RESEARCH

Posttraumatic stress disorder, anxiety and depression

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Art by Jessica Johnson

A significant focus of my work seeks to elucidate the biological mechanisms underlying anxiety and trauma-related disorders, including posttraumatic stress disorder and major depressive disorder. We apply large-scale functional genomic assays (i.e. RNA-sequencing, methylation arrays) to peripheral tissues (i.e. whole blood, umbilical cord blood, serum, plasma) to study the  role of innate immunity and inflammation in individuals that are vulnerable or resilient to these disorders (see here, here and here).  These projects have translational research potential with the goal of identifying objective diagnostic/prognostic blood biomarkers for stress reactivity and resilience. I am also working with large-scale molecular data derived from clinical trials that test the efficacy of anti-inflammatory medications to alleviate core symptoms of PTSD. We also explore integrating molecular data  with cognitive, electrophysiological, and neuroimaging techniques to  identify unique combinations of biosignatures able to distinguish between finer gradients of health and disease.

Child health and development

A second theme of my research has been to elucidate risk factors, biomarkers and mechanisms underlying impaired neurodevelopment, using a variety of methodologies. For example, our work has recently demonstrated that prenatal exposure to maternal PTSD and depression induces neuronal, immunological and behavioral abnormalities in the affected offspring, using transcriptomic technologies (see here). We have also led the construction of the first molecular map depicting protein expression, function and interaction in the human cortex throughout postnatal development using unbiased proteomic technologies (see here). In parallel, I am leading studies with both academic and industry researchers, applying transcriptome technologies to identify diagnostic peripheral blood RNA biomarkers for idiopathic and syndromic forms of autism spectrum disorder, including Phelan-McDermid Syndrome (PMS). We are also actively integrating molecular data from human, cellular and animal models of PMS with the goal of identifying the exact molecular mechanisms underpinning this rare monogenic form of autism. I am also statistical adviser for clinical trials, which test the efficacy of IGF-1 treatment to alleviate core symptomatology of PMS.

Schizophrenia and environmental models of schizophrenia

Another tier of my work investigates the molecular underpinnings of schizophrenia and environmental models of schizophrenia, including methamphetamine-associated psychosis. This work seeks to better understand the neurocognitive and molecular deficits underlying the development of MAP and its progression in order to develop improved diagnostics and treatment approaches. Our work has identified highly accurate peripheral blood (RNA) and serum (protein) biomarkers of MAP and their relationships with neuroimaging (sMRI and DTI) (see here and here). More recently, I am extending the knowledge gained from these studies directly into schizophrenia research through collaboration with the CommonMind Consortium, for which I am analyzing transcriptome data from hundreds of postmortem brain samples from SCZ cases and controls. Beyond these applications, I have also diligently endeavored to help the field mature theoretically by introducing a strategy to integrate gene expression with other levels of biological data to derive more complex, but also more powerful biomarker profiles to set a stage for sensitive blood-based biomarkers in mental disorders (see here).

Algorithm Development

A critical component of biomarker discovery and validation is a rigorous statistical analysis. I am especially interested in solving emerging biological and algorithmic problems arising in computational/molecular biology, such as: a) comparative transcriptomics and proteomics (here); b) modelling epistatic interactions (here); c) predicting cellular frequencies from heterogeneous biological tissue (here); d) multi-modal integrative deep machine-learning applications; e) modelling RNA-editing in from RNA-sequencing data; f) gene network reconstruction and multi-modal data integrations. In addition to generating new data in support of these aims, I also use just about any high-throughput data I can get my hands in the public domain, which can ultimately be translated into better understanding the molecular mechanisms underlying neurodevelopmental and nueropsychiatric disorders.

Download computational code for quality control and analysis of RNA-sequencing gene expression data from my evolving github account, here.
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