ORIGINAL ARTICLE

Sonic vibration affects the metabolism of yeast cells growingin liquid culture: a metabolomic study

Raphael Bastos Mereschi Aggio • Victor Obolonkin •

Silas Granato Villas-Boas

Received: 18 July 2011 / Accepted: 19 August 2011 / Published online: 4 September 2011

� Springer Science+Business Media, LLC 2011

Abstract It is undeniable that music and sounds can

affect our emotions and mood, but so far the study of

physical stimuli provoked by sound on living organisms

has been mostly focused on brain and sensorimotor struc-

tures rather than cellular metabolism. Using metabolomics,

we compared the physiology of yeast cells growing in

defined liquid medium exposed to music, high and low

frequency sonic vibration and silence. All sonic stimuli

tested not only increased the growth rate of the yeast cells

by 12% but they also reduced biomass production by 14%.

The intra- and extracellular metabolite profiles differed

significantly depending on the sonic stimulus applied

showing that different metabolic pathways are affected

differently by different sound frequency. Therefore, our

results clearly demonstrate that sound does affect microbial

cell metabolism when growing in liquid culture, opening an

entirely new perspective for scientific investigation inter-

facing acoustics, biophysics and biochemistry.

Keywords Metabolomics � Metabolite profiling �Bioacoustics � Yeast physiology � Cell metabolism

1 Introduction

Although humanity has puzzled over the effect of sound on

animal behaviour and physiology for centuries we have

limited knowledge of the role of sound on single cells.

Audible sounds, consist of oscillating pressure waves

transmitted through solids, liquids, or gases, that if suffi-

ciently strong stimulate mechanosensory cells (Pickett

et al. 2000). Sound can govern emotions and mood via

structures involved in cognitive, sensorimotor, and emo-

tional processing, and music has even been proposed as an

alternative form of therapy to treat various illnesses

(Koelsch et al. 2010).

To date little is known about the effects of sonic waves

on the cellular metabolism. Nonetheless, some scientific

studies have reported that plants respond to different

music (Xiujuan et al. 2003) and high frequency sound

waves can significantly increase protective antioxidative

enzyme expression in plants (Xiujuan et al. 2003; Li et al.

2008). Sonic waves at specific frequencies have also been

found to stimulate intestinal contractions and duodenal

passage of barium in humans (Polous and Kurko 1991)

and Syroeshkin et al. (1998) demonstrated that sonic

waves induce contractive conformational changes of the

mitochondrial transmembrane ATPase, affecting ATP

production. Despite this tantalising evidence, audible

sound’s intrinsic impact on cellular metabolism has

received limited attention from the scientific community.

Although biological interactions of sonic and ultrasonic

vibration appears to have been explored since 1900

(Naimark et al. 1951), since the 1920’s the work has

almost exclusively focused on ultrasound (\20 kHz;

Wood and Loomis 1927; Harvey and Loomis 1928;

Harvey et al. 1928).

Using a single cell organism as a model, we have

exposed baker’s yeast, Saccharomyces cerevisiae to sound,

to test whether sonic waves of different frequencies affect

basal cell metabolism and growth. We choose a unicellular

microorganism to exclude the complexity of multicellular

Raphael Bastos Mereschi Aggio and Victor Obolonkin contributed

equally to this work.

R. B. M. Aggio � V. Obolonkin � S. G. Villas-Boas (&)

Centre for Microbial Innovation, School of Biological Sciences,

The University of Auckland, 3A Symonds Street,

Private Bag 92019, Auckland 1142, New Zealand

e-mail: s.villas-boas@auckland.ac.nz

123

Metabolomics (2012) 8:670–678

DOI 10.1007/s11306-011-0360-x

organisms, and to eliminate the influence of nervous and

auditory systems of animals. Moreover the genome, bio-

chemistry and metabolome of S. cerevisiae are extremely

well characterised (Sherman 1997), and as such provide us a

powerful model to test the role of sound on cell metabolism.

2 Materials and methods

2.1 Yeast strain

Saccharomyces cerevisiae strain VIN13 was maintained on

YPD (yeast extract, peptone, dextrose) agar plates at 30�C

and used to prepare all pre-inoculums.

2.2 Shake flask cultures

The yeast strain was cultivated aerobically in triplicate,

using shake-flasks containing glucose (20 g l-1) as single

carbon source, (NH4)2SO4 (5.0 g l-1), MgSO47H2O

(0.5 g l-1), KH2PO4 (3.0 g l-1), vitamins and trace ele-

ments according to Verduyn et al. (1992). The culture

flasks were inoculated with an overnight grown pre-inoc-

ulum prepared in the same growth medium. Cultivations

were performed using the same rotatory shaker and incu-

bated in the same incubation room at 28 ± 1�C and 190

rev./min, in 500 ml shake flasks containing 100 ml of

medium and cotton plugs. Each sound treatment was car-

ried out at different days but using broth medium prepared

in a single bulk to ensure identical composition and inoc-

ulated with identical initial optical density using a pre-

inoculum grown overnight in the same medium.

2.3 Sound stimuli

The experimental set up as shown in Fig. 1 consisted of

two middle-high frequency speakers and low frequency

subwoofer connected to a computer. They were placed on

flat panel shaker surrounding the 500 ml shake-flasks. The

distance between the speakers and shake-flasks did not

exceed 20 cm. Due to the small within-group variation

(samples from the different flasks clustered close together

and presented small standard deviations in the different

measurements, n = 15), we assumed the sound pressure to

be roughly equal for all technical replicates. The yeast

cultures were exposed to four different conditions: (HF)

high frequency, 10 kHz, recording 89 dBA, sampled at 705

Kbps; (LF) low frequency, 100 Hz, recording 92 dBA,

sampled at 705 Kbps; broad-band music, sampled at 320

Kbps and 80–92 dBA; and ‘‘silence’’—control conditions

including only the mechanical noise of the incubator room

at approx 60 dBA. The sound stimuli were played contin-

uously for the duration of the experiment.

2.4 Fermentation parameters

Optical density (OD) of culture broth was measured at

600 nm using a Hitachi (model U-1100) spectrophotometer

each hour until the stationary growth phase was reached.

Presence of contamination was monitored using light

microscopy and growth rate (l) was calculated as

l ¼ 2:303ðlogðODÞ � logðOD0ÞÞðt � t0Þ

Fig. 1 Scheme of experimental set up. The experimental set up

consisted of two middle-high frequency speakers and low frequency

subwoofer connected to a computer. They were placed on flat panel

shaker surrounding the 500 ml shake-flasks. The distance between the

speakers and shake-flasks did not exceed 20 cm

Table 1 Classification table obtained from leave-one-out cross val-

idation of 15 samples containing intracellular metabolites

Original source Predicted source

HF LF M S

HF 15 0 0 0

LF 0 15 0 0

M 0 0 15 0

S 0 0 0 15

HF high frequency sonic wave, LF low frequency sonic wave,

M music, S silence

Sonic vibration affects yeast metabolism 671

123

where l is the growth rate, OD is the optical density

measured at the end of the exponential growth, OD0 is the

optical density measured in the beginning of the expo-

nential growth, t is the time at the end of the exponential

growth, and t0 is the time at the beginning of the expo-

nential growth.

2.5 Sampling for metabolome analysis

Five 5 ml cellular samples were rapid harvested from each

flask when the cells had reached an OD600 = 2.3–2.4 (early

to middle exponential growth phase). The cell metabolism

was quenched using cold-glycerol saline solution (-23�C)

followed by cold centrifugation at -20�C. Internal stan-

dard, 20 ll of 2,3,3,3-d4 alanine (10 mM), was added to

the cell pellets and the intracellular metabolites were

extracted using freeze/thaw cycles and cold methanol–

water (1:1v/v) solution. Detailed protocol can be found in

our previously published work (Smart et al. 2010).

2.6 Metabolite analyses

The cell extracts containing intracellular metabolites were

freeze-dried using a 12L Labconco Freeze Dryer (Labconco

Corporation). The freeze-dried solids were resuspended in

200 ll of sodium hydroxide solution (1 M) and derivatized

according to our standard laboratorial procedure (Smart et al.

2010). The derivatized samples were analyzed using a GC–

MS system (GC7890 coupled to a MSD5975, Agilent tech-

nologies), with a quadrupole mass selective detector (EI)

operated at 70 eV. The column used for all analyzes was a

ZB-1701 (Phenomenex), 30 m 9 250 m (internal diameter)

9 0.15 (film thickness), with 5 m guard column. The MS was

operated in scan mode (start after 6 min; mass range 38–650

a.m.u. at 1.47 scans/s (Smart et al. 2010).

2.7 Compound identification

We have used the Automated Mass Spectral Deconvolution

and Identification System (AMDIS) to identify compounds

Fig. 2 Growth curves and growth rates of Saccharomyces cerevisiaeat different experimental conditions. Cultivations occurred aerobi-

cally in shake flasks at 28�C under constant agitation at 190

rev. min-1. Flasks in triplicate were subject to different sound

stimuli: Low frequency sonic waves (100 Hz at 92 dBA), high

frequency sonic waves (10 kHz at 89 dBA), broad-band music

(sampled at 320 kbps at 80–92 dBA), and silence (background noise

at *60 dBA). Each data point represents mean values of three flasks

672 R. B. M. Aggio et al.

123

Table 2 All intracellular metabolites identified in samples of Saccharomyces cerevisiae growing aerobically on glucose at different growth

conditions (sound stimuli)

Mean of relative intensity (n = 15) ± standard deviationa

Silence Music Low frequency High frequency

Aspartate 0.041 ± 0.008 0.018 ± 0.001 0.068 ± 0.013 0.103 ± 0.031

Oleate 0.063 ± 0.017 0.060 ± 0.017 0.032 ± 0.007 0.053 ± 0.039

Proline 0.231 ± 0.034 0.084 ± 0.011 0.049 ± 0.008 0.058 ± 0.023

Citrate 0.000 ± 0.000 0.000 ± 0.000 0.068 ± 0.011 0.076 ± 0.020

Isoleucine 0.090 ± 0.020 0.040 ± 0.010 0.047 ± 0.007 0.058 ± 0.009

Leucine 0.103 ± 0.018 0.051 ± 0.005 0.042 ± 0.006 0.049 ± 0.009

14-methylpentadecanoate 0.043 ± 0.014 0.035 ± 0.008 0.032 ± 0.005 0.040 ± 0.018

Glutamine 0.098 ± 0.023 0.040 ± 0.005 0.035 ± 0.021 0.027 ± 0.010

Succinate 0.009 ± 0.002 0.004 ± 0.001 0.039 ± 0.007 0.029 ± 0.006

Phenol derivative 0.138 ± 0.055 0.089 ± 0.021 0.035 ± 0.005 0.043 ± 0.013

Glutamate 0.171 ± 0.029 0.069 ± 0.016 0.479 ± 0.073 0.558 ± 0.202

Stearate 0.039 ± 0.012 0.021 ± 0.006 0.023 ± 0.004 0.029 ± 0.010

Caproicate 0.015 ± 0.002 0.009 ± 0.005 0.045 ± 0.004 0.029 ± 0.004

Tyrosine 0.017 ± 0.003 0.013 ± 0.004 0.012 ± 0.002 0.015 ± 0.006

2-methoxysuccinate 0.071 ± 0.017 0.033 ± 0.009 0.011 ± 0.001 0.012 ± 0.006

Asparagine 0.000 ± 0.000 0.000 ± 0.000 0.015 ± 0.003 0.017 ± 0.009

NADPH/NADP? 0.000 ± 0.000 0.000 ± 0.000 0.022 ± 0.009 0.017 ± 0.008

NADH/NAD? 0.000 ± 0.000 0.000 ± 0.000 0.020 ± 0.009 0.020 ± 0.010

Caprylate 0.000 ± 0.000 0.000 ± 0.000 0.023 ± 0.002 0.014 ± 0.002

Myristate 0.017 ± 0.001 0.000 ± 0.000 0.006 ± 0.001 0.007 ± 0.003

2-oxoglutarate 0.000 ± 0.000 0.000 ± 0.000 0.008 ± 0.001 0.008 ± 0.001

Malate 0.000 ± 0.000 0.000 ± 0.000 0.004 ± 0.001 0.006 ± 0.002

Caprinate 0.000 ± 0.000 0.000 ± 0.000 0.008 ± 0.001 0.009 ± 0.001

2-aminobutyrate 0.033 ± 0.004 0.015 ± 0.002 0.002 ± 0.0001 0.006 ± 0.0008

Benzoate 0.004 ± 0.0004 0.002 ± 0.001 0.004 ± 0.004 0.003 ± 0.001

Fumarate 0.005 ± 0.0007 0.000 ± 0.000 0.004 ± 0.001 0.004 ± 0.002

Nicotinate 0.000 ± 0.000 0.000 ± 0.000 0.005 ± 0.001 0.004 ± 0.001

Histidine 0.000 ± 0.000 0.003 ± 0.001 0.007 ± 0.002 0.004 ± 0.002

Indol derivative 0.033 ± 0.011 0.031 ± 0.014 0.005 ± 0.004 0.005 ± 0.001

Alanine 0.177 ± 0.032 0.126 ± 0.022 0.359 ± 0.031 0.319 ± 0.035

Serine 0.000 ± 0.000 0.000 ± 0.000 0.003 ± 0.001 0.004 ± 0.001

4-aminobutyrate 0.019 ± 0.001 0.015 ± 0.008 0.003 ± 0.001 0.002 ± 0.001

Adipate 0.008 ± 0.0008 0.000 ± 0.000 0.005 ± 0.0007 0.003 ± 0.0003

Cis-aconitate 0.000 ± 0.000 0.000 ± 0.000 0.002 ± 0.001 0.002 ± 0.001

Levulinate 0.003 ± 0.001 0.000 ± 0.000 0.003 ± 0.001 0.003 ± 0.001

Norvaline 0.000 ± 0.000 0.009 ± 0.0005 0.000 ± 0.000 0.002 ± 0.0001

Phenylpropanoate 0.005 ± 0.0005 0.000 ± 0.000 0.001 ± 0.000 0.001 ± 0.001

Lysine 0.224 ± 0.043 0.176 ± 0.052 0.204 ± 0.042 0.174 ± 0.071

Glycine 0.027 ± 0.004 0.014 ± 0.002 0.009 ± 0.001 0.013 ± 0.006

Norleucine 0.043 ± 0.002 0.037 ± 0.003 0.000 ± 0.000 0.064 ± 0.005

Threonine 0.046 ± 0.007 0.020 ± 0.004 0.018 ± 0.003 0.020 ± 0.005

Valine 0.021 ± 0.003 0.071 ± 0.072 0.216 ± 0.020 0.196 ± 0.021

Hydroxybenzoate 0.009 ± 0.001 0.004 ± 0.0002 0.000 ± 0.000 0.000 ± 0.000

Methionine 0.000 ± 0.000 0.000 ± 0.000 0.003 ± 0.0003 0.005 ± 0.001

10,13-dimethyltetradecanoate 0.045 ± 0.012 0.038 ± 0.009 0.028 ± 0.007 0.038 ± 0.022

Phenylalanine 0.014 ± 0.003 0.000 ± 0.000 0.348 ± 0.032 0.523 ± 0.057

Sonic vibration affects yeast metabolism 673

123

present in each sample. AMDIS is a software freely dis-

tributed by the National Institute of Standards and Tech-

nology and has been largely applied to metabolomics.

Although AMDIS algorithm is considered powerful in

deconvoluting and identifying chromatographic peaks, it

produces some inaccuracies in relation to compound

quantification. Therefore, we used an in-house R (www.

r-project.org) script which recalculates the correct intensity

of each compound previously identified by AMDIS. A

detailed description of this process and our in-house R

script can be found in Aggio et al. (2011).

2.8 Data normalization

As described above, many technical steps precede the

generation of metabolomics data (e.g. sampling, quench-

ing, metabolite extraction and freeze-drying). Each one of

these steps is a potential source of variability, which can

possibly interfere in the identification of trends and corre-

lations present in our data-set. Therefore, we used the

2,3,3,3-d4 alanine as internal standard to minimise this

variability. This way, we divided the intensity of each

metabolite by the intensity of the internal standard detected

in each sample. More details about data normalization

procedure can be found in Smart et al. (2010).

2.9 Metabolomic data analysis

The difference in metabolite profiles of the different samples

was assessed by three different statistical methods. Firstly we

short-listed the relative level of each identified metabolite in

all the growth conditions. Conditions for which the level of a

metabolite was below the detection limit of the method it was

assigned absent. The qualitative results are summarised in

the Venn diagram (Fig. 3). Secondly, the metabolites that

were detected in all samples from all conditions were

selected for Fisher discriminant analysis (FDA). The pro-

jections of samples to three dimensions were computed by

dicrcoord function from fpc package (http://www.homepa

ges.ucl.ac.uk/*ucakche/). The visual clustering was

achieved by plotting the first three FDA projections. lda

function from the MASS package was used to classify

samples into four experimental categories. The results were

validated using leave-one-out cross validation technique,

using a single observation as the testing data for the FDA and

the remaining data as the training set. The procedure was

repeated until all observations were used as the testing

samples. Results are showed in Table 1. We achieved 100%

correct classification for all of the experimental conditions,

which validates the separation of samples in the four data

classes (growth conditions). Finally each metabolite

Fig. 3 Fisher discriminant analysis (FDA) for sample visualisation.

GC–MS metabolite data successfully distinguishes the different

growth conditions. Projections of the log transformed intracellular

metabolite data from 60 samples into 3D space shows four distinct

clusters of the four data classes. For each sample the projection values

were calculated as the linear combination of metabolite values

determined by FDA. Only metabolites that were detected in all

samples of all four growth conditions were used for the analysis

Table 2 continued

Mean of relative intensity (n = 15) ± standard deviationa

Silence Music Low frequency High frequency

4-aminobenzoate 0.002 ± 0.0002 0.000 ± 0.000 0.004 ± 0.000 0.000 ± 0.000

Pyruvate 0.000 ± 0.000 0.000 ± 0.000 0.004 ± 0.0005 0.003 ± 0.0004

Lactate 0.037 ± 0.019 0.017 ± 0.010 0.155 ± 0.014 0.149 ± 0.030

Malonate 0.000 ± 0.000 0.000 ± 0.000 0.004 ± 0.004 0.001 ± 0.000

Carbamate 0.089 ± 0.040 0.016 ± 0.008 0.000 ± 0.000 0.000 ± 0.000

Glutarate 0.000 ± 0.000 0.000 ± 0.000 0.002 ± 0.0003 0.000 ± 0.000

Ornithine 0.570 ± 0.069 0.304 ± 0.052 0.196 ± 0.029 0.106 ± 0.037

Pyroglutamate 0.000 ± 0.000 0.000 ± 0.000 0.253 ± 0.072 0.162 ± 0.089

a Mean of GC–MS peak normalized by internal standard

674 R. B. M. Aggio et al.

123

expression level was tested for the difference between

growth conditions with univariate analysis of variance

(ANOVA). The data was log-transformed to fit the normal

distribution criteria. Post-hoc turkey multiple comparison

test was used to estimate significantly different pairs of

conditions. Analysis was performed using R 2.9.0 software (

http://www.r-project.com).

3 Results and discussion

A strain of S. cerevisiae commonly used in wine fermenta-

tion was cultured in a chemically defined broth with glucose

as the only carbon source. All culture flasks were inoculated

with freshly prepared (exponentially growing) pre-inoculum

at identical initial optical density and were incubated at 28�C

under constant agitation using the same shaker-incubator.

Three different sound stimuli were applied constantly to the

culture flasks in triplicate throughout the incubation period

(Fig. 1): (LF) low frequency sonic waves (100 Hz at

92 dBA), (HF) high frequency sonic waves (10 kHz

at 89 dBA), and (M) broad-band music (sampled at 320 kbps

at 80–92 dBA); which were compared to culture flasks

growing in the silence (background noise at*60 dBA). The

yeast cells growing under sonic stimuli presented longer lag

phase (?1 h) and shorter exponential growth (-2 h) when

compared to cells growing in the silence (Fig. 2). In addition,

the growth rate of cells grown under sound stimuli was found

to be significantly different than the growth rate of yeast cells

growing in the silence (P value \ 0.005—Student’s t-test)

(Fig. 2). Yeasts growing under music presented the highest

growth rate of 0.362 h-1, which was 12.4% faster

(P \ 0.005) than cells growing in the silence (0.322 h-1).

The growth rate of cells growing under low frequency

(0.350 h-1) and high frequency sonic waves (0.359 h-1)

grew also significantly faster than in the silence (P \ 0.005).

Interestingly, the increased growth rates of sound exposed

yeast resulted in up to 14% reduction in biomass production

(Fig. 2). The maximum optical densities (600 nm) reached

by cultures incubated under sound stimuli were between 3.44

and 3.72 while the cultures kept in the silence reached 4.03

(P \ 0.05).

We reasoned that sonically modulated growth and bio-

mass accumulation must have impacted cellular metabolism

and metabolites. Therefore we explored the metabolome of

cultures from each sound treatment. Cells from all treatments

were harvested at middle exponential growth phase

(OD600nm 2.3–2.4) and promptly quenched at -23�C to stop

cell metabolism (Smart et al. 2010). Cells were washed and

intracellular metabolites extracted at low temperature prior

Fig. 4 Venn visualization of

the different identified

metabolites comparing

experimental conditions.

Experimental conditions:

S silence, M music, LF low

frequency, and HF high

frequency

Sonic vibration affects yeast metabolism 675

123

to be chemical derivatized and analysed by gas chromatog-

raphy-mass spectrometry (Smart et al. 2010).

Approximately 50 different metabolites, which play

important metabolic roles in the central carbon metabo-

lism, lipid and amino acid biosynthesis, were identified

among hundreds of detected compounds (Table 2). To

distinguish samples among classes, we projected the

identified metabolite level data from each sample to a

lower dimensional space. Two often-used data projection

methods are PCA (principal component analysis) and FDA

(Fisher discriminant analysis). PCA maximizes variation in

the reduced dimensions, whereas FDA maximizes separa-

tion between classes (Villas-Boas et al. 2005). For this

reason, we applied FDA to visualize samples in an attempt

to distinguish them among classes, which revealed very

clear separation as shown in Fig. 3. Our metabolomics data

successfully demonstrated that each data class (sound

stimuli and silence) present distinct metabolite profiles,

with samples from same data class clustering very close to

each other. For instance, we achieved 100% correct clas-

sification for all of the experimental conditions using the

leave-one-out cross validation technique (Table 1)

55 different intracellular metabolites were consistently

identified from over 120 metabolites searched for in our

MS library (Table 2). Of these 32 metabolites were

detected at significant different levels in each growth

condition and 9 were unique to specific conditions (Fig. 5).

On comparison of the most significantly changed metabo-

lite levels among growth conditions, some metabolites

detected in all four conditions showed a linear decrease in

levels when comparing silence, music, low frequency and

high frequency sonic waves (e.g. ornithine, glutamine,

2-methoxysuccinate). On the other hand, several com-

pounds were detected only in samples growing under fixed

sonic waves such as NADP?, NAD?, myristate, 2-oxo-

glutarate, malate, fumarate, and others (Fig. 5), whereas

fewer metabolites were only detected in music and silence

(Figs. 4, 5).

Although non-detection for a given condition illustrates

that some metabolites were below our detection limits,

differential metabolite level data still permits interpretation

and provides proof-of-concept that sound impacts on cel-

lular metabolism of yeast. Using our recently developed

approach for predicting metabolic pathway activity based

Fig. 5 Some of the metabolites detected at significant different

levels. Stripcharts of the metabolites shows statistically significant

difference between experimental conditions tested using univariate

ANOVA. Arrows represent 95% turkey confidence intervals for

multiple comparisons between growth conditions. Only metabolites

showing greatest difference were selected for the plot. Experimental

conditions: S silence, M music, LF low frequency, and HF high

frequency

676 R. B. M. Aggio et al.

123

on metabolite profile data (Aggio et al. 2010), we have

explored metabolic traits evident in our data (summarised

in Fig. 6).

Based on the hypothesis generating algorithm (PAPi),

which explores metabolite profile data and the KEGG

database to predict and compare metabolic pathway

activities among different experimental conditions (Aggio

et al. 2010), it is apparent that different sound stimuli have

distinct effects on cell metabolism (Fig. 6). For instance,

low frequency sound induces an apparent increase in

activity of numerous metabolic pathways, with exception

of glycolysis. All sound stimuli inhibited the activity of

pantothenate and CoA biosynthetic pathways, whilst the

activity of five other pathways under sonic treatment (e.g.

pentose phosphate pathway and pentose-glucoronate in-

terconversions) increased. Interestingly, only music stim-

ulus negatively impacted lysine biosynthesis and cysteine

and methionine metabolism. High frequency sound

depressed apparent activities of several pathways generally

related to aromatic amino acid biosynthesis and

metabolism.

Ultrasonic vibrations have been long recognized as a

powerful energy source able to affect cell structures and

growth (Jomdecha and Prateepasen 2006). There is some

evidence that bacteria can enhance the proliferation of

stressed neighbouring cells by emitting ultrasound over

distances of 30 cm (Matsuhashi et al. 1998). Xiujuan et al.

(2003) observed that the H?-ATPase activity was per-

turbed by low frequency sound and this redistributed

cytoplasmic calcium in plant cells (Xiujuan et al. 2003).

The protective antioxidant peroxidase isoenzymes also

increased on sonic treatment (Xiujuan et al. 2003). These

data suggest that specific sound intensities and frequencies

altered membrane ion permeabilities and may alter free

radical formation. Sound frequencies of 170 and 340 Hz

also drove ATP formation in bovine F1/F0 ATPase function

in submitochondrial particles (Syroeshkin et al. 1998).

Potentially sound may alter ATP rotation, or membrane

proton gradients given that other transmembrane enzymes

and transporters might also be susceptible to sonic vibra-

tions. Future studies should certainly involve the mea-

surement of redox potential of the cells and their nucleotide

levels during sound stimuli under continuous growth cul-

tures, where the growth rate can be kept constant. This will

certainly assist in the understanding of the mechanism

behind the effect of sound on yeast cell metabolism.

Fig. 6 Comparative metabolic pathway activities of Saccharomycescerevisiae under different growth conditions based on intracellular

metabolomic data. The activity scores (AS) for each pathway were

calculated using our Pathway Activity Profiling (PAPi) algorithm

(Aggio et al. 2010). PAPi calculates for each metabolic pathway listed

in KEEG database an activity score based on the number of

metabolites identified from each pathway and their relative abun-

dances. As a result, the activity score represents the likelihood that a

metabolic pathway is active inside the cell and, consequently, allows

the comparison of metabolic pathway activities using metabolite

profile data

Sonic vibration affects yeast metabolism 677

123

4 Conclusions

By using a metabolomic approach, we demonstrate that

audible frequency sound influences yeast cell metabolism.

We not only showed that the intracellular metabolite profile

of yeasts cells harvested at same growth phase (early

exponential growth) but growing at different sound stimuli

were significantly different as demonstrated by FDA

analysis (Fig. 3), we also showed that some identified

metabolites were only detected in specific group of samples

(sound treatments, n = 15) (Fig. 4), while those metabo-

lites detected in all samples also presented significant dif-

ferent levels (Fig. 5). Moreover, our data indicate that

different sound frequencies induce different metabolic and

physiological responses in yeast cells. This opens new

perspectives for scientific investigation interfacing acous-

tics, biophysics and biochemistry, and perhaps provides

powerful tools for manipulating cell metabolism and

growth control (i.e.; in bioreactors) and proliferation (i.e.;

cancer treatment).

Acknowledgments We are very grateful to Gregory Cook, Matthew

Goddard, Richard Gardner, and Vladimir Obolonkin for valuable

comments and to Anthony Hickey for discussion and critical reading

of this manuscript. We also thank Farhana Pinu and Sang Kim for

technical assistance with media and sample preparation.

References

Aggio, R. B. M., Ruggiero, K., & Villas-Boas, S. G. (2010). Pathway

Activity Profiling (PAPi): From the metabolite profile to the

metabolic pathway activity. Bioinformatics, 26, 2969–2976.

Aggio, R. B. M., Villas-Boas, S. G., & Ruggiero, K. (2011). Metab:

An R package for high-throughput analysis of metabolomics data

generated by GC-MS. Bioinformatics, 27, 2316–2318.

Harvey, E. N., Harvey, E. B., & Loomis, A. L. (1928). Further

observations on the effect of high frequency sound waves on

living matter. Biological Bulletin Marine Biological Laboratory,55, 459–469.

Harvey, E. N., & Loomis, A. L. (1928). High frequency sound waves

of small intensity and their biological effects. Nature, 121,

622–624.

Jomdecha, C., & Prateepasen, A. (2006). The research of low-

ultrasonic energy effects on yeast growth in fermentation

process. In 12th Asia-Pacific Conference on NDT, 5th–10thNovember 2006, Auckland, New Zealand.

Koelsch, S., Offermanns, K., & Franzke, P. (2010). Music in the

treatment of affective disorders: An exploratory investigation of

a new method for music-therapeutic research. Music Perception,27, 307–316.

Li, B., Wei, J., Tang, K., Liang, Y., Shu, K., & Wang, B. (2008).

Effect of sound wave stress on antioxidant enzyme activities and

lipid peroxidation of Dendrobium candidum. Colloids andSurfaces B: Biointerfaces, 63, 269–275.

Matsuhashi, M., Pankrushina, A. N., Takeuchi, S., Ohshima, H.,

Miyoi, H., Endoh, K., et al. (1998). Production of sound waves

by bacterial cells and the response of bacterial cells to sound.

The Journal of General and Applied Microbiology, 44, 49–55.

Naimark, G. M., Klair, J., & Mosher, W. A. (1951). A bibliography

on sonic and ultrasonic vibration: Biological, biochemical and

biophysical applications. Journal of The Franklin Institute, 251,

279–299.

Pickett, J. P., et al. (2000). The American Heritage� Dictionary of theEnglish Language (4th ed.). Boston: Houghton Mifflin.

Polous, Y. U. M., & Kurko, V. S. (1991). Sound-wave stimulation of

duodenal motility in chronic duodenal ileus. KlinicheskayaMeditsina, 69, 42–44.

Sherman, F. (1997). Yeast genetics. In R. A. Meyers (Ed.), Theencyclopaedia of molecular biology and molecular medicine(Vol. 6, pp. 302–325). Weinheim: VCH Publisher.

Smart, K. F., Aggio, R. B. M., Van Houtte, J. R., & Villas-Boas, S. G.

(2010). Analytical platform for metabolome analysis microbial

cells using methyl chloroformate derivatization followed by gas

chromatography–mass spectrometry. Nature Protocols, 5,

1709–1729.

Syroeshkin, A. V., Bakeeva, L. E., & Cherepanov, D. A. (1998).

Contraction transitions of F1–F0 ATPase during catalytic

turnover. Biochimica et Biophysica Acta, 1409, 59–71.

Verduyn, C., Postma, E., Scheffers, W. A., & van Dijken, J. P. (1992).

Effect of benzoic acid on metabolic fluxes in yeasts: A

continuous-culture study on regulation of respiration and alco-

holic fermentation. Yeast, 8, 501–517.

Villas-Boas, S. G., Moxley, J. F., Akesson, M., Stephanopoulos, G., &

Nielsen, J. (2005). High-throughput metabolic state analysis:

The missing link in integrated functional genomics of yeasts.

Biochemical Journal, 388, 669–677.

Wood, R. W., & Loomis, A. L. (1927). The physical and biological

effects of high frequency sound waves of great intensity. TheLondon, Edinburgh, and Dublin Philosophical Magazine, 4,

417–436.

Xiujuan, W., Bochu, W., Yi, J., Defang, L., Chuanren, D., Xiaocheng,

Y., et al. (2003). Effects of sound stimulation on protective

enzyme activities and peroxidise isoenzymes of chrysanthemum.

Colloids and Surfaces B: Biointerfaces, 27, 59–63.

678 R. B. M. Aggio et al.

123