Gene Expression Data Explorer
Info Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples. You can learn more about ARCHS4 and its pipeline here.
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GROUP CONDITION SAMPLES
HK-2
GSM5519154 GSM5519155 GSM5519156
GSM5519157 GSM5519158
Description

Submission Date: Aug 15, 2021

Summary: Diabetic kidney disease (DKD) is the leading cause of both chronic kidney disease (CKD) and end-stage renal disease (ESRD). In this study, we performed transcriptome gene expression profiling of kidney tissues in human renal proximal epithelial tubular cell line (HK-2) treated with high D-glucose (HG) for 7 days before the addition of 40 mM oxamate for a further 24 hours in the presence of HG. Afterwards, we analyzed the differentially expressed (DE) genes and investigated gene relationships based on weighted gene co-expression network analysis (WGCNA). Accordingly, enrichment analyses of GO terms and KEGG pathways showed that several pathways (e.g., lysosome (hsa04142) and p53 signaling pathway (hsa04115)) may be involved in a response of HK-2 cells to oxamate. Moreover, via WGCNA, we identified two modules: both the turquoise and blue modules were enriched in pathways associated with lysosome. However, the P53 signaling pathway was only found using all 3,884 DE genes. Furthermore, the key hub genes IGFBP3 interacted with 6 up-regulated and 12 down-regulated DE genes in the network that were enriched in the P53 signaling pathway. This is the first study reporting co-expression patterns of a gene network after lactate dehydrogenase inhibition in HK-2 cells. Our results may contribute to our understanding of the underlying molecular mechanism of in vitro reprogramming under hyperglycemic stress that orchestrates the survival and functions of HK-2 cells.

GEO Accession ID: GSE182138

PMID: 35370938

Description

Submission Date: Aug 15, 2021

Summary: Diabetic kidney disease (DKD) is the leading cause of both chronic kidney disease (CKD) and end-stage renal disease (ESRD). In this study, we performed transcriptome gene expression profiling of kidney tissues in human renal proximal epithelial tubular cell line (HK-2) treated with high D-glucose (HG) for 7 days before the addition of 40 mM oxamate for a further 24 hours in the presence of HG. Afterwards, we analyzed the differentially expressed (DE) genes and investigated gene relationships based on weighted gene co-expression network analysis (WGCNA). Accordingly, enrichment analyses of GO terms and KEGG pathways showed that several pathways (e.g., lysosome (hsa04142) and p53 signaling pathway (hsa04115)) may be involved in a response of HK-2 cells to oxamate. Moreover, via WGCNA, we identified two modules: both the turquoise and blue modules were enriched in pathways associated with lysosome. However, the P53 signaling pathway was only found using all 3,884 DE genes. Furthermore, the key hub genes IGFBP3 interacted with 6 up-regulated and 12 down-regulated DE genes in the network that were enriched in the P53 signaling pathway. This is the first study reporting co-expression patterns of a gene network after lactate dehydrogenase inhibition in HK-2 cells. Our results may contribute to our understanding of the underlying molecular mechanism of in vitro reprogramming under hyperglycemic stress that orchestrates the survival and functions of HK-2 cells.

GEO Accession ID: GSE182138

PMID: 35370938

Visualize Samples

Info Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.

Precomputed Differential Gene Expression

Info Differential expression signatures are automatically computed using the limma R package. More options for differential expression are available to compute below.

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Differential Gene Expression Analysis
Info Differential expression signatures can be computed using DESeq2 or characteristic direction.
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Bulk RNA-seq Appyter

This pipeline enables you to analyze and visualize your bulk RNA sequencing datasets with an array of downstream analysis and visualization tools. The pipeline includes: PCA analysis, Clustergrammer interactive heatmap, library size analysis, differential gene expression analysis, enrichment analysis, and L1000 small molecule search.