Molecular Diagnostic Outcomes from 700 Cases: What Can We Learn from a Retrospective Analysis of Clinical Exome Sequencing?

Clinical exome sequencing (CES) aids in the diagnosis of rare genetic disorders. Herein, we report the molecular diagnostic yield and spectrum of genetic alterations contributing to disease in 700 pediatric cases analyzed at the Children's Hospital of Philadelphia. The overall diagnostic yield was 23%, with three cases having more than one molecular diagnosis and 2.6% having secondary/additional findings. A candidate gene finding was reported in another 8.4% of cases. The clinical indications with the highest diagnostic yield were neurodevelopmental disorders (including seizures), whereas immune- and oncology-related indications were negatively associated with molecular diagnosis. The rapid expansion of knowledge regarding the genome's role in human disease necessitates reanalysis of CES samples. To capture these new discoveries, a subset of cases (n = 240) underwent reanalysis, with an increase in diagnostic yield. We describe our experience reporting CES results in a pediatric setting, including reporting of secondary findings, reporting newly discovered genetic conditions, and revisiting negative test results. Finally, we highlight the challenges associated with implementing critical updates to the CES workflow. Although these updates are necessary, they demand an investment of time and resources from the laboratory. In summary, these data demonstrate the clinical utility of exome sequencing and reanalysis, while highlighting the critical considerations for continuous improvement of a CES test in a clinical laboratory.

Total levels of hippocampal histone acetylation predict normal variability in mouse behavior.

BACKGROUND: Genetic, pharmacological, and environmental interventions that alter total levels of histone acetylation in specific brain regions can modulate behaviors and treatment responses. Efforts have been made to identify specific genes that are affected by alterations in total histone acetylation and to propose that such gene specific modulation could explain the effects of total histone acetylation levels on behavior - the implication being that under naturalistic conditions variability in histone acetylation occurs primarily around the promoters of specific genes. METHODS/RESULTS: Here we challenge this hypothesis by demonstrating with a novel flow cytometry based technique that normal variability in open field exploration, a hippocampus-related behavior, was associated with total levels of histone acetylation in the hippocampus but not in other brain regions. CONCLUSIONS: Results suggest that modulation of total levels of histone acetylation may play a role in regulating biological processes. We speculate in the discussion that endogenous regulation of total levels of histone acetylation may be a mechanism through which organisms regulate cellular plasticity. Flow cytometry provides a useful approach to measure total levels of histone acetylation at the single cell level. Relating such information to behavioral measures and treatment responses could inform drug delivery strategies to target histone deacetylase inhibitors and other chromatin modulators to places where they may be of benefit while avoiding areas where correction is not needed and could be harmful.

Global state measures of the dentate gyrus gene expression system predict antidepressant-sensitive behaviors.

BACKGROUND: 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. METHODS: We take a novel approach to this problem by demonstrating that the gene expression system of the dentate gyrus responds to fluoxetine (FLX), a commonly used antidepressant medication, in a stereotyped-manner involving changes in the expression levels of thousands of genes. The aggregate behavior of this large-scale systemic response was quantified with principal components analysis (PCA) yielding a single quantitative measure of the global gene expression system state. RESULTS: Quantitative measures of system state were highly correlated with variability in levels of antidepressant-sensitive behaviors in a mouse model of depression treated with fluoxetine. Analysis of dorsal and ventral dentate samples in the same mice indicated that system state co-varied across these regions despite their reported functional differences. Aggregate measures of gene expression system state were very robust and remained unchanged when different microarray data processing algorithms were used and even when completely different sets of gene expression levels were used for their calculation. CONCLUSIONS: System state measures provide a robust method to quantify and relate global gene expression system state variability to behavior and treatment. State variability also suggests that the diversity of reported changes in gene expression levels in response to treatments such as fluoxetine may represent different perspectives on unified but noisy global gene expression system state level responses. Studying regulation of gene expression systems at the state level may be useful in guiding new approaches to augmentation of traditional antidepressant treatments.