Differential scaling between g1 protein production and cell size dynamics promotes commitment to the cell division cycle in budding yeast

ABSTRACT In the unicellular eukaryote _Saccharomyces cerevisiae_, Cln3–cyclin-dependent kinase activity enables Start, the irreversible commitment to the cell division cycle. However, the

concentration of Cln3 has been paradoxically considered to remain constant during G1, due to the presumed scaling of its production rate with cell size dynamics. Measuring metabolic and

biosynthetic activity during cell cycle progression in single cells, we found that cells exhibit pulses in their protein production rate. Rather than scaling with cell size dynamics, these

pulses follow the intrinsic metabolic dynamics, peaking around Start. Using a viral-based bicistronic construct and targeted proteomics to measure Cln3 at the single-cell and population

levels, we show that the differential scaling between protein production and cell size leads to a temporal increase in Cln3 concentration, and passage through Start. This differential

scaling causes Start in both daughter and mother cells across growth conditions. Thus, uncoupling between two fundamental physiological parameters drives cell cycle commitment. Access

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TEMPORAL SEGREGATION OF BIOSYNTHETIC PROCESSES IS RESPONSIBLE FOR METABOLIC OSCILLATIONS DURING THE BUDDING YEAST CELL CYCLE Article Open access 27 February 2023 CELL SIZE SETS THE DIAMETER

OF THE BUDDING YEAST CONTRACTILE RING Article Open access 11 June 2020 REGULATION WITH CELL SIZE ENSURES MITOCHONDRIAL DNA HOMEOSTASIS DURING CELL GROWTH Article Open access 07 September

2023 DATA AVAILABILITY Source data are available online for Figs. 1–6 and Extented Data Figs. 1–8. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium

PRIDE81 partner repository with the dataset identifier PXD015327. All other data are available from the authors on reasonable request. CODE AVAILABILITY At

https://github.com/molecular-systems-biology/Litsios-et-al-2019, we provide one CSV file containing raw microscopy data, together with the respective MATLAB file as an example of our

data-processing pipeline (smoothing, rate estimation and so on). These data were used in the construction of Fig. 5c and Extended Data Fig. 4h. The custom-made Python script used for

analysis of the confocal images is also provided. All other MATLAB scripts used for processing are available from the authors on reasonable request. REFERENCES * Johnson, A. & Skotheim,

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47, D442–D450 (2019). Article  CAS  PubMed  Google Scholar  Download references ACKNOWLEDGEMENTS The authors thank B. Tu and A. D. Ortega for critical comments on an early version of the

manuscript, Z. Zhang for advice on microscopy, C. Åberg for discussions on model-based analysis of microscopy data, and the Ida van der Klei laboratory for provision of the pSNA10 plasmid.

Financial support was provided by the EU ITN project ISOLATE (grant agreement 289995). AUTHOR INFORMATION Author notes * Daphne H. E. W. Huberts Present address: Cancer Research UK Cambridge

Institute, University of Cambridge, Cambridge, UK * Georg Hubmann Present address: Department of Biology, Laboratory of Molecular Cell Biology, Institute of Botany and Microbiology, KU

Leuven, Heverlee, Belgium * Georg Hubmann Present address: Center for Microbiology, VIB, Heverlee, Belgium * Marten Exterkate Present address: Molecular Microbiology, Groningen Biomolecular

Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands AUTHORS AND AFFILIATIONS * Molecular Systems Biology, Groningen Biomolecular Sciences and

Biotechnology Institute, University of Groningen, Groningen, the Netherlands Athanasios Litsios, Daphne H. E. W. Huberts, Hanna M. Terpstra, Paolo Guerra, Alexandros Papagiannakis, Mattia

Rovetta, Johan Hekelaar, Georg Hubmann, Marten Exterkate, Andreas Milias-Argeitis & Matthias Heinemann * Proteomics Core Facility, Biozentrum, University of Basel, Basel, Switzerland

Alexander Schmidt & Katarzyna Buczak Authors * Athanasios Litsios View author publications You can also search for this author inPubMed Google Scholar * Daphne H. E. W. Huberts View

author publications You can also search for this author inPubMed Google Scholar * Hanna M. Terpstra View author publications You can also search for this author inPubMed Google Scholar *

Paolo Guerra View author publications You can also search for this author inPubMed Google Scholar * Alexander Schmidt View author publications You can also search for this author inPubMed 

Google Scholar * Katarzyna Buczak View author publications You can also search for this author inPubMed Google Scholar * Alexandros Papagiannakis View author publications You can also search

for this author inPubMed Google Scholar * Mattia Rovetta View author publications You can also search for this author inPubMed Google Scholar * Johan Hekelaar View author publications You

can also search for this author inPubMed Google Scholar * Georg Hubmann View author publications You can also search for this author inPubMed Google Scholar * Marten Exterkate View author

publications You can also search for this author inPubMed Google Scholar * Andreas Milias-Argeitis View author publications You can also search for this author inPubMed Google Scholar *

Matthias Heinemann View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS A.L. and M.H. conceived the study. A.L., M.H. and A.M.-A. designed the

study. A.L. constructed the strains, performed the experiments and analysed the data. D.H.E.W.H. performed the preliminary experiments and contributed conceptually. H.M.T. participated in

strain construction and culture sampling for targeted proteomics. A.M.-A. performed the smoothing and derivative estimation for the single-cell time-lapse data. P.G. performed and analysed

the verification experiments with confocal microscopy. A.S. and K.B. performed the targeted proteomics and analysed the data. A.P. participated in strain construction and metabolite

measurements during batch cultivation, and performed the preliminary data analysis. M.R. performed the elutriation, and participated in culture sampling for targeted proteomics and

respective data analysis. J.H. prepared the protein samples for mass spectrometry. G.H. performed the model-based analysis of the metabolite data for estimation of cellular physiology. M.E.

participated in strain construction. A.L. and M.H. wrote the manuscript with input from A.M.-A. M.H. and A.M.-A. supervised the study. CORRESPONDING AUTHORS Correspondence to Andreas

Milias-Argeitis or Matthias Heinemann. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains

neutral with regard to jurisdictional claims in published maps and institutional affiliations. EXTENDED DATA EXTENDED DATA FIG. 1 CELL CYCLE ARREST IN LOW FLUX CONDITIONS. (A) Total sfGFP

content of non-dividing cells (n = 10 cells) over time. sfGFP expressed via TEF1 promoter. (B) Left: Whi5 localizes primarily in the nucleus during G1 and shuttles in the cytoplasm during

the Start transition69. Example merged phase-contrast and fluorescent images showing Whi5-mGFP localization in non-dividing and dividing TM6* (middle) and VW100-tet-Hxt1 (right) cells.

Experiments repeated independently 3 times with similar results. (C) Glucose uptake rate assayed via 2-NBDG uptake, versus G1 duration in individual TM6* cells (n = 86 cells, Spearman r:

-0.3784, p-value: 0.0003). (D) Percentage of G1-arrested cells as a function of glucose concentration in the microfluidics device. The number of cells analysed per condition is indicated in

the parentheses. Grey line shows the exponential fit. (E) Hxt1 expression in response to tetracycline in the HXT-null strain carrying an Hxt1 copy under the control of a Tet-On promoter.

Data from 3 technical replicates per condition are shown (except for VW100-tet-HXT1). (F) Rates of carbon uptake, ethanol and CO2 production, and O2 consumption for TM6* and wild type during

growth on glucose and maltose. Centre values present means, and error bars propagated standard errors, as determined in the model-based regression analysis (data from 3 independent

experiments used in regression; see Methods) with Maximum Likelihood formulation. (G) Distribution of time of first Start (as indicated by bud emergence) in TM6* cells (n = 956 cells from 4

experiments). (H) Dynamics of yeGFP production rate in G1-arrested TM6* cells (n = 20 cells) in response to increase in glycolytic flux achieved by switching the feed to 10 gL−1 maltose

after ≈40 hours of cultivation on 10 gL−1 glucose. Vertical red line indicates the time when cells started passing Start (as indicated by bud emergence) in response to the nutrient switch.

Source data for A and C-H are provided in Source Data Extended Data Fig. 1. EXTENDED DATA FIG. 2 NAD(P)H AUTOFLUORESCENCE AS A REPORTER OF METABOLIC DYNAMICS AND DETERMINATION OF PROTEIN

PRODUCTION RATE. (A) Dynamics of NAD(P)H autofluorescence in wild type cells (n = 30 cells) in response to an increase in glycolytic flux achieved via a switch from glucose-low to

glucose-rich media. Values of each single-cell NAD(P)H trajectory were normalized by division with the mean NAD(P)H value of the whole trajectory. (B) Dynamics of NAD(P)H autofluorescence

aligned for the moment of birth in wild type daughter cells (n = 53 cells) in steady nutrient conditions. Histogram shows the distribution of the timing of Start in the same cells, as

reported by the exit of Whi5-mCherry from the nucleus. (C) Example of smoothing the total sfGFP time series of a single cell during G1 using Gaussian process regression. Blue squares:

measurement data; Red curve: posterior mean function; Grey band: 95% posterior confidence region. (D) The posterior Gaussian processes (from (C)) can be used to analytically derive an

estimate of the rate of sfGFP production. This is again a Gaussian process, whose posterior distribution can be analytically obtained (see Methods). Red line: posterior mean of the

derivative; Grey band: 95% posterior confidence region. (E) Comparison of cellular-autofluorescence-corrected and non-corrected total sfGFP measurements (n = 50 cells). For correction, the

average total cellular autofluorescence of WT cells (n = 25 cells) at the GFP channel was smoothed, and was then subtracted from the average smoothed total sfGFP fluorescence. Data are

normalized to the fluorescence value at the moment of bud appearance. Note that control cells undergo also unperturbed growth, and thus, corrected signals include correction for

cell-size-dependent changes in cellular autofluorescence. Source data for Extended Data Fig. 2 are provided in Source Data Extended Data Fig. 2. EXTENDED DATA FIG. 3 CONFIRMATION OF

DIFFERENTIAL SCALING WITH CONFOCAL MICROSCOPY AND DIFFERENT ANALYSES. (A) Rate of sfGFP production versus cell size in daughter cells during G1, estimated by confocal microscopy (n = 27

cells), and by (B) widefield microscopy but determining total sfGFP on the basis of the integrated fluorescence over the whole cell area (n = 43 cells). (C) Coefficient of variation of

Whi5-sfGFP fluorescence as a measure of Whi5 localization in a single cell, in comparison to the ratio between nuclear to whole-cell mean Whi5-sfGFP fluorescence (apart from Whi5-sfGFP, this

cell expressed Hta2-mCherry for defining the nuclear area, which was used for the determination of the nuclear mean Whi5-sfGFP intensity). (D) Dynamics of sfGFP production rate and

Whi5-mCherry localization (determined as shown in (C)) during G1 for the single cell in Fig. 3f, displayed in absolute time. As can be observed, Whi5 exhibits only a transient, partial exit

from the nucleus during the first pulse in protein production, and exits completely the nucleus during the second pulse, suggesting that in the case of more than one pulses in protein

production, cells attempt, but fail to pass Start during the first pulse likely due to insufficient phosphorylation of Whi5. In all cases sfGFP expression was driven by the TEF1 promoter,

and cells were grown on 20 gL−1 glucose. Source data for Extended Data Fig. 3 are provided in Source Data Extended Data Fig. 3. EXTENDED DATA FIG. 4 DIFFERENTIAL SCALING BETWEEN CLN3

PRODUCTION RATE AND CELL SIZE DYNAMICS. (A) Likely due to the rapid Cln3 degradation, Cln3-sfGFP fusions do not generate detectable fluorescent signals, as can be seen in (B) (merged

phase-contrast and fluorescent images of Cln3-sfGFP wild type cells mixed with wild type Hta2-mRFP1 cells as control for cellular autofluorescence). Experiment performed once with cells in

multiple imaging positions, with each position yielding similar results. (C) Mean cell sfGFP in wild type (n = 43) and A-315T/CLN3 (n = 66) cells expressing the CLN3-2A-sfGFP fusion during

growth on poor carbon source (20 gL−1 lactate) (Mann Whitney test p-value <0.0001). Horizontal lines denote the median. (D) Relative change in Cln3 levels across different growth rates,

measured via the Cln3-2A-sfGFP fusion in single cells (this study), or via immunoblots in chemostat cultures (data from27). In both cases Cln3 levels are normalized against the highest Cln3

value. (E) Example of smoothing the total sfGFP originating from the Cln3-2A-sfGFP fusion for a single cell during G1 using Gaussian processes and (F) respective analytically derived

estimate of Cln3 production rate (similarly to Extended Data Fig. 2c, d). (G) Comparison of cellular-autofluorescence-corrected and non-corrected total sfGFP measurements from the

Cln3-2A-sfGFP construct (n = 38 cells). For correction, the average total cellular autofluorescence of WT cells (n = 35 cells) at the GFP channel was smoothed, and then subtracted from the

average smoothed total sfGFP fluorescence. Data are normalized to fluorescence value at moment of bud appearance. Note that control cells undergo unperturbed growth, and thus, corrected

signals include also correction for cell-size-dependent changes in cellular autofluorescence. (H) Rate of Cln3 production versus cell size in daughter cells for normalized G1 duration (n = 

41 cells). (I) Same as (H) but for cells aligned for the moment of bud appearance. Unless otherwise indicated, cells were grown on 20 gL−1 glucose. Source data for C-I are provided in Source

Data Extended Data Fig. 4. EXTENDED DATA FIG. 5 THE INCREASE IN CLN3 CONCENTRATION DETERMINES THE TIMING OF START. (A) Comparison of cellular-autofluorescence-corrected and non-corrected

mean Whi5-sfGFP (n = 50 cells) and Whi5-mCherry (n = 50 cells) concentrations. For correction, the mean cellular autofluorescence (integrated intensity divided by cell volume) of WT cells at

the GFP (n = 20 cells) or RFP (n = 20 cells) channels was subtracted from the mean Whi5-sfGFP or Whi5-mCherry based estimated Whi5 concentration respectively. Data are normalized to the

value at the moment of bud appearance. Note that control cells undergo also unperturbed growth, and thus, corrected signals include correction for cell-size-dependent changes in cellular

autofluorescence. (B) Whi5 concentration dynamics normalized to t = 0 in small G1 cells isolated by centrifugal elutriation and released to YPD. Whi5 concentration was calculated by

measuring cell size changes, in parallel with Whi5 abundance via targeted proteomics, in the same samples as in Fig. 4e, f. The continuous line denotes the smoothing spline. Error bars show

propagated SEM (n = 4 independent biological replicates). (C) Addition of higher NAA concentration leads to even further delay of Start. Duration of pre-Start G1 before (n = 49 and 24 cells)

and after (n = 30 and 19 cells) addition of 2 mM NAA in OsTIR Cln3-AID and OsTIR Cln3 (control) cells. Indicated p-value from Mann Whitney test. Horizontal lines denote the median. (D)

sfGFP production rate (driven by TEF1 promoter) in OsTIR Cln3-AID (n = 19 cells) and OsTIR Cln3 (control) (n = 17) cells treated with NAA (1 mM) before the second budding. Data are shown in

normalized time between the two subsequent buddings, aligned for the moment of last bud appearance before addition of NAA (t = 0), and first bud appearance after addition of NAA (t = 100).

Source data for Extended Data Fig. 5 are provided in Source Data Extended Data Fig. 5. EXTENDED DATA FIG. 6 THERE IS DIFFERENTIAL SCALING BETWEEN CLN3 PRODUCTION RATE AND CELL SIZE ACROSS

DIFFERENT GROWTH CONDITIONS. (A) Whi5 concentration in wild type daughter cells for galactose (n = 50 and 46 cells for WF-1 and WF-2, respectively) and lactate (n = 50 cells for both WF-1

and WF-2) conditions, normalized for concentration at birth and aligned for the moment of Start. Dashed lines: WF-1 (mean cell fluorescence). Solid lines: WF-2 (integrated fluorescence over

whole cell area divided by cell volume). (B) Change in cell size and Whi5-mCherry concentration (integrated fluorescence over whole cell area divided by cell volume) between cytokinesis and

Start in mother cells on galactose (n = 42 cells) and (C) lactate (n = 42 cells). The vertical lines denote the respective population average. (D) Dynamics of sfGFP production rate and rate

of NAD(P)H change in a single wild type cell at steady galactose (20 gL−1) or (E) lactate (20 gL−1) environment. (F) Cln3 production rate and respective cell size dynamics in wild type

daughter cells aligned for the moment of bud appearance, on galactose (n = 36 cells) or (G) lactate (n = 43 cells) conditions. Source data for Extended Data Fig. 6 are provided in Source

Data Extended Data Fig. 6. EXTENDED DATA FIG. 7 THE DIFFERENTIAL SCALING BETWEEN CLN3 PRODUCTION RATE AND CELL SIZE CAUSES START ACROSS DIFFERENT GROWTH CONDITIONS. (A) Heatmap showing the

dynamics of the Cln3 production rate during G1 in single wild type daughter cells on galactose and (B) lactate. The dark squares indicate the moment of Start in each cell. (C) Heatmap

showing the dynamics of the Cln3 production rate in single wild type mother cells on galactose and (D) lactate. Cells are aligned for Start (t = 0) and cytokinesis is indicated in each cell

by a dark square. In all cases data are normalized as described in Fig. 5b. Source data for Extended Data Fig. 7 are provided in Source Data Extended Data Fig. 7. EXTENDED DATA FIG. 8 THE

EFFECT OF PROTEIN DEGRADATION ON PROTEIN ABUNDANCE DYNAMICS. To demonstrate the impact of degradation rate on protein abundance dynamics, we simulated the system described by Eq. (1) for

different values of _k__d_. To facilitate comparisons with the experimental data across different _k__d_ values, we used a smoothed version of the estimated Cln3 production rate (Fig. 5c) as

_k__p_(_t_) and assumed that the system is at equilibrium at _t_ = 0 (that is \(p\left( 0 \right) = \frac{{k_p\left( 0 \right)}}{{k_d}}\)) for all values of _k__d_ except _k__d_ = 0 and

_k__d_ = ∞. Furthermore, for all values of _k__d_≠0 the plotted trajectories were normalized to their initial point by dividing _p_(_t_) by _p_(0). Finally, in the case of _k__d_ = 0 (no

degradation), _p_(0) was set to zero. The use of the experimentally determined production rate allows us to assess the impact of different values of _k__d_ on time scales that are relevant

for protein synthesis in the G1 phase. Altering _k__d_ affects the protein half-life (_T_1/2) since the two quantities are connected by the formula _T_1/2=ln(2)/_k__d_. The curve

corresponding to the asymptotic case _T_1/2→0 (_k__d_→∞) is just _k__p_(_t_) itself (normalized to its initial value), while the case _T_1/2 = ∞ corresponds to the accumulation of a highly

stable protein such as GFP. As can be observed, a range of short protein half-lives (such as those reported for Cln3) between these two extremes results in protein abundance profiles that

are very similar in shape to _k__p_(_t_). As _T_1/2 increases, the peak of the protein abundance gets shifted to the right and the overall variation of the response gets reduced. This

happens because the bandwidth of the system decreases with increasing half-life (decreasing degradation rate). In the limit of zero protein half-life, the protein abundance tracks the time

integral of _k__p_(_t_) (cyan line). Source data for Extended Data Fig. 8 are provided in Source Data Extended Data Fig. 8. SUPPLEMENTARY INFORMATION REPORTING SUMMARY SUPPLEMENTARY TABLE 1

List of the _S. cerevisiae_ strains used in this study SUPPLEMENTARY TABLE 2 List of the primers, and information on how they were used in strain construction SUPPLEMENTARY TABLE 3 Detailed

widefield microscopy settings SUPPLEMENTARY TABLE 4 Peptides and transitions for PRM analysis SOURCE DATA SOURCE DATA FIG. 1 SOURCE DATA FIG. 2 SOURCE DATA FIG. 3 SOURCE DATA FIG. 4 SOURCE

DATA FIG. 5 SOURCE DATA FIG. 6 SOURCE DATA EXTENDED DATA FIG. 1 SOURCE DATA EXTENDED DATA FIG. 2 SOURCE DATA EXTENDED DATA FIG. 3 SOURCE DATA EXTENDED DATA FIG. 4 SOURCE DATA EXTENDED DATA

FIG. 5 SOURCE DATA EXTENDED DATA FIG. 6 SOURCE DATA EXTENDED DATA FIG. 7 SOURCE DATA EXTENDED DATA FIG. 8 RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE

Litsios, A., Huberts, D.H.E.W., Terpstra, H.M. _et al._ Differential scaling between G1 protein production and cell size dynamics promotes commitment to the cell division cycle in budding

yeast. _Nat Cell Biol_ 21, 1382–1392 (2019). https://doi.org/10.1038/s41556-019-0413-3 Download citation * Received: 08 October 2018 * Accepted: 25 September 2019 * Published: 04 November

2019 * Issue Date: November 2019 * DOI: https://doi.org/10.1038/s41556-019-0413-3 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get shareable

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