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Cell Specific Gene Expression01:58

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Cell Type-specific Gene Expression Profiling in the Mouse Liver
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Deciphering the Developmental Dynamics of the Mouse Liver Transcriptome.

Sumedha S Gunewardena1, Byunggil Yoo2, Lai Peng3

  • 1Department of Molecular and Integrative Physiology, Department of Biostatistics, University of Kansas Medical Center, Kansas City, Kansas, 66160, United States of America.

Plos One
|October 27, 2015
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Summary

This study reveals how liver gene expression and RNA splicing change during development in mice. It identifies key regulators controlling age-specific liver functions and highlights the increasing complexity of RNA splicing with age.

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Area of Science:

  • Developmental Biology
  • Genomics
  • Molecular Biology

Background:

  • The liver's function shifts dramatically during development from hematopoiesis to drug metabolism and nutrient homeostasis.
  • Understanding age-related changes in liver gene expression and RNA splicing is crucial but remains limited.

Purpose of the Study:

  • To comprehensively analyze the liver transcriptome dynamics during mouse development.
  • To identify age-regulated genes, RNA splicing variants, and their upstream regulators.
  • To elucidate the molecular mechanisms governing age-specific hepatic functions.

Main Methods:

  • RNA sequencing (RNA-Seq) was employed to profile the liver transcriptome across 12 developmental ages in mice, from late embryonic to mature stages.
  • Statistical analyses, including factor analysis, were used to identify differentially expressed genes and RNA splicing variants.
  • Bioinformatic approaches were utilized to identify upstream regulators and gene expression modules.

Main Results:

  • Out of 21,889 annotated genes, 9,641 were expressed, and 7,289 showed age-dependent regulation.
  • Six distinct temporal expression patterns were identified for hepatic genes.
  • Gene expression related to metabolism and structure increased with age, while immune-related factors decreased; growth factors peaked early.
  • 1,455 novel splicing isoform candidates were discovered, indicating significant alternative splicing complexity.

Conclusions:

  • Hepatic ontogeny involves significant transcriptome remodeling, with distinct gene expression patterns and increasing alternative splicing complexity.
  • Key regulatory networks and upstream factors orchestrating age-specific liver functions were identified.
  • Age-related changes in RNA splicing contribute significantly to the functional specialization of the adult liver.