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 PMID:22120178  

Using iPSC-derived neurons to uncover cellular phenotypes associated with Timothy syndrome.

Sergiu P Paşca | Thomas Portmann | Irina Voineagu | Masayuki Yazawa | Aleksandr Shcheglovitov | Anca M Paşca | Branden Cord | Theo D Palmer | Sachiko Chikahisa | Seiji Nishino | Jonathan A Bernstein | Joachim Hallmayer | Daniel H Geschwind | Ricardo E Dolmetsch
Nature medicine | 2011

Monogenic neurodevelopmental disorders provide key insights into the pathogenesis of disease and help us understand how specific genes control the development of the human brain. Timothy syndrome is caused by a missense mutation in the L-type calcium channel Ca(v)1.2 that is associated with developmental delay and autism. We generated cortical neuronal precursor cells and neurons from induced pluripotent stem cells derived from individuals with Timothy syndrome. Cells from these individuals have defects in calcium (Ca(2+)) signaling and activity-dependent gene expression. They also show abnormalities in differentiation, including decreased expression of genes that are expressed in lower cortical layers and in callosal projection neurons. In addition, neurons derived from individuals with Timothy syndrome show abnormal expression of tyrosine hydroxylase and increased production of norepinephrine and dopamine. This phenotype can be reversed by treatment with roscovitine, a cyclin-dependent kinase inhibitor and atypical L-type-channel blocker. These findings provide strong evidence that Ca(v)1.2 regulates the differentiation of cortical neurons in humans and offer new insights into the causes of autism in individuals with Timothy syndrome.

Pubmed ID: 22120178

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Associated grants

  • Agency: NIMH NIH HHS, United States
    Id: R01 MH094714
  • Agency: NIMH NIH HHS, United States
    Id: DP1 MH099904
  • Agency: NIH HHS, United States
    Id: DP1 OD003889
  • Agency: NIMH NIH HHS, United States
    Id: R21 MH087898
  • Agency: NIMH NIH HHS, United States
    Id: R33 MH087898

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Bioconductor (tool)

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SAM (tool)

RRID:SCR_011888

Software for genomic expression data mining using a statistical technique for finding significant genes in a set of microarray experiments.

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