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Related Experiment Video

Updated: Jun 17, 2026

Isolating Brown Adipocytes from Murine Interscapular Brown Adipose Tissue for Gene and Protein Expression Analysis
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Published on: March 12, 2021

Filling gaps in PPAR-alpha signaling through comparative nutrigenomics analysis.

Duccio Cavalieri1, Enrica Calura, Chiara Romualdi

  • 1Department of Pharmacology, University of Firenze, Firenze, Italy. duccio.cavalieri@unifi.it

BMC Genomics
|December 17, 2009
PubMed
Summary

This study integrated gene expression data to identify new targets of PPARalpha, a key fatty acid sensor. Findings reveal conserved genes and potential epigenetic regulation in response to diet.

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Area of Science:

  • Genomics
  • Nutritional Science
  • Bioinformatics

Background:

  • High-throughput genomic tools are common in nutrition research, but individual studies are insufficient for comprehensive understanding.
  • Large public expression datasets present analysis challenges.
  • Peroxisome proliferator-activated receptor alpha (PPARalpha) is a crucial fatty acid sensor regulating metabolic gene expression, with its full physiological role still under investigation.

Purpose of the Study:

  • To investigate PPARalpha function by applying a cross-species meta-analysis approach.
  • To integrate sixteen microarray datasets examining high-fat diet and PPARalpha signaling perturbations across different organisms.

Main Methods:

  • Cross-species meta-analysis of sixteen microarray datasets.
  • Identification of consistently differentially expressed genes (MDEGs) under high-fat diet or PPARalpha signaling perturbations.
  • Screening MDEGs for transcription factor binding sites, including Peroxisome Proliferating Response Elements (PPREs).

Main Results:

  • Identified 164 differentially expressed genes (MDEGs) consistently responding to high-fat diet or PPARalpha signaling.
  • Discovered five conserved yeast genes homologous to mammalian PPARalpha targets, serving as potential model genes.
  • Identified 20 new candidate genes with both PPRE binding sites and altered expression, alongside a non-random genomic localization of MDEGs.

Conclusions:

  • In silico analysis combined with evolutionary conservation effectively synthesizes expression data.
  • Identified potential gene candidates to elucidate PPARalpha signaling pathways.
  • Non-random genomic localization suggests the importance of epigenetic mechanisms in PPARalpha-mediated transcriptional regulation.