Dataset Information

Exon level machine learning analyses elucidate novel candidate miRNA targets in an avian model of fetal alcohol spectrum disorder.

ABSTRACT: Gestational alcohol exposure causes fetal alcohol spectrum disorder (FASD) and is a prominent cause of neurodevelopmental disability. Whole transcriptome sequencing (RNA-Seq) offer insights into mechanisms underlying FASD, but gene-level analysis provides limited information regarding complex transcriptional processes such as alternative splicing and non-coding RNAs. Moreover, traditional analytical approaches that use multiple hypothesis testing with a false discovery rate adjustment prioritize genes based on an adjusted p-value, which is not always biologically relevant. We address these limitations with a novel approach and implemented an unsupervised machine learning model, which we applied to an exon-level analysis to reduce data complexity to the most likely functionally relevant exons, without loss of novel information. This was performed on an RNA-Seq paired-end dataset derived from alcohol-exposed neural fold-stage chick crania, wherein alcohol causes facial deficits recapitulating those of FASD. A principal component analysis along with k-means clustering was utilized to extract exons that deviated from baseline expression. This identified 6857 differentially expressed exons representing 1251 geneIDs; 391 of these genes were identified in a prior gene-level analysis of this dataset. It also identified exons encoding 23 microRNAs (miRNAs) having significantly differential expression profiles in response to alcohol. We developed an RDAVID pipeline to identify KEGG pathways represented by these exons, and separately identified predicted KEGG pathways targeted by these miRNAs. Several of these (ribosome biogenesis, oxidative phosphorylation) were identified in our prior gene-level analysis. Other pathways are crucial to facial morphogenesis and represent both novel (focal adhesion, FoxO signaling, insulin signaling) and known (Wnt signaling) alcohol targets. Importantly, there was substantial overlap between the exomes themselves and the predicted miRNA targets, suggesting these miRNAs contribute to the gene-level expression changes. Our novel application of unsupervised machine learning in conjunction with statistical analyses facilitated the discovery of signaling pathways and miRNAs that inform mechanisms underlying FASD.

SUBMITTER: Al-Shaer AE

PROVIDER: S-EPMC6478348 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Exon level machine learning analyses elucidate novel candidate miRNA targets in an avian model of fetal alcohol spectrum disorder.

Al-Shaer Abrar E AE Flentke George R GR Berres Mark E ME Garic Ana A Smith Susan M SM

PLoS computational biology 20190411 4

Gestational alcohol exposure causes fetal alcohol spectrum disorder (FASD) and is a prominent cause of neurodevelopmental disability. Whole transcriptome sequencing (RNA-Seq) offer insights into mechanisms underlying FASD, but gene-level analysis provides limited information regarding complex transcriptional processes such as alternative splicing and non-coding RNAs. Moreover, traditional analytical approaches that use multiple hypothesis testing with a false discovery rate adjustment prioritize ...[more]

PMID: 30973878

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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.

Project description:BackgroundFetal alcohol spectrum disorders (FASD) is a leading cause of neurodevelopmental disability. Genetic factors can modify vulnerability to FASD, but these elements are poorly characterized.MethodsWe performed high-throughput transcriptional profiling to identify gene candidates that could potentially modify vulnerability to ethanol's (EtOH's) neurotoxicity. We interrogated a unique genetic resource, neuroprogenitor cells from 2 closely related Gallus gallus lines having well-characterized robust or attenuated EtOH responses with respect to intracellular calcium mobilization and CaMKII/β-catenin-dependent apoptosis. Samples were not exposed to EtOH prior to analysis.ResultsWe identified 363 differentially expressed genes in neuroprogenitors from these 2 lines. Kyoto Encyclopedia of Genes and Genomes analysis revealed several gene clusters having significantly differential enrichment in gene expression. The largest and most significant cluster comprised ribosomal proteins (38 genes, p = 1.85 × 10(-47) ). Other significantly enriched gene clusters included metabolism (25 genes, p = 0.0098), oxidative phosphorylation (18 genes, p = 1.10 × 10(-11) ), spliceosome (13 genes, p = 7.02 × 10(-8) ), and protein processing in the endoplasmic reticulum (9 genes, p = 0.0011). Inspection of gene ontogeny (GO) terms identified 24 genes involved in the calcium/β-catenin signals that mediate EtOH's neurotoxicity in this model, including β-catenin itself and both calmodulin isoforms.ConclusionsFour of the identified pathways with altered transcript abundance mediate the flow of cellular information from RNA to protein. Importantly, ribosome biogenesis also senses nucleolar stress and regulates p53-mediated apoptosis in neural crest. Human ribosomopathies produce craniofacial malformations and 11 known ribosomopathy genes were differentially expressed in this model of neural crest apoptosis. Rapid changes in ribosome expression are consistently observed in EtOH-treated mouse embryo neural folds, a model that is developmentally similar to ours. The recurring identification of ribosome biogenesis suggests it is a candidate modifier of EtOH vulnerability. These results highlight this approach's efficacy to formulate new, mechanistic hypotheses regarding EtOH's developmental damage.

Dataset Information

Exon level machine learning analyses elucidate novel candidate miRNA targets in an avian model of fetal alcohol spectrum disorder.

Publications

Exon level machine learning analyses elucidate novel candidate miRNA targets in an avian model of fetal alcohol spectrum disorder.

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