E X P E R I M E N T A L C E L L R E S E A R C H 3 1 7 ( 2 0 1 1 ) 2 9 5 8 – 2 9 6 8

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www.e l sev i e r . com/ loca te /yexc r

Research Article

Synchronous protein cycling in batch cultures of the yeastSaccharomyces cerevisiae at log growth phase

Gabriele Romagnolia, 1, Enrico Cundarib, Rodolfo Negria, c, Marco Crescenzid, Lorenzo Farinae,Alessandro Giulianif, 2, Michele M. Bianchia, c,⁎, 2

aDept. of Biology and Biotechnology ‘Charles Darwin’, Sapienza Università di Roma, Rome, ItalybIBPM, Consiglio Nazionale della Ricerca, Rome, ItalycIstituto Pasteur Fondazione Cenci-Bolognetti, Sapienza Università di Roma, Rome, ItalydDept. of Cell Biology and Neurosciences, Istituto Superiore di Sanità, Rome, ItalyeDept. of Computer and System Sciences “A. Ruberti”, Sapienza Università di Roma, Rome, ItalyfDept. of Environment and Health, Istituto Superiore di Sanità, Rome, Italy

A R T I C L E I N F O R M A T I O N

⁎ Corresponding author at: Dept. of Biology andFax: +39 0649912351.

E-mail addresses: gabriele.biotek@gmail.commarco.crescenzi@iss.it (M. Crescenzi), lorenzo.fa(M.M. Bianchi).

Abbreviations: SVD, Singular Value Decompo1 Present address: Kluyver Laboratory of Biote2 These authors contributed equally to this wo

0014-4827/$ – see front matter © 2011 Elseviedoi:10.1016/j.yexcr.2011.09.007

A B S T R A C T

Article Chronology:

Received 22 June 2011Revised version received9 September 2011Accepted 12 September 2011Available online 24 September 2011

The assumption that cells are temporally organized systems, i.e. showing relevant dynamics oftheir state variables such as gene expression or protein and metabolite concentration, whiletacitly given for granted at the molecular level, is not explicitly taken into account wheninterpreting biological experimental data. This conundrum stems from the (undemonstrated)assumption that a cell culture, the actual object of biological experimentation, is a population ofbillions of independent oscillators (cells) randomly experiencing different phases of their cyclesand thus not producing relevant coordinated dynamics at the population level. Moreover thefact of considering reproductive cycle as by far the most important cyclic process in a cellresulted in lower attention given to other rhythmic processes. Here we demonstrate that

growing yeast cells show a very repeatable and robust cyclic variation of the concentration ofproteins with different cellular functions. We also report experimental evidence that themechanism governing this basic oscillator and the cellular entrainment is resistant to externalchemical constraints. Finally, cell growth is accompanied by cyclic dynamics of medium pH.These cycles are observed in batch cultures, different from the usual continuous cultures inwhich yeast metabolic cycles are known to occur, and suggest the existence of basic,spontaneous, collective and synchronous behaviors of the cell population as a whole.

© 2011 Elsevier Inc. All rights reserved.

Keywords:

Cell communication

ExpressionEntrainmentPopulationRhythmic processpH

Biotechnology ‘Charles Darwin’, Sapienza Università di Roma, p.le Aldo Moro 5, 00185 Rome, Italy.

(G. Romagnoli), enrico.cundari@uniroma1.it (E. Cundari), rodolfo.negri@uniroma1.it (R. Negri),rina@uniroma1.it (L. Farina), alessandro.giuliani@iss.it (A. Giuliani), michele.bianchi@uniroma1.it

sition; MUSIC, Multiple Signal Classification.chnology, Delft University of Technology, Julianalaan 67, NL-2628 BC Delft, The Netherlands.rk.

r Inc. All rights reserved.

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Introduction

Cycles are intrinsic to nature. The most evident biological cycle isthe circadian cycle entrained by the day/night alternation. Manydifferent organisms follow the circadian cycle but biological cycleswith longer or shorter periods also exist, or co-exist in the sameorganism. The circadian cycle can be considered as an adaptationto environmental changes. Some cycles, instead, are clearly inher-ent to a specific function: heart beat, for example, ensures oxygensupply to tissues and its frequency and intensity depend on oxy-gen requirement. At the cell population level, the connection of anumber of cyclic activities with a specific function is not alwaysself-evident, although the involvement of metabolic changes inthe establishment of such cycles has been often ascertained [1].Cycles emerging at the cell population level are a consequence ofthe close interconnection among the various molecular constitu-ents (genes, proteins, metabolites) as well as of the establishmentof direct or mediated cell-to-cell communication [2]. The contribu-tion of intrinsic cellular periodicities and environmental con-straints in the establishment of such cyclic behaviors is still amatter of debate.

Many cyclic activities in different organisms are governed byclocks or oscillators that have been studied and characterized in de-tail. The presence of a functional clock in a cell is sufficient to ensurea cyclic activity and, possibly, cyclic outputs. The analysis at singlecell level [3,4] confirms this statement. In complex organisms,which are composed of multicellular tissues and organs, the rhythmcould be given by a master metronome: in mammals, for example,the functioning of peripheral organs is temporally coordinated bythe pacemaker activity of the suprachiasmic nucleus [5].

In single-cell organisms, the study of clocks is apparently sim-plified by the absence of a hierarchical structure. Cyclic behaviorsare investigated by collecting samples of a large ensemble of cellsat regular time points and observing if a relevant dynamics (i.e.one or more parameters largely different from a random behaviorin time) does appear. A cell population synchronizes following asignal or some form of communication or ‘common sensing’ thatmakes cells non independent of each other. The establishment ofspatial correlations among the cells ending up into temporal cor-relations, detectable as time-dependent coordinated activities,contradicts the ergodic hypothesis according to which cell cul-tures are ensembles of completely independent individuals. Innon-hierarchical systems, like homogeneous and isotropic cellcultures, the entraining signal is produced by equipotent neigh-boring cells. In this case a cyclic behavior of the whole culture isdetectable if also the signal is produced in a pulsating fashion bythe whole population. Flat signaling will not maintain the entrain-ment of the cellular oscillators and the synchronized populationwill soon become an ergodic system in the dimension of time.

Signals known to induce collective changes in yeast cell popu-lations are the pheromone response, that can synchronize cell re-productive cycle; the diauxic shift, that induces the metabolictransition from fermentation to respiration in batch cultures orthe accumulation of aromatic alcohols, that produces quorumsensing changes. These signals are the result of a developmentalprocess of the cell or of the culture as a whole [2]. However, thediauxic shift and quorum sensing are not cyclic. A proper cellularoscillator is probably the mechanism that produces whole-cell cy-clic changes in yeast cells continuously cultured at high cell

density [6,7]. In such conditions, cells collectively and synchro-nously move from a reductive to an oxidative physiological status,with coordinated changes of the transcriptome and metabolitecomposition [8,9]. Important basic cellular activities, such asDNA duplication, electron transport and ATP synthesis [7,10] arecarried out in parallel and in cyclic fashion. Similar cycles, derivingfrom respiratory oscillations, are also observed in batch cultureson trehalose as carbon source [11]. The yeast glycolytic oscillation[12] is also a collective behavior in defined conditions [13]. A met-abolic circadian cycle can be induced in yeast by cyclically chang-ing the growth temperature in continuous cultures [14].

As mentioned above, yeast growing cells in batch cultures arecommonly considered as an ensemble of dividing individuals, pro-gressively increasing in number with time but equally distributedin each phase of the duplication process (ergodic system). As aconsequence, cellular and molecular properties of culture samples(a large number of cells collected at different time points) are sup-posed to be constant, with no relevant collective temporal struc-ture, or eventually to change linearly in short time windows. In aprevious work [15] we proved that this was not the case for cellcultures. We demonstrated that cyclic transcriptomic changescould be detected in yeast—but also in mammalian—cell cultures.

In the present work we tried to answer to the immediate fol-lowing question: does protein content varies cyclically and syn-chronously in the cell population as transcripts do? This was nota trivial question. For example, a consistent fraction of hepatic cir-cadian proteins are not correlated to cycling gene transcription[16]. We focused our work on low cell density yeast batch culturesand we showed here that, in this condition, yeast cells exhibitedcollective and cyclic protein content variation. We also demon-strated that medium pH varied cyclically but medium bufferinghad no effect on protein concentration cycles. The evidenced cy-cles were demonstrated to be fairly invariant in frequency acrossdifferent protein species, while changing in amplitude.

Materials and methods

Strains and media

Yeast strains were from Yeast GFP (Green Fluorescent Protein)Clone Collection of Invitrogen. Each Yeast-GFP clone representsan individual S. cerevisiae strain containing an open readingframe with a C-terminal Aequorea victoria GFP (S65T) fusion tag.The genotype of the parent haploid S. cerevisiae strain (ATCC201388) is: MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0. The genes test-ed were: RPL11 (YPR102C), CIT2 (YCR005C), RAP1 (YNL216W) andMGS1 (YNL218W). Rpl11 is a component of the large ribosomalsubunit and its promoter shows the typical structure and tran-scriptional regulation of RP genes [17]. Cit2 is a citrate synthase,a respiratory enzyme, and it is preferentially expressed at diauxicshift or at stationary phase [18]. Rap1 is a DNA binding protein in-volved in activation or repression of gene transcription and intelomeric functions [19], and Mgs1 is a protein involved in DNAmetabolism and genome stability [20]. The wild type yeast strainwas BY4741 (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0) from EURO-SCARF. Standard medium (YPD) was 1% Yeast Extract (Becton-Dickinson), 2% Peptone (Becton-Dickinson) and 2% glucose. Cyclo-heximide was from Sigma-Aldrich (C7698) and sodium metava-nadate from Fluka (72060).

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Cultivation in flask

Precultures were prepared by growing yeast strains in YPD to thelate log phase (about 1×107 cell/ml) and used to inoculate a 5 lflask containing 2.5 l of YPD. Precultures were diluted about 100fold in the flask (to about 1×105 cell/ml) and cells were allowedto recover growth in the flask for at least 4 h at 26 °C before sam-pling. The medium in the flask was mixed by magnetic stirring(100 rpm) and aerated with a sparger supplying air at 0.4 l/min.Samples were withdrawn through an outlet piping at the bottomside of the flask at 5 min intervals. The piping volume was dis-carded at each culture withdrawal and cell samples were analyzedimmediately for fluorescence.

Cultivation in bioreactor

We used a BiostatQ B-Braun bioreactor endowed with four 1 l ves-sels, each containing 600 ml of YPD and inoculated with late logprecultures at starting cell concentrations of 0.5–1×107 cell/ml.Incubations were performed at 28 °C with magnetic stirring(300 rpm) and air supply (0.4 l/min). A doubling-time period ormore, measured as OD600 increase, was left before sampling. Sam-pling was performed at 5 min intervals when OD600 ranged be-tween 0.4 and 1.0 and the piping volumes were discarded ateach withdrawal. Since the distance between the bioreactorplant and the flow-cytometer could not allow immediate analysisof samples, cells were collected by centrifugation (microfuge, at10 g for 2 min) and frozen on dry ice/ethanol mixture. Frozencells were maintained at −70 °C. Groups of ten samples were si-multaneously thawed by the addition of PBS buffer and fluores-cence of each sample was immediately measured withoutintervals, but waiting just for the technical time of the instrumentfor reading. This procedure excluded the possibility that flores-cence outcomes were artifacts of sample reading at regular inter-vals or noise background of the measuring device.

Flow cytometry

Samples were analyzed using an EPICS xl (Beckman-Coulter)flow-cytometer. Three parameters were acquired for each sample:forward light scatter (FS) and side light scatter (SS) which accountfor cell size and granularity, respectively and green fluorescence(FL1) to determine GFP-associated fluorescence emission (wavelength: 509 nm). 10,000 events were acquired for each sample.At the beginning, in the middle and at the end of each experiment,standard fluorescent micro-beads were analyzed to verify acquisi-tion efficiency. Acquired data were analyzed using the WinMDIsoftware by Joe Trotter, available at http://facs.scripps.edu.

Northern protocol

Culture samples (1.5 ml) for RNA extraction were harvested every5 min, immediately centrifuged (3 min at 14,000 rpm, benchmicrofuge) and collected cells were frozen by immersion in etha-nol/dry ice mixture. Frozen cells were maintained at −70 °C.RNAs were extracted following the hot phenol protocol [21].RNAs preparations were suspended in 50% (v/v) formamide,2.2 M formaldehyde andMOPS buffer (0.56%MOPS pH7, 5 mM so-dium citrate, 1 mM EDTA), heated at 65 °C for 15 min and loaded(10 μg) onto a formaldehyde/agarose gel (1% w/v agarose, 6%

formaldehyde, 50 mM NaCl, 4 mM EDTA). After electrophoresis,RNAs were transferred to Nytran-N membrane (Schleicher andSchuell, Dassel, Germany) following the Northern blotting proce-dure. The filter was hybridized with 32P-labeled RPL11 DNAprobe, obtained from PCR amplification (forward: ccctatgcgtgattt-gaaga; reverse: ggtacccttacatctctttc). The hybridized filter was ex-posed to Amersham Biosciences Storage Phosphor Screen and theimage was subsequently acquired by Typhoon 9200 phosphoima-ger for signal quantification.

Data analysis

The temporal structure of cell cultures was investigated by meansof SVD (Singular Value Decomposition) and the power spectrumdensity estimated using the MUSIC (Multiple Signal Classification)protocol. SVD was applied to an 8 dimensional embedding matrixof the original discrete time series of the concentration of thestudied protein in terms of GFP-fluorescence or of the mRNA hy-bridization signals. The pattern of correlations among the columnsof the EM (autocorrelation matrix) will convey the entire informa-tion of the dynamics of the studied system [22]. The choice of aneight dimensional embedding was demonstrated to be optimalfor maintaining a sufficient dimensionality for the derived attrac-tors while not being too detrimental for the short length of the se-ries [23]. In the present case, the SVD procedure was particularlyappropriate given the presence of an overwhelming linear trenddue to the non-stationary character of batch conditions whichends up into a linear ‘size’ component (pc1). SVD decomposedthe signal into mutually orthogonal components, and consequent-ly it allowed to filter out the trend in a natural, unsupervised wayand to keep the oscillatory character of the dynamics into minorcomponents [24,25]. The determination of periodic componentsin time series is a long standing problem of wide interests in appli-cations and, in fact, researchers from diverse field have contribut-ed to this effort. The power spectral density (PSD) functiondescribes the distribution of power with frequency and, ideally,it displays a sharp peak for any cyclical component present inthe data. For a signal x[n] of infinite length the PSD can beexpressed as:

P fð Þ ¼ limM→∞E1

2Mþ 1 ∑M

n¼−Mx n½ �e−j2πfn

��������2( )

:

In the practical case of a time series of finite length N, the esti-mation of the PSD has traditionally been based on Fourier trans-form. The recent interest in alternative methods is motivated bythe important and difficult case of high frequency resolutionusing short data records, as in our case. There have been severalapproaches to such problems including the so-called maximumlikelihood (ML) method of Capon (1969) and Burg's (1967) max-imum entropy (ME) method (reviewed in [26]). Although oftensuccessful and widely used, these methods have certain funda-mental limitations (especially bias and sensitivity in parameter es-timates), largely because they use an incorrect model (e.g., ARrather than special ARMA) of the measurements. Therefore, a cru-cial point is the use of the correct data model. In our study, wewanted to characterize the presence of oscillatory modes in data,so that an appropriate data model is to consider sinusoidal compo-nents embedded in noise. The most powerful method available is

2961E X P E R I M E N T A L C E L L R E S E A R C H 3 1 7 ( 2 0 1 1 ) 2 9 5 8 – 2 9 6 8

certainly the MUltiple SIgnal Classification (MUSIC) algorithm,proposed by Schmidt in 1986 [27]. MUSIC estimates the frequencycontent of a signal using an eigenspace method. This method as-sumes that a signal x[n] consists of p sinusoidal components (com-plex exponentials) of frequency fi in the presence of Gaussianwhite noise z[n]:

x n½ � ¼ ∑p

i¼1Aiej2πfinþφi þ z n½ �

where, for real sinusoids pmust be chosen to be twice the numberof real sinusoids n=0,1,…,N−1. The frequency estimation func-tion for MUSIC is

PMUS fð Þ ¼ 1

∑pþq

i¼pþ1eH fð Þvij j

where

e fð Þ ¼ 1 ej2πf ej2π2f … ej2π N−1ð Þf� �Tp is the dimension of the signal subspace (twice the number of os-cillatory components), q is the dimension of the noise subspaceand the vi's are the eigenvectors of the signal correlation matrixordered according to the decreasing magnitude of the correspond-ing eigenvalues. An exhaustive description of the analytical proce-dures is reported in SM1.

Results

Selection of proteins

In a previous work [15] we showed that, in populations of cellscultured in standard flask conditions (yeast) or plates (mammali-an cells), mRNA accumulation of a large number of genes is cyclicand synchronized. Taking this result into account, we decided toinvestigate i) whether protein accumulation follows the sametemporal pattern as mRNA and ii) whether a correlation betweenmedium or growth conditions and biological synchrony can beestablished. Yeast was chosen to address these questions, becauseof the availability of a collection of yeast strains, each one with theGFP reporter gene fused in frame to the coding region of a differ-ent gene. The average content of an individual protein could beevaluated by measuring the fluorescence of GFP associated tothat protein in cell population samples. Four proteins (Rpl11,Cit2, Rap1 and Mgs1) with different cellular functions and differ-ent expression patterns were selected (see Materials and methodsfor detail).

RPL11 mRNA determination and validation of dataanalysis protocols

The first experiment aimed at confirming, in our growth condi-tions, the cyclic nature of RPL11 mRNA transcription and to testthe robustness of our analytical protocols (SVD and MUSIC) ondataset. The wild type yeast culture growing in the aerated andstirred flask (OD=0.35 to 0.7) was sampled at 5 min intervalsfor 120 min and RNA prepared as described in Materials andmethods. Fig. 1A shows Northern blot signals of RNA preparations

after hybridization with RPL11 probe. Although the content ofmost individual mRNA varies cyclically in a population of cells,the total amount of RNAs might reasonably be assumed as con-stant, due to the buffering capacity of ribosomal RNA which isvery stable and accounts for 80% of total RNA. Signal intensitieswere then acquired and normalized by total RNA loading: rawdata are reported in Supplementary material SM2, where all theraw experimental data—hybridization signals, fluorescence valuesand pH values—of our work are presented in table and graphformats.

SVD results are reported in Fig. 1B. RPL11 mRNA expressionshowed a trend-like first component (pc1), explaining 54% ofRPL11 mRNA variance, and two cyclic components that could ac-count for approximately 33% of the variance (pc2 and pc3; 18.5%and 14% of variance explained, respectively). The observed 90°shift, that correlated pc 2 and pc3 as sine/cosine pair, was anexpected component feature. Period of cycles and robustnessof periodicity were determined by computing a P value bymeans of the Multiple Signal Classification (MUSIC) algorithm.Although our cultures were not intentionally synchronized, cy-cles possibly correlated to the cell division cycle (90–120 min)were intentionally excluded from our analysis. A graphical rep-resentation of power spectrum estimate of the RPL11 mRNAtime profile using the MUSIC algorithm is reported in Fig. 1C:the first peak on the left corresponded to the ‘low’ frequencysignal related to the presence of a trend component, whereasthe appearance of a second peak on the right was the result ofa ‘high’ frequency signal corresponding to the presence of theoscillatory behavior. In summary, the analysis of RPL11 mRNAtime profile revealed an extremelysignificant period (Pvalue=5·10−14) of 26.4 min (2π/26.4=0.379 rad/min usingnormalized angular frequency units). For simplicity, wereported the graphical output of the MUSIC analysis only forRpl11 mRNA, while for all other experiments just the numericaloutputs of MUSIC analysis were reported (Table 1).

Protein cycles and growth onset are correlated events

Experiments were carried out to determine Rpl11 dynamics inflask cultured cells growing exponentially at different points ofthe batch cultivation. The first culture was started by inoculatingcells at the estimated cell density of 1–5×105 cell/ml (OD below0.01). At the end of the lag phase, samples of approximately 2 mlwere harvested at 5 min intervals and submitted to FACS analysis.Start of culture growth was accompanied by the progressive accu-mulation of proliferating cells [28,29] characterized by smallersize and lower fluorescence intensity emission, as compared tothe large resting cell, resulting in reduction of the average fluores-cence (Figs. 2A and B). SVD analysis of the fluorescence timecourse revealed the presence of three predominant factors(Figs. 2B and C). The first component (Fig. 2B) was strongly corre-lated (Pearson r=0.90) with the percentage of small cells in theculture, suggesting that the most relevant driving force of Rpl11concentration variability in the considered time window was thechanging proportion in time of small-sized dividing cells. Thetransition phase in which small cells started to accumulate wasidentified around 60 min. This corresponded to the presence ofmajor peaks in the second most important component (pc2,Fig. 2C). In this experiment, the cyclic nature of Rpl11 protein ex-pression was revealed by the third component that displayed a

Fig. 1 – Analysis of RPL11 transcription. In Fig. 1A the RPL11hybridization signals after Northern blotting analysis are shown. In Fig. 1Bthe SVD analysis of the hybridization signals is reported. The principal components 1, 2 and 3 are represented by triangles, black dotsandwhite dots, respectively. In Fig. 1CMUSIC spectral analysis is reported: the peak on the left corresponds to the ‘low’ frequency signal(component 1) related to the presence of a trend component, whereas the peak on the right is a ‘high’ frequency signal correspondingto the presence of an oscillatory behavior (components 2 and 3). The height of the peaks is proportional to the relative proportion ofvariance explained by the trend and oscillatory modes.

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clear temporal order. These results suggested a correlation be-tween growth onset (increase of small cell population and pc1 de-crease) and the appearance of steady Rpl11 cycles (amplitudeincrease of pc2 and pc3 oscillations).

Rpl11 protein cycles during exponential growth

Rpl11 concentration cycles were determined in repeated and in-dependent batch cultures at OD 0.05, OD 0.3 and OD 1 (approxi-mately from 106 to 5×107 cell per ml) with substantiallyidentical results. We report here in detail SVD analysis of the ex-periment at OD 0.05. The average fluorescence could be separatedinto a linear trend (not shown) and the cyclic components pc2 andpc3, having the usual reciprocal sine/cosine coupling (Fig. 3). Pc2

Table 1 – MUSIC analysis of GFP fluorescence.

Protein Cultivation system Cell population Speci

Rpl11 Flask Whole –

Large –

Small –

Whole HWhole 10

Rpl11 Bioreactor Whole –

Whole 50401

Cit2 Flask Whole –

Mgs1 Bioreactor Whole –

Rap1 Bioreactor Whole –

10

Special conditions: KP=potassium phosphate pH7 or pH5.8, CHX=cyclohexadditions.a Average of 3 to 4 independent experiments with standard deviation (±).b Range of variation in the repeated and independent experiments.

and pc3 globally explained around 1.5% of total variance. Hereagain, the cell population was composed of cells of differentsizes that could be easily discriminated into small and large cellsub-populations. Fig. 4 shows the cyclic components of SVD anal-ysis on fluorescence data of small and large cells separately. Com-ponents 2 and 3 of both cell types were cyclic and phase shifted,although pc2 and pc3 of large cells appeared less harmonic. Pc2and pc3 accounted for 3% and 2.6% of explained variance (smallcells) and 3.3% and 2.4% of explained variance (large cells), respec-tively. The application of the MUSIC algorithm to the fluorescencedata allowed to exactly evaluate the periodicity of protein concen-tration changes in terms of frequency and probability. The entireRpl11 data set was analyzed and the results are reported inTable 1. A reproducible periodicity ranging from 17 to 20 min

al conditions Period (minutes) P value

19.8±3.1 a 1 b

17±3.9 a 4·10−3÷1b

20.1±2.6 a 1.6·10−3÷7.9·10−1b

ypoxic 21.2 10 mM KP 21.3 1

18.3±5.1 a 1.6·10−2÷2·10−1b

mM KP 22.7 2.5·10−2

mg/l CHX 17 3.1·10−2

mM MEV 31.6 2·10−3

12.1 8·10−1

26.1 1.6·10−1

29.1 1·10−2

0 mM KP* 29.1 1

imide, MEV=sodium metavanadate,–=standard YPD medium without

Fig. 2 – SVD analysis of FACS data of Rpl11::GFP at growth onset.In Fig. 2A the relative fluorescence vs the cell size at threedifferent time points is reported: 20, 100 and 200 min from thebeginning of fluorescence measurements. The growingpopulation of small proliferating cells is circled. In Fig. 2B theprincipal component 1 (pc1, triangles) and the relative fractionof small cells (white squares) vs time are reported. The pc1 scoresare strongly negatively correlatedwith the fraction of small cells.In Fig. 2C the principal components pc2 and pc3 (black andwhitedots, respectively) are reported which account for the cyclicshape of Rpl11 concentration vs time at the time point of growthonset, around 60min from the beginning of the fluorescencemeasurement. Time is expressed in minutes.

Fig. 3 – SVD analysis of FACS data of Rpl11::GFP duringexponential growth. In Fig. 3 the fluorescence analysis of theRpl11::GFP fused protein in the whole cell population during theexponential growth (OD600=0.05) is reported. The principalcyclic components 2 and 3 are black andwhite dots, respectively,vs time. Time is expressed in minutes.

Fig. 4 – SVD analysis of small and large cells. In Fig. 4 theseparated SVD analysis of FACS data of small (Fig. 4A) and largecells (Fig. 4B) are reported. The oscillating components pc2 andpc3 are indicated by black dots and white dots, respectively.

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was found, both for the whole cell population and the large andsmall cells subsets. In some cases highly significant P-valueswere obtained.

Medium buffering and aeration effects

After having demonstrated the existence of Rpl11 concentrationcycles, we focused on the investigation of the possible effect of mi-croenvironment conditions on the above mentioned cycles. Welldescribed cycles in yeast are correlated to respiratory activities.These cycles are remarkably stable and can last for months in con-tinuous cultures, showing a period length of the same order ofmagnitude of cycles described in the present article [7]. We there-fore measured Rpl11 protein cycles in flask without insufflated airduring culture growth. Also in this condition a 20 min cycle withcoupled pc2 and pc3 components could be detected (Table 1, spe-cial conditions: hypoxic), accounting for 4.9% and 2.2% ofexplained variance, respectively (data not shown). Both subpopu-lations of small and large cells showed cyclic components (notshown). This experiment suggested that aeration had no effecton the observed periodicity.

Successively, we addressed medium ionic strength and/or pHdynamics as possible influential factors on the described cyclic be-havior. Cells were incubated in YPD medium buffered with100 mM potassium phosphate at pH7. We could not find any rel-evant change in the cyclic pattern of Rpl11 concentration by ana-lyzing the fluorescence data by SVD (not shown) or by MUSIC(results reported in Table 1, special conditions: 100 mM KP). Asimilar experiment was performed on cells grown in bioreactor ei-ther in YPD or in YPD buffered with 50 mM potassium phosphatepH7. Fluorescence was measured after collection of all samples(see Materials and methods section for detail). SVD analysis ofthe fluorescence (whole cells) is reported in Fig. 5. The shortertime window allowed to reduce or eliminate the linear trend. In

Fig. 5 – SVD analysis of Rpl11 frombioreactor cultivation. In Figs.5A and B data from cells cultivated in a bioreactor on YPD andbuffered-YPD media are reported, respectively. The cyclicprincipal components pc1, pc2 andpc3 are indicated by triangles,black dots and white dots, respectively, in both panels.

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this experiment components 1 and 2 (not buffered: 32% and 18.9%of explained variance, buffered: 31.8% and 28.6% of explained var-iance, respectively) showed a cyclic behavior. The application ofthe MUSIC algorithm to both the whole cell data set (Table 1, spe-cial conditions: 50 mM KP) and the small and large subsets (notshown) revealed strong periodicities of cycles with frequenciessimilar to those measured in flask growing cultures. The smalltemperature difference between flask (26 °C) and bioreactor(28 °C) cultivation was not influent on results. Actually, cyclesgoverned by clocks are by definition resistant to temperaturechanges [30]. In addition, yeast respiratory cycles present in con-tinuous cultivations are stable from 26 °C to 34 °C [31].

Cit2, Rap1 and Mgs1

The cyclic variation of concentration of Cit2, Rap1 and Mgs1 wastested. We measured Cit2::GFP fusion protein expression in cellsgrowing exponentially in flask. Two cyclic components (pc2 and

Fig. 6 – SVD analysis of Cit2. In Fig. 6 the cyclic components 2(black dots) and 3 (white dots) of Cit2 fluorescence arereported.

pc3, with sine/cosine pairing, Fig. 6) responsible, in this case, oflarger fractions of variance (about 5% each, for a total of 10% ofexplained variance of the cyclic component) were detected. Periodlength (Table 1) was of 12.1 min. The cyclic factors independentlycomputed for large and small cell populations (not shown)strongly correlated to each other in time (Pearson r=0.90). Inter-estingly, the amplitude of the cyclic components dramaticallychanged in time (Fig. 6), while the period length remained steady.This behavior pointed to the robustness of the detected cycles thatcould be modulated in amplitude but remained unchanged intheir characteristic frequency. Amplitude modulation might bethe result of a peculiar regulation of expression of Cit2, which isa growth-phase dependent protein, indicating the interference ofadditional effectors to those governing the cycles of Cit2 proteinlevel in the cells.

The Mgs1 and Rap1 fusion strains with GFP reporter were bothgrown in bioreactor in standard YPD medium. Rap1::GFP strainwas also cultured in YPD buffered with 100 mM potassium phos-phate. Both Rap1 and Mgs1 showed a behavior very similar tothe other analyzed proteins with cycles of comparable period, in-dependent on buffering conditions (Table 1).

Effect of cycloheximide and metavanadate on Rpl11protein cycles

The protein level within a cell depends primarily on the balancebetween translation and polypeptide degradation. In order todetermine whether the described protein cycles derived fromde novo cyclic synthesis rather than degradation, we measuredRpl11 variations in the presence of 40 μg/ml of cycloheximide,a compound known to block protein synthesis [32]. In prelimi-nary tests we added cycloheximide to yeast cells exponentiallygrowing in bioreactor and we measured fluorescence beforeand after addition. Fluorescence data (SM2) showed a clear cy-clic behavior associated to a strong linear trend that changedin correspondence to cycloheximide addition. The cyclic fluores-cence had a significant periodicity of 17 min (Table 1, specialconditions: 40 mg/l CHX). SVD analysis (not shown) revealedthe presence of a new component pc2 that was probably origi-nated by the perturbation caused by drug addition. We there-fore analyzed fluorescence data from two parallel independentbioreactor cultures with and without cycloheximide. SVD analy-sis, reported in Figs. 7A and B, indicated opposite trends of thefirst principal components, as it was expected from the blockageof protein synthesis and a general reduction of protein concen-tration in the cells. The cyclic components pc2 and pc3, on thecontrary, were still present and substantially unchanged in theculture containing cycloheximide (Fig. 7B). This result sug-gested that protein cycling was generated by a mechanism inde-pendent on bulk protein synthesis.

Cellular pH is maintained stable by proton traffickingthrough the plasma membrane in response to cytoplasmic pro-duction by metabolism of organic acids or to external mediumacidification. In addition, GFP fluorescence is sensitive tochanges of environmental pH. Therefore we tested the effect ofsodium metavanadate, an inhibitor of the main ATPase protonpump Pma1 [33]. No significant or abrupt changes of fluores-cence could be detected after the addition of metavanadate(not shown), indicating that pH homeostasis was still

Fig. 7 – SVD analysis of Rpl11 in the presence of cycloheximide.In Figs. 7A and B SVD analyses of the average fluorescence ofRpl11::GFP from parallel cultivations in the bioreactor without(7A) and with (7B) the presence of 40 mg/ml chycloheximideare reported. The trend component pc1 is indicated bytriangles. The cyclic components pc2 and pc3 are reported asblack and white dots, respectively. Time expressed in minutes.

Table 2 –MUSIC analysis of medium pH in bioreactor.

Special conditions Period (minutes) P value

– 34.7±4.9 a 5·10−14÷2.5·10−3, b

50 mM KP 39±10.5 a 5·10−14

40 mg/l CHX 18.4 6.3·10−3

1 mM MEV 33.7 1·10−4

100 mM KP* 34.6 1·10−4

Special conditions: KP=potassium phosphate pH7 or pH5.8,CHX=cycloheximide, MEV=sodium metavanadate,−=standardYPD medium without additions.The noise background of the measuring device was evaluated bymonitoring pH in the medium without cells and resulted in a non-significant cycle of 13.3 min.a Average of 3 to 4 repeated independent experiments with standarddeviation (±).b Range of variation in the repeated and independent experiments.

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maintained, in case by other proton exchangers, like the H+/Na+ antiporter Nha1 [34]. Fluorescence cycles were steadilymaintained also in the presence of metavanadate, but the periodwas almost doubled with respect to the untreated cells (Table 1,special conditions: 1 mM MEV).

Fig. 8 – pHprofiles in bioreactor. Fig. 8 reports the comparison ofpHprofiles in the timewindows of the exponential growthphaseanalyzed in our experiments. In Fig. 8A are reported pH outputsof a non-inoculated vessel containing sterile YPD medium(upper profile) and a standard YPD growth profile (lowerprofile). Fig. 8B shows the effect on pH of the addition ofpotassiumphosphate pH5.8 (KP, final concentration 100mM) inexponentially growing yeast cultures on YPD medium. The timepoint of addition is indicated by an arrow. Time expressed inhours.

pH analysis

In parallel with fluorescencemeasurements, wemonitoredmediumpH in bioreactor experiments with in situ electrodes. pH variationprofiles of representative experiments are reported in Fig. 8. In stan-dard YPDmedium (Fig. 8A) pH changed in amonocline fashion dur-ing the exponential growth. The addition of potassium phosphate(Fig. 8B) produced an abrupt pH shift without an evident effect onthe cellular cycles of concentration of the studied proteins (Rpl11and Rap1: Table 1). The addition of cycloheximide and sodiummetavanadate produced only small and transient changes in thepHprofile (not shown)without effect on protein cycles. The detailedanalysis of the pH meter output vs time also revealed cyclic compo-nents. An example of SVD analysis—buffered medium culture andstandard medium culture—is reported in SM2. Results of MUSICanalysis are reported in Table 2. The presence of growing yeastresulted in the generation of an extremely significant and reproduc-ible pH cycle of about 35 min in the medium. In all tested mediumconditions, pH cycles were present and strongly significant, thussuggesting that they resulted from a coordinated cellular activity, in-sensitive to the tested conditions. Interestingly, the period of pH cy-cles was roughly two times longer than that of protein cycles(Table 1), except when cycloheximide was used.

It is well known that intensity of GFP fluorescence is sensitive tosurrounding environmental pH [35]. However, we excluded that thedetected protein cycles based on GFP fluorescence within the cellwere merely the chemical effect of a cycling medium pH on a con-stant cellular level of protein. As a matter of fact, cellular homeosta-sis is able to contrast medium pH variations much larger [36] thanthe pH cycles we measured. Furthermore, GFP fluorescence pattern(average level and cycles) remained stable even when subjected tothe macroscopic pH variations due to the addition of chemicals(Fig. 8). Finally, we could not observe an effect on the amplitude offluorescence cycles after the addition of metavanadate, a compoundthat might specifically affect cellular pH homeostasis.

Discussion

Values of a cellular parameter measured with high frequency froma large number of individuals growing without constraints might

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assume different profiles in function of time: flat, if the conditionsare stationary; increasing or decreasing, if conditions are varyingprogressively; changing the sign/value of variation, if the parame-ter is subjected to phase variation. These profiles are the (macro-scopic) result of measures when (microscopic) cells arerandomly distributed around an average state. In all the experi-ments of the present work, we actually have met such situations,which are described by the trend components, often coincidentwith the major components of SVD. Cyclic functions of time of cel-lular parameters also occur in individual cells, but require collec-tive cell synchronization to be detected from the cell population,as is routinely performed in cell cycle works, for example. In thiswork we report evidence of spontaneous connection between cy-clic activities and collective behaviors in growing yeast cultures.

We have previously demonstrated [15] that mRNA abundanceof single genes varies cyclically in yeast andmammalian cell popu-lations growing in standard laboratory conditions. This behavior isnot confined to a particular gene product but has a genome-widescale and contradicts to the common belief that growing cells areensembles of independent individuals. In fact, the synchronousexpression of a single gene in a cell population deprived of exter-nal signals for entrainment, requires the existence of cell-to-cellcommunication to enable the synchronization process to occur.We show here that the yeast gene RPL11 in a batch and low celldensity culture has a cyclic and synchronous expression. A consis-tent fraction (about one third) of RPL11 mRNA in the cell popula-tion is subject to cyclic and collective variation. Since mRNA leveldepends on synthesis and degradation, cellular cross-talk mightgovern the cyclic character of transcription or mRNA stability, orboth. This basic oscillation of gene expression will not exclude var-iations of mRNA synthesis and degradation linked to other addi-tive mechanisms of cell culture dynamics, such as cell cycleprogression, phase transitions and so on.

Due to the strict relationship between mRNA and protein syn-thesis, the question arises as to whether the cyclic concentrationof mRNA results in a cyclic concentration of its product (proteins).Ideally this question should be extended to the entire proteome. Inpractice, we confined our analysis to four proteins having differentfunctions: the transcription factor Rap1, the ribosomal proteinRpl11, the mitochondrial metabolic protein Cit2 and Mgs1, a pro-tein required for chromosome maintenance. The average cellularcontent of each protein was determined by measuring the fluores-cence of the fused reporter protein GFP. Our present results showthat, despite the different functions of the tested proteins, the cul-ture growth phase or the culture medium or conditions, these pro-teins have a cyclic temporal profile of concentration, with aperiodicity comparable to that of mRNA. The variance of proteinconcentration showing a cyclic property is not negligible andranges from few to 50%. Our specific analysis (MUSIC) of data ex-cludes that these oscillations, even when small, are due to noisebackground, random experimental errors or biased by measuringdevices. These oscillations will be hereafter referred to as CBB(Collective Basic Batch) cycles. Protein CBB cycles are also presentin subpopulations of cells with different physiological states, sug-gesting the existence of a very general mechanism of entrainment.

The tested proteins showed rather constant amplitude of cyclicexpression except for Cit2, which showed pulses of amplitudemodulation. This could be due to phase dependent induction ofCit2 gene expression, consistently with the physiological functionof this protein. This is completely in line with the system dynamics

theory: while the typical frequency of an oscillator is an intrinsicproperty of the system, the amplitude of oscillations is a tunableparameter in response to external stimuli. We can infer that theoscillation frequency of CBB cycles is a basic characteristic of thewhole cell (omic level) and of the entire culture (populationlevel), while the amplitude of the oscillation, which is still at thepopulation level, depends on the function of the single gene (orfunctionally correlated genes) and can be rapidly adjusted in re-sponse to environmental contingencies and changing needs ofthe system. It is noteworthy that cellular cycles might be favorablefor adaptation. In fact, a global cellular oscillating system, beingnever off, is optimal to increase the responsiveness and the fitnessof the cell population to external stimuli, in comparison with asteady state system.

Different culture conditions and chemical treatments weretested aiming at disturbing CBB cycles in order to obtain informa-tion about the underlying cellular mechanisms or the synchroniz-ing cell-to-cell messenger. However, CBB cycles proved to beresistant to all tested conditions. Oxygen availability indepen-dence suggests that CBB cycles might not be connected to redoxcycles ([8,9,37] for a recent review). When protein synthesis wasblocked by cycloheximide, the cellular protein concentration pro-gressively decreased, but still in a cyclic fashion, suggesting thatmRNA translation is not the (unique) mechanism involved inCBB protein cycles. We can therefore hypothesize that also proteinfolding and degradation might occur cyclically or the cyclic mech-anism of protein synthesis might escape cycloheximide inhibition.

CBB cycles were not affected by medium buffering and sharppH transitions. At the same time the expected modification of me-dium pH due to yeast cell growth followed a cyclic pattern. An ob-vious explanation of this phenomenon could be found in the cyclicmetabolism and cyclic production and consumption of acidic me-tabolites intrinsic to the occurrence of CBB cycles at a proteomiclevel. Interestingly, the acidic metabolite sulphidric acid has beenproposed to be, together with acetaldehyde, the entrainer mes-senger of the yeast metabolic cycle [38,39]. If the pH cycle was cor-related to cell entrainment, it would be interesting to understandhow this message is transduced from the medium into the cell.One possibility could be the involvement of proton pumps, evenif this would not necessarily imply a cyclic variation of cytosolicpH. The perturbation of proton trafficking by metavanadate didnot suppress CBB cycles but increased (doubled) its periodicityand tuned it to that of medium pH cycles suggesting that CBB cy-cles might be modulated in frequency by affecting the dynamics ofthe cell–medium interface.

It is known that environmental perturbations and stress condi-tions might affect the duration of the cell cycle that leads to cellduplication. In our experiments the duplication times rangedfrom 2.4 to 3.8 h, measured by optical density of the cultures,depending on the growth phase, on the strain and/or on the culti-vation conditions: flask, bioreactor or presence of chemicals. Nev-ertheless we always have found very stable CBB cycles, in terms ofperiod length. Although the intimate nature of the CBB cycles re-mains to be explored and defined, this observation suggests thattheir origin might derive from an intrinsic feature of the cellulardynamics, rather independent on external stimuli. For example,the yeast metabolic cycles are the outcome of synchronous and cy-clic discharging and reloading of the respiration apparatus towhich various cellular activities seems to be functionally coupled[40,41], including the reproductive cycle [10] and, eventually, a

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yeast circadian system [14]. Yeast metabolic cycles are observed incontinuous cultivations where respiration is operating. In our cul-tivation conditions, other cyclic and well rooted cellular dynamicscould be pointed out, like the CBB cycle. Similarly to what has beenproposed for the metabolic cycles, CBB cycles could constitute thebuilding blocks of less fundamental, though essential, cellular ac-tivities, such as the reproductive cycle.

As stated above, cell population synchrony requires individualcells to communicate by the transmission of signals through themedium. Cell contact, e.g. in the case of dense cultures, mightalso contribute to this process. However, in our experimental con-ditions, no perturbation of cyclic parameters was observed whencell density increased. Instead, the onset of CBB cycles was ob-served when cells transited from the lag phase into the growthphase. This suggests that cell internal clock and cross-talk be-tween cells and the environment leading to synchronicity arebased on the cellular metabolism correlated to population growth.The rate of communicating through the medium has to be muchhigher than the cellular cycle period in order to ensure the stabil-ity and synchrony of CBB cycles independently on cell density.This is a condition that can be easily obtained if the messengermolecules are highly diffusible molecules, cyclically producedand consumed by actively growing cells.

In conclusion, our study suggests that a proliferating yeastbatch culture is an ensemble of communicating and synchronizedindividuals which collectively coordinate their life activities in acyclic, basic and pervasive fashion. This behavior results in medi-um modifications that, in their turn, are collectively sensed bythe cells and regulate the cellular activities, thus completing andreiterating the cycle.

Supplementary materials related to this article can be foundonline at doi:10.1016/j.yexcr.2011.09.007.

Acknowledgments

This work was supported by Istituto Pasteur-Fondazione Cenci-Bolognetti, by MIUR-Cofin (200651483 and 20075HF7A9), by theExcellence Centre of Biologia e Medicina Molecolari (BEMM) ofSapienzaUniversity of Rome andbyAteneo della Scienza e della Tec-nologia (AST) of Sapienza University of Rome. Authors are gratefultoMs. Donata Carbone for excellent help in revising themanuscript.

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