Research
Defining Disease Mechanisms
Once the genes implicated in a disorder are identified, understanding the mechanism(s) by which they lead to disease is approachable. We use genetic engineering in model systems such as rodents (in vivo), and human neural stem cells (in vitro) to understand the biological impact of risk variants (schematic shown below).
Informative Experimentation Disease-relevant genes require experimental modelling to better understand their contribution to disease.
However, since genes do not act in isolation and many complex human disorders involve the interaction of many genes, we also rely on systems biology methods to understand the biological pathways that may be impacted by disease associated variation. We employ multi-dimensional approaches to link genotype to phenotype by profiling genome-wide measures such as whole genome sequencing, transcriptomics, and epigenetics. This has necessitated the development and application of gene network methods, which form the backbone of our systems biology framework.
Leveraging Multi-Dimensional Data Understanding the complexity of gene-gene interactions in the human brain requires the combination of multiple data sources. Translating this high-dimensional data back into gene networks that are biologically interpretable is essential for downstream analyses.
To move from gene networks to understanding disease mechanism(s) we need to be able to connect multiple levels of analysis, traversing the distance from gene to protein, to cell to circuit to behavior and cognition. Connecting these different levels necessitates multidisciplinary collaborations, ranging from those that study the effects of risk variants in humans, to studies that try to bridge the gap from DNA variant to cellular function and physiology using human neural stem cells (Pa?ca et al. 2015) and animal models (de la Torre-Ubieta et al. 2016, Peñagarikano et al. 2011 (watch the video abstract here), Peñagarikano et al. 2015, Chandran et al. 2017).
Model Organisms and Organoids Identifying relevant genes, biologically modifying their function in model organisms or organoids, and evaluating the impact on phenotype permits a deep understanding of disease biology.
See also:
Gandal et al. 2016, The road to precision psychiatry: translating genetics into disease mechanisms, Nat Neurosci.
Geschwind and Konopka 2009, Neuroscience in the era of functional genomics and systems biology, Nature.
Parikshak et al. 2015, Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders, Nat Rev Genet.
de la Torre-Ubieta et al. 2016, Advancing the understanding of autism disease mechanisms through genetics., Nat Med.
Pa?ca et al. 2015, Functional cortical neurons and astrocytes from human pluripotent stem cells in 3D culture, Nat Methods.
Peñagarikano et al. 2011, Absence of CNTNAP2 leads to epilepsy, neuronal migration abnormalities, and core autism-related deficits, Cell. (Watch the video abstract here.)
Peñagarikano et al. 2015, Exogenous and evoked oxytocin restores social behavior in the Cntnap2 mouse model of autism, Sci Transl Med.
Chandran et al. 2017, Inducible and reversible phenotypes in a novel mouse model of Friedreich’s Ataxia, eLife.