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
Peripheral blood
GSM5811730 GSM5811731 GSM5811732 GSM5811733
GSM5811726 GSM5811727 GSM5811728 GSM5811729
Description

Submission Date: Jan 12, 2022

Summary: Aims/hypothesis. Ectopic calcification is a typical feature of diabetic vascular disease and resembles an accelerated aging phenotype. We previously found an excess of myeloid calcifying cells (MCCs) in diabetic patients. We herein examined molecular and cellular pathways linking atherosclerotic calcification with calcification by myeloid cells in the diabetic milieu.

Methods. We first examined the associations among coronary calcification, MCC levels, and mononuclear cell gene expression in a cross-sectional study of 87 type 2 diabetic patients undergoing elective coronary angiography. Then, we undertook in vitro studies on mesenchymal stem cells (MSCs) and on the THP-1 myeloid cells line to verify the causal relationships of the observed associations.

Results. Coronary calcification was associated with 2.8-times higher MCC levels (p=0.037) and 50% elevated expression of the osteogenic gene RUNX2 in mononuclear cells, whereas expression of Sirtuin-7 (SIRT7) was inversely correlated with calcification. In standard differentiation assays of MSCs, SIRT7 knockdown activated the osteogenic program and worsened calcification, especially in the presence of high (20 mM) glucose. In the monocytic cell line THP-1, SIRT7 downregulation drove a pro-calcific phenotype, whereas SIRT7 overexpression prevented high-glucose induced calcification. Through the JAK/STAT pathway, high glucose induced miR-125b-5p, which in turn targeted SIRT7 in myeloid cells and was directly associated with coronary calcification.

Conclusions/interpretation. We describe a new pathway elicited by high glucose trough the JAK/STAT cascade, involving regulation of SIRT7 by mir-125b-5p driving calcification by myeloid cells. This pathway is associated with coronary calcification in diabetic patients and may be a target to tackle diabetic vascular disease.

GEO Accession ID: GSE193510

PMID: 35708762

Description

Submission Date: Jan 12, 2022

Summary: Aims/hypothesis. Ectopic calcification is a typical feature of diabetic vascular disease and resembles an accelerated aging phenotype. We previously found an excess of myeloid calcifying cells (MCCs) in diabetic patients. We herein examined molecular and cellular pathways linking atherosclerotic calcification with calcification by myeloid cells in the diabetic milieu.

Methods. We first examined the associations among coronary calcification, MCC levels, and mononuclear cell gene expression in a cross-sectional study of 87 type 2 diabetic patients undergoing elective coronary angiography. Then, we undertook in vitro studies on mesenchymal stem cells (MSCs) and on the THP-1 myeloid cells line to verify the causal relationships of the observed associations.

Results. Coronary calcification was associated with 2.8-times higher MCC levels (p=0.037) and 50% elevated expression of the osteogenic gene RUNX2 in mononuclear cells, whereas expression of Sirtuin-7 (SIRT7) was inversely correlated with calcification. In standard differentiation assays of MSCs, SIRT7 knockdown activated the osteogenic program and worsened calcification, especially in the presence of high (20 mM) glucose. In the monocytic cell line THP-1, SIRT7 downregulation drove a pro-calcific phenotype, whereas SIRT7 overexpression prevented high-glucose induced calcification. Through the JAK/STAT pathway, high glucose induced miR-125b-5p, which in turn targeted SIRT7 in myeloid cells and was directly associated with coronary calcification.

Conclusions/interpretation. We describe a new pathway elicited by high glucose trough the JAK/STAT cascade, involving regulation of SIRT7 by mir-125b-5p driving calcification by myeloid cells. This pathway is associated with coronary calcification in diabetic patients and may be a target to tackle diabetic vascular disease.

GEO Accession ID: GSE193510

PMID: 35708762

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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.