This study includes both cross sectional and an 8-week prospective design. Healthy adults were enrolled, comprising 2 groups: individuals with a long-term RR practice (group M; n = 19) or those with no prior RR experience (novice; group N₁; n = 19). Group N₁ novices, furthermore, underwent 8-weeks of RR training (Group N₂; n = 20) for the prospective analysis. In the cross sectional study, we compare gene expression profiles (GEP) in whole blood between groups M and N₁, whereas in the prospective study GEP is compared for each individual novice subject before and after RR experience, matched individuals of groups N₁ versus N₂ respectively.

Transcriptional differences between the different groups and within individuals before and after the RR are assessed by microarray analysis using Affymetrix HG-U133 Plus 2.0 genechips (www.affymetrix.com). This technology is a well established and reliable method to assess global gene expression differences [18]. Comparing group M (subjects with long term RR practice) to group N₁ (subjects prior to RR training), we find statistically significant differential expression of 2209 genes; 1275 significantly up-regulated and 934 significantly down-regulated in M vs. N₁. Additionally, 1561 genes are differentially expressed in novices after RR experience, N₂ vs. N₁; 874 significantly up-regulated and 687 significantly down-regulated. Comparison of gene lists from M vs. N₁, N₂ vs. N₁ and M vs. N₂ with Venn diagrams reveals significant overlap (Fig. 1a). Significance of overlaps is calculated using hypergeometric distributional assumption [19] and p-values are adjusted using Bonferroni correction for multiple comparisons [20]. Heatmaps were generated from genes in the intersecting areas of the Venn diagrams (Fig. 1b). We find 316 up-regulated and 279 down-regulated genes are differentially expressed in group M compared to both group N₁ and N₂; these changes in GEP are only observed in long-term RR practitioners. Similarly, 260 genes are up-regulated and 168 genes are down-regulated in both groups M and N₂ compared to N₁; they represent GEP changes characteristic of RR practice over at least 8 weeks.

Heatmaps generated using these genes exhibit consistent GEP changes across the three groups with a few samples in each group resembling the GEP of another group. To determine if any demographic characteristics (e.g. age, ethnicity , etc.) influences this observation, we clustered each group separately using the same set of genes. For each cluster analysis, we calculated the significance of observing a characteristic among the samples in the subgroups formed. We found that number of times M subjects reported eliciting the RR per week was significantly associated with the subgroups formed when M samples were clustered using genes differentially expressed in long term RR practitioners only. Specifically, there were 316 up-regulated and 279 down-regulated genes differentially expressed in group M compared to both group N₁ and N₂; (Fig. 1b). All remaining cluster analyses revealed no such significant influence of demographic characteristics (see online supplementary data).

Finally, the intersection of all 3 areas (M vs. N₁, N₂ vs. N₁ and M vs. N₂) identifies genes with expression behavior that is monotonically changed between N₁ to N₂ to M (Fig. 1c). These results clearly demonstrate that short term as well as long term RR practice lead to distinct and consistent gene expression changes in hematopoietic cells.

We performed Expression Analysis Systematic Explorer (EASE) analysis [21] using M vs. N₁, and N₂ vs. N₁ data-sets, to identify Gene Ontology (GO) categories where specific genes in these data occur more often than would be expected by random distribution of genes. These findings (and those of the validation data-set below) are summarized in Table 1, where select over-represented GO categories are listed along with specific genes differentially expressed in our data-sets. These categories include oxidative phosphorylation, ubiquitin-dependent protein catabolism, nuclear messenger RNA (mRNA) splicing, ribosomes, metabolic processes, regulation of apoptosis, NF-κB pathways, cysteine-type endo-peptidase activity and antigen processing. Most are significant in both long-term (M vs. N₁) and short-term (N₂ vs. N₁) practitioners of daily RR practice (see Table).

Even though our analyses of differentially expressed genes and GO categories associated with RR practice meet widely accepted criteria for statistical significance, we were concerned about the relatively small fold changes that were observed (see Supplementary Methods). To address this issue we employed Gene Set Enrichment Analysis (GSEA). GSEA has proven to be useful for capturing subtle expression changes in complex gene signatures based on predefined gene sets or pathways [22]. As described above, we examined expression data for 2 comparisons, M vs. N₁ and N₂ vs. N₁. The selected pathways or gene sets that are significantly enriched (False Discovery Rate (FDR)<50%, nominal p-value (NPV)< = 0.02) are shown in Figure 2, with gene sets for N₂ vs. N₁ and M vs. N₁ in Fig 2A and Fig 2B respectively.

GSEA analysis of N₂ vs. N₁ showed highly significant enrichment in gene sets related to various cellular stressors/stress responses and metabolism. To a pronounced degree these observations complement the results of GO analysis presented in the Table, also depicting significant alterations in cellular response to stress, oxidative and primary metabolism. The transition effect of the RR from novice to short term (8 weeks) to long term RR practitioners has been denoted through a colorgram of ribosomal proteins and ubiquitin mediated proteolysis gene sets (Fig. 2C). Whereas expression of ribosomal genes is significantly upregulated in RR practitioners at 8 weeks and more pronounced in long term practitioners, ubiquitin mediated proteolysis gene expression in general shows an opposite trend. Closer inspection of the colorgrams for ribosomal proteins and Ub proteolysis gene sets shows some variation in the GEP in each subgroup (N₁, N₂ M). The GEP of a few N₁ or N₂ subjects resembles the GEP of M subjects and vice versa. To elucidate the association between GEP and subject characteristics (Race, Age, etc), we performed clustering of each subgroup separately (N₁, M) using the enriched gene sets (Ribosomal and Ub Proteolysis). This analysis identified a subcluster in the N₂ subgroup that has significant over-representation of Asian subjects (P value <0.05) when the clustering was performed using the ribosomal protein gene set. This observation needs further validation on a larger dataset as the current study contains only five Asian subjects. No other characteristic exhibited significant association with the ribosomal protein gene set. No significant association between the GEP profiles and subject characteristic was found when clustering was performed using the Ub proteolysis gene set. This analysis provides further insight into the stress response related genes that are influenced by RR practice.

As a validation of our results, we repeated the experimental and analysis procedures defined in the “Methods” section on a new set of samples consisting of 5 N₁, 5 N₂ and 6 M subjects. We found 1846 and 2390 probe sets differentially expressed between M vs. N₁, and N₂ vs. N₁ groups. The validation data-set showed a significant (p<10⁻⁵) number of genes in common with the original analysis of 58 samples. We also found that 70–75% of all GO categories from the original analysis were retained in the validation set (p ∼0), and 30–65% of significantly over-represented GO categories were shared. Of note, biologically relevant GO categories such as oxidative phosphorylation, regulation of apoptosis, and antigen presentation, come up as significantly over-represented in both the original and validation analyses. Results of the validation set and comparison analyses can be found in the online supplementary data. In addition, validation GSEA analysis on N₂ vs. N₁ subjects shows enrichment of ribosomal proteins and platelet expressed gene sets and enrichment of ribosomal proteins, oxidative phosphorylation and electron transport chain gene sets in M vs. N₁ subjects (Fig. 2A and 2B). The similarities between the original and validation results from GSEA analysis argues against random chance accounting for the observed enrichment of these gene sets.

Nineteen healthy practitioners of various RR eliciting techniques (including several types of meditation, Yoga, and repetitive prayer) participated (M group; n = 19). Years of practice averaged 9.4 years (5.0 sd) and ranged from 4 to 20 years. Twenty individuals without any prior RR eliciting experience served as controls (N group; n = 20).. As shown in Table 2, the M and N groups are matched with respect to age, gender, race, height, weight, and marital status, which do not exhibit significant difference between the groups (p>0.05, t- and chi-square test).

The study protocol was approved by the Committee on Clinical Investigations at the Beth Israel Deaconess Medical Center (BIDMC), Boston MA. All subjects provided written informed consent and the study was conducted in the General Clinical Research Center (GCRC) of the BIDMC. After providing written informed consent, participants were screened by a physician and had blood drawn to ensure good health. All participants completed a testing session in the GCRC. N₁ (novice) subjects had 8-weeks of RR training, listened to a 20-minute RR-eliciting CD daily and returned to the GCRC for a repeat testing session (hereafter classified as the N₂ group).

N subjects received 8 weeks of RR training. Training included information about reducing daily stress, and a 20-minute elicitation of the RR [35]. Subjects randomized to the RR group received 8 weekly individual RR-training sessions from an experienced clinician as per our manualized research protocol [35]. The first session provided an educational overview of the stress response and the RR, instructions on how to elicit the RR, and a 20-minute guided RR experience. Sessions 2 through 8 consisted of a review of the subject's home practice card for compliance and a 20-minute guided RR experience.

During the RR elicitation in the weekly session, the subject was guided through a RR sequence including: diaphragmatic breathing, body scan, as well as mantra and mindfulness meditation, while subjects passively ignored intrusive thoughts. The specific CD guided the subject through the same sequence and has our clinical research studies and clinical practice for more than 15 years [35]. Subjects were asked to listen to the RR-eliciting CD once a day for 20 minutes at home.

To measure compliance, participants' daily home practice logs were reviewed each week and at the end of the 8 week training. These logs indicate that N subjects listened for an average of 17.5 minutes per day (3.7 sd) over 8-weeks.

Following previously described protocols, the transcriptional profile of samples were probed using Affymetrix HG-U 133 Plus 2.0 chips representing over 47,000 transcripts and variants using more than 54,000 probesets. Scanned image output files were visually examined for major chip defects and hybridization artifacts and then analyzed with Affymetrix GeneChip Microarray Analysis Suite 5.0 (MAS5) software (Affymetrix). The image from each chip was scaled such that the 2% trimmed mean intensity value for all arrays was adjusted to target intensity and reported as a non-negative quantity. Chips used for subsequent analysis consisted of 19 M, 19 N₁ and 20 N₂ samples (one chip from the N₁ group had insufficient signal values). A hierarchical clustering technique was used to construct an Unweighted Pair Group Method with Arithmetic-mean (UPGMA) tree using Pearson's correlation as the metric of similarity [36]. The expression data matrix was row-normalized for each gene prior to the application of average linkage clustering. When comparing 2 groups of samples to identify genes enriched in a given group, we used combination of three criteria. We considered genes with significantly different expression across the two groups using t-test (p<0.05) that further remained significant at a 5% false discovery rate (FDR) using permutation testing with 1,000 permutations [37], [38]. In order to finalize a set of genes significantly up-regulated in a given group compared to another group, among the genes that passed the aforementioned steps, we filtered the ones that are “present” in at least half of the samples in the enriched group using Affymetrix' MAS5 Presence/Absence (P/A) calls. We used a paired t-test when comparing samples in groups N₁ and N₂.

Data deposition: All data sets have been deposited in the Gene Expression Omnibus, www.ncbi.nlm.nih.gov/geo (accession nos. GSE10041 and GSM253663-253734).

Differentially expressed genes between the 3 groups (N₂ vs. N₁, M vs. N₁ and M vs. N₂) were separately analyzed using EASE to identify biologically relevant categories that are over-represented in the input set [21]. EASE analyses tested each list against all genes on the chip and overrepresentation describes a group of genes belonging to a certain GO category that appear more often in the given input list than expected to occur if the distribution were random. GO categories that had EASE scores of 0.05 or lower were selected as significantly over-represented. Gene Set Enrichment Analysis (GSEA 2.0 package http://www.broad.mit.edu/gsea/) was used to determine whether an a priori defined set of genes showed statistically significant, concordant differences between 2 groups (N₂ vs. N₁, and M vs. N₁) in the context of known biological pathways. We tested expression values of all the genes in the relevant sample groups against 1687 gene sets obtained from the MSigDB2.0 for enrichment belonging to various metabolic pathways, chromosomal locations and functional sets (gene sets related to cancer/cancer cells are not included). The enriched gene sets have nominal p-value (NPV) less than 1% and False Discovery Rate (FDR) <50% after 100 random permutations. These criteria ensure that there is minimal chance of identifying false positives.

Supplementary Methods are located at http://bidmcgenomics.org/MIND_BODY_RR/(Login: benson) (Password: test1).