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Comparative transcriptomic rhythms in the mouse and human prefrontal cortex.

Jennifer N Burns1,2, Aaron K Jenkins1,2, Xiangning Xue3

  • 1Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15219.

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Circadian rhythms in the prefrontal cortex (PFC) are conserved across mice and humans, but with sex-specific phase shifts and unique gene expression patterns, impacting psychiatric disease research.

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Area of Science:

  • Neuroscience
  • Genomics
  • Chronobiology

Background:

  • Prefrontal cortex (PFC) alterations are linked to psychiatric disorders.
  • Circadian gene expression rhythms exist in the brain, including the PFC, and are disrupted in disease.
  • Translational research is challenged by human PFC specialization compared to rodents.

Purpose of the Study:

  • To compare transcriptomic rhythms between mouse medial PFC (mPFC) and human anterior cingulate cortex (ACC) and dorsolateral PFC (DLPFC).
  • To provide a foundation for translational studies on molecular rhythms.
  • To investigate sex-specific differences in cross-species transcriptomic rhythms.

Main Methods:

  • RNA sequencing was employed for transcriptomic analysis.
  • A sex-specific, cross-species comparison was conducted.
  • Rhythmic transcripts were analyzed in mouse mPFC and human ACC/DLPFC.

Main Results:

  • Circadian rhythm signaling is conserved across species and PFC subregions.
  • A sex-dependent phase shift in core clock gene expression exists between mouse mPFC and human PFC.
  • Transcriptomic rhythm identity is largely unique, with highest overlap between mouse mPFC and human ACC in females.
  • Core biological processes show enrichment for rhythmic transcripts, with regional and sex-specific variations.

Conclusions:

  • Transcriptomic rhythms are evolutionarily conserved but show significant advancement in the human PFC.
  • Cross-species differences, particularly sex-specific ones, must be considered in animal models for psychiatric disease research.
  • This study provides crucial insights for developing more accurate translational models.