In the last decades, few mechanistically novel therapeutic agents have been developed to treat mental and neurodegenerative disorders. Numerous studies suggest that targeting BDNF and its TrkB receptor could be a promising therapeutic strategy for the treatment of brain disorders. However, the development of potent small ligands for the TrkB receptor has proven to be difficult. By using a peptidomimetic approach, we developed a highly potent and selective TrkB inhibitor, cyclotraxin-B, capable of altering TrkB-dependent molecular and physiological processes such as synaptic plasticity, neuronal differentiation and BDNF-induced neurotoxicity. Cyclotraxin-B allosterically alters the conformation of TrkB, which leads to the inhibition of both BDNF-dependent and -independent (basal) activities. Finally, systemic administration of cyclotraxin-B to mice results in TrkB inhibition in the brain with specific anxiolytic-like behavioral effects and no antidepressant-like activity. This study demonstrates that cyclotraxin-B might not only be a powerful tool to investigate the role of BDNF and TrkB in physiology and pathology, but also represents a lead compound for the development of new therapeutic strategies to treat brain disorders.

Despite sharing the same genes, identical twins demonstrate substantial variability in behavioral traits and in their risk for disease. Epigenetic factors-DNA and chromatin modifications that affect levels of gene expression without affecting the DNA sequence-are thought to be important in establishing this variability. Epigenetically-mediated differences in the levels of gene expression that are associated with individual variability traditionally are thought to occur only in a gene-specific manner. We challenge this idea by exploring the large-scale organizational patterns of gene expression in an epigenetic model of behavioral variability.

A causal role of mutations in multiple general transcription factors in neurodevelopmental disorders including autism suggested that alterations in global levels of gene expression regulation might also relate to disease risk in sporadic cases of autism. This premise can be tested by evaluating for changes in the overall distribution of gene expression levels. For instance, in mice, variability in hippocampal-dependent behaviors was associated with variability in the pattern of the overall distribution of gene expression levels, as assessed by variance in the distribution of gene expression levels in the hippocampus. We hypothesized that a similar change in variance might be found in children with autism. Gene expression microarrays covering greater than 47,000 unique RNA transcripts were done on RNA from peripheral blood lymphocytes (PBL) of children with autism (n = 82) and controls (n = 64). Variance in the distribution of gene expression levels from each microarray was compared between groups of children. Also tested was whether a risk factor for autism, increased paternal age, was associated with variance. A decrease in the variance in the distribution of gene expression levels in PBL was associated with the diagnosis of autism and a risk factor for autism, increased paternal age. Traditional approaches to microarray analysis of gene expression suggested a possible mechanism for decreased variance in gene expression. Gene expression pathways involved in transcriptional regulation were down-regulated in the blood of children with autism and children of older fathers. Thus, results from global and gene specific approaches to studying microarray data were complimentary and supported the hypothesis that alterations at the global level of gene expression regulation are related to autism and increased paternal age. Global regulation of transcription, thus, represents a possible point of convergence for multiple etiologies of autism and other neurodevelopmental disorders.

Selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine are the most common form of medication treatment for major depression. However, approximately 50% of depressed patients fail to achieve an effective treatment response. Understanding how gene expression systems respond to treatments may be critical for understanding antidepressant resistance.