Two patients walk into a clinic...a genomics perspective on the future of schizophrenia

Potentially repurposable drugs for schizophrenia identified from its interactome

We previously presented the protein-protein interaction network of schizophrenia associated genes, and from it, the drug-protein interactome which showed the drugs that target any of the proteins in the interactome. Here, we studied these drugs further to identify whether any of them may potentially be repurposable for schizophrenia. In schizophrenia, gene expression has been described as a measurable aspect of the disease reflecting the action of risk genes. We studied each of the drugs from the interactome using the BaseSpace Correlation Engine, and shortlisted those that had a negative correlation with differential gene expression of schizophrenia. This analysis resulted in 12 drugs whose differential gene expression (drug versus normal) had an anti-correlation with differential expression for schizophrenia (disorder versus normal). Some of these drugs were already being tested for their clinical activity in schizophrenia and other neuropsychiatric disorders. Several proteins in the protein interactome of the targets of several of these drugs were associated with various neuropsychiatric disorders. The network of genes with opposite drug-induced versus schizophrenia-associated expression profiles were significantly enriched in pathways relevant to schizophrenia etiology and GWAS genes associated with traits or diseases that had a pathophysiological overlap with schizophrenia. Drugs that targeted the same genes as the shortlisted drugs, have also demonstrated clinical activity in schizophrenia and other related disorders. This integrated computational analysis will help translate insights from the schizophrenia drug-protein interactome to clinical research - an important step, especially in the field of psychiatric drug development which faces a high failure rate.

Schizophrenia interactome with 504 novel protein-protein interactions

Genome-wide association studies of schizophrenia (GWAS) have revealed the role of rare and common genetic variants, but the functional effects of the risk variants remain to be understood. Protein interactome-based studies can facilitate the study of molecular mechanisms by which the risk genes relate to schizophrenia (SZ) genesis, but protein–protein interactions (PPIs) are unknown for many of the liability genes. We developed a computational model to discover PPIs, which is found to be highly accurate according to computational evaluations and experimental validations of selected PPIs. We present here, 365 novel PPIs of liability genes identified by the SZ Working Group of the Psychiatric Genomics Consortium (PGC). Seventeen genes that had no previously known interactions have 57 novel interactions by our method. Among the new interactors are 19 drug targets that are targeted by 130 drugs. In addition, we computed 147 novel PPIs of 25 candidate genes investigated in the pre-GWAS era. While there is little overlap between the GWAS genes and the pre-GWAS genes, the interactomes reveal that they largely belong to the same pathways, thus reconciling the apparent disparities between the GWAS and prior gene association studies. The interactome including 504 novel PPIs overall, could motivate other systems biology studies and trials with repurposed drugs. The PPIs are made available on a webserver, called Schizo-Pi at http://severus.dbmi.pitt.edu/schizo-pi with advanced search capabilities.

Application of fatty acid and lipid measurements in neuropsychiatry

The importance of lipids in the understanding of disease states is constantly increasing. Whilst the link between metabolic disorders and lipids seems to be clear, interpreting lipid regulation in the context of neuropsychiatric disorders is a new approach. Mental disorders account for almost 15% of the total global disease burden with Alzheimer’s disease, depression or schizophrenia being amongst the most widespread mental disorders in the general population. For this reason rapid and early diagnosis is crucial and finding the right biomarkers is of great importance. Lipids appear to be essential in learning the aetiopathology of neuropsychiatric diseases as well as in biomarker research as they are most abundantly present in the brain. This study discusses recent findings in neuropsychiatry in the context of lipid analysis.

Schizophrenia biomarkers: translating the descriptive into the diagnostic

While schizophrenia and mental health are qualitatively distinct at the level of clinical presentation, the specific molecular signatures that underlie, or associate with, illness are not. Biomarker identification in schizophrenia is intended to offer a number of important benefits to patient well-being including prediction of future illness, diagnostic clarity and a level of disease description that would guide treatment choice. However, the choice of sample and form of analysis used to produce useful biomarkers is still uncertain. In this review, advances from recent studies spanning the technical spectrum are presented together with comment on their comparative strengths and weaknesses. To date, these studies have aided our understanding of the pathological processes associated with illness much more than they have provided robust biomarkers. A number of reasons for this observation are suggested, as are new strategies for the extraction of biomarkers from large '-omics' datasets.

Genome-wide association studies: findings at the major histocompatibility complex locus in psychosis

The major histocompatibility complex (MHC) is one of the most intensively investigated, genetically diverse regions of the genome. In its extended form, it encodes more than 400 genes critical to immunity but is also involved in many other functions. In 2009, three simultaneously published genome-wide association studies (GWAS) reported the first compelling evidence for involvement of the MHC in schizophrenia susceptibility. In this review, we describe the structure and function of the MHC, discuss some of the challenges for genetic analysis of the region, and provide an update on findings from GWAS studies before describing potential approaches to interpreting the role of the locus in schizophrenia etiology. The GWAS literature supports involvement of the MHC locus in schizophrenia susceptibility. Current evidence suggests that the MHC plays a more significant role in schizophrenia susceptibility than in other psychiatric disorders. Because of the substantial diversity at the locus, there are differences in the implicated risk variants between ancestral groups, as there are for many other disorders. This is somewhat different than the pattern emerging at other loci. The association findings presently capture large genomic regions, with at least some evidence to suggest that multiple signals may be involved. Based on notable successes in other disorders, we suggest approaches to refining association signals at the locus. Finally, we discuss that these genetic data may be used to understand how the MHC contributes to the complex etiology of schizophrenia.

Genetics of schizophrenia from a clinicial perspective

Schizophrenia (SZ) is a common disorder that runs in families. It has a relatively high heritability, i.e., inherited factors account for the major proportion of its etiology. The high heritability has motivated gene mapping studies that have improved in sophistication through the past two decades. Belying earlier expectations, it is now becoming increasingly clear that the cause of SZ does not reside in a single mutation, or even in a single gene. Rather, there are multiple DNA variants, not all of which have been identified. Additional risk may be conferred by interactions between individual DNA variants, as well as 'gene-environment' interactions. We review studies that have accounted for a fraction of the heritability. Their relevance to the practising clinician is discussed. We propose that continuing research in DNA variation, in conjunction with rapid ongoing advances in allied fields, will yield dividends from the perspective of diagnosis, treatment prediction through pharmacogenetics, and rational treatment through discoveries in pathogenesis.

Recent advances in nutrition, genes and brain health

Molecular mechanisms underlying brain structure and function are affected by nutrition throughout the life cycle, with profound implications for health and disease. Responses to nutrition are in turn influenced by individual differences in multiple target genes. Recent advances in genomics and epigenomics are increasing understanding of mechanisms by which nutrition and genes interact. This review starts with a short account of current knowledge on nutrition-gene interactions, focusing on the significance of epigenetics to nutritional regulation of gene expression, and the roles of SNP and copy number variants (CNV) in determining individual responses to nutrition. A critical assessment is then provided of recent advances in nutrition-gene interactions, and especially energy status, in three related areas: (i) mental health and well-being, (ii) mental disorders and schizophrenia, (iii) neurological (neurodevelopmental and neurodegenerative) disorders and Alzheimer's disease. Optimal energy status, including physical activity, has a positive role in mental health. By contrast, sub-optimal energy status, including undernutrition and overnutrition, is implicated in many disorders of mental health and neurology. These actions are mediated by changes in energy metabolism and multiple signalling molecules, e.g. brain-derived neurotrophic factor (BDNF). They often involve epigenetic mechanisms, including DNA methylation and histone modifications. Recent advances show that many brain disorders result from a sophisticated network of interactions between numerous environmental and genetic factors. Personal, social and economic costs of sub-optimal brain health are immense. Future advances in understanding the complex interactions between nutrition, genes and the brain should help to reduce these costs and enhance quality of life.