Following the genes: a framework for animal modeling of psychiatric disorders

Genetic insights into the neurobiology of anxiety

Anxiety and fear are evolutionarily conserved emotions that increase the likelihood of an organism surviving threatening situations. Anxiety and vigilance states are regulated by neural networks involving multiple brain regions. In anxiety disorders, this intricate regulatory system is disturbed, leading to excessive or prolonged anxiety or fear. Anxiety disorders have both genetic and environmental risk factors. Genetic research has the potential to identify specific genetic variants causally associated with specific phenotypes. In recent decades, genome-wide association studies (GWASs) have revealed variants predisposing to neuropsychiatric disorders, suggesting novel neurobiological pathways in the etiology of these disorders. Here, we review recent human GWASs of anxiety disorders, and genetic studies of anxiety-like behavior in rodent models. These studies are paving the way for a better understanding of the neurobiological mechanisms underlying anxiety disorders.

Toward Best Practices for Imaging Transcriptomics of the Human Brain

Modern brainwide transcriptional atlases provide unprecedented opportunities for investigating the molecular correlates of brain organization, as quantified using noninvasive neuroimaging. However, integrating neuroimaging data with transcriptomic measures is not straightforward, and careful consideration is required to make valid inferences. In this article, we review recent work exploring how various methodological choices affect 3 main phases of imaging transcriptomic analyses, including 1) processing of transcriptional atlas data; 2) relating transcriptional measures to independently derived neuroimaging phenotypes; and 3) evaluating the functional implications of identified associations through gene enrichment analyses. Our aim is to facilitate the development of standardized and reproducible approaches for this rapidly growing field. We identify sources of methodological variability, key choices that can affect findings, and considerations for mitigating false positive and/or spurious results. Finally, we provide an overview of freely available open-source toolboxes implementing current best-practice procedures across all 3 analysis phases.

Using animal models for the studies of schizophrenia and depression: The value of translational models for treatment and prevention

Animal models of psychiatric disorders have been highly effective in advancing the field, identifying circuits related to pathophysiology, and identifying novel therapeutic targets. In this review, we show how animal models, particularly those based on development, have provided essential information regarding circuits involved in disorders, disease progression, and novel targets for intervention and potentially prevention. Nonetheless, in recent years there has been a pushback, largely driven by the US National Institute of Mental Health (NIMH), to shift away from animal models and instead focus on circuits in normal subjects. This has been driven primarily from a lack of discovery of new effective therapeutic targets, and the failure of targets based on preclinical research to show efficacy. We discuss why animal models of complex disorders, when strongly cross-validated by clinical research, are essential to understand disease etiology as well as pathophysiology, and direct new drug discovery. Issues related to shortcomings in clinical trial design that confound translation from animal models as well as the failure to take patient pharmacological history into account are proposed to be a source of the failure of what are likely effective compounds from showing promise in clinical trials.

MCF2 is linked to a complex perisylvian syndrome and affects cortical lamination

The combination of congenital bilateral perisylvian syndrome (CBPS) with lower motor neuron dysfunction remains unusual and suggests a potential common genetic insult affecting basic neurodevelopmental processes. Here we identify a putatively pathogenic missense mutation in the MCF2 gene in a boy with CBPS. Using in utero electroporation to genetically manipulate cortical neurons during corticogenesis, we demonstrate that the mouse Mcf2 gene controls the embryonic migration of cortical projection neurons. Strikingly, we find that the CBPS-associated MCF2 mutation impairs cortical laminar positioning, supporting the hypothesis that alterations in the process of embryonic neuronal migration can lead to rare cases of CBPS.

Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry

INTRODUCTION

Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an introduction on how machine learning/Artificial Intelligence (AI) may instantiate such capabilities, as well as provide rationale for its application to psychiatry in both research and clinical ecosystems.

METHODS

Databases PubMed and PsycINFO were searched from 1966 to June 2016 for keywords:Big Data, Machine Learning, Precision Medicine, Artificial Intelligence, Mental Health, Mental Disease, Psychiatry, Data Mining, RDoC, and Research Domain Criteria. Articles selected for review were those that were determined to be aligned with the objective of this particular paper.

RESULTS

Results indicate that AI is a viable option to build useful predictors of outcome while offering objective and comparable accuracy metrics, a unique opportunity, particularly in mental health research. The approach has also consistently brought notable insight into disease models through processing the vast amount of already available multi-domain, semi-structured medical data. The opportunity for AI in psychiatry, in addition to disease-model refinement, is in characterizing those at risk, and it is likely also relevant to personalizing and discovering therapeutics.

CONCLUSIONS

Machine learning currently provides an opportunity to parse disease models in complex, multi-factorial disease states (e.g. mental disorders) and could possibly inform treatment selection with existing therapies and provide bases for domain-based therapeutic discovery.

A Novel Zebrafish ret Heterozygous Model of Hirschsprung Disease Identifies a Functional Role for mapk10 as a Modifier of Enteric Nervous System Phenotype Severity

Hirschsprung disease (HSCR) is characterized by absence of enteric neurons from the distal colon and severe intestinal dysmotility. To understand the pathophysiology and genetics of HSCR we developed a unique zebrafish model that allows combined genetic, developmental and in vivo physiological studies. We show that ret mutant zebrafish exhibit cellular, physiological and genetic features of HSCR, including absence of intestinal neurons, reduced peristalsis, and varying phenotype expressivity in the heterozygous state. We perform live imaging experiments using a UAS-GAL4 binary genetic system to drive fluorescent protein expression in ENS progenitors. We demonstrate that ENS progenitors migrate at reduced speed in ret heterozygous embryos, without changes in proliferation or survival, establishing this as a principal pathogenic mechanism for distal aganglionosis. We show, using live imaging of actual intestinal movements, that intestinal motility is severely compromised in ret mutants, and partially impaired in ret heterozygous larvae, and establish a clear correlation between neuron position and organised intestinal motility. We exploited the partially penetrant ret heterozygous phenotype as a sensitised background to test the influence of a candidate modifier gene. We generated mapk10 loss-of-function mutants, which show reduced numbers of enteric neurons. Significantly, we show that introduction of mapk10 mutations into ret heterozygotes enhanced the ENS deficit, supporting MAPK10 as a HSCR susceptibility locus. Our studies demonstrate that ret heterozygous zebrafish is a sensitized model, with many significant advantages over existing murine models, to explore the pathophysiology and complex genetics of HSCR.

Hirschsprung Disease (HSCR) is a common congenital intestinal motility disorder diagnosed at birth by absence of enteric neurons in the distal gut, leading to intestinal obstruction that requires life-saving surgery. HSCR exhibits complex inheritance patterns and its genetic basis is not fully understood. Although well studied by human geneticists, and modelled using mouse, significant questions remain about the cellular and genetic causes of the disease and the relationship between neuron loss and defective intestinal motility. Here we use accessible, transparent zebrafish to address these outstanding questions. We establish that ret mutant zebrafish display key features of HSCR, including absence of intestinal neurons, reduced gut motility and varying phenotype expressivity. Using live imaging, possible in zebrafish but not in mouse, we demonstrate that decreased migration speed of enteric neuron progenitors colonising the gut is the principal defect leading to neuron deficits. By direct examination of gut motility in zebrafish larvae, we establish a clear correlation between neurons and motility patterns. Finally, we show that mapk10 mutations worsen the enteric neuron deficit of ret mutants, indicating that mutations in MAPK10 may increase susceptibility to HSCR. We show many benefits of modelling human genetic diseases in zebrafish and advance our understanding of HSCR.

Translational approaches to anxiety: focus on genetics, fear extinction and brain imaging

Anxiety disorders are highly prevalent and debilitating psychiatric disorders. Owing to the complex aetiology of anxiety disorders, translational studies involving multiple approaches, including human and animal genetics, molecular, endocrinological and imaging studies, are needed to get a converging picture of function or dysfunction of anxiety-related circuits. An advantage of anxiety disorders is that the neural circuitry of fear is comparatively well understood, with striking analogies between animal and human models, and this article aims to provide a brief overview of current translational approaches to anxiety. Experimental models that involve similar tasks in animals and humans, such as fear conditioning and extinction, seem particularly promising and can be readily integrated with imaging, behavioural and physiological readouts. The cross-validation between animal and human genetics models is essential to examine the relevance of candidate genes, as well as their neural pathways, for anxiety disorders; a recent example of such cross-validation work is provided by preclinical and clinical work on TMEM132D, which has been identified as a candidate gene for panic disorder. Further integration of epigenetic data and gene × environment interaction are promising approaches, as highlighted by FKPB5 and PACAP, early life trauma and stress-related anxiety disorders. Finally, connecting genetic and epigenetic data with functionally relevant imaging readouts will allow a comparison of overlap and differences across species in mechanistic pathways from genes to brain functioning and behaviour.