Genomics and bioinformatics offer methods ( Lander, 2011 ) to explore the biology and evolution of music and sounds at the molecular level. To date, few genome-wide scans have been performed in musical traits in humans ( Asher et al., 2008 ; Pulli et al., 2008 ; Park et al., 2012 ; Oikkonen et al., 2014 ). Genome-wide expression analysis can be applied to study human traits in an unbiased, hypothesis-free fashion based on their molecular properties, rather than anatomic regions. Here, we have utilized a combination of genomic and bioinformatic methods to analyze the effect of classical music on the peripheral whole blood transcriptome. Peripheral blood was used, as brain samples are inaccessible in humans.

Materials and Methods

Ethics statement The Ethics Committee of Helsinki University Central Hospital approved this study. Written informed consent was obtained from all the participants.

Participants and phenotypes A total of 48 individuals (aged 18–73; mean 42.5) participated in the study (Table S1). All the 48 participants were characterized for musical aptitude and music education. Musical aptitude was measured using three tests: the auditory structuring ability test (Karma music test) (Karma, 2007), and the Seashore tests for pitch and for time (Seashore, Lewis & Saetveit, 1960). Details of the tests have been described in Oikkonen et al. (2014). A combined score of the three tests (COMB score; range 0–150) was used to define musical aptitude. COMB scores were classified as either high or low based on the upper and lower quartiles of score distribution. Data on music education were collected using a questionnaire. The self-reported levels of music education (referred to as edu classes 1–4), received through studies or degrees from music schools, institutes, or universities, were classified as follows: 1 represents no music education; 2 represents music education of less than two years; 3 represents music education of more than ten years; and 4 represents a professional musician. The approximate time of systematic music education and training was 21.42 years on average, in participants of edu classes 3–4. Table 1 shows the sample’s phenotype characteristics. Phenotype n * Edu classes 1–2 19 Edu classes 3–4 29 Low COMB scores 12 High COMB scores 12 Male 22 Female 26 DOI: 10.7717/peerj.830/table-1

Exposure to music To our knowledge, no previous studies have systematically studied the effect of listening to music on genome-wide transcriptional profiles of humans. We have previously shown that music-listening habits vary a lot among listeners (Ukkola-Vuoti et al., 2011). To start with, we chose to study the effect of classical music, Wolfgang Amadeus Mozart’s Violin Concerto No. 3 in G major, K.216 because it is relatively familiar in the western culture. As the human brain perceives complex sounds in a millisecond-level time frame (Wang et al., 2009; Kayser, Logothetis & Panzeri, 2010), we expected that the 20 min-listening session (duration that the concerto lasts) will induce an effect of music on human transcriptome. In studies on the effect of pain in humans, very short durations of pain induction (8 to 150 s) have been used (Hubbard et al., 2011). The participants were unaware of the type of music that was intended for the listening session. Peripheral blood samples were collected from all the 48 participants just before and after the listening session. From here on, participants who listened to music are referred to as listeners throughout the text.

Control study The same 48 participants were invited to a control study. Of these, 15 participants could attend. The participants were advised to avoid listening to music and hard exercise the day before the control study. The control study was performed in a ‘music-free’ environment, where the participants had an opportunity to converse, read a magazine, or take a walk outside (no exercise) during the session. Peripheral blood samples were collected from the participants just before and after 20 min in the control session (the same duration as in the listening session). From here on, participants of the control session are referred to as controls throughout the text.

Genome-wide expression profiling For this procedure, 2 × 2.5 ml samples of peripheral blood were drawn into PAXgene blood RNA tubes (PreAnalytiX GmbH, Hombrechtikon, Switzerland) as per the kit instructions, in both of the sessions. Total RNA was isolated using the PAXgene blood miRNA Kit (PreAnalytiX GmbH, Hombrechtikon, Switzerland) as per the kit manual. Purified RNA samples were measured for concentration and purity on the NanoDrop 1000 v.3.7 (Thermo Fisher Scientific, Waltham, Massachusetts, USA). Globin mRNA was depleted from our samples using Ambion’s Human GLOBINclear™ kit (Applied Biosystems, Carlsbad, California, USA) as per the kit insert. The samples were measured on the NanoDrop 1000 to determine the sample concentration and purity and for integrity on the 2100 Bioanalyzer (Agilent Technologies, Waldbronn, Germany) before being diluted to 50 ng/µl using RNase-free water. A total of 2 µg of RNA was assayed on the Illumina HumanHT-12 v4 bead array (Illumina Inc., San Diego, California, USA), which targets more than 47,000 probes. The gene expression profiling assays for the listening and control sessions were conducted in two separate batches. To account for the batch effect corrections, six samples from the listening session were assayed together with the control session samples. Intensity data were exported through Bead Studio software. The data reported in this article have been deposited in the Gene Expression Omnibus database, www.ncbi.nlm.nih.gov/geo (accession no. GSE48624).

Data preprocessing The Lumi package was used to read and preprocess the signal intensity data. Specifically, pre-processing included background correction, variance stabilizing transformation, and quantile normalization. Data from both the listening (n = 48) and control (n = 15) sessions were normalized separately. Five samples from the control session were excluded from further analyses owing to data quality. In addition, we used the ComBat method (Johnson, Li & Rabinovic, 2007) to adjust for batch effects and determine if this correction affected the pre-post fold-changes across conditions. However, we did not find significant differences between the fold-changes of corrected and uncorrected data over time across conditions. Therefore, we chose to proceed with the uncorrected data owing to the strengths of our analysis methods as described below. After normalization, duplicate and un-annotated probes were excluded using the genefilter package (R package version 1.40.0). Before extracting the expression values from the normalized data, Illumina’s detection p-values (threshold: 0.01) were used to filter out probes with low intensities corresponding to the background signal. Finally, only those probes that were expressed in at least half of all of the arrays (listening and control sessions) were chosen for the study.