Dataset Information

Kcnn2 blockade reverses learning deficits in a mouse model of fetal alcohol spectrum disorders.

ABSTRACT: Learning disabilities are hallmarks of congenital conditions caused by prenatal exposure to harmful agents. These include fetal alcohol spectrum disorders (FASDs) with a wide range of cognitive deficiencies, including impaired motor skill development. Although these effects have been well characterized, the molecular effects that bring about these behavioral consequences remain to be determined. We previously found that the acute molecular responses to alcohol in the embryonic brain are stochastic, varying among neural progenitor cells. However, the pathophysiological consequences stemming from these heterogeneous responses remain unknown. Here we show that acute responses to alcohol in progenitor cells altered gene expression in their descendant neurons. Among the altered genes, an increase of the calcium-activated potassium channel Kcnn2 in the motor cortex correlated with motor learning deficits in a mouse model of FASD. Pharmacologic blockade of Kcnn2 improves these learning deficits, suggesting Kcnn2 blockers as a new intervention for learning disabilities in FASD.

SUBMITTER: Mohammad S

PROVIDER: S-EPMC7131887 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Kcnn2 blockade reverses learning deficits in a mouse model of fetal alcohol spectrum disorders.

Mohammad Shahid S Page Stephen J SJ Wang Li L Ishii Seiji S Li Peijun P Sasaki Toru T Basha Aiesha A Salzberg Anna A Quezado Zenaide Z Imamura Fumiaki F Nishi Hirotaka H Isaka Keiichi K Corbin Joshua G JG Liu Judy S JS Kawasawa Yuka Imamura YI Torii Masaaki M Hashimoto-Torii Kazue K

Nature neuroscience 20200316 4

Learning disabilities are hallmarks of congenital conditions caused by prenatal exposure to harmful agents. These include fetal alcohol spectrum disorders (FASDs) with a wide range of cognitive deficiencies, including impaired motor skill development. Although these effects have been well characterized, the molecular effects that bring about these behavioral consequences remain to be determined. We previously found that the acute molecular responses to alcohol in the embryonic brain are stochast ...[more]

PMID: 32203497

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Project description:The pathology of fetal alcohol syndrome and the less severe fetal alcohol spectrum disorders includes brain dysmyelination. Recent studies have shed light on the molecular mechanisms underlying these white matter abnormalities. Rodent models of fetal alcohol syndrome and human studies have shown suppressed oligodendrocyte differentiation and apoptosis of oligodendrocyte precursor cells. Ethanol exposure led to reduced expression of myelin basic protein and delayed myelin basic protein expression in rat and mouse models of fetal alcohol syndrome and in human histopathological specimens. Several studies have reported increased expression of many chemokines in dysmyelinating disorders in central nervous system, including multiple sclerosis and fetal alcohol syndrome. Acute ethanol exposure reduced levels of the neuroprotective insulin-like growth factor-1 in fetal and maternal sheep and in human fetal brain tissues, while ethanol increased the expression of tumor necrosis factor α in mouse and human neurons. White matter lesions have been induced in the developing sheep brain by alcohol exposure in early gestation. Rat fetal alcohol syndrome models have shown reduced axon diameters, with thinner myelin sheaths, as well as reduced numbers of oligodendrocytes, which were also morphologically aberrant oligodendrocytes. Expressions of markers for mature myelination, including myelin basic protein, also were reduced. The accumulating knowledge concerning the mechanisms of ethanol-induced dysmyelination could lead to the development of strategies to prevent dysmyelination in children exposed to ethanol during fetal development. Future studies using fetal oligodendrocyte- and oligodendrocyte precursor cell-derived exosomes isolated from the mother's blood may identify biomarkers for fetal alcohol syndrome and even implicate epigenetic changes in early development that affect oligodendrocyte precursor cell and oligodendrocyte function in adulthood. By combining various imaging modalities with molecular studies, it may be possible to determine which fetuses are at risk and to intervene therapeutically early in the pregnancy.

Project description:BackgroundFetal alcohol syndrome (FAS) is a lifelong developmental disability that occurs among individuals with prenatal alcohol exposure (PAE). With improved prediction models, FAS can be diagnosed or treated early, if not completely prevented.ObjectiveIn this study, we sought to compare different machine learning algorithms and their FAS predictive performance among women who consumed alcohol during pregnancy. We also aimed to identify which variables (eg, timing of exposure to alcohol during pregnancy and type of alcohol consumed) were most influential in generating an accurate model.MethodsData from the collaborative initiative on fetal alcohol spectrum disorders from 2007 to 2017 were used to gather information about 595 women who consumed alcohol during pregnancy at 5 hospital sites around the United States. To obtain information about PAE, questionnaires or in-person interviews, as well as reviews of medical, legal, or social service records were used to gather information about alcohol consumption. Four different machine learning algorithms (logistic regression, XGBoost, light gradient-boosting machine, and CatBoost) were trained to predict the prevalence of FAS at birth, and model performance was measured by analyzing the area under the receiver operating characteristics curve (AUROC). Of the total cases, 80% were randomly selected for training, while 20% remained as test data sets for predicting FAS. Feature importance was also analyzed using Shapley values for the best-performing algorithm.ResultsOverall, there were 20 cases of FAS within a total population of 595 individuals with PAE. Most of the drinking occurred in the first trimester only (n=491) or throughout all 3 trimesters (n=95); however, there were also reports of drinking in the first and second trimesters only (n=8), and 1 case of drinking in the third trimester only (n=1). The CatBoost method delivered the best performance in terms of AUROC (0.92) and area under the precision-recall curve (AUPRC 0.51), followed by the logistic regression method (AUROC 0.90; AUPRC 0.59), the light gradient-boosting machine (AUROC 0.89; AUPRC 0.52), and XGBoost (AUROC 0.86; AURPC 0.45). Shapley values in the CatBoost model revealed that 12 variables were considered important in FAS prediction, with drinking throughout all 3 trimesters of pregnancy, maternal age, race, and type of alcoholic beverage consumed (eg, beer, wine, or liquor) scoring highly in overall feature importance. For most predictive measures, the best performance was obtained by the CatBoost algorithm, with an AUROC of 0.92, precision of 0.50, specificity of 0.29, F1 score of 0.29, and accuracy of 0.96.ConclusionsMachine learning algorithms were able to identify FAS risk with a prediction performance higher than that of previous models among pregnant drinkers. For small training sets, which are common with FAS, boosting mechanisms like CatBoost may help alleviate certain problems associated with data imbalances and difficulties in optimization or generalization.

Dataset Information

Kcnn2 blockade reverses learning deficits in a mouse model of fetal alcohol spectrum disorders.

Publications

Kcnn2 blockade reverses learning deficits in a mouse model of fetal alcohol spectrum disorders.

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