Ari Allyn-Feuer

A glutamatergic network mediates lithium response in bipolar disorder as defined by epigenome pathway analysis

AIM A regulatory network in the human brain mediating lithium response in bipolar patients was revealed by analysis of functional SNPs from genome-wide association studies (GWAS) and published gene association studies, followed by epigenome mapping. METHODS An initial set of 23,312 SNPs in linkage disequilibrium with lead SNPs, and sub-threshold GWAS SNPs rescued by pathway analysis, were studied in the same populations. These were assessed using our workflow and annotation by the epigenome roadmap consortium. RESULTS Twenty-seven percent of 802 SNPs that were associated with lithium response (13 published studies gene association studies and two GWAS) were shared in common with 1281 SNPs from 18 GWAS examining psychiatric disorders and adverse events associated with lithium treatment. Nineteen SNPs were annotated as active regulatory elements such as enhancers and promoters in a tissue-specific manner. They were located within noncoding regions of ten genes: ANK3, ARNTL, CACNA1C, CACNG2, CDKN1A, CREB1, GRIA2, GSK3B, NR1D1 and SLC1A2. Following gene set enrichment and pathway analysis, these genes were found to be significantly associated (p = 10(-27); Fisher exact test) with an AMPA2 glutamate receptor network in human brain. Our workflow results showed concordance with annotation of regulatory elements from the epigenome roadmap. Analysis of cognate mRNA and enhancer RNA exhibited patterns consistent with an integrated pathway in human brain. CONCLUSION This pharmacoepigenomic regulatory pathway is located in the same brain regions that exhibit tissue volume loss in bipolar disorder. Although in silico analysis requires biological validation, the approach provides value for identification of candidate variants that may be used in pharmacogenomic testing to identify bipolar patients likely to respond to lithium.

Network Reconstruction Reveals that Valproic Acid Activates Neurogenic Transcriptional Programs in Adult Brain Following Traumatic Injury

ObjectivesTo determine the mechanism of action of valproic acid (VPA) in the adult central nervous system (CNS) following traumatic brain injury (TBI) and hemorrhagic shock (HS).MethodsData were analyzed from different sources, including experiments in a porcine model, data from postmortem human brain, published studies, public and commercial databases.ResultsThe transcriptional program in the CNS following TBI, HS, and VPA treatment includes activation of regulatory pathways that enhance neurogenesis and suppress gliogenesis. Genes which encode the transcription factors (TFs) that specify neuronal cell fate, including MEF2D, MYT1L, NEUROD1, PAX6 and TBR1, and their target genes, are induced by VPA. VPA represses genes responsible for oligodendrogenesis, maintenance of white matter, T-cell activation, angiogenesis, and endothelial cell proliferation, adhesion and chemotaxis. NEUROD1 has regulatory interactions with 38% of the genes regulated by VPA in a swine model of TBI and HS in adult brain. Hi-C spatial mapping of a VPA pharmacogenomic SNP in the GRIN2B gene shows it is part of a transcriptional hub that contacts 12 genes that mediate chromatin-mediated neurogenesis and neuroplasticity.ConclusionsFollowing TBI and HS, this study shows that VPA administration acts in the adult brain through differential activation of TFs responsible for neurogenesis, genes responsible for neuroplasticity, and repression of TFs that specify oligodendrocyte cell fate, endothelial cell chemotaxis and angiogenesis.Short title: Mechanism of action of valproic acid in traumatic brain injury

The epigenome, 4D nucleome and next-generation neuropsychiatric pharmacogenomics

The 4D nucleome has the potential to render challenges in neuropsychiatric pharmacogenomics more tractable. The epigenome roadmap consortium has demonstrated the critical role that noncoding regions of the human genome play in determination of human phenotype. Chromosome conformation capture methods have revealed the 4D organization of the nucleus, bringing interactions between distant regulatory elements into close spatial proximity in a periodic manner. These functional interactions have the potential to elucidate mechanisms of CNS drug response and side effects that previously have been unrecognized. This perspective assesses recent advances likely to reveal novel pharmacodynamic regulatory pathways in human brain, charting a future new avenue of pharmacogenomics research, using the spatial and temporal architecture of the human epigenome as its foundation.

Mining the topography and dynamics of the 4D Nucleome to identify novel CNS drug pathways

The pharmacoepigenome can be defined as the active, noncoding province of the genome including canonical spatial and temporal regulatory mechanisms of gene regulation that respond to xenobiotic stimuli. Many psychotropic drugs that have been in clinical use for decades have ill-defined mechanisms of action that are beginning to be resolved as we understand the transcriptional hierarchy and dynamics of the nucleus. In this review, we describe spatial, temporal and biomechanical mechanisms mediated by psychotropic medications. Focus is placed on a bioinformatics pipeline that can be used both for detection of pharmacoepigenomic variants that discretize drug response and adverse events to improve pharmacogenomic testing, and for the discovery of novel CNS therapeutics. This approach integrates the functional topology and dynamics of the transcriptional hierarchy of the pharmacoepigenome, gene variant-driven identification of pharmacogenomic regulatory domains, and mesoscale mapping for the discovery of novel CNS pharmacodynamic pathways in human brain. Examples of the application of this pipeline are provided, including the discovery of valproic acid (VPA) mediated transcriptional reprogramming of neuronal cell fate following injury, and mapping of a CNS pathway glutamatergic pathway for the mood stabilizer lithium. These examples in regulatory pharmacoepigenomics illustrate how ongoing research using the 4D nucleome provides a foundation to further insight into previously unrecognized psychotropic drug pharmacodynamic pathways in the human CNS.

3D Cell Nuclear Morphology: Microscopy Imaging Dataset and Voxel-Based Morphometry Classification Results

Cell deformation is regulated by complex underlying biological mechanisms associated with spatial and temporal morphological changes in the nucleus that are related to cell differentiation, development, proliferation, and disease. Thus, quantitative analysis of changes in size and shape of nuclear structures in 3D microscopic images is important not only for investigating nuclear organization, but also for detecting and treating pathological conditions such as cancer. While many efforts have been made to develop cell and nuclear shape characteristics in 2D or pseudo-3D, several studies have suggested that 3D morphometric measures provide better results for nuclear shape description and discrimination. A few methods have been proposed to classify cell and nuclear morphological phenotypes in 3D, however, there is a lack of publicly available 3D data for the evaluation and comparison of such algorithms. This limitation becomes of great importance when the ability to evaluate different approaches on benchmark data is needed for better dissemination of the current state of the art methods for bioimage analysis. To address this problem, we present a dataset containing two different cell collections, including original 3D microscopic images of cell nuclei and nucleoli. In addition, we perform a baseline evaluation of a number of popular classification algorithms using 2D and 3D voxel-based morphometric measures. To account for batch effects, while enabling calculations of AUROC and AUPR performance metrics, we propose a specific cross-validation scheme that we compare with commonly used k-fold cross-validation. Original and derived imaging data are made publicly available on the project web-page: http://www.socr.umich.edu/projects/3d-cell-morphometry/data.html.

Deep learning in pharmacogenomics: from gene regulation to patient stratification

This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future, deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical and demographic datasets.

3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification.

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We used robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we computed geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compared over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieved accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.

Publisher Correction: 3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification

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