Evaluation of TrkB and BDNF transcripts in prefrontal cortex, hippocampus, and striatum from subjects with schizophrenia, bipolar disorder, and major depressive disorder

Identification of Vulnerable Cell Types in Major Brain Disorders Using Single Cell Transcriptomes and Expression Weighted Cell Type Enrichment

The cell types that trigger the primary pathology in many brain diseases remain largely unknown. One route to understanding the primary pathological cell type for a particular disease is to identify the cells expressing susceptibility genes. Although this is straightforward for monogenic conditions where the causative mutation may alter expression of a cell type specific marker, methods are required for the common polygenic disorders. We developed the Expression Weighted Cell Type Enrichment (EWCE) method that uses single cell transcriptomes to generate the probability distribution associated with a gene list having an average level of expression within a cell type. Following validation, we applied EWCE to human genetic data from cases of epilepsy, Schizophrenia, Autism, Intellectual Disability, Alzheimer's disease, Multiple Sclerosis and anxiety disorders. Genetic susceptibility primarily affected microglia in Alzheimer's and Multiple Sclerosis; was shared between interneurons and pyramidal neurons in Autism and Schizophrenia; while intellectual disabilities and epilepsy were attributable to a range of cell-types, with the strongest enrichment in interneurons. We hypothesized that the primary cell type pathology could trigger secondary changes in other cell types and these could be detected by applying EWCE to transcriptome data from diseased tissue. In Autism, Schizophrenia and Alzheimer's disease we find evidence of pathological changes in all of the major brain cell types. These findings give novel insight into the cellular origins and progression in common brain disorders. The methods can be applied to any tissue and disorder and have applications in validating mouse models.

Identification of Vulnerable Cell Types in Major Brain Disorders Using Single Cell Transcriptomes and Expression Weighted Cell Type Enrichment

The cell types that trigger the primary pathology in many brain diseases remain largely unknown. One route to understanding the primary pathological cell type for a particular disease is to identify the cells expressing susceptibility genes. Although this is straightforward for monogenic conditions where the causative mutation may alter expression of a cell type specific marker, methods are required for the common polygenic disorders. We developed the Expression Weighted Cell Type Enrichment (EWCE) method that uses single cell transcriptomes to generate the probability distribution associated with a gene list having an average level of expression within a cell type. Following validation, we applied EWCE to human genetic data from cases of epilepsy, Schizophrenia, Autism, Intellectual Disability, Alzheimer's disease, Multiple Sclerosis and anxiety disorders. Genetic susceptibility primarily affected microglia in Alzheimer's and Multiple Sclerosis; was shared between interneurons and pyramidal neurons in Autism and Schizophrenia; while intellectual disabilities and epilepsy were attributable to a range of cell-types, with the strongest enrichment in interneurons. We hypothesized that the primary cell type pathology could trigger secondary changes in other cell types and these could be detected by applying EWCE to transcriptome data from diseased tissue. In Autism, Schizophrenia and Alzheimer's disease we find evidence of pathological changes in all of the major brain cell types. These findings give novel insight into the cellular origins and progression in common brain disorders. The methods can be applied to any tissue and disorder and have applications in validating mouse models.

Animal models of bipolar mania: The past, present and future

Bipolar disorder (BD) is the sixth leading cause of disability in the world according to the World Health Organization and affects nearly six million (∼2.5% of the population) adults in the United State alone each year. BD is primarily characterized by mood cycling of depressive (e.g., helplessness, reduced energy and activity, and anhedonia) and manic (e.g., increased energy and hyperactivity, reduced need for sleep, impulsivity, reduced anxiety and depression), episodes. The following review describes several animal models of bipolar mania with a focus on more recent findings using genetically modified mice, including several with the potential of investigating the mechanisms underlying 'mood' cycling (or behavioral switching in rodents). We discuss whether each of these models satisfy criteria of validity (i.e., face, predictive, and construct), while highlighting their strengths and limitations. Animal models are helping to address critical questions related to pathophysiology of bipolar mania, in an effort to more clearly define necessary targets of first-line medications, lithium and valproic acid, and to discover novel mechanisms with the hope of developing more effective therapeutics. Future studies will leverage new technologies and strategies for integrating animal and human data to reveal important insights into the etiology, pathophysiology, and treatment of BD.

Association between Val66Met polymorphisms in brain-derived neurotrophic factor gene and asthma risk: a meta-analysis

OBJECTIVE

The Val66Met polymorphisms in brain-derived neurotrophic factor (BDNF) gene have been reported to be associated with asthma risk, while the results are inconclusive. Considering a single study may lack the power to provide reliable conclusion, we performed a meta-analysis to investigate the association between the Val66Met polymorphisms and asthma susceptibility.

METHODS

A comprehensive literature search of PubMed, Embase, China National Knowledge Infrastructure (CNKI) and Wanfang databases was conducted before February 12, 2015. The pooled odds ratio (OR) with 95 % confidence intervals (CIs) were calculated.

RESULTS

Six eligible studies with a total of 3501 subjects were finally included in this meta-analysis. Overall, a significantly increased risk was detected in the Val66Met G allele (G vs. A: OR 1.33, 95 % CI 1.19-1.49, P = 5.61E-07; GG vs. GA + AA: OR 1.48, 95 % CI 1.20-1.83, P = 3.14E-04; GG vs. GA: OR 1.48, 95 % CI 1.17-1.89, P = 0.001; GG vs. AA: OR 1.62, 95 % CI 1.20-2.19, P = 0.002). Moreover, stratification by ethnicity indicated marked association between the Val66Met G allele and asthma risk in Caucasians (G vs. A: OR 1.29, 95 % CI 1.12-1.49, P = 0.001; GG + GA vs. AA: OR 1.59, 95 % CI 1.03-2.46, P = 0.039; GG vs. GA + AA: OR 1.32, 95 % CI 1.11-1.57, P = 0.001; GG vs. GA: OR 1.28, 95 % CI 1.07-1.53, P = 0.007; GG vs. AA: OR 1.72, 95 % CI 1.11-2.68, P = 0.015).

CONCLUSION

Our present meta-analysis suggests that the Val66Met polymorphisms in BDNF gene are potentially associated with asthma risk in Caucasians. Further well-designed case-control studies with larger sample size and more ethnic groups are needed to confirm these conclusions.