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

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Research Article

Synchronous protein cycling in batch cultures of the yeast Saccharomyces cerevisiae at log growth phase Gabriele Romagnolia, 1 , Enrico Cundarib , Rodolfo Negria, c , Marco Crescenzid , Lorenzo Farinae , Alessandro Giuliani f, 2 , Michele M. Bianchia, c,⁎, 2 a

Dept. of Biology and Biotechnology ‘Charles Darwin’, Sapienza Università di Roma, Rome, Italy IBPM, Consiglio Nazionale della Ricerca, Rome, Italy c Istituto Pasteur Fondazione Cenci-Bolognetti, Sapienza Università di Roma, Rome, Italy d Dept. of Cell Biology and Neurosciences, Istituto Superiore di Sanità, Rome, Italy e Dept. of Computer and System Sciences “A. Ruberti”, Sapienza Università di Roma, Rome, Italy f Dept. of Environment and Health, Istituto Superiore di Sanità, Rome, Italy b

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

A B S T R A C T

Article Chronology:

The assumption that cells are temporally organized systems, i.e. showing relevant dynamics of

Received 22 June 2011

their state variables such as gene expression or protein and metabolite concentration, while

Revised version received

tacitly given for granted at the molecular level, is not explicitly taken into account when

9 September 2011

interpreting biological experimental data. This conundrum stems from the (undemonstrated)

Accepted 12 September 2011

assumption that a cell culture, the actual object of biological experimentation, is a population of

Available online 24 September 2011

billions of independent oscillators (cells) randomly experiencing different phases of their cycles and thus not producing relevant coordinated dynamics at the population level. Moreover the

Keywords:

fact of considering reproductive cycle as by far the most important cyclic process in a cell

Cell communication

resulted in lower attention given to other rhythmic processes. Here we demonstrate that

Expression

growing yeast cells show a very repeatable and robust cyclic variation of the concentration of

Entrainment

proteins with different cellular functions. We also report experimental evidence that the

Population

mechanism governing this basic oscillator and the cellular entrainment is resistant to external

Rhythmic process

chemical constraints. Finally, cell growth is accompanied by cyclic dynamics of medium pH.

pH

These cycles are observed in batch cultures, different from the usual continuous cultures in which 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.

⁎ Corresponding author at: Dept. of Biology and Biotechnology ‘Charles Darwin’, Sapienza Università di Roma, p.le Aldo Moro 5, 00185 Rome, Italy. Fax: + 39 0649912351. E-mail addresses: [email protected] (G. Romagnoli), [email protected] (E. Cundari), [email protected] (R. Negri), [email protected] (M. Crescenzi), [email protected] (L. Farina), [email protected] (A. Giuliani), [email protected] (M.M. Bianchi). Abbreviations: SVD, Singular Value Decomposition; MUSIC, Multiple Signal Classification. 1 Present address: Kluyver Laboratory of Biotechnology, Delft University of Technology, Julianalaan 67, NL-2628 BC Delft, The Netherlands. 2 These authors contributed equally to this work. 0014-4827/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.yexcr.2011.09.007

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Introduction Cycles are intrinsic to nature. The most evident biological cycle is the circadian cycle entrained by the day/night alternation. Many different organisms follow the circadian cycle but biological cycles with longer or shorter periods also exist, or co-exist in the same organism. The circadian cycle can be considered as an adaptation to environmental changes. Some cycles, instead, are clearly inherent to a specific function: heart beat, for example, ensures oxygen supply to tissues and its frequency and intensity depend on oxygen requirement. At the cell population level, the connection of a number of cyclic activities with a specific function is not always self-evident, although the involvement of metabolic changes in the establishment of such cycles has been often ascertained [1]. Cycles emerging at the cell population level are a consequence of the close interconnection among the various molecular constituents (genes, proteins, metabolites) as well as of the establishment of direct or mediated cell-to-cell communication [2]. The contribution of intrinsic cellular periodicities and environmental constraints in the establishment of such cyclic behaviors is still a matter of debate. Many cyclic activities in different organisms are governed by clocks or oscillators that have been studied and characterized in detail. The presence of a functional clock in a cell is sufficient to ensure a cyclic activity and, possibly, cyclic outputs. The analysis at single cell level [3,4] confirms this statement. In complex organisms, which are composed of multicellular tissues and organs, the rhythm could be given by a master metronome: in mammals, for example, the functioning of peripheral organs is temporally coordinated by the pacemaker activity of the suprachiasmic nucleus [5]. In single-cell organisms, the study of clocks is apparently simplified by the absence of a hierarchical structure. Cyclic behaviors are investigated by collecting samples of a large ensemble of cells at regular time points and observing if a relevant dynamics (i.e. one or more parameters largely different from a random behavior in time) does appear. A cell population synchronizes following a signal or some form of communication or ‘common sensing’ that makes cells non independent of each other. The establishment of spatial correlations among the cells ending up into temporal correlations, detectable as time-dependent coordinated activities, contradicts the ergodic hypothesis according to which cell cultures are ensembles of completely independent individuals. In non-hierarchical systems, like homogeneous and isotropic cell cultures, the entraining signal is produced by equipotent neighboring cells. In this case a cyclic behavior of the whole culture is detectable if also the signal is produced in a pulsating fashion by the whole population. Flat signaling will not maintain the entrainment of the cellular oscillators and the synchronized population will soon become an ergodic system in the dimension of time. Signals known to induce collective changes in yeast cell populations are the pheromone response, that can synchronize cell reproductive cycle; the diauxic shift, that induces the metabolic transition from fermentation to respiration in batch cultures or the accumulation of aromatic alcohols, that produces quorum sensing changes. These signals are the result of a developmental process of the cell or of the culture as a whole [2]. However, the diauxic shift and quorum sensing are not cyclic. A proper cellular oscillator is probably the mechanism that produces whole-cell cyclic changes in yeast cells continuously cultured at high cell

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density [6,7]. In such conditions, cells collectively and synchronously move from a reductive to an oxidative physiological status, with coordinated changes of the transcriptome and metabolite composition [8,9]. Important basic cellular activities, such as DNA duplication, electron transport and ATP synthesis [7,10] are carried out in parallel and in cyclic fashion. Similar cycles, deriving from respiratory oscillations, are also observed in batch cultures on trehalose as carbon source [11]. The yeast glycolytic oscillation [12] is also a collective behavior in defined conditions [13]. A metabolic circadian cycle can be induced in yeast by cyclically changing the growth temperature in continuous cultures [14]. As mentioned above, yeast growing cells in batch cultures are commonly considered as an ensemble of dividing individuals, progressively increasing in number with time but equally distributed in each phase of the duplication process (ergodic system). As a consequence, cellular and molecular properties of culture samples (a large number of cells collected at different time points) are supposed to be constant, with no relevant collective temporal structure, or eventually to change linearly in short time windows. In a previous work [15] we proved that this was not the case for cell cultures. We demonstrated that cyclic transcriptomic changes could be detected in yeast—but also in mammalian—cell cultures. In the present work we tried to answer to the immediate following question: does protein content varies cyclically and synchronously in the cell population as transcripts do? This was not a trivial question. For example, a consistent fraction of hepatic circadian proteins are not correlated to cycling gene transcription [16]. We focused our work on low cell density yeast batch cultures and we showed here that, in this condition, yeast cells exhibited collective and cyclic protein content variation. We also demonstrated that medium pH varied cyclically but medium buffering had no effect on protein concentration cycles. The evidenced cycles were demonstrated to be fairly invariant in frequency across different 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 represents an individual S. cerevisiae strain containing an open reading frame with a C-terminal Aequorea victoria GFP (S65T) fusion tag. The genotype of the parent haploid S. cerevisiae strain (ATCC 201388) is: MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0. The genes tested were: RPL11 (YPR102C), CIT2 (YCR005C), RAP1 (YNL216W) and MGS1 (YNL218W). Rpl11 is a component of the large ribosomal subunit and its promoter shows the typical structure and transcriptional regulation of RP genes [17]. Cit2 is a citrate synthase, a respiratory enzyme, and it is preferentially expressed at diauxic shift or at stationary phase [18]. Rap1 is a DNA binding protein involved in activation or repression of gene transcription and in telomeric functions [19], and Mgs1 is a protein involved in DNA metabolism and genome stability [20]. The wild type yeast strain was BY4741 (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0) from EUROSCARF. Standard medium (YPD) was 1% Yeast Extract (BectonDickinson), 2% Peptone (Becton-Dickinson) and 2% glucose. Cycloheximide was from Sigma-Aldrich (C7698) and sodium metavanadate from Fluka (72060).

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Cultivation in flask Precultures were prepared by growing yeast strains in YPD to the late log phase (about 1 × 107 cell/ml) and used to inoculate a 5 l flask containing 2.5 l of YPD. Precultures were diluted about 100 fold in the flask (to about 1 × 105 cell/ml) and cells were allowed to recover growth in the flask for at least 4 h at 26 °C before sampling. 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 bottom side of the flask at 5 min intervals. The piping volume was discarded at each culture withdrawal and cell samples were analyzed immediately for fluorescence.

Cultivation in bioreactor We used a BiostatQ B-Braun bioreactor endowed with four 1 l vessels, each containing 600 ml of YPD and inoculated with late log precultures 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 or more, measured as OD600 increase, was left before sampling. Sampling was performed at 5 min intervals when OD600 ranged between 0.4 and 1.0 and the piping volumes were discarded at each withdrawal. Since the distance between the bioreactor plant and the flow-cytometer could not allow immediate analysis of samples, cells were collected by centrifugation (microfuge, at 10 g for 2 min) and frozen on dry ice/ethanol mixture. Frozen cells were maintained at −70 °C. Groups of ten samples were simultaneously thawed by the addition of PBS buffer and fluorescence of each sample was immediately measured without intervals, but waiting just for the technical time of the instrument for reading. This procedure excluded the possibility that florescence outcomes were artifacts of sample reading at regular intervals 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 account for cell size and granularity, respectively and green fluorescence (FL1) to determine GFP-associated fluorescence emission (wave length: 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 acquisition efficiency. Acquired data were analyzed using the WinMDI software by Joe Trotter, available at http://facs.scripps.edu.

Northern protocol Culture samples (1.5 ml) for RNA extraction were harvested every 5 min, immediately centrifuged (3 min at 14,000 rpm, bench microfuge) and collected cells were frozen by immersion in ethanol/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 and MOPS buffer (0.56% MOPS pH7, 5 mM sodium 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 and Schuell, Dassel, Germany) following the Northern blotting procedure. The filter was hybridized with 32P-labeled RPL11 DNA probe, obtained from PCR amplification (forward: ccctatgcgtgatttgaaga; reverse: ggtacccttacatctctttc). The hybridized filter was exposed to Amersham Biosciences Storage Phosphor Screen and the image was subsequently acquired by Typhoon 9200 phosphoimager for signal quantification.

Data analysis The temporal structure of cell cultures was investigated by means of SVD (Singular Value Decomposition) and the power spectrum density estimated using the MUSIC (Multiple Signal Classification) protocol. SVD was applied to an 8 dimensional embedding matrix of the original discrete time series of the concentration of the studied protein in terms of GFP-fluorescence or of the mRNA hybridization signals. The pattern of correlations among the columns of the EM (autocorrelation matrix) will convey the entire information of the dynamics of the studied system [22]. The choice of an eight dimensional embedding was demonstrated to be optimal for maintaining a sufficient dimensionality for the derived attractors while not being too detrimental for the short length of the series [23]. In the present case, the SVD procedure was particularly appropriate given the presence of an overwhelming linear trend due to the non-stationary character of batch conditions which ends up into a linear ‘size’ component (pc1). SVD decomposed the signal into mutually orthogonal components, and consequently it allowed to filter out the trend in a natural, unsupervised way and to keep the oscillatory character of the dynamics into minor components [24,25]. The determination of periodic components in time series is a long standing problem of wide interests in applications and, in fact, researchers from diverse field have contributed to this effort. The power spectral density (PSD) function describes the distribution of power with frequency and, ideally, it displays a sharp peak for any cyclical component present in the data. For a signal x[n] of infinite length the PSD can be expressed as: ( Pðf Þ ¼ limM→∞ E



2 )
1

M −j2πfn
x ½ n e ∑
: 2M þ 1
n¼−M

In the practical case of a time series of finite length N, the estimation of the PSD has traditionally been based on Fourier transform. The recent interest in alternative methods is motivated by the important and difficult case of high frequency resolution using short data records, as in our case. There have been several approaches to such problems including the so-called maximum likelihood (ML) method of Capon (1969) and Burg's (1967) maximum entropy (ME) method (reviewed in [26]). Although often successful and widely used, these methods have certain fundamental limitations (especially bias and sensitivity in parameter estimates), largely because they use an incorrect model (e.g., AR rather than special ARMA) of the measurements. Therefore, a crucial point is the use of the correct data model. In our study, we wanted to characterize the presence of oscillatory modes in data, so that an appropriate data model is to consider sinusoidal components embedded in noise. The most powerful method available is

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certainly the MUltiple SIgnal Classification (MUSIC) algorithm, proposed by Schmidt in 1986 [27]. MUSIC estimates the frequency content of a signal using an eigenspace method. This method assumes that a signal x[n] consists of p sinusoidal components (complex exponentials) of frequency fi in the presence of Gaussian white noise z[n]: p

x½n ¼ ∑ Ai ej2πfi nþφi þ z½n i¼1

where, for real sinusoids p must be chosen to be twice the number of real sinusoids n = 0, 1, …, N− 1. The frequency estimation function for MUSIC is PMUS ðf Þ ¼

1 pþq

∑ jeH ðf Þvi j

i¼pþ1

where  eðf Þ ¼ 1 ej2πf

ej2π2f

… ej2πðN−1Þf

T

p is the dimension of the signal subspace (twice the number of oscillatory components), q is the dimension of the noise subspace and the vi's are the eigenvectors of the signal correlation matrix ordered according to the decreasing magnitude of the corresponding eigenvalues. An exhaustive description of the analytical procedures is reported in SM1.

Results Selection of proteins In a previous work [15] we showed that, in populations of cells cultured in standard flask conditions (yeast) or plates (mammalian cells), mRNA accumulation of a large number of genes is cyclic and synchronized. Taking this result into account, we decided to investigate i) whether protein accumulation follows the same temporal pattern as mRNA and ii) whether a correlation between medium or growth conditions and biological synchrony can be established. Yeast was chosen to address these questions, because of the availability of a collection of yeast strains, each one with the GFP reporter gene fused in frame to the coding region of a different gene. The average content of an individual protein could be evaluated by measuring the fluorescence of GFP associated to that protein in cell population samples. Four proteins (Rpl11, Cit2, Rap1 and Mgs1) with different cellular functions and different expression patterns were selected (see Materials and methods for detail).

RPL11 mRNA determination and validation of data analysis protocols

The first experiment aimed at confirming, in our growth conditions, the cyclic nature of RPL11 mRNA transcription and to test the robustness of our analytical protocols (SVD and MUSIC) on dataset. The wild type yeast culture growing in the aerated and stirred flask (OD = 0.35 to 0.7) was sampled at 5 min intervals for 120 min and RNA prepared as described in Materials and methods. Fig. 1A shows Northern blot signals of RNA preparations

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after hybridization with RPL11 probe. Although the content of most individual mRNA varies cyclically in a population of cells, the total amount of RNAs might reasonably be assumed as constant, due to the buffering capacity of ribosomal RNA which is very stable and accounts for 80% of total RNA. Signal intensities were then acquired and normalized by total RNA loading: raw data are reported in Supplementary material SM2, where all the raw experimental data—hybridization signals, fluorescence values and pH values—of our work are presented in table and graph formats. SVD results are reported in Fig. 1B. RPL11 mRNA expression showed a trend-like first component (pc1), explaining 54% of RPL11 mRNA variance, and two cyclic components that could account 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 an expected component feature. Period of cycles and robustness of periodicity were determined by computing a P value by means of the Multiple Signal Classification (MUSIC) algorithm. Although our cultures were not intentionally synchronized, cycles possibly correlated to the cell division cycle (90–120 min) were intentionally excluded from our analysis. A graphical representation of power spectrum estimate of the RPL11 mRNA time profile using the MUSIC algorithm is reported in Fig. 1C: the first peak on the left corresponded to the ‘low’ frequency signal related to the presence of a trend component, whereas the appearance of a second peak on the right was the result of a ‘high’ frequency signal corresponding to the presence of the oscillatory behavior. In summary, the analysis of RPL11 mRNA time profile revealed an extremelysignificant period (P value = 5 · 10 −14) of 26.4 min (2π/26.4 = 0.379 rad/min using normalized angular frequency units). For simplicity, we reported the graphical output of the MUSIC analysis only for Rpl11 mRNA, while for all other experiments just the numerical outputs of MUSIC analysis were reported (Table 1).

Protein cycles and growth onset are correlated events Experiments were carried out to determine Rpl11 dynamics in flask cultured cells growing exponentially at different points of the batch cultivation. The first culture was started by inoculating cells at the estimated cell density of 1–5 × 105 cell/ml (OD below 0.01). At the end of the lag phase, samples of approximately 2 ml were harvested at 5 min intervals and submitted to FACS analysis. Start of culture growth was accompanied by the progressive accumulation of proliferating cells [28,29] characterized by smaller size and lower fluorescence intensity emission, as compared to the large resting cell, resulting in reduction of the average fluorescence (Figs. 2A and B). SVD analysis of the fluorescence time course revealed the presence of three predominant factors (Figs. 2B and C). The first component (Fig. 2B) was strongly correlated (Pearson r = 0.90) with the percentage of small cells in the culture, suggesting that the most relevant driving force of Rpl11 concentration variability in the considered time window was the changing proportion in time of small-sized dividing cells. The transition phase in which small cells started to accumulate was identified around 60 min. This corresponded to the presence of major peaks in the second most important component (pc2, Fig. 2C). In this experiment, the cyclic nature of Rpl11 protein expression was revealed by the third component that displayed a

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Fig. 1 – Analysis of RPL11 transcription. In Fig. 1A the RPL11 hybridization signals after Northern blotting analysis are shown. In Fig. 1B the SVD analysis of the hybridization signals is reported. The principal components 1, 2 and 3 are represented by triangles, black dots and white dots, respectively. In Fig. 1C MUSIC 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 corresponding to the presence of an oscillatory behavior (components 2 and 3). The height of the peaks is proportional to the relative proportion of variance explained by the trend and oscillatory modes.

clear temporal order. These results suggested a correlation between growth onset (increase of small cell population and pc1 decrease) and the appearance of steady Rpl11 cycles (amplitude increase of pc2 and pc3 oscillations).

Rpl11 protein cycles during exponential growth Rpl11 concentration cycles were determined in repeated and independent batch cultures at OD 0.05, OD 0.3 and OD 1 (approximately from 106 to 5 × 107 cell per ml) with substantially identical results. We report here in detail SVD analysis of the experiment at OD 0.05. The average fluorescence could be separated into a linear trend (not shown) and the cyclic components pc2 and pc3, having the usual reciprocal sine/cosine coupling (Fig. 3). Pc2

and pc3 globally explained around 1.5% of total variance. Here again, the cell population was composed of cells of different sizes that could be easily discriminated into small and large cell sub-populations. Fig. 4 shows the cyclic components of SVD analysis on fluorescence data of small and large cells separately. Components 2 and 3 of both cell types were cyclic and phase shifted, although pc2 and pc3 of large cells appeared less harmonic. Pc2 and pc3 accounted for 3% and 2.6% of explained variance (small cells) and 3.3% and 2.4% of explained variance (large cells), respectively. The application of the MUSIC algorithm to the fluorescence data allowed to exactly evaluate the periodicity of protein concentration changes in terms of frequency and probability. The entire Rpl11 data set was analyzed and the results are reported in Table 1. A reproducible periodicity ranging from 17 to 20 min

Table 1 – MUSIC analysis of GFP fluorescence. Protein

Cultivation system

Rpl11

Flask

Rpl11

Bioreactor

Cit2 Mgs1 Rap1

Flask Bioreactor Bioreactor

Cell population

Special conditions

Period (minutes)

P value

Whole Large Small Whole Whole Whole Whole

– – – Hypoxic 100 mM KP – 50 mM KP 40 mg/l CHX 1 mM MEV – – – 100 mM KP*

19.8 ± 3.1 a 17 ± 3.9 a 20.1 ± 2.6 a 21.2 21.3 18.3 ± 5.1 a 22.7 17 31.6 12.1 26.1 29.1 29.1

1b 4 · 10−3 ÷ 1 b 1.6 · 10−3 ÷ 7.9 · 10−1 b 1 1 1.6 · 10−2 ÷ 2 · 10−1 b 2.5 · 10−2 3.1 · 10−2 2 · 10−3 8 · 10−1 1.6 · 10−1 1 · 10−2 1

Whole Whole Whole

Special conditions: KP = potassium phosphate pH7 or pH5.8, CHX = cycloheximide, MEV = sodium metavanadate,– = standard YPD medium without additions. a Average of 3 to 4 independent experiments with standard deviation (±). b Range of variation in the repeated and independent experiments.

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Fig. 4 – SVD analysis of small and large cells. In Fig. 4 the separated SVD analysis of FACS data of small (Fig. 4A) and large cells (Fig. 4B) are reported. The oscillating components pc2 and pc3 are indicated by black dots and white dots, respectively.

was found, both for the whole cell population and the large and small cells subsets. In some cases highly significant P-values were obtained. Fig. 2 – SVD analysis of FACS data of Rpl11::GFP at growth onset. In Fig. 2A the relative fluorescence vs the cell size at three different time points is reported: 20, 100 and 200 min from the beginning of fluorescence measurements. The growing population of small proliferating cells is circled. In Fig. 2B the principal component 1 (pc1, triangles) and the relative fraction of small cells (white squares) vs time are reported. The pc1 scores are strongly negatively correlated with the fraction of small cells. In Fig. 2C the principal components pc2 and pc3 (black and white dots, respectively) are reported which account for the cyclic shape of Rpl11 concentration vs time at the time point of growth onset, around 60 min from the beginning of the fluorescence measurement. Time is expressed in minutes.

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

Medium buffering and aeration effects After having demonstrated the existence of Rpl11 concentration cycles, we focused on the investigation of the possible effect of microenvironment conditions on the above mentioned cycles. Well described cycles in yeast are correlated to respiratory activities. These cycles are remarkably stable and can last for months in continuous cultures, showing a period length of the same order of magnitude of cycles described in the present article [7]. We therefore measured Rpl11 protein cycles in flask without insufflated air during culture growth. Also in this condition a 20 min cycle with coupled pc2 and pc3 components could be detected (Table 1, special conditions: hypoxic), accounting for 4.9% and 2.2% of explained variance, respectively (data not shown). Both subpopulations of small and large cells showed cyclic components (not shown). This experiment suggested that aeration had no effect on the observed periodicity. Successively, we addressed medium ionic strength and/or pH dynamics as possible influential factors on the described cyclic behavior. Cells were incubated in YPD medium buffered with 100 mM potassium phosphate at pH7. We could not find any relevant change in the cyclic pattern of Rpl11 concentration by analyzing the fluorescence data by SVD (not shown) or by MUSIC (results reported in Table 1, special conditions: 100 mM KP). A similar experiment was performed on cells grown in bioreactor either in YPD or in YPD buffered with 50 mM potassium phosphate pH7. Fluorescence was measured after collection of all samples (see Materials and methods section for detail). SVD analysis of the fluorescence (whole cells) is reported in Fig. 5. The shorter time window allowed to reduce or eliminate the linear trend. In

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Fig. 5 – SVD analysis of Rpl11 from bioreactor cultivation. In Figs. 5A and B data from cells cultivated in a bioreactor on YPD and buffered-YPD media are reported, respectively. The cyclic principal components pc1, pc2 and pc3 are indicated by triangles, black dots and white dots, respectively, in both panels.

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

Cit2, Rap1 and Mgs1 The cyclic variation of concentration of Cit2, Rap1 and Mgs1 was tested. We measured Cit2::GFP fusion protein expression in cells growing 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 are reported.

pc3, with sine/cosine pairing, Fig. 6) responsible, in this case, of larger fractions of variance (about 5% each, for a total of 10% of explained variance of the cyclic component) were detected. Period length (Table 1) was of 12.1 min. The cyclic factors independently computed for large and small cell populations (not shown) strongly correlated to each other in time (Pearson r = 0.90). Interestingly, the amplitude of the cyclic components dramatically changed in time (Fig. 6), while the period length remained steady. This behavior pointed to the robustness of the detected cycles that could be modulated in amplitude but remained unchanged in their characteristic frequency. Amplitude modulation might be the result of a peculiar regulation of expression of Cit2, which is a growth-phase dependent protein, indicating the interference of additional effectors to those governing the cycles of Cit2 protein level in the cells. The Mgs1 and Rap1 fusion strains with GFP reporter were both grown in bioreactor in standard YPD medium. Rap1::GFP strain was also cultured in YPD buffered with 100 mM potassium phosphate. Both Rap1 and Mgs1 showed a behavior very similar to the other analyzed proteins with cycles of comparable period, independent on buffering conditions (Table 1).

Effect of cycloheximide and metavanadate on Rpl11 protein cycles

The protein level within a cell depends primarily on the balance between translation and polypeptide degradation. In order to determine whether the described protein cycles derived from de novo cyclic synthesis rather than degradation, we measured Rpl11 variations in the presence of 40 μg/ml of cycloheximide, a compound known to block protein synthesis [32]. In preliminary tests we added cycloheximide to yeast cells exponentially growing in bioreactor and we measured fluorescence before and after addition. Fluorescence data (SM2) showed a clear cyclic behavior associated to a strong linear trend that changed in correspondence to cycloheximide addition. The cyclic fluorescence had a significant periodicity of 17 min (Table 1, special conditions: 40 mg/l CHX). SVD analysis (not shown) revealed the presence of a new component pc2 that was probably originated by the perturbation caused by drug addition. We therefore analyzed fluorescence data from two parallel independent bioreactor cultures with and without cycloheximide. SVD analysis, reported in Figs. 7A and B, indicated opposite trends of the first principal components, as it was expected from the blockage of protein synthesis and a general reduction of protein concentration in the cells. The cyclic components pc2 and pc3, on the contrary, were still present and substantially unchanged in the culture containing cycloheximide (Fig. 7B). This result suggested that protein cycling was generated by a mechanism independent on bulk protein synthesis. Cellular pH is maintained stable by proton trafficking through the plasma membrane in response to cytoplasmic production by metabolism of organic acids or to external medium acidification. In addition, GFP fluorescence is sensitive to changes of environmental pH. Therefore we tested the effect of sodium metavanadate, an inhibitor of the main ATPase proton pump Pma1 [33]. No significant or abrupt changes of fluorescence could be detected after the addition of metavanadate (not shown), indicating that pH homeostasis was still

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Table 2 – MUSIC analysis of medium pH in bioreactor. Special conditions – 50 mM KP 40 mg/l CHX 1 mM MEV 100 mM KP*

Period (minutes) a

34.7 ± 4.9 39 ± 10.5 a 18.4 33.7 34.6

P value −14

5·10 ÷ 2.5 · 10−3, b 5 · 10−14 6.3 · 10−3 1 · 10−4 1 · 10−4

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

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

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

Fig. 8 – pH profiles in bioreactor. Fig. 8 reports the comparison of pH profiles in the time windows of the exponential growth phase analyzed in our experiments. In Fig. 8A are reported pH outputs of a non-inoculated vessel containing sterile YPD medium (upper profile) and a standard YPD growth profile (lower profile). Fig. 8B shows the effect on pH of the addition of potassium phosphate pH 5.8 (KP, final concentration 100 mM) in exponentially growing yeast cultures on YPD medium. The time point of addition is indicated by an arrow. Time expressed in hours.

pH analysis

In parallel with fluorescence measurements, we monitored medium pH in bioreactor experiments with in situ electrodes. pH variation profiles of representative experiments are reported in Fig. 8. In standard YPD medium (Fig. 8A) pH changed in a monocline fashion during the exponential growth. The addition of potassium phosphate (Fig. 8B) produced an abrupt pH shift without an evident effect on the cellular cycles of concentration of the studied proteins (Rpl11 and Rap1: Table 1). The addition of cycloheximide and sodium metavanadate produced only small and transient changes in the pH profile (not shown) without effect on protein cycles. The detailed analysis of the pH meter output vs time also revealed cyclic components. An example of SVD analysis—buffered medium culture and standard medium culture—is reported in SM2. Results of MUSIC analysis are reported in Table 2. The presence of growing yeast resulted in the generation of an extremely significant and reproducible pH cycle of about 35 min in the medium. In all tested medium conditions, pH cycles were present and strongly significant, thus suggesting that they resulted from a coordinated cellular activity, insensitive to the tested conditions. Interestingly, the period of pH cycles 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 to surrounding environmental pH [35]. However, we excluded that the detected protein cycles based on GFP fluorescence within the cell were merely the chemical effect of a cycling medium pH on a constant cellular level of protein. As a matter of fact, cellular homeostasis is able to contrast medium pH variations much larger [36] than the pH cycles we measured. Furthermore, GFP fluorescence pattern (average level and cycles) remained stable even when subjected to the macroscopic pH variations due to the addition of chemicals (Fig. 8). Finally, we could not observe an effect on the amplitude of fluorescence cycles after the addition of metavanadate, a compound that might specifically affect cellular pH homeostasis.

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

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assume different profiles in function of time: flat, if the conditions are stationary; increasing or decreasing, if conditions are varying progressively; changing the sign/value of variation, if the parameter is subjected to phase variation. These profiles are the (macroscopic) result of measures when (microscopic) cells are randomly distributed around an average state. In all the experiments of the present work, we actually have met such situations, which are described by the trend components, often coincident with the major components of SVD. Cyclic functions of time of cellular parameters also occur in individual cells, but require collective cell synchronization to be detected from the cell population, as is routinely performed in cell cycle works, for example. In this work we report evidence of spontaneous connection between cyclic activities and collective behaviors in growing yeast cultures. We have previously demonstrated [15] that mRNA abundance of single genes varies cyclically in yeast and mammalian cell populations growing in standard laboratory conditions. This behavior is not confined to a particular gene product but has a genome-wide scale and contradicts to the common belief that growing cells are ensembles of independent individuals. In fact, the synchronous expression of a single gene in a cell population deprived of external signals for entrainment, requires the existence of cell-to-cell communication to enable the synchronization process to occur. We show here that the yeast gene RPL11 in a batch and low cell density culture has a cyclic and synchronous expression. A consistent fraction (about one third) of RPL11 mRNA in the cell population is subject to cyclic and collective variation. Since mRNA level depends on synthesis and degradation, cellular cross-talk might govern the cyclic character of transcription or mRNA stability, or both. This basic oscillation of gene expression will not exclude variations of mRNA synthesis and degradation linked to other additive mechanisms of cell culture dynamics, such as cell cycle progression, phase transitions and so on. Due to the strict relationship between mRNA and protein synthesis, the question arises as to whether the cyclic concentration of mRNA results in a cyclic concentration of its product (proteins). Ideally this question should be extended to the entire proteome. In practice, we confined our analysis to four proteins having different functions: the transcription factor Rap1, the ribosomal protein Rpl11, the mitochondrial metabolic protein Cit2 and Mgs1, a protein required for chromosome maintenance. The average cellular content of each protein was determined by measuring the fluorescence of the fused reporter protein GFP. Our present results show that, despite the different functions of the tested proteins, the culture growth phase or the culture medium or conditions, these proteins have a cyclic temporal profile of concentration, with a periodicity comparable to that of mRNA. The variance of protein concentration showing a cyclic property is not negligible and ranges from few to 50%. Our specific analysis (MUSIC) of data excludes that these oscillations, even when small, are due to noise background, random experimental errors or biased by measuring devices. These oscillations will be hereafter referred to as CBB (Collective Basic Batch) cycles. Protein CBB cycles are also present in subpopulations of cells with different physiological states, suggesting the existence of a very general mechanism of entrainment. The tested proteins showed rather constant amplitude of cyclic expression except for Cit2, which showed pulses of amplitude modulation. This could be due to phase dependent induction of Cit2 gene expression, consistently with the physiological function of this protein. This is completely in line with the system dynamics

theory: while the typical frequency of an oscillator is an intrinsic property of the system, the amplitude of oscillations is a tunable parameter in response to external stimuli. We can infer that the oscillation frequency of CBB cycles is a basic characteristic of the whole cell (omic level) and of the entire culture (population level), while the amplitude of the oscillation, which is still at the population level, depends on the function of the single gene (or functionally correlated genes) and can be rapidly adjusted in response to environmental contingencies and changing needs of the system. It is noteworthy that cellular cycles might be favorable for adaptation. In fact, a global cellular oscillating system, being never off, is optimal to increase the responsiveness and the fitness of the cell population to external stimuli, in comparison with a steady state system. Different culture conditions and chemical treatments were tested aiming at disturbing CBB cycles in order to obtain information about the underlying cellular mechanisms or the synchronizing cell-to-cell messenger. However, CBB cycles proved to be resistant to all tested conditions. Oxygen availability independence suggests that CBB cycles might not be connected to redox cycles ([8,9,37] for a recent review). When protein synthesis was blocked by cycloheximide, the cellular protein concentration progressively decreased, but still in a cyclic fashion, suggesting that mRNA translation is not the (unique) mechanism involved in CBB protein cycles. We can therefore hypothesize that also protein folding and degradation might occur cyclically or the cyclic mechanism of protein synthesis might escape cycloheximide inhibition. CBB cycles were not affected by medium buffering and sharp pH transitions. At the same time the expected modification of medium pH due to yeast cell growth followed a cyclic pattern. An obvious explanation of this phenomenon could be found in the cyclic metabolism and cyclic production and consumption of acidic metabolites intrinsic to the occurrence of CBB cycles at a proteomic level. Interestingly, the acidic metabolite sulphidric acid has been proposed to be, together with acetaldehyde, the entrainer messenger of the yeast metabolic cycle [38,39]. If the pH cycle was correlated to cell entrainment, it would be interesting to understand how this message is transduced from the medium into the cell. One possibility could be the involvement of proton pumps, even if this would not necessarily imply a cyclic variation of cytosolic pH. The perturbation of proton trafficking by metavanadate did not suppress CBB cycles but increased (doubled) its periodicity and tuned it to that of medium pH cycles suggesting that CBB cycles might be modulated in frequency by affecting the dynamics of the cell–medium interface. It is known that environmental perturbations and stress conditions might affect the duration of the cell cycle that leads to cell duplication. In our experiments the duplication times ranged from 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 cultivation conditions: flask, bioreactor or presence of chemicals. Nevertheless we always have found very stable CBB cycles, in terms of period length. Although the intimate nature of the CBB cycles remains to be explored and defined, this observation suggests that their origin might derive from an intrinsic feature of the cellular dynamics, rather independent on external stimuli. For example, the yeast metabolic cycles are the outcome of synchronous and cyclic discharging and reloading of the respiration apparatus to which 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 in continuous cultivations where respiration is operating. In our cultivation conditions, other cyclic and well rooted cellular dynamics could be pointed out, like the CBB cycle. Similarly to what has been proposed for the metabolic cycles, CBB cycles could constitute the building blocks of less fundamental, though essential, cellular activities, such as the reproductive cycle. As stated above, cell population synchrony requires individual cells to communicate by the transmission of signals through the medium. Cell contact, e.g. in the case of dense cultures, might also contribute to this process. However, in our experimental conditions, no perturbation of cyclic parameters was observed when cell density increased. Instead, the onset of CBB cycles was observed when cells transited from the lag phase into the growth phase. This suggests that cell internal clock and cross-talk between cells and the environment leading to synchronicity are based on the cellular metabolism correlated to population growth. The rate of communicating through the medium has to be much higher than the cellular cycle period in order to ensure the stability and synchrony of CBB cycles independently on cell density. This is a condition that can be easily obtained if the messenger molecules are highly diffusible molecules, cyclically produced and consumed by actively growing cells. In conclusion, our study suggests that a proliferating yeast batch culture is an ensemble of communicating and synchronized individuals which collectively coordinate their life activities in a cyclic, basic and pervasive fashion. This behavior results in medium modifications that, in their turn, are collectively sensed by the cells and regulate the cellular activities, thus completing and reiterating the cycle. Supplementary materials related to this article can be found online at doi:10.1016/j.yexcr.2011.09.007.

[6]

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Acknowledgments This work was supported by Istituto Pasteur-Fondazione CenciBolognetti, by MIUR-Cofin (200651483 and 20075HF7A9), by the Excellence Centre of Biologia e Medicina Molecolari (BEMM) of Sapienza University of Rome and by Ateneo della Scienza e della Tecnologia (AST) of Sapienza University of Rome. Authors are grateful to Ms. Donata Carbone for excellent help in revising the manuscript.

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