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
Liver
GSM6705681 GSM6705682 GSM6705683
GSM6705678 GSM6705679 GSM6705680
Description

Submission Date: Nov 02, 2022

Summary: Aging is associated with the development of insulin resistance and hypertension. Aim of this study was to identify factors regulating these processes. We studied mice with complete knockout (KO) of the neuroendocrine prohormone Chromogranin A (CgA) as a model for healthy aging. These mice display two opposite aging phenotypes: hypertension but heightened insulin sensitivity at young age, whereas the blood pressure normalizes at older age and insulin sensitivity further improves. In comparison, aging WT mice gradually lost glucose tolerance and insulin sensitivity and developed hypertension. Quantitative RT-PCR revealed increased (~35-fold) accumulation of bacterial DNA in the heart of 2-yr-old WT mice compared to only ~2-fold increase in age-matched CgA-KO mice. Similarly, RNA sequencing showed increased expression of the Vsig4 gene (which removes bacterial DNA) in the liver of 2-yr-old CgA-KO mice, which possibly accounts for the very low accumulation of microbial DNA in the heart. The reversal of hypertension in aging CgA-KO mice is possibly due to (i) low accumulation of microbial DNA (i.e., reduced inflammation), and (ii) decreased spillover of norepinephrine (measured by ultra-pressure liquid chromatography) in the heart and kidneys. The reverse is true for aging WT mice. Moreover, while aging WT mice had increased inflammation with higher plasma TNF-⍺, IFN-ɣ, CCL2 and increased mitochondrial fission, these phenotypes were the opposite in aging CgA-KO mice. We conclude that Vsig4 plays a crucial role in "healthy aging" by counteracting age-associated insulin resistance and hypertension.

GEO Accession ID: GSE217145

PMID: 36440192

Description

Submission Date: Nov 02, 2022

Summary: Aging is associated with the development of insulin resistance and hypertension. Aim of this study was to identify factors regulating these processes. We studied mice with complete knockout (KO) of the neuroendocrine prohormone Chromogranin A (CgA) as a model for healthy aging. These mice display two opposite aging phenotypes: hypertension but heightened insulin sensitivity at young age, whereas the blood pressure normalizes at older age and insulin sensitivity further improves. In comparison, aging WT mice gradually lost glucose tolerance and insulin sensitivity and developed hypertension. Quantitative RT-PCR revealed increased (~35-fold) accumulation of bacterial DNA in the heart of 2-yr-old WT mice compared to only ~2-fold increase in age-matched CgA-KO mice. Similarly, RNA sequencing showed increased expression of the Vsig4 gene (which removes bacterial DNA) in the liver of 2-yr-old CgA-KO mice, which possibly accounts for the very low accumulation of microbial DNA in the heart. The reversal of hypertension in aging CgA-KO mice is possibly due to (i) low accumulation of microbial DNA (i.e., reduced inflammation), and (ii) decreased spillover of norepinephrine (measured by ultra-pressure liquid chromatography) in the heart and kidneys. The reverse is true for aging WT mice. Moreover, while aging WT mice had increased inflammation with higher plasma TNF-⍺, IFN-ɣ, CCL2 and increased mitochondrial fission, these phenotypes were the opposite in aging CgA-KO mice. We conclude that Vsig4 plays a crucial role in "healthy aging" by counteracting age-associated insulin resistance and hypertension.

GEO Accession ID: GSE217145

PMID: 36440192

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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Perturbation Condition

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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.