DYX1C1 functions in neuronal migration in developing neocortex

Department of Physiology and Neurobiology, University of Connecticut, Storrs, CT 06268, USA.
Neuroscience (Impact Factor: 3.36). 01/2007; 143(2):515-22. DOI: 10.1016/j.neuroscience.2006.08.022
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Rodent homologues of two candidate dyslexia susceptibility genes, Kiaa0319 and Dcdc2, have been shown to play roles in neuronal migration in developing cerebral neocortex. This functional role is consistent with the hypothesis that dyslexia susceptibility is increased by interference with normal neural development. In this study we report that in utero RNA interference against the rat homolog of another candidate dyslexia susceptibility gene, DYX1C1, disrupts neuronal migration in developing neocortex. The disruption of migration can be rescued by concurrent overexpression of DYX1C1, indicating that the impairment is not due to off-target effects. Transfection of C- and N-terminal truncations of DYX1C1 shows that the C-terminal TPR domains determine DYX1C1 intracellular localization to cytoplasm and nucleus. RNAi rescue experiments using truncated versions of DYX1C1 further indicate that the C-terminus of DYX1C1 is necessary and sufficient to DYX1C1's function in migration. In conclusion, DYX1C1, similar to two other candidate dyslexia susceptibility genes, functions in neuronal migration in rat neocortex.

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Available from: Jan Voskuil, Oct 01, 2015
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    • "In particular, variations in DCDC2 were found to associate with wide-spread gray matter changes and reading-related brain activation (Cope et al., 2012; Meda et al., 2008). Moreover, variations in DCDC2, DYX1C1, and KIAA0319 were shown to affect temporo-parietal white matter (Darki, Peyrard-Janvid, Matsson, Kere, & Klingberg, 2012; Marino et al., 2014), even though these structural features may be only indirectly affected by genetically determined alterations in neuronal migration (Meng et al., 2005; Paracchini et al., 2006; Wang et al., 2006). "
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    ABSTRACT: Developmental dyslexia, a severe impairment of literacy acquisition, is known to have a neurological basis and a strong genetic background. However, effects of individual genetic variations on dyslexia-associated deficits are only moderate and call for the assessment of the genotype's impact on mediating neuro-endophenotypes by the imaging genetics approach. Using voxel-based morphometry (VBM) in German participants with and without dyslexia, we investigated gray matter changes and their association with impaired phonological processing, such as reduced verbal working memory. These endophenotypical alterations were, together with dyslexia-associated genetic variations, examined on their suitability as potential predictors of dyslexia. We identified two gray matter clusters in the left posterior temporal cortex related to verbal working memory capacity. Regional cluster differences correlated with genetic risk variants in TNFRSF1B. High-genetic-risk participants exhibit a structural predominance of auditory-association areas relative to auditory-sensory areas, which may partly compensate for deficient early auditory-sensory processing stages of verbal working memory. The reverse regional predominance observed in low-genetic-risk participants may in turn reflect reliance on these early auditory-sensory processing stages. Logistic regression analysis further supported that regional gray matter differences and genetic risk interact in the prediction of individuals' diagnostic status: With increasing genetic risk, the working-memory related structural predominance of auditory-association areas relative to auditory-sensory areas classifies participants with dyslexia versus control participants. Focusing on phonological deficits in dyslexia, our findings suggest endophenotypical changes in the left posterior temporal cortex could comprise novel pathomechanisms for verbal working memory-related processes translating TNFRSF1B genotype into the dyslexia phenotype.
    Cortex 07/2015; 71:291-305. DOI:10.1016/j.cortex.2015.06.029 · 5.13 Impact Factor
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    • "The roles of genes implicated in dyslexia were investigated, among others, in RAN interference (RANi) studies with animals. For instance, the results revealed that DYX1C1 (Rosen et al., 2007; Wang et al., 2006), KIAA0319 (Paracchini et al., 2006) and DCDC2 (Burbridge et al., 2008; Meng et al., 2005) are involved in neuronal migration. ROBO1 has been implicated in axon growth (Zhu, Li, Zhou, Wu, & Rao, 1999). "
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    ABSTRACT: Neuroimaging has had an enormous impact on the way human cognition is studied in vivo. With the advent of fMRI, scientists have been able to ask questions regarding individual differences which could not have been addressed with PET. These, are particularly important for studying developmental disorders, such as dyslexia because research suggests that participants with dyslexia (DPs) form a heterogeneous population. There are three main theories of dyslexia: the phonological (PDT), visual magnocellular (MDT) and cerebellar (CDT). The majority of neuroimaging studies, motivated by these theories, have shortcomings. First, they have relied on group comparisons which can obscure the less frequent differences between DPs and controls (CPs). Second, they mostly tested one underlying cause, postulated by one theory. Third, the majority of them focused on detecting a deficit without empirically demonstrating its relationship with reading deficit which defines this disorder. The goal of this research is to shed more light on the neural correlates of reading deficit in dyslexia and address these criticisms. First, by using a multiple case study to investigate individual differences among DPs. Such comparisons would not have been possible with PET. Second, by contrasting the predictions, of each of the main theories, on the neural correlates of the reading impairment in one sample of DPs. The behavioural studies suggest that there are subtypes of dyslexia, however, they cannot be tested by focusing on one theory. Third, by using a reading task and fMRI - which provides an opportunity for directly testing the relationship between the predictions of a given theory and the neural correlates of reading impairment. Eighteen individual DPs and 16 CPs were tested in a multiple case study. Participants were right-handed, with native English, normal vision and hearing and without clinical ADHD and DCD. To ensure that no confounds were present due to differences between a DP and CPs in IQ, handedness and age, these were entered into the analysis as covariates. The neuroimaging task involved silent reading of individual words and fixating on a cross (baseline) in an event-related design. Four findings were revealed. First, the neural correlates of reading deficit in dyslexia in all cases, except one, are consistent with the predictions of the PDT and CDT, in one case with the visual MDT and in ten cases with a hypothetical visual (not magnocellular) deficit theory; most cases are consistent with more than one theory. Second, there are considerable individual differences in the neural correlates of reading deficit. Even where they are consistent with the same theory in two DPs, the areas can differ. Third, the results reveal that the lack of deficit on a behavioural test does not mean that the neural correlates of reading are intact. Fourth, a large number of DPs who have a similar profile on the behavioural measures exhibited dissimilar profiles on the neural level. These findings suggest that endophenotypes uncovered by neuroimaging will enable researchers to find important links between the behavioural and genetic characteristics of DPs. Multidisciplinary research on individual differences, including neuroimaging, behavioural and genetic measures within a multiple-deficit model holds significant promise.
    Advances in Neuroimaging Research, Edited by Victoria Asher-Hansley, 05/2014: chapter 1: pages 1-119; Nova Science Publishers., ISBN: 978-1-63321-307-4
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    • "Additional genes have also been proposed as dyslexia candidates: DYX1C1 (Nopola- Hemmi et al., 2000), ROBO1 (Hannula-Jouppi et al., 2005), KIAA0319L (Couto et al., 2008), MLPR19, and C2ORF3 (Anthoni et al., 2007). Among them, DYX1C1, ROBO1, DCDC2, and KIAA0319 have been implicated in global brain-developmental processes such as neural migration and axonal guidance (Hivert et al., 2002, Meng et al., 2005, Wang et al., 2006; Paracchini et al., 2006). These results provide convincing evidence that these genes are interesting study targets on the basis of previous evidence from post-mortem studies in individuals with RD that have identified abnormalities in neuronal migration (Galaburda et al., 1985). "
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    ABSTRACT: Dyslexia or reading disability (RD) is the most common childhood learning disorder and a significantly heritable trait. Many recent studies have investigated the genetic basis of dyslexia, and several candidate genes have been proposed. Among these, DCDC2 and KIAA0319 have emerged as the strongest candidate genes for dyslexia; however studies have not provided uniformly supportive results. The aim of this study was to assess the contribution of proposed candidate genes to the molecular etiology of dyslexia in a Brazilian sample. Large deletions and duplications in the candidate genes DCDC2, KIAA0319, and ROBO1 were investigated in 51 dyslexic subjects. Furthermore, a family-based association study was performed to investigate whether associations observed in other populations with variants in the DCDC2 and KIAA0319 genes were reproducible in Brazilian dyslexic individuals. Our analysis did not detect any deletions or duplications in the genes studied, and we found no evidence that the allelic variants in the two candidate genes were significantly associated with RD in our sample. Our data do not support a role of the DCDC2/KIAA0319 locus in influencing dyslexia as a categorical trait. Given the genetic complexity of dyslexia, it is plausible that both genes contribute to an increased risk, but the relative influence of these 2 genes on RD varies in different study samples, and/or depends on analytical approaches.
    Genetics and molecular research: GMR 11/2013; 12(4):5356-5364. DOI:10.4238/2013.November.7.10 · 0.78 Impact Factor
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