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HCN2 Rescues brain defects by enforcing endogenous voltage pre-patterns.

ABSTRACT: Endogenous bioelectrical signaling coordinates cell behaviors toward correct anatomical outcomes. Lack of a model explaining spatialized dynamics of bioelectric states has hindered the understanding of the etiology of some birth defects and the development of predictive interventions. Nicotine, a known neuroteratogen, induces serious defects in brain patterning and learning. Our bio-realistic computational model explains nicotine's effects via the disruption of endogenous bioelectrical gradients and predicts that exogenous HCN2 ion channels would restore the endogenous bioelectric prepatterns necessary for brain patterning. Voltage mapping in vivo confirms these predictions, and exogenous expression of the HCN2 ion channel rescues nicotine-exposed embryos, resulting in normal brain morphology and molecular marker expression, with near-normal learning capacity. By combining molecular embryology, electrophysiology, and computational modeling, we delineate a biophysical mechanism of developmental brain damage and its functional rescue.

SUBMITTER: Pai VP

PROVIDER: S-EPMC5843655 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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HCN2 Rescues brain defects by enforcing endogenous voltage pre-patterns.

Pai Vaibhav P VP Pietak Alexis A Willocq Valerie V Ye Bin B Shi Nian-Qing NQ Levin Michael M

Nature communications 20180308 1

Endogenous bioelectrical signaling coordinates cell behaviors toward correct anatomical outcomes. Lack of a model explaining spatialized dynamics of bioelectric states has hindered the understanding of the etiology of some birth defects and the development of predictive interventions. Nicotine, a known neuroteratogen, induces serious defects in brain patterning and learning. Our bio-realistic computational model explains nicotine's effects via the disruption of endogenous bioelectrical gradients ...[more]

PMID: 29519998

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Project description:Among the challenges arising in brain imaging genetic studies, estimating the potential links between neurological and genetic variability within a population is key. In this paper, we propose a multivariate, multimodal formulation for variable selection that leverages co-expression patterns across various data modalities. Our approach is based on an intuitive combination of two widely used statistical models: sparse regression and canonical correlation analysis (CCA). While the former seeks multivariate linear relationships between a given phenotype and associated observations, the latter searches to extract co-expression patterns between sets of variables belonging to different modalities. In the following, we propose to rely on a "CCA-type" formulation in order to regularize the classical multimodal sparse regression problem (essentially incorporating both CCA and regression models within a unified formulation). The underlying motivation is to extract discriminative variables that are also co-expressed across modalities. We first show that the simplest formulation of such model can be expressed as a special case of collaborative learning methods. After discussing its limitation, we propose an extended, more flexible formulation, and introduce a simple and efficient alternating minimization algorithm to solve the associated optimization problem. We explore the parameter space and provide some guidelines regarding parameter selection. Both the original and extended versions are then compared on a simple toy data set and a more advanced simulated imaging genomics data set in order to illustrate the benefits of the latter. Finally, we validate the proposed formulation using single nucleotide polymorphisms data and functional magnetic resonance imaging data from a population of adolescents ( subjects, age 16.9 ± 1.9 years from the Philadelphia Neurodevelopmental Cohort) for the study of learning ability. Furthermore, we carry out a significance analysis of the resulting features that allow us to carefully extract brain regions and genes linked to learning and cognitive ability.

Dataset Information

HCN2 Rescues brain defects by enforcing endogenous voltage pre-patterns.

Publications

HCN2 Rescues brain defects by enforcing endogenous voltage pre-patterns.

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