Abstract
BackgroundSelective 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.MethodsWe 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.ResultsQuantitative 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.ConclusionsSystem 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.
Highlights
Measurement of changes in gene expression levels in response to treatments is a commonly used approach to understanding biological processes
We have found that the noisiness of gene expression measurements at the individual gene expression level does not translate to the systems level, where measurements of global gene expression system state, an aggregate measure of the behavior of thousands of gene expression levels such as those occurring during the progression of a developmental gene expression program, are highly robust[3,4]
Because stereotyped gene expression programs, such as those occurring during development or in response to stimuli, are characterized by a large fraction of monotonically changing gene expression levels, we have found that the first principal component score (PCA1), which describes the monotonically changing fraction of genes, can be used to quantify the progression of gene expression programs under multiple conditions[4]
Summary
Measurement of changes in gene expression levels in response to treatments is a commonly used approach to understanding biological processes This is because gene expression levels frequently approximate protein levels, yet are much easier to measure. We, and others, have used covariance-based analyses such as principal components analysis (PCA), often referred to as singular value decomposition (SVD) when applied to gene expression data, to quantify the aggregate behavior of covarying gene expression levels[4,5,6,7,8,9] Such methods reduce thousands of gene expression measurement into principal components scores that describe the central tendency of large groups of covarying genes. Understanding how gene expression systems respond to treatments may be critical for understanding antidepressant resistance
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