Related Experiment Video
Updated: Feb 15, 2026

Microarray Analysis for Saccharomyces cerevisiae
Published on: April 7, 2011
Regulatory network analysis of hypertension and hypotension microarray data from mouse model.
Yanli Zhu1, Jingming Zhuo1, Chunmei Li1
1a Department of Cardiology , Shandong Provincial Hospital affiliated to Shandong University , Jinan City , China.
This study identifies key genes involved in regulating high blood pressure (BPH) and low blood pressure (BPL). Sept6 and Pigx are implicated in BPH, while Gtf2ird1, Urb2, and Wif1 show potential for BPL treatment.
Area of Science:
- Genomics
- Cardiovascular Biology
- Systems Biology
Background:
- Blood pressure regulation is complex, involving multiple genetic factors.
- Identifying specific genes for high blood pressure (BPH) and low blood pressure (BPL) is crucial for targeted therapies.
Purpose of the Study:
- To identify potential genes regulating blood pressure.
- To screen for target genes for high blood pressure (BPH) and low blood pressure (BPL) treatment.
Main Methods:
- Utilized the GSE19817 microarray dataset from mice with BPH, BPL, and normotensive controls.
- Performed principal component analysis (PCA), screened differentially expressed genes (DEGs), and conducted pathway enrichment analysis.
- Constructed gene regulatory networks (GRNs) for aorta, liver, heart, and kidney tissues.
Main Results:
- Identified 2,726 BPH-related DEGs and 2,472 BPL-related DEGs, primarily enriched in immune response pathways.
- Gene regulatory network topology showed similarities across heart, kidney, and liver tissues.
- Sept6 and Pigx were top BPH-related DEGs in aorta compared to other tissues.
- Gtf2ird1, Urb2, and Wif1 were top BPL-related DEGs in tissues excluding the kidney.
Conclusions:
- Sept6 and Pigx may play a role in the pathogenesis of high blood pressure.
- Gtf2ird1, Urb2, and Wif1 represent potential therapeutic targets for low blood pressure treatment.
Related Concept Videos
Cis-regulatory Sequences
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Analysis of Population Pharmacokinetic Data
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

