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Collins, Christin D.

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Collins

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Christin D.

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Collins, Christin D.

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2012 - 20142

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Author's Publications: search.filters.isAuthorOfPublication.6c2c37ef-2e51-4a08-9bb8-85761767ab28

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    Divergent dysregulation of gene expression in murine models of fragile X syndrome and tuberous sclerosis
    (BioMed Central, 2014) Kong, Sek Won; Sahin, Mustafa; Collins, Christin D.; Wertz, Mary H; Campbell, Malcolm G; Leech, Jarrett D; Krueger, Dilja; Bear, Mark F; Kunkel, Louis; Kohane, Isaac
    Background: Fragile X syndrome and tuberous sclerosis are genetic syndromes that both have a high rate of comorbidity with autism spectrum disorder (ASD). Several lines of evidence suggest that these two monogenic disorders may converge at a molecular level through the dysfunction of activity-dependent synaptic plasticity. Methods: To explore the characteristics of transcriptomic changes in these monogenic disorders, we profiled genome-wide gene expression levels in cerebellum and blood from murine models of fragile X syndrome and tuberous sclerosis. Results: Differentially expressed genes and enriched pathways were distinct for the two murine models examined, with the exception of immune response-related pathways. In the cerebellum of the Fmr1 knockout (Fmr1-KO) model, the neuroactive ligand receptor interaction pathway and gene sets associated with synaptic plasticity such as long-term potentiation, gap junction, and axon guidance were the most significantly perturbed pathways. The phosphatidylinositol signaling pathway was significantly dysregulated in both cerebellum and blood of Fmr1-KO mice. In Tsc2 heterozygous (+/−) mice, immune system-related pathways, genes encoding ribosomal proteins, and glycolipid metabolism pathways were significantly changed in both tissues. Conclusions: Our data suggest that distinct molecular pathways may be involved in ASD with known but different genetic causes and that blood gene expression profiles of Fmr1-KO and Tsc2+/− mice mirror some, but not all, of the perturbed molecular pathways in the brain.
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    Characteristics and Predictive Value of Blood Transcriptome Signature in Males with Autism Spectrum Disorders
    (Public Library of Science, 2012) Kong, Sek Won; Collins, Christin D.; Shimizu-Motohashi, Yuko; Holm, Ingrid; Campbell, Malcolm G.; Lee, In-Hee; Brewster, Stephanie J.; Hanson, Ellen; Harris, Heather; Lowe, Kathryn R.; Saada, Adrianna; Mora, Andrea; Madison, Kimberly; Hundley, Rachel; Egan, Jessica; McCarthy, Jillian; Eran, Ally; Galdzicki, Michal; Rappaport, Leonard; Kunkel, Louis; Kohane, Isaac
    Autism Spectrum Disorders (ASD) is a spectrum of highly heritable neurodevelopmental disorders in which known mutations contribute to disease risk in 20% of cases. Here, we report the results of the largest blood transcriptome study to date that aims to identify differences in 170 ASD cases and 115 age/sex-matched controls and to evaluate the utility of gene expression profiling as a tool to aid in the diagnosis of ASD. The differentially expressed genes were enriched for the neurotrophin signaling, long-term potentiation/depression, and notch signaling pathways. We developed a 55-gene prediction model, using a cross-validation strategy, on a sample cohort of 66 male ASD cases and 33 age-matched male controls (P1). Subsequently, 104 ASD cases and 82 controls were recruited and used as a validation set (P2). This 55-gene expression signature achieved 68% classification accuracy with the validation cohort (area under the receiver operating characteristic curve (AUC): 0.70 [95% confidence interval [CI]: 0.62–0.77]). Not surprisingly, our prediction model that was built and trained with male samples performed well for males (AUC 0.73, 95% CI 0.65–0.82), but not for female samples (AUC 0.51, 95% CI 0.36–0.67). The 55-gene signature also performed robustly when the prediction model was trained with P2 male samples to classify P1 samples (AUC 0.69, 95% CI 0.58–0.80). Our result suggests that the use of blood expression profiling for ASD detection may be feasible. Further study is required to determine the age at which such a test should be deployed, and what genetic characteristics of ASD can be identified.