N-myc downstream regulated gene 2 ameliorates myocardial remodeling and cardiac function in heart failure rats

Zhao, Jing; Zhao, Quanhong; Mao, Shuai

doi:10.1177/0960327121993208

Cited by 4 publications

References 41 publications

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A pluripotent stem cell atlas of multilineage differentiation revealsTMEM88as a developmental regulator of mammalian blood pressure

Shen

Werner

Sun

et al. 2022

Preprint

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This study develops a versatile cell multiplexing and data analysis platform to accelerate knowledge gain into mechanisms of cell differentiation. We engineer a cell barcoding system in human cells enabling multiplexed single-cell RNA sequencing for high throughput perturbation of customisable and versatile experimental conditions. This is coupled with a new computational analysis pipeline that overcomes the limitations of existing algorithms by using an unsupervised, genome-wide, orthogonal biological reference point to reveal the cell diversity and regulatory networks of any input scRNA-seq data set. We implement this pipeline by engineering transcribed barcodes into induced pluripotent stem cells and multiplex 62 independent experimental conditions comprising eight differentiation time points and nine developmental signalling perturbations with duplicates. We identify and deconstruct the temporal, signalling, and gene regulatory basis of multilineage iPSC differentiation into ectoderm, mesoderm, and endoderm cell types. This study provides a cellular and computational pipeline to study cell differentiation applicable to diverse fields of developmental biology, drug discovery, and disease modelling.

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A pluripotent stem cell atlas of multilineage differentiation revealsTMEM88as a developmental regulator of mammalian blood pressure

Shen

Werner

Sun

et al. 2022

Preprint

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Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis. Next generation sequencing (NGS) dataset GSE243039 was obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein-protein interaction (PPI) network was constructed and modules were analysed using the Human Integrated Protein-Protein Interaction rEference (HIPIE) database and Cytoscape software, and hub genes were identified. Subsequantely, a network between miRNAs and hub genes, and network between TFss and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve (ROC) analysis was used to validate the hub genes. A total of 958 DEGs, including 479 up regulated genes and 479 down regulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including VCAM1, SNCA, PRKCB, ADRB2, FOXQ1, MDFI, ACTBL2, PRKD1, DAPK1 and ACTC1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including TCF3 and CLOCK, were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network. This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment, and monitoring of endometriosis.

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Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

Vastrad,

Vastrad

2024

Egypt J Med Hum Genet

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Background Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis. Methods Next-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes. Results A total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network. Conclusions This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis.

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N-myc downstream regulated gene 2 ameliorates myocardial remodeling and cardiac function in heart failure rats

Cited by 4 publications

References 41 publications

A pluripotent stem cell atlas of multilineage differentiation revealsTMEM88as a developmental regulator of mammalian blood pressure

A pluripotent stem cell atlas of multilineage differentiation revealsTMEM88as a developmental regulator of mammalian blood pressure

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

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