Genomic counter-stress changes induced by the relaxation response

Abstract

Background: Mind-body practices that elicit the relaxation response (RR) have been used worldwide for millennia to prevent and treat disease. The RR is characterized by decreased oxygen consumption, increased exhaled nitric oxide, and reduced psychological distress. It is believed to be the counterpart of the stress response that exhibits a distinct pattern of physiology and transcriptional profile. We hypothesized that RR elicitation results in characteristic gene expression changes that can be used to measure physiological responses elicited by the RR in an unbiased fashion.

Methods/principal findings: We assessed whole blood transcriptional profiles in 19 healthy, long-term practitioners of daily RR practice (group M), 19 healthy controls (group N(1)), and 20 N(1) individuals who completed 8 weeks of RR training (group N(2)). 2209 genes were differentially expressed in group M relative to group N(1) (p<0.05) and 1561 genes in group N(2) compared to group N(1) (p<0.05). Importantly, 433 (p<10(-10)) of 2209 and 1561 differentially expressed genes were shared among long-term (M) and short-term practitioners (N(2)). Gene ontology and gene set enrichment analyses revealed significant alterations in cellular metabolism, oxidative phosphorylation, generation of reactive oxygen species and response to oxidative stress in long-term and short-term practitioners of daily RR practice that may counteract cellular damage related to chronic psychological stress. A significant number of genes and pathways were confirmed in an independent validation set containing 5 N(1) controls, 5 N(2) short-term and 6 M long-term practitioners.

Conclusions/significance: This study provides the first compelling evidence that the RR elicits specific gene expression changes in short-term and long-term practitioners. Our results suggest consistent and constitutive changes in gene expression resulting from RR may relate to long term physiological effects. Our study may stimulate new investigations into applying transcriptional profiling for accurately measuring RR and stress related responses in multiple disease settings.

Figure 1. Gene Ontology Analysis.

Analysis of differentially expressed genes: a) Venn diagrams: * indicates significant overlaps (p<10⁶); b) Heatmaps of the 595 differentially regulated genes in both M vs. N₁ and M vs. N₂ (left) and the 418 differentially regulated genes in both M vs. N₁ and N₂ vs. N₁; c) Heatmap of 15 genes in the intersection of all three groups (gene symbols listed on the right). In heatmaps, rows represent genes and columns represent samples from N₁, N₂, and M groups. Genes are clustered using row-normalized signals and mapped to the [−1,1] interval (shown in scales beneath each heatmap). Red and green represent high and low expression values, respectively.

Figure 2. GSEA Analysis.

The analysis has been performed for >1200 predefined datasets using GSEA 2.0 software. Signal values for each gene are obtained by collapsing the probe values using max_probe algorithm. Representative datasets, significantly enriched (FDR<50%, or NPV< = 0.01) between any two groups and corresponding heatmaps (depicting relative gene expression changes of core enrichment) are shown in a) N₂ vs. N₁ and b) M vs. N₁. Datasets that are enriched in both the original and validation analyses are marked with *. c) Heatmaps of ribosomal proteins and ubiquitin mediated proteolysis illustrate transitional trends in gene expression across the N₁, N₂ and M groups.