Link, N. | bioRxiv Science

bioRxiv2019

ANKLE2, a target of Zika virus, controls asymmetric cell division of neuroblasts and uncovers a novel microcephaly pathway

Neuroblasts in flies divide asymmetrically by establishing polarity, distributing cell fate determinants asymmetrically, and positioning their spindle for cell division. The apical complex contains aPKC, Bazooka (Par3), and Par6, and its activity depends on L(2)gl. We show that Ankle2 interacts with L(2)gl and affects aPKC. Reducing Ankle2 levels disrupts ER and nuclear envelope morphology, releasing the kinase Ballchen/VRK1 into the cytosol. These defects are associated with reduced phosphorylation of aPKC, disruption of Par complex localization, and spindle alignment defects. Importantly, removal of one copy of ballchen/VRK1 or l(2)gl suppresses the loss of Ankle2 and restores viability and brain size. The Zika virus NS4A protein interacts with Drosophila Ankle2 and VRK1 in dividing neuroblasts. Human mutational studies implicate this neural cell division pathway in microcephaly and motor neuron disease. In summary, NS4A, ANKLE2, VRK1 and LLGL1 define a novel pathway that impinges on asymmetric determinants of neural stem cell division.

genetics↗

bioRxiv2019

High-throughput Multimodal Automated Phenotyping (MAP) with Application to PheWAS

ObjectiveElectronic health records (EHR) linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. The objective of this study was to develop an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP).\n\nMethodWe developed a mapping method for automatically identifying relevant ICD and NLP concepts for a specific phenotype leveraging the UMLS. Aggregated ICD and NLP counts along with healthcare utilization were jointly analyzed by fitting an ensemble of latent mixture models. The MAP algorithm yields a predicted probability of phenotype for each patient and a threshold for classifying subjects with phenotype yes/no. The algorithm was validated using labeled data for 16 phenotypes from a biorepository and further tested in an independent cohort PheWAS for two SNPs with known associations.\n\nResultsThe MAP algorithm achieved higher or similar AUC and F-scores compared to the ICD code across all 16 phenotypes. The features assembled via the automated approach had comparable accuracy to those assembled via manual curation (AUCMAP 0.943, AUCmanual 0.941). The PheWAS results suggest that the MAP approach detected previously validated associations with higher power when compared to the standard PheWAS method based on ICD codes.\n\nConclusionThe MAP approach increased the accuracy of phenotype definition while maintaining scalability, facilitating use in studies requiring large scale phenotyping, such as PheWAS.

bioinformatics↗