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Our main goal is to identify the transcript expression changes induced by mutations in microtubule-related genes associated with schizophrenia (SZ) and stress exposure in human cell and mouse models, which are enriched among genes specifically regulated in adolescent human brains and differentially expressed in psychiatric disease conditions, such as SZ. Aim 1 will examine the transcript expression changes in iPS cells- derived human neuronal cells resulting from CRISPR/Cas9 mediated mutagenesis of KCTD13, DPYSL2, SDCCAG8 and CKAP5 by conducting a RNA-seq analysis. We will also use these genetic cell models to determine changes in transcript expression with and without treatment with dexamethasone as a model of stress exposure in vitro. Aim 2 will examine the transcript expression changes in the prefrontal cortex (PFC) of Pcm1 knockout mice, 16p11(dup) mice model, 16p11(dup)/Kctd13+/- mice, and other mouse models such as Dpysl2 conditional knockout mice by conducting a RNA-seq analysis. We will also examine if observed changes are exacerbated by adolescent social isolation. Aim 3 will compare genomic regions and transcripts identified in Aims 1 and 2 as differentially expressed due to genetic mutations and stress exposure in cell and mouse models to transcripts and genes identified in large-scale postmortem human brain tissue RNA-seq collections. Using unique human post-mortem RNA-seq datasets, we will address whether our findings can be translated in two contexts relevant to human adolescent brain maturation and psychiatric disease conditions. We will also cross-validate differentially expressed stress-associated molecules in serum of prospective human subjects developed SZ and mouse models resulting from genetic mutations and social isolation as well as adolescent human brains and PFC region of the mouse models.
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Data analysis tools for leveraging massive public data to improve hypothesis-driven research
  • 批准号:
    10598130
  • 项目类别:
  • 资助金额:
    $42.82万
  • 财政年份:
    2022
  • 负责人:
    Jeffrey T. Leek
  • 依托单位:
Data analysis tools for leveraging massive public data to improve hypothesis-driven research
  • 批准号:
    10330636
  • 项目类别:
  • 资助金额:
    $2.68万
  • 财政年份:
    2022
  • 负责人:
    Jeffrey T. Leek
  • 依托单位:
Data analysis tools for leveraging massive public data to improve hypothesis-driven research
  • 批准号:
    10654376
  • 项目类别:
  • 资助金额:
    $40.44万
  • 财政年份:
    2022
  • 负责人:
    Jeffrey T. Leek
  • 依托单位:
A massive study of data science to address the scientific reproducibility crisis
  • 批准号:
    9100338
  • 项目类别:
  • 资助金额:
    $36.45万
  • 财政年份:
    2016
  • 负责人:
    Jeffrey T. Leek
  • 依托单位:
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