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

Valproic acid-induced changes of 4D nuclear morphology in astrocyte cells

Histone deacetylase inhibitors, such as valproic acid (VPA), have important clinical therapeutic and cellular reprogramming applications. They induce chromatin reorganization that is associated with altered cellular morphology. However, there is a lack of comprehensive characterization of VPA-induced changes of nuclear size and shape. Here, we quantify 3D nuclear morphology of primary human astrocyte cells treated with VPA over time (hence, 4D). We compared volumetric and surface-based representations and identified seven features that jointly discriminate between normal and treated cells with 85% accuracy on day 7. From day 3, treated nuclei were more elongated and flattened and then continued to morphologically diverge from controls over time, becoming larger and more irregular. On day 7, most of the size and shape descriptors demonstrated significant differences between treated and untreated cells, including a 24% increase in volume and 6% reduction in extent (shape regularity) for treated nuclei. Overall, we show that 4D morphometry can capture how chromatin reorganization modulates the size and shape of the nucleus over time. These nuclear structural alterations may serve as a biomarker for histone (de-)acetylation events and provide insights into mechanisms of astrocytes-to-neurons reprogramming.

Druggable Transcriptional Networks in the Human Neurogenic Epigenome

Chromosome conformation capture methods have revealed the dynamics of genome architecture which is spatially organized into topologically associated domains, with gene regulation mediated by enhancer-promoter pairs in chromatin space. New evidence shows that endogenous hormones and several xenobiotics act within circumscribed topological domains of the spatial genome, impacting subsets of the chromatin contacts of enhancer-gene promoter pairs in cis and trans Results from the National Institutes of Health-funded PsychENCODE project and the study of chromatin remodeling complexes have converged to provide a clearer understanding of the organization of the neurogenic epigenome in humans. Neuropsychiatric diseases, including schizophrenia, bipolar spectrum disorder, autism spectrum disorder, attention deficit hyperactivity disorder, and other neuropsychiatric disorders are significantly associated with mutations in neurogenic transcriptional networks. In this review, we have reanalyzed the results from publications of the PsychENCODE Consortium using pharmacoinformatics network analysis to better understand druggable targets that control neurogenic transcriptional networks. We found that valproic acid and other psychotropic drugs directly alter these networks, including chromatin remodeling complexes, transcription factors, and other epigenetic modifiers. We envision a new generation of CNS therapeutics targeted at neurogenic transcriptional control networks, including druggable parts of chromatin remodeling complexes and master transcription factor-controlled pharmacogenomic networks. This may provide a route to the modification of interconnected gene pathways impacted by disease in patients with neuropsychiatric and neurodegenerative disorders. Direct and indirect therapeutic strategies to modify the master regulators of neurogenic transcriptional control networks may ultimately help extend the life span of CNS neurons impacted by disease.

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.

The pharmacoepigenomics informatics pipeline defines a pathway of novel and known warfarin pharmacogenomics variants

AIM

'Pharmacoepigenomics' methods informed by omics datasets and pre-existing knowledge have yielded discoveries in neuropsychiatric pharmacogenomics. Now we evaluate the generality of these methods by discovering an extended warfarin pharmacogenomics pathway.

MATERIALS & METHODS

We developed the pharmacoepigenomics informatics pipeline, a scalable multi-omics variant screening pipeline for pharmacogenomics, and conducted an experiment in the genomics of warfarin.

RESULTS

We discovered known and novel pharmacogenomics variants and genes, both coding and regulatory, for warfarin response, including adverse events. Such genes and variants cluster in a warfarin response pathway consolidating known and novel warfarin response variants and genes.

CONCLUSION

These results can inform a new warfarin test. The pharmacoepigenomics informatics pipeline may be able to discover new pharmacogenomics markers in other drug-disease systems.

Trauma care: Finding a better way

In a Perspective, Hasan Alam discusses emerging treatment approaches in trauma care.