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Systematic analysis of gene expression patterns associated with postmortem interval in human tissues.

Yizhang Zhu1,2, Likun Wang1, Yuxin Yin3,4

  • 1Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, 100191, China.

Scientific Reports
|July 16, 2017
PubMed
Summary

Postmortem mRNA degradation varies by tissue, gene, and genotype. Understanding the postmortem interval (PMI) is crucial for accurate gene expression research in human tissues.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Postmortem mRNA degradation poses challenges for gene expression research using human tissues.
  • The postmortem interval (PMI) is a critical factor influencing mRNA stability.
  • Comprehensive understanding of global gene expression changes related to PMI across human tissues is limited.

Purpose of the Study:

  • To systematically analyze gene expression alterations associated with PMI in diverse human tissues.
  • To characterize the tissue-specific, gene-specific, and genotype-dependent patterns of postmortem mRNA degradation.
  • To identify genes with significant expression variability between short and long PMI groups.

Main Methods:

  • Utilized the Genotype-Tissue Expression (GTEx) database.
  • Analyzed gene expression levels from 2,016 high-quality postmortem samples across 316 donors.
  • Examined samples with postmortem intervals ranging from 1 to 27 hours.

Main Results:

  • Postmortem mRNA degradation exhibits significant tissue-specific, gene-specific, and genotype-dependent patterns.
  • Identified 266 differentially variable (DV) genes, including DEFB4B and IFNG, showing distinct expression between short and long PMI groups.
  • Provided a detailed profile of PMI-associated gene expression across various human tissues.

Conclusions:

  • The study offers a comprehensive overview of how PMI affects gene expression in human postmortem tissues.
  • Findings aid in the interpretation of gene expression data from postmortem samples.
  • Highlights the importance of considering PMI and its interacting factors in gene expression studies.