUGR). Moreover, the greater (−9.1 kcal/mol) molecular binding of flavoxate to PTGS2 was found to have satisfactory pharmacokinetic properties compared with those of other compounds. The flavoxate-bound PTGS2 protein complex remained stable throughout the simulation; with a ligand fit to protein, i.e., a RMSD ranging from ∼2.0 to 4.0 Å and a RMSF ranging from ∼0.5 to 2.9 Å, was observed throughout the 100 ns analysis.

          Conclusion:

          The findings of this study may be useful for treating PE and IUGR in the management of PTB.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            clusterProfiler 4.0: A universal enrichment tool for interpreting omics data

            Summary Functional enrichment analysis is pivotal for interpreting high-throughput omics data in life science. It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible. To meet these requirements, we present here an updated version of our popular Bioconductor package, clusterProfiler 4.0. This package has been enhanced considerably compared with its original version published 9 years ago. The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases. It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization. Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists. We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.
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              NCBI GEO: archive for functional genomics data sets—update

              The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/2191048/overviewRole: Role: Role: Role: Role: Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2672507/overviewRole: Role: Role: Role: Role: Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2653080/overviewRole: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/511415/overviewRole: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/1942441/overviewRole: Role: Role:
                Role: Role: Role:
                Journal
                Front Mol Biosci
                Front Mol Biosci
                Front. Mol. Biosci.
                Frontiers in Molecular Biosciences
                Frontiers Media S.A.
                2296-889X
                22 April 2024
                2024
                : 11
                : 1384214
                Affiliations
                [1] 1 Computational Biochemistry Research Laboratory , Department of Biochemistry , Dow Medical College , Dow University of Health Sciences , Karachi, Pakistan
                [2] 2 Department of Biosciences , Faculty of Life Sciences , Mohammad Ali Jinnah University , Karachi, Pakistan
                [3] 3 Department of Biochemistry , Medicine Program , Batterjee Medical College , Jeddah, Saudi Arabia
                [4] 4 Department of Pharmaceutics , College of Pharmacy , King Saud University , Riyadh, Saudi Arabia
                [5] 5 College of Pharmacy , Mercer University , Atlanta, GA, United States
                [6] 6 Department of Biochemistry , University of Karachi , Karachi, Pakistan
                Author notes

                Edited by: Vikram Dalal, Washington University in St. Louis, United States

                Reviewed by: Maulikkumar P. Patel, Washington University in St. Louis, United States

                Yusra Rahman, The University of Iowa, United States

                Gunjan Saini, Purdue University, United States

                Semanti Ghosh, Swami Vivekananda University, India

                *Correspondence: Muhammad Bilal Azmi, bilal.azmi@ 123456duhs.edu.pk
                [ † ]

                ORCID: Uzma Asif, orcid.org/0000-0002-4659-9173; Muhammad Bilal Azmi, orcid.org/0000-0001-8320-4479; Mohsin Kazi, orcid.org/0000-0002-5611-0378

                [ ‡ ]

                These authors have contributed equally to this work and share first authorship

                Article
                1384214
                10.3389/fmolb.2024.1384214
                11070483
                38712342
                1bc118f6-8167-4171-afce-cdba25689d93
                Copyright © 2024 Azmi, Nasir, Asif, Kazi, Uddin and Qureshi.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 February 2024
                : 22 March 2024
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research work is supported by RSP2024R301, Saudi Arabia.
                Categories
                Molecular Biosciences
                Original Research
                Custom metadata
                Biological Modeling and Simulation

                hub genes,intrauterine growth restriction,cytohubba,flavoxate,preeclampsia,preterm birth

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