Identification of Transcriptional and Metabolic Programs Related to Mammalian Cell Size

Identification of Transcriptio

Current Biology 24, 598–608, March 17, 2014 ª2014 The Authors http://dx.doi.org/10.1016/j.cub.2014.01.071

Articlenal

and Metabolic ProgramsRelated to Mammalian Cell Size

Teemu P. Miettinen,1 Heli K.J. Pessa,1 Matias J. Caldez,2,3

Tobias Fuhrer,4 M. Kasim Diril,2 Uwe Sauer,4

Philipp Kaldis,2,3 and Mikael Bjorklund1,*1Division of Cell and Developmental Biology, College of LifeSciences, University of Dundee, Dundee DD1 5EH, UK2Institute of Molecular and Cell Biology, Agency for Science,Technology and Research, 61 Biopolis Drive, Proteos #03–09,Singapore 138673, Singapore3Department of Biochemistry, National University ofSingapore, Singapore 117597, Singapore4Institute of Molecular Systems Biology, EidgenossischeTechnische Hochschule Zurich, Wolfgang-Pauli Strasse 16,8093 Zurich, Switzerland

Summary

Background: Regulation of cell size requires coordination ofgrowth and proliferation. Conditional loss of cyclin-dependentkinase 1 in mice permits hepatocyte growth without cell divi-sion, allowing us to study cell size in vivo using transcriptomicsand metabolomics.Results: Larger cells displayed increased expression of cyto-skeletal genes but unexpectedly repressed expression ofmany genes involved in mitochondrial functions. This effectappears to be cell autonomous because cultured Drosophilacells induced to increase cell size displayed a similar gene-expression pattern. Larger hepatocytes also displayed areduction in the expression of lipogenic transcription factors,especially sterol-regulatory element binding proteins. Inhibi-tion of mitochondrial functions and lipid biosynthesis, whichis dependent on mitochondrial metabolism, increased thecell size with reciprocal effects on cell proliferation in severalcell lines.Conclusions: We uncover that large cell-size increase isaccompanied by downregulation of mitochondrial geneexpression, similar to that observed in diabetic individuals.Mitochondrial metabolism and lipid synthesis are used tocouple cell size and cell proliferation. This regulatory mecha-nismmay provide a possiblemechanism for sensingmetazoancell size.

Introduction

Cell size can be increased by impeding with cell-cycle pro-gression, increasing the rate of biosynthesis, or both. Inunicellular organisms, cell size and proliferation are mainlycontrolled by nutrient levels, whereas regulation throughgrowth and mitogenic and survival signals is additionallyimportant in metazoan cells [1]. Cell size increases with ploidyin many organisms, although the mechanism behind this iselusive [2, 3]. Saccharomyces cerevisiae has been the pre-dominant model used to study cell size [2, 4]. Genes affecting

*Correspondence: [email protected]

This is an open access article under the CC BY license (http://

creativecommons.org/licenses/by/3.0/).

cell size have been identified through loss-of-function studiesin yeast [5, 6] and Drosophila [7, 8], as well as through gene-expression studies of yeast cell-cycle mutants and strainswith variable ploidy [9–11]. However, in mammals, practicallyall insights are derived from cultured cells with a focus inunderstanding whether there is an active cell-size control[12–14]. Mechanisms that affect cell size in vivo have receivedless attention, apart from the role of mTOR.Liver is a homogenous tissue mainly composed of hepato-

cytes. Liver regenerates to its normal size after partial hepa-tectomy ([PH]; removal of w70% of the liver) through cellgrowth and division of the remaining cells. Interestingly,mouse liver with a cyclin-dependent kinase 1 (Cdk1) liver-specific knockout (Cdk1Flox/Flox Albumin-Cre, hereafter namedCdk1Liv2/2) can also regenerate. However, this occurs in theabsence of cell divisions, resulting in enlarged hepatocytes[15]. Because Cdk1 is essential for cell-cycle progression,this model separates growth and proliferation effects, allowingus to analyze how mammalian cells respond to cell-sizechanges in vivo. We identify how gene-expression andmetab-olite levels correlate with cell size and discover that both mito-chondrial metabolism and lipid biosynthesis are used tocouple cell size and cell proliferation.

Results

Correlation of Gene Expression and Metabolite Levels with

Cell Size In VivoLiver samples from control (Cdk1Flox/Flox) and Cdk1Liv2/2 ani-mals, before and after partial hepatectomy, form a series ofsamples with different nuclear sizes (Figure 1A). Hepatocytesfrom Cdk1Liv2/2 mice after PH have 2–3 times larger radiithan those from Cdk1Flox/Flox mice ([15]; Figure 1B), with rela-tively uniform size increase because the variation is similar tocontrols (Figures 1A and 1B). We measured gene expressionand relative metabolite levels in these four nearly isogenicsample types using nuclear radius as a proxy for cell size[2, 3]. We then correlated all gene expression and metabolitechanges to cell size (Figures 1C and 1D; Figures S1A andS1B available online; Tables S1 and S2). Gene-expressiondata were validated by comparing samples before and afterPH (Figure S1C) and by quantitative RT-PCR (Figures S1Dand S1E). To our knowledge, there are no prior data regardingglobal gene expression and metabolic changes related to cellsize from metazoan organisms in vivo.The metabolomics data contained semiquantitative ion in-

tensities, which potentially account for >2,200 metabolitesbased on accurate mass annotation and covering a large frac-tion of the metabolome (Figure S1F). We observed manychanges related to hepatectomy (Figures S1B and S1G),including known changes in levels of glycogen, glucose,taurine, betaine, and creatine [16]. We could also identifychanges related to Cdk1 deletion and cell size (Figure S1B).By plotting the correlation of the nuclear radius and changein metabolite and gene-expression levels between the largestand the smallest cells, we observed that the strongest correla-tions with cell-size change are usually not associated with thelargest fold changes (Figure S1G; Tables S1 and S2).

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Figure 1. Correlation of Gene-Expression and Metabolite Levels with Cell Size in Mouse Liver

(A) Representative Feulgen-stained histological sections of Cdk1Flox/Flox and Cdk1Liv2/2 liver before and 96 hr after PH. The Cdk1Liv2/2 hepatocytes regen-

erate by growing in size because they are unable to divide, whereas the cell size in Cdk1Flox/Flox liver is not significantly changed. All images were taken with

the same magnification. Scale bar represents 20 mm.

(B) Quantification of the nuclear sizes in liver samples. The data shown indicate mean 6 SD of nuclear radius relative to control Cdk1Flox/Flox before PH

(n = 13–55 cells).

(C) Analysis of gene expression by RNA-seq. Four genes displaying strong correlation with nuclear radius are shown as examples with correlation,

and 690% confidence intervals are shown with solid and dotted line, respectively.

(D) A density plot of gene-expression correlations with nuclear radius for all genes. Median Pearson correlation (0.222) for all genes is indicated with the

dotted line.

See also Figure S1 and Tables S1 and S2.

Transcriptomic and Metabolic Effects of Cell Size599

At gene-expression level, the fatty acid transporterCd36dis-played almost perfect linear correlation (r = 0.968) with nuclearsize (Figure 1C). We also identified genes with strong negativecorrelation (for example,Ndufb10 [NADH dehydrogenase sub-unit, r = 20.947]) (Figure 1C), although these were less abun-dant (118geneswith correlation <20.8withnuclear size versus302 genes with correlation > 0.8 with nuclear size). Such acoordinated global gene expression with cell size is consistentwith yeast data [9, 10]. The distribution pattern of all gene andmetabolite correlations with cell size is in Figures 1D andS1A. The observed gene-expression pattern could resultfrom downregulation of a few highly expressed genes. How-ever, the most abundant genes are on average only slightlydownregulated, and the observed positive correlation is dueto increased expression of many genes with low expression(Figure S1H). Because many of these are regulatory proteins,these changes might be necessary to support cell growth.

In contrast to yeast, in which G1 cyclins are repressed withincreased cell size [9], the expression of many cell-cycle genes

correlated positively with nuclear size. Cyclins D1–D3, E1, E2,A2, B1, and B2 displayed a positive correlation with cell size(r = 0.344–0.761; Table S1), suggesting that repression ofcyclins is not universally required for cell-size increase.

Mitochondrial and Cytoskeletal Genes Strongly Correlatewith Cell Size

Rather than focusing on individual genes, wewanted to identifywhether expression of genes related to various subcellularcomponents is coordinated with cell size. Comparison of sizecorrelation distributions for various subcellular structuresbasedongeneontology (GO)classifications revealed twostruc-tures that deviated from thewhole-cell profile. These structureswere cytoskeleton andmitochondria, correlating positively andnegatively, respectively,with cell size (Figures2A, 2B, andS2A).Because the cytoskeleton is required to mechanically supportcells and is an integral part of various cellular transport mecha-nisms, the upregulation of cytoskeleton was not unexpectedand has been observed in yeast [9]. Analysis of protein

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Figure 2. Correlation of Gene Expression with Cell Size for Different Subcellular Components Identifies Downregulation of Mitochondrial Genes

(A)Mouse genes annotated to individual subcellular components using gene ontology (GO) analysis were identified, andmedian correlationwith nuclear size

was calculated. Dotted orange line indicates median cell correlation for all genes included in this analysis. We calculated p values using Kolmogorov-

Smirnov test.

(B) Expression correlations for genes annotated to mitochondria and cytoskeleton. Correlations were binned to obtain scaling profiles (bars) for each sub-

cellular component. For comparison, the whole-cell profile (only genes with annotation in any of the subcellular component analyses, as opposed to all

genes in Figure 1D, orange line) is overlaid on the bar chart. The number of genes in thewhole-cell profile was normalized to the number of genes in individual

subcellular components to simplify comparison.

(C) Connectivity of genes correlating negatively (adjusted p value < 0.05) with cell size, as identified using the STRING database. Groups of functionally in-

teracting genes are indicated with green circles and named. Note that one-carbon metabolism genes, such as adenosylhomocysteinase (Ahcy), are impor-

tant for glutathione synthesis, indicating possible coregulation.

(D) Drosophila genes annotated to individual subcellular components as for liver data. Dotted orange line indicates median of log2 fold change for all genes

included in this analysis.

(E) Histograms of mitochondrial and cytoskeletal gene expression compared to all genes (orange line) in Drosophila Kc167 cells.

See also Figure S2 and Tables S3 and S4.

Current Biology Vol 24 No 6600

complexes indicated that the Wave2 and Arp2/3 complexesresponsible for actinnucleationwereamong themostpositivelycorrelating complexes (Figure S2B). The negative correlationbetween mitochondrial gene expression and nuclear size wasunexpected because mitochondrial deletion mutants in yeastdisplay small cell size [5, 6], and mitochondrial content scaleswith cell size [17, 18]. The genes annotated in the inner mem-brane and matrix were the most negatively correlating genesets within mitochondria (Figures S2C and S2D).

Next, we analyzed the connectivity of the genes correlatingwith cell size by using a protein-association network database.The positively correlating network contained DNA replicationgenes, ribosomal protein-coding genes, Rho GTPase-relatedgenes, cytoskeleton and cell-adhesion-related genes, E2F-related, and Hippo pathway genes (Figure S2E), all of whichare likely to be involved in growth. The negatively correlatingnetwork contained a large cluster of mitochondrial genesand smaller clusters containing cholesterol biosynthesisgenes, apolipoprotein and serine protease inhibitors (serpin),and genes involved in glutathione, phenylalanine and tyrosine,and one-carbon metabolism (Figure 2C). These networks had2.3 and 8.1 times more connections per gene, respectively,than similarly sized random networks, indicating functional in-teractions (Figure S2E). Many of these findings were corrobo-rated by metabolomics data, which showed marked changesin glutathione, one-carbon, and DNA-replication-relatedmeta-bolism (Table S3). Because one of the major functions of mito-chondria is oxidative phosphorylation (OxPhos), we analyzed

the expression of OxPhos genes. These displayed strongnegative correlation with nuclear size (Figure S2F).The identified gene-expression patterns could potentially be

caused by Cdk1 deletion rather than by change in cell size.However, genes affected by Cdk1 deletion had very limitedoverlap with size-correlating genes. This overlap was only4% (22 of 526) of positively correlating genes and 6% (36 of569) of negatively correlating genes (Figure S2G), indicatingthat the observed effects are not Cdk1 dependent. Addition-ally, we used gene-expression data from cultured DrosophilaKc167 cells. Knockdown of Pop2 deadenylase, which causesdegradation of mRNA polyA tails, increases cell size w20%without major effects on cell cycle (Figures S2H and S2I).The CCR4-NOT complex, which contains a Pop2 ortholog,has one of the strongest cell-size effects in yeast [6]. Analysisof Drosophila RNA-expression data (Table S4) indicated thatmitochondrial genes were significantly downregulated andthat cytoskeletal genes were upregulated (Figures 2D and2E). The exception to liver data was that ribosomal geneexpression was repressed, and this may be a feedback mech-anism related to stabilization ofmRNAs. The similarity of gene-expression signatures in mouse and Drosophila cells impliesthat these gene-expression changes are cell-autonomouseffects related to cell size.

Aerobic Glycolysis Fuels Cell Size Increases

The negative correlation of mitochondrial gene expressionwith cell size suggested changes in energy metabolism. We


did not observe significant changes in mitochondrial number,size, or number of cristae, although the mitochondria in largercells tended to be smaller and slightly more abundant, withincreased electron density (Figures 3A and S3A). Furtheranalysis of the gene-expression levels of mitochondrial DNA-replication machinery (Figure S3B) and mitochondrial DNAamount relative to genomic DNA (Figure S3C) did not indicatedepletion of mitochondria.

Despite negative correlation, the absolute reduction ofmRNA and protein expression of OxPhos complexes inCdk1Liv2/2 post-PH samples compared to Cdk1Flox/Flox pre-PH samples was only w20% (Figure 3B), which could explainthe phenotypic difference of yeast deletion mutants [5, 6].Metabolomics data indicated no changes in ATP levels but areduction in AMP levels, which correlated with cell size (Fig-ure 3C). Consistently, analysis of the cellular energy sensorAMP-activated kinase (AMPK) indicated downregulation ofAMPK activity in Cdk1Liv2/2 cells (Figure 3B). Thus, ATP levelsare unlikely to be limiting in larger cells, and the lack of AMPKactivation could provide permissive conditions for the cell-sizeincrease. We conclude that the observed mitochondrial gene-expression correlation is due to moderate transcriptionaldownregulation.

Compensatory increases in glycolysis could maintain ATPlevels, and we indeed observed upregulation of genes relatedto three key regulatory steps (Figure 3D). Hexokinase expres-sion correlated well with cell size, whereas pyruvate kinasePkm2 displayed a mixed hepatectomy and cell-size effect.Additionally, lactate dehydrogenase (Ldha) correlated posi-tively and pyruvate dehydrogenase (Pdha) correlated nega-tively with cell size (Figure 3D). Analysis of the metabolitelevels indicated that, whereas changes in glucose levels incontrol and Cdk1Liv2/2 animals were roughly similar, changescaused by PH in pyruvate levels at the end of the glycolyticpathway were different (Figure S3D). For a summary of allglycolysis data, see Figures S3D and S3E.

We did not observe significant changes in tricarboxylic acid(TCA) cycle metabolites (Figure S3D), although we observedchanges in isocitrate dehydrogenases 1 and 2 (Idh1 andIdh2) as well as in mitochondrial glutaminase 2 (Gls2) andglutamate dehydrogenase 1 (Glud1), with concomitant in-crease in glutamate levels (Figure 3E). The glycolytic inhibi-tor 2-deoxyglucose (2-DG) and the glutamine antagonist6-Diazo-5-oxo-L-norleucine (DON) abolished the cell-size in-crease caused by respiratory inhibitor sodium azide in humanosteosarcoma cell line U2OS (Figure 3F). Altogether, thesedata suggest that glycolysis and glutaminolysis are requiredto fuel cell growth caused by mitochondrial inhibition (bysodium azide) in vitro and possibly in vivo.

Interestingly, although metabolic changes in early glycol-ysis displayed a clear hepatectomy effect, Cdk1Liv2/2 geno-type and cell size had more effect on metabolite levels atlater stages of glycolysis. For example, we observed thatCdk1Liv2/2 suppressed the increased pyruvate levels causedby hepatectomy in control animals (Figures S3D and S3E).Furthermore, hepatectomized Cdk1Liv2/2 knockout mice dis-played increased metabolite levels related to serine andglycerol synthesis. These metabolic changes and the positivecorrelation of pyruvate kinase Pkm2 expression with cell sizeare consistent with tumor-like metabolic phenotype [19].Overall, the observed cell-size-related metabolic and gene-expression changes are conceptually similar to the Warburgeffect, in which mitochondrial activity is reduced relative toglycolysis.

Mitochondria Regulate the Balance between Cell Size andCell Proliferation

To investigate this putative functional link between mitochon-dria and cell size, we screened a set of small molecules,including compounds that target mitochondria and glycolysis,glutaminolysis, and the pentose phosphate pathway (PPP).Mitochondria-targeting inhibitors frequently increased cellsize and reduced cell numbers, with a modest inverse correla-tion (R2 = 0.27) (Figure 4A). The mitochondria-targetingcompounds included uncoupling agents (FCCP and CCCP),ionophore (valinomycin), mitochondrial division inhibitor(Mdivi-1), translation inhibitors (minocycline and thiostrepton),and drugs with mitochondrial off-targets (tamoxifen).The reciprocal effects on cell size and proliferation are

illustrated with Mdivi-1, which targets the dynamin-relatedprotein 1 (Drp1), and sodium azide, an inhibitor of OxPhoscomplex IV (Figures 4B, S4A, and S4B). Increases in cell sizewere similarly detected by electrical current exclusion methodand by measurement of protein amount per cell, arguingagainst osmotic effects (Figures S4C and S4D). In contrast tomitochondrial inhibitors, phenylbutyrate, which enhancesthe metabolic flux from glycolysis to mitochondria, causedincreased proliferation and decreased cell size, although itslowed down proliferation at high concentrations (Figure 4C).Most nonmitochondria-targeted chemicals had little effecton cell size, although they reduced cell number and conse-quently displayed no correlation with cell size (R2 = 0.07) (Fig-ure 4A). These data, together with our RNAi screen inDrosophila [7] and recent yeast data [20], indicate that cellsize is, in most cases, not connected to effects in cell prolifer-ation (cell cycle) as commonly believed.Genetic means of targeting mitochondrial functions also

increased cell size. U2OSrho0 cells, which do not contain mito-chondrial DNA and thus are defective in many mitochondrialfunctions, were larger than wild-type cells (Figure S4E). Cellsize was also increased by RNAi of the transcriptional coacti-vator PGC-1a (Figures 4D and 4E), which has been implicatedas an integrator of metabolism and mitochondrial geneexpression by regulating OxPhos, TCA cycle, and lipid synthe-sis genes.Because two OxPhos complex inhibitors, antimycin A and

oligomycin, did not increase cell size (TableS5andFigureS4F),we examinedwhat othermitochondrial functions could explainthe cell-size phenotype. Mitochondrial metabolism is closelylinked to oxidative phosphorylation and proliferation [19, 21].A key function of mitochondria is to provide acetyl-coenzymeA (CoA) for histone acetylation as well as for mevalonate andcholesterol and fatty acid synthesis (Figure S4G). Mitochon-drial acetyl-CoA is exported to the cytoplasm as citrate.Although our metabolomic data cannot distinguish subcellularpools of metabolites, all enzymes involved in the citrate andacetyl-CoA transport process correlated negatively with cellsize in our gene-expression data (Figure S4G). Additionally,RNAi of the mitochondrial citrate transporter SLC25A1 in-creased the size of U2OS and HeLa cells (Figure S4H).In yeast, acetylation of histones binding to growth gene loci

is important for promoting transcription and inducing prolifer-ation [22]. We thus tested whether the cell-size effects in ourmodels are linked to histone acetylation. Histone acetylationwas reduced in larger Cdk1Liv2/2 cells in vivo (Figure S4I), aswell as in U2OS cells treated with rotenone, an OxPhos com-plex I inhibitor and a potent cell-size inducer (Figure S4J).However, although all of the histone acetyltransferase inhibi-tors that were tested reduced cell proliferation, none of these

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Figure 3. Glycolysis Increases with Cell Size

(A) Representative electron microscopy images of Cdk1Flox/Flox and Cdk1Liv2/2 liver before and after hepatectomy. Arrows and ‘‘M’’ indicate glycogen and

mitochondria, respectively. All scale bars represent 500 nm. For quantification, see Figure S3A.

(B) mRNA expression (red line) and protein levels (blue bars) of selected OxPhos proteins.Western blot shows themeasured OxPhos complex components,

phospho-Thr172-AMPK (pAMPK) levels, and GAPDH (loading control).

(C) Relative ATP and AMP levels in liver samples, as measured by mass spectrometry. Statistical significance was measured by ANOVA.

(D) Proportional expression of key glycolytic genes based on liver RNA-seq data.

(E) Glutamate metabolite levels (orange) and Idh expression levels (blue and gray) correlate with cell size.

(F) Inhibition of glycolysis and glutaminolysis by 2-DG and DON rescue U2OS cell size increase by 1 mM sodium azide (p < 0.001 in both; t test,

mean 6 SD, n = 3).

See also Figure S3.


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Figure 4. Inhibition of Mitochondrial Functions

Increases Cell Size in Cultured Cells

(A) Changes in cell size and cell number in U2OS

cells by small molecules. Compoundswith known

effects on mitochondria are displayed in red.

Glycolysis, glutaminolysis, and PPP compounds

are displayed in blue, and others are displayed

in green. Red and black solid lines display linear

regression for mitochondria targeting and for all

other compounds, respectively, with 90% confi-

dence intervals shown as dotted line. See Table

S5 for all compounds and concentrations used.

(B) U2OS cell number (red line) and cell size (blue

line) were analyzed as a function of Mdivi-1 con-

centration (n = 3, 48 hr).

(C) HeLa cell number (red line) and cell size (blue

line) as a function of phenylbutyrate concentra-

tion in delipidated FBS (n = 3, 48 hr).

(D) Representative cell-size profiles for PGC-1a

knockdown in U2OS and HeLa cells.

(E) Quantification of cell-size changes by two

PGC-1a targeting siRNAs (25 nM) compared to

control RNAi-treated cells (n = 3, 48 hr), with a

western blot showing the knockdown efficiency

in U2OS cells. All treatments except siRNA1 in

U2OS cells had p value < 0.01 (t test).

(F) Rescue of SLC25A1 RNAi (15 nM) by LipidMix

(50 ml/ml) (n = 3, 48 hr). Data shown indicate

mean 6 SD with t test (ns, not significant).

See also Figure S4 and Table S5.


inhibitors increased cell size (Figure S4K). Thus, histone acet-ylation levels are important for cell proliferation but do notexplain cell size increases.

Becausemitochondrially derived acetyl-CoA is also used forlipid biosynthesis, we attempted to rescue citrate transporterSLC25A1 RNAi by supplementing U2OS cells with a commer-cially available lipid mixture (LipidMix). This almost completelyrescued the cell-size increase caused by SLC25A1 RNAi (Fig-ure 4F). Interestingly, the effect of Mdivi-1 was also rescued byaddition of LipidMix (Figure S4L).

Repression of Lipid Biosynthesis Increases Cell SizeWe considered whether coupling of lipid synthesis and cellproliferation could explain our observations of mitochondriaand cell size. Gene expression related to de novo lipid biosyn-thesis negatively correlated with cell size (Figure 5A). Analysisof individual transcription factors identified the sterol-regula-tory element binding transcription factor 2 (SREBF2/SREBP2)as themost negatively correlating (Figure 5B). Analysis of tran-scription factor families identified E2F, ARID, and ETS factorscorrelating positively and STATs and PPARs correlating nega-tively with cell size (Figure S5A). Interestingly, 17 out of 55transcription factors with a negative size correlation of <20.3clustered based on network analysis (Figure S5B), and theseare involved in regulation of lipid metabolism either directly(SREBPs, PPAR-a, retinoic acid receptors, LXR/Nr1h2,ChREBP/Mlxipl, and HNF4A) or via inflammatory responses(STATs and IRFs). The coordinated downregulation of thisnetwork demonstrates the well-known crosstalk betweenmetabolic and inflammatory signals [23], which is clinicallyimportant in diabetes, obesity, and atherosclerosis.

SREBP1 preferentially activates fattyacid metabolism, whereas SREBP2 acti-vates cholesterol metabolism [24], andthe activities of SREBP1 and SREBP2

are regulated by phosphatidylcholine and cholesterol, respec-tively [24, 25]. Expression of genes involved in SREBPmatura-tion was also negatively correlated (Figure S5C). Becausecholesterol synthesis and one-carbon metabolism are SREBPtargets [25], this likely explains their negative correlation ofgene-expression andmetabolite levels with cell size (Figure 2Cand Table S3B).RNAi of both SREBP1 and SREBP2 increased cell size in

U2OS and hTERT-RPE cells (Figures 5C and S5D). The effectwas dose dependent (Figure S5E), and silencing of SREBP1and SREBP2 increased cell size more than either treatmentalone (Figure S5F). It has been reported that SREBP RNAi de-creases the size of RPE cells [26], but this observation may bedue to the combined effects of AKT and hydroxytamoxifen(note that tamoxifen potently increases cell size) or to morecomplete knockdown because SREBP knockout mice arelethal. Importantly, the SREBP1 and SREBP2 knockdown-induced cell-size increase in our experiments could be seenwith multiple small interfering RNAs (siRNAs) and could berescued with lipid mixture, making it unlikely that this is anoff-target effect (Figures 5C and 5D).Of all lipid classes, triacylglycerides displayed the best

correlation with cell size (Figure 5E and Table S3C), and thisaccumulation of lipids may explain the downregulation of thelipogenic transcription factors [24, 25]. Although accumulationof hepatic lipids may lead to fatty liver, Cdk1Liv2/2 mice do nothave fatty liver disease based on PPARg expression (FiguresS5G and S5I).Increased cell size should result in a decrease of the relative

surface area compared to volume. Metabolomics data indi-cated that the total levels of the detected phospholipids

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Figure 5. SREBP-Mediated Lipid Biosynthesis Is Involved in Modulation of Cell Size

(A) Relative expression of genes in the mevalonate and cholesterol synthesis pathway and fatty acid synthesis pathway decreases with cell size in mouse

liver. The expression values were normalized to the highest expression for each gene.

(B) Histogram of individual transcription factor expression correlation with cell size in mouse liver. Median correlation of all transcription factors (r = 0.275) is

indicated with the dotted line.

(C) Quantification of U2OS cell-size changes by targeting SREBP1 and SREBP2 with nonoverlapping siRNAs (25 nM, n = 3, 60 hr). Knockdown of SREBP2

was validated by western blotting. b-actin was used as loading control. Compared to control, p value < 0.001 with all SREBP siRNAs (t test).

(D) Rescue of cell size by SREBP RNAi using LipidMix in U2OS cells. Significance was analyzed by t test (n = 3, 55 hr).

(E) Correlations (blue bars) and log2 fold changes (red line) for all lipid classes containing more than four metabolites, as classified in LIPID MAPS (http://

www.lipidmaps.org).

(F) Log2 fold changes between smallest and largest liver cells for individual glycerolipids and glycerophospholipids based on the metabolomics measure-

ment. Horizontal line indicates mean (t test).

(G) Measurement of total phospholipids using a colorimetric assay from liver extracts. Phospholipids were normalized to tissue weight. Expected cell-

surface area relative to volume is in red. The differences in phospholipid levels are significant (p < 0.01, ANOVA).

Data shown in (C), (D), and (G) indicate mean 6 SD (n = 3).

See also Figure S5.


http://www.lipidmaps.org

http://www.lipidmaps.org

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G I

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Figure 6. Lipids Modulate Cell Size and Proliferation Ratio

(A) Cell number (red line) and cell size (blue line) were measured after 48 hr (n = 3).

(B) Fatostatin (25 mM) effects on U2OS cell size were rescued by 50 ml/ml LipidMix (n = 3, 64 hr, t test).

(C) U2OS cell number (red line) and cell size (blue line) were analyzed as a function of simvastatin concentration (n = 3, 48 hr).

(D) Simvastatin (7.5 mM) effects on U2OS cell size and cell proliferation were rescued by 5 mM mevalonolactone (n = 3, 60 hr, t test).

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(reflecting membrane synthesis) were not changed in largercells, whereas storage lipids (triglycerides) were clearlyincreased (Figures 5E and 5F). Direct measurement of totalphospholipids in liver samples displayed a minor increase(Figure 5G). We also reanalyzed a yeast lipidomics experiment[27] that used haploid and diploid cells. These data revealedthat two of the three most abundant phospholipids areincreased in larger cells. It appears that total phospholipids,which are present in both plasma membrane and internalmembranes, increase with cell size, and the change in phos-pholipid levels does not match the change in cell-surfacearea (Figure 5G).

To further investigate the relationship between lipids andcell size, we inhibited SREBP processing using fatostatin.Fatostatin increased cell size with a reduction in cellnumber in multiple cell lines (Figures 6A and S6A). Thiseffect was almost completely rescued by lipid addition(Figure 6B). Similar effects were obtained by inhibition ofcholesterol synthesis by simvastatin, which inhibits HMG-CoA reductase (Figure 6C). Specific rescue of simvastatinwas obtained by mevalonolactone, the end product of thereaction (Figures 6D and S6B). Opposite effects on cell prolif-eration and cell size could be seen by supplying HeLacells grown in lipid-depleted fetal bovine serum (FBS) withLipidMix. This treatment dose-dependently increased cellnumber and reduced cell size (Figure 6E). This was alsoobserved to a lesser extent with normal FBS, although higherconcentrations of lipids caused lipotoxicity (Figure S6C). Thelipids were not used as an energy source because inhibitionof beta-oxidation with etomoxir could not rescue this effect(Figure 6F). Hence, the lipids are either used as buildingblocks for membrane or for signaling to the cell-proliferationmachinery.

To further demonstrate that lipids regulate the balancebetween cell size and cell proliferation, we increased cellsize by blocking cell-cycle progression using Cdk inhibitorRO-3306 and DNA synthesis inhibitor gemcitabine (Figures6G and 6H). Although these treatments significantly in-creased cell size, cell size could not be rescued by LipidMix.Thus, cell-cycle arrest and mitochondria-mediated increasesin cell size are distinct, and LipidMix supplementation doesnot inhibit cell-size increase but acts by stimulating cellproliferation.

In diabetic and/or obese patients, excess lipids are associ-ated with both the decline in mitochondrial functions andthe decline in mitochondrial gene expression [28–30]. Weobserved that inhibition of SREBP function with fatostatinand SREBP RNAi resulted in increased mitochondrial mem-brane potential (Figures S6D and S6E), indicative of thewell-known feedback mechanism between lipids and mito-chondrial function. Altogether, our data validate a role formito-chondria and lipids in regulating the balance between cell sizeand cell proliferation.

(E) Dose dependence of increased HeLa cell proliferation by LipidMix in delipi

(F) Effect of etomoxir (50 mM) on LipidMix induced HeLa cell proliferation in 10

(G) Effect of LipidMix (50 ml/ml) on cell size in U2OS cells arrested with 7.5 mM

Data shown in (A)–(G) indicate mean 6 SD.

(H) Cell-cycle arrests in G2/M and early S phase by RO-3306 and Gemcitabine

(I) When cells proliferate, highmitochondrial metabolic activity and lipogenic tra

need for plasma membrane lipids decreases. Intracellularly accumulating lipid

lipid synthesis-related gene expression. Downregulated lipid biosynthesis, in tu

are inhibited, proliferation is reduced without directly inhibiting cell growth. SR

See also Figure S6.

Discussion

We have investigated how mouse liver cells respond toincreased cell size caused by Cdk1 inactivation and hepa-tectomy. Whereas cytoskeletal gene expression positivelycorrelates with cell size, unexpectedly, the expression of mito-chondrial and de novo lipid biosynthesis genes inversely cor-relates with cell size. Inhibition of mitochondrial functionsand lipid synthesis increases cell size in culture, suggestingcausality. Although decline in nutrient transport efficiencyand increase in time required for diffusion-limited processescould potentially limit cell size, Cdk1Liv2/2 cells grow withoutsigns of energy deprivation. The liver and Drosophila modelsindicate that the observed mitochondrial link is not Cdk1dependent or cell-cycle dependent and that it is a cell-auton-omous response related to cell size.A fundamental unresolved issue in cell biology is the

coupling of cell size and cell proliferation. We demonstratethat the balance between cell size and cell proliferation canbe changed by targeting mitochondria and/or lipid biosyn-thesis, providing one possible mechanism for this coupling.Many mitochondrial and lipid metabolism genes are downre-gulated in proliferating hepatocytes in vivo [31]. However,because most proliferating cells grow in size before cell divi-sion, the complete lack of proliferation in our model allowsseparation of growth effects. Neutral lipids, such as triglycer-ides, substantially increase after PH [32], and growth immedi-ately after hepatectomy occurs by cellular hypertrophy beforeinitiation of the cell division [33]. This physiological coinci-dence of growth with lipid accumulation before hepatocyteproliferation is consistent with our data.The mitochondrial and glycolytic changes observed in our

model bear similarities to theWarburg effect. Our data suggestthat the Warburg effect is primarily driving cell growth and notproliferation, as is often thought. Our findings are supported bya recent study in which the Warburg effect is not needed forT cell proliferation [34]. Furthermore, increased aerobic glycol-ysis decouples cell proliferation and biomass production inyeast [35], and PGC-1a expression correlates with prolifera-tion of melanoma cells [36]. It is possible that the change inglycolysis and mitochondrial activity related to cell size maybe more related to optimization of metabolic-precursor pro-duction than energy production.At first glance, our results conflict with mTOR effects. mTOR

activity increases cell size and activates lipid biosynthesis,which increases proliferation and, together with increasedprotein biosynthesis, results in increased biomass. In ourexperiments, cell size is increased at the cost of reduced pro-liferation with no increase in biomass production. We alsoshow that the opposite is true by supplying cells with lipids,which reduces cell size and increases proliferation. Our resultsare consistent with physical laws in which increased cell size isexpected to result in relative reduction of plasma membrane

dated FBS-containing medium (n = 3, 48 hr).

% lipid-free FBS (n = 3, 60 hr, t test).

RO-3306 or 1 mM Gemcitabine (n = 3, 34 hr).

, respectively, were verified by DNA staining.

nscription-factor levels aremaintained.When cell size increases, the relative

s repress the activity of lipogenic transcription factors and, consequently,

rn, reduces the need formitochondrial metabolism. Similarly, if mitochondria

EBP is shown as an example; SRE is sterol-regulatory element DNA motif.


production as volume grows faster than surface area (r3 versusr2, where r is radius) and generates a scaling problem for agrowing cell. The physical scaling problem assumes that lipidsare used for plasma membrane production and does not takeinternal membranes into account. Our analysis suggests thatthere is an increase in internal membranes, in membranecomposition, or in free phospholipids because total phospho-lipid levels are greater in larger cells. On the other hand,cholesterol is highly plasma membrane enriched, and themost affected transcription factor was SREBPF2, which isresponsible for cholesterol biosynthesis. To understand therole of lipids in the scaling problem, we will need to measurequantities and membrane selectivity of individual lipid speciesin detail.

In summary, increased cell size results in the relative reduc-tion of mitochondrial metabolism and lipid biosynthesis inmouse liver (Figure 6I). When proliferation is reduced, theneed for plasma membrane components is reduced, andexcess lipids, which are not incorporated into the plasmamembrane, accumulate and inhibit lipogenic transcription fac-tors. This reduces lipid biosynthesis and, consequently, theneed for mitochondrial metabolism. This negative feedbackthus matches the cell-size and cell-proliferation rate and mayprovide a solution for the scaling problem. Thus, lipids, whichare not incorporated into membranes, can potentially be partof a cell-size-sensing mechanism.

Experimental Procedures

The complete details of the experimental procedures are provided in the

Supplemental Experimental Procedures.

Cdk1 conditional knockout mice have been described previously [15]. All

the procedures performed were in accordance with institutional guidelines

at the Institute of Molecular and Cell Biology, A*STAR, Singapore. Livers

were collected before and 96 hr after partial (70%) hepatectomy. Nuclear

size was calculated from histological sections and normalized to

Cdk1Flox/Flox mice before hepatectomy. Pearson correlation coefficients

were calculated using all samples for each gene and metabolite.

Liver samples were analyzed by RNA sequencing (RNA-seq) and by mass

spectrometry for metabolomics. Cell-size and cell-number measurements

were conducted using flow cytometry. Small molecules were from Sigma-

Aldrich, Tocris, Santa-Cruz, or Calbiochem. RNAi was performed by

transfecting siRNAwith HiPerfect (QIAGEN). The siRNA and oligonucleotide

sequences are in Table S6. Antibodies were detected using infrared-dye

conjugated secondary antibodies and LICOR Odyssey detection system.

For electron microscopy, liver pieces were fixed and exposed to osmium

tetroxide and then embedded in Spurr’s resin.

Accession Numbers

The ArrayExpress Archive accession number for the liver gene-expression

data in this paper is E-MTAB-1297.

Supplemental Information

Supplemental Information includes Supplemental Experimental Proce-

dures, six figures, and six tables and can be found with this article online

at http://dx.doi.org/10.1016/j.cub.2014.01.071.

Acknowledgments

We thank C.J. Weijer for comments, A. McLeod for technical assistance,

N.V. Gounko and A. Boey of the IMB-IMCB Joint ElectronMicroscopy Suite,

the Agency for Science, Technology and Research (A*STAR), Singapore for

assistance in preparing and imaging specimens, and the GenePool facility

at the University of Edinburgh for Drosophila RNA-seq. This study was

funded by the Wellcome Trust Career Development Fellowship (089999)

and Scottish Universities Life Sciences Alliance (SULSA) to M.B. and by

the Biomedical Research Council of A*STAR, Singapore to P.K.

Received: October 29, 2013

Revised: December 20, 2013

Accepted: January 30, 2014

Published: March 6, 2014

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Current Biology, Volume 24

Supplemental Information

Identification of Transcriptional

and Metabolic Programs

Related to Mammalian Cell Size

Teemu P. Miettinen, Heli K.J. Pessa, Matias J. Caldez, Tobias Fuhrer, M. Kasim Diril,

Uwe Sauer, Philipp Kaldis, and Mikael Björklund

Figure S1, related to figure 1. Transcriptomics and metabolomics analysis of cell size in

vivo. (A) Analysis of metabolite levels by mass spectrometry. A density plot of all metabolite

level correlations with nuclear radius is shown. Median Pearson correlation (0.066) is

Number of metabolite ions

median

-1 10

80

40

0

Correlation with nuclear radius

Metabolomics

A B

Size

Hepatectomy

Cdk1

Metabolite level (log2 ion count )

Dehydroxycarnitine

12

11

13-Dihydro PGF-1a

11

9

Glutaconylcarnitine

11.6

10.6

Ubiquinone Q2

11

10

Heptadecanoic acid16.8

16.0

N-Glycolylneuraminic acid

15

14

O-Phosphoethanolamine

15

14

D-Glucuronic acid-1-phosphate

14

12

Guanidinosuccinic acid12

11

Phosphoglyceric acid

13

12

S-Lactoylglutathione

11

15

AdenosineDeoxyguanosineNeuraminic acid

13

12

Glycocholic acidTrihydroxyoxocholanyl-Glycine

13

9

Protopine

11

10

dATP

11

12

Uridine monophosphatePseudouridine 5'-phosphate

14

17

Cdk1fl/fl PrePH

Cdk1fl/fl PostPH

Cdk1Liv-/- PrePH

Cdk1Liv-/- PostPH

Sample order

Phosphoserine

Ergothioneine

11

13

15

1413

14

Glycochenodeoxy-cholate 3-sulfate

Log2FC Cdk1Flox/Flox Post-PH vs Pre-PH

Log2FC Cdk1L

iv-/-

Post-PH vs Pre-PH

-4 -2 0 2 4 6

6

4

2

0

-2

-4

Prtn3Scara5

Ttf3

Lcn2Ly6d

Esco2Mybl1Diap3

Saa2Mt2

Mt1

S100a8

Usp2

Per3Dbp

Gadd45aCux2

Scd1

Lrit1Hao2

A1bgGm15439Sult3a1

Rfx4

Mup16

Mup17

Tubb2aTubb2bTubb3

Cxcl13

Gm10290Acot10

Ica1Atp4a

Tnfrsf12a

Loxl4Gm13855

4

0

4

2

0

2

4

0

4

8

1

0

1

2

2

0

2

75

0

25 0

50

4

0

4

4

0

4

0

5

0

qPCR

RNAs

eq

qPCR

RNAs

eq

qPCR

RNAs

eq

qPCR

RNAs

eqqP

CRRN

Aseq

qPCR

RNAs

eq

qPCR

RNAs

eq

qPCR

RNAs

eqqP

CRRN

Aseq

qPCR

RNAs

eq

qPCR

RNAs

eq

Ccnd1Csnk2b Hmgcr Hsd17b2

Htatsf1 Mki67 Mup20 Sc4mol

Scd1 Slco1a1 Top2a qPCR changein expressionrelative to control

RNAseq changein expressionrelative to control

PrePostFlox/Flox Liv-/-

PostPre

R2= 0.83

Expression correlation (qPCR)

Expression correlation (RNAseq) 1

1

-1

-1

-0.5

-0.5 0.5

0.5

150

150

100

150

150

75

2.5

2.5

Lipid metabolism

Glycan Biosynthesisand metabolism

Carbohydratemetabolism

Nucleotidemetabolism

Cofactors and vitamins

Amino acid metabolismEnergy metabolism

Metabolite correlation with cell size

Metabolite log2FC

Cdk1L

iv-/- postPH vs Cdk1F

lox/Flox prePH

1-1 0.5-0.5

6

4

2

-6

-4

-2

GlycogenMaltotriose

AmylopectinGlycogen Maltohexaose

ErgothioneineAllyl isothiocyanate

Pimelylcarnitine13,14-Dihydro PGF-1a

Aminoadipic acidAminoadipic acid

PG(16:0/20:3)

O-Phosphohomoserine

1b-Hydroxycholic acid

Glycochenodeoxy-cholate-3-sulfatePhosphoserine

Glycocholic acid

NADP

Glucose

mRNA expression (log2 reads/million)

Log2FC Cdk1Liv-/- Post-PH

vs Cdk1Flox/FloxPre-PH

C

D E F

G H

-5 5 10 15 20

-8.0

-6.0

-4.0

-2.0

6.0

8.0

10.0 median log2FC of100 genes sliding window

2.0

4.0

indicated with a dotted line. (B) Examples of metabolites displaying cell size, hepatectomy or

Cdk1 effects. Note that we identified also metabolites such as ergothioneine, trehalose and

protopine which are not produced by mammalian metabolism, but maybe derived from food

or microbial metabolism. (C) Scatter plot of gene expression changes in response to

hepatectomy in Cdk1Flox/Flox (x-axis) and Cdk1Liv-/- livers (y-axis). Mean values of the

replicate samples were used to calculate fold-changes. As expected, we observed

upregulation of Scara5, serum amyloid A and metallothionein genes in response to partial

hepatectomy. (D) Validation of RNAseq data with quantitative RT-PCR analysis of a

selection of genes. Mean expression levels relative to Cdk1Flox/Flox control animal before

partial hepatectomy as measured by quantitative PCR (red bars) or RNAseq (blue bars).

qPCR data shown is mean expression of three technical replicates for each liver sample.

RNAseq data is calculated from the mean expression of technical replicates and plotted as

negative values for clarity. The gene names are indicated above the individual histograms.

Sample identities are shown in the Csnk2b graph only but are the same in all graphs. Note

that in a few cases, qPCR shows reduced expression compared to RNAseq, e.g., the last bar

in Sc4mol. (E) Correlation plot for Pearson correlation coefficients with nuclear radius for

the genes shown in (D), as analyzed by qPCR (x-axis) and RNAseq (y-axis). The correlation

between the two gene expression methods is shown (R2 = 0.83). (F) Annotation coverage of

the metabolomics data using 3 mD mass tolerance overlaid on the KEGG human metabolome

map (hsa01100). The map is colored by pathways. Dot size reflects log10 average intensity

of metabolite levels over all samples. (G) Scatter plot of metabolite ion correlations with

nuclear/cell size and log2 fold changes between smallest and largest cells. Data is derived

from both aqueous and organic extractions of the metabolomics data. (H) Comparison of

mRNA expression levels (in Cdk1flox/flox livers before hepatectomy) and fold changes

between smallest and largest cells. Red line indicates median log2 fold change of 100 genes

sliding window. Our data suggests that genes whose expression is low are most sensitive to

cell size changes.

Figure S2, related to figure 2. Additional analysis of cell size gene expression

programs. (A) Mouse liver mRNA expression correlations for genes annotated to individual

controlPop2

75

25

50

0G1 G2P

ercentage of cells

Cell size (% control)

control Pop2

130

90

110

400

200

0

Cell count

Cell size (FSC-A x106)0 2 4

controlPop2

A

C

Cyfip1 Wasf2

Nckap1

6720456B07Rik

Abi1

Wave-2 (#186)Spc24 Spc25

Nuf2 Ndc80

NDC80 kinetochore (#127)

Mcm4

Mcm2

Mcm6

Mcm3

Mcm5

Mcm7

MCM (#387)

Gins2 Gins4

Gins1 Gins3

GINS (#678)Rfc1 Rfc4

Rfc2 Rfc5 Rfc3

RFC (#277)

Actr3 Actr2

Arpc2 Arpc4

Arpc1b

Arpc3

Arp2/3 protein (#27)

Ccnb2 Ccnd1

Ccnb1 Cdk1

Pcna

Cdkn1a

Cell cycle kinase CDC2 (#310)

Vti1b Vamp8Stx8

SNARE (#736)

Ecsit (#2938)

mt-Nd1 Traf6

Ndufaf1 Ndufa1

mt-Co2

Tomm20 Ecsit Ndufs3

Psma4 Psma7

Psmb4Psmb3

Psmb7

Psma3

Psma6 Psmb5Psmb1

Psma2

Psma5

Psmb6

Psmb2

20S proteasome (#38)

gene

expr

essio

nco

rrelat

ion w

ithnu

clear

size

10.5

-0.5-1

0

B

-0.2-0.4 0 0.1

Median correlation

innermembranematrixintermembranespace

outer membrane

p<2.2x10-16

p=4.1x10-13

Hepatectomyin controlbackground

Hepatectomyin Cdk1 nullbackground

Cdk1 deletion

Cdk1 deletionand

hepatectomy

97

281

22

292

PositiveHepatectomyin controlbackground

Hepatectomyin Cdk1 nullbackground

Cdk1 deletion

Cdk1 deletionand

hepatectomy

94

289

36

486

Negative

526 genes correlatingpositively with cell size(adj. p. value <0.01)

569 genes correlatingnegatively with cell size(adj. p. value <0.05)

G

D

F

ERandom

Negative correl.(p<0.05)

Positive correl.(p<0.01)

0 2Connections/gene

4

H

Number of genes

Expression correlation with nuclear radius

plasmamembrane

0

50

100

150

-1 10

nucleolus50

0

25

-1 10

Golgi

0

25

50

75

-1 10

nucleus

0

200

400

-1 10

cytoplasm

0

200

400

-1 10

extracellular

0

25

50

75

-1 10

ribosome

0

20

40

-1 10

cytosol

0

25

50

75

-1 10

membrane

0

100

200

-1 10

ER

0

25

50

75

-1 10

OxPhosI II III IV V

MCM

0.0

0.5

1.0

Expression correlation

with nuclear radius

Complex:

innermembrane30

20

10

0

8

4

0

3

2

1

0

12

0

6

matrix outermembrane

intermembranespace

-1 10 -1 10 -1 10-1 10

Number of genes

Expression correlation with nuclear radius

I400

200

0

600

Cell count

DNA content

controlPop2

Nop56Stmn1Rpsa

Mthfd2Lsm3

Marcks

Basp1Smc5

H2afz

Trpm7

Anxa5 Ssh2

Psat1

Mcm6

Setd7Gins2

Mcm5 E2f2

Suv39h1

Rpl5Eid1 NptnIer3

Klf6

Ywhah

Selh

Sh3bgrl

Stag2

Cdc25b

Gins4Mcm4

Gtpbp4Nap1l1

Timeless

Prim2

E2f1Dgcr8

Rpa1

Rpa2

Pole

Myst3

Atf3

Rbbp8Tead1

Rps8

Gm6030Rps13Rplp0Rpl13

AsphLgals1Ssr3

Rpl19

Rpl12

Btaf1

Gm5271

Gm6336Gm7263

Cenpj

Mcph1Tcf12

Jund

Batf3

Iqgap1

Galnt1

Nras

Camk2d

Msn

Zeb2

Igsf8

Fcer1gS100b

Mme

Ankrd26

Sykb

Hcls1

Flna

Pawr

Cybb

Grm1Rac2

Gnai2 Nckap1l

Fads3

Nedd4

Cd63

Cyfip1Arf6

Apc

Cfp

Agap1

Ap3b1

Cd300ldClec4a2

Laptm5

Uchl5

Itgb2

Cd53Lyz1

Lpl

Il3raLamp1

Ucp2Ncf1

DgkhAcsl4Dgke

Ppt1

Jak3

Cln6

Rlf

Tyrobp

Ptgs1

Cd68

Actn1

App

FybDmpk

Rif1

Mbnl2

Gpc1

Aplp2

TbcaCugbp2

Arhgef5

Heph

Capns1

Myof

Zfp217

Rhog

Arhgef2Arap2

Sox4

Cdk6

Dock10

Gdf10

Arhgap4

Pmepa1

Rgs19

Pou2f2

Gmfb

Mmp15

Cd5l Timp2

Ren1

Myo9aNcam1

ArhgdibOphn1

Rgs10

Rhoq

Bgn

Mmp14Smoc2

Senp1

Lum

Sdc3

Ccdc80Cd44Spc24

Cenpt

Nup155

Smg1Apaf1

Cfl2

Abcg3Anxa4Tmed10Mllt3 Actb

Ccar1

Plec1Vim Olfml3

Tnfaip8Casp8

Bicc1

Nol3S100a6

Mlec

MefvTmsb10

F13a1Sh3kbp1

Erbb2ip

Ap1s2

Cltc

Actr3

Galns

Myl6

VaspAtp9a

Cdk14

Nacc1

Rock2

Gpnmb

Dync1li1

Spp1FlnbCd36

Thbs1Fstl1

Adh1

ENSMUSG00000034868Epcam

Col1a2Prom1 Fkbp9Crtap Col1a1 Itgb1

Slc22a3

Tmsb4x

Col5a2Col6a3

Col5a1

Col3a1

Abcc2

Il17rb

Abcg2Spc25

Ranbp2

Calu

Ctse

Gpx3

Hk1Gpx4

Gsta4

Gstm5

Prpf31

Emr1

Tmem176b

Tmem176a

Aif1

Hnmt

Alg8

Cst3 Alg3

Ctss

C3ar1

Cd276

Soat1

C1qa

DNA replicationE2F andHippo

Ribosome

GTPase

Cytoskeleton/cell adhesion

subcellular components were binned to obtain scaling profiles (bars) for each subcellular

component. For comparison, the whole cell profile (all genes with annotation in any of the

subcellular component, orange line) is overlaid on the bar chart. The number of genes in the

whole cell profile was normalized to the number of genes in individual subcellular

component to simplify comparison. Interestingly, plasma membrane annotated genes are not

coordinately downregulated. (B) Examples of protein complexes, which gene expression

positively and negatively correlates with cell size. The CORUM complex database numbers

for each complex are indicated (mips.helmholtz-muenchen.de/genre/proj/corum ). Note that

we detect expression of Cdk1 mRNA as the knockout is a result of Cdk1 exon 3 deletion only

in hepatocytes and the signal may stem from other cell types in the liver. (C) Analysis of

gene expression profiles for mitochondrial substructures using GO analysis. (D) Summary of

the mitochondrial gene expression changes. p values were calculated using Kolmogorov-

Smirnov test. (E) Connectivity of genes correlating positively (adj.p.value <0.01) with cell

size as identified using the STRING database. Groups of functionally interacting genes are

indicated with green circles and named. Number of connections in positively and negatively

correlating gene sets (Fig. 2C) as well as in similar sized random networks (mean and SD of

five random networks) is show in the inset. Note that the number of connections in the gene

set correlating negatively with cell size is more than three times higher than that of the

positively correlating gene set. (F) Correlation of individual OxPhos complexes (I-V) and the

minichromosome maintenance complex (MCM) with nuclear radius. Boxplot indicates

median correlation. Outliers are shown with black dots. (G) Venn diagrams depicting the

overlap between differentially expressed genes in pairwise sample and size correlating genes.

Our data indicates a poor overlap between identified cell size genes and genes responding to

Cdk1 deletion or partial hepatectomy. (H) Cell size histogram and relative cell change in

dsRED control and Pop2 RNAi treated Kc167 cells. Cells were treated by RNAi for 4 days.

(I) Percentage of cells in G1 and G2 in dsRED and Pop2 RNAi treated Kc167 cells used for

Drosophila RNAseq analysis. G1 and G2 populations were analysed from DNA content

histograms. Data shown in G and H is mean and standard deviation (n=3). All data except H

and I are from mouse liver gene expression data set.

Figure S3, related to figure 3. Gene expression and metabolic changes related to

glycolysis and TCA cycle. (A) Quantification of mitochondrial number per image (n=7-‐

Glucose

Glucose-6-Phosphate

Fructose-6-Phosphate

Fructose-1,6-Bisphosphate

Dihydroxyacetone phosphate

Glyceraldehyde-3-Phosphate

1,3-Bisphosphoglycerate

3-Phosphoglycerate

2-Phosphoglycerate

Phosphoenylpyruvate

Pyruvate

Hexokinase

Glucose phosphate isomerase

Phosphofructokinase

Fructose diphosphate aldolase

Triosephosphate isomerase

Glyceraldehyde phosphate dehydrogenase

Phosphoglycerate kinase

Phosphoglycerate mutase

Enolase

Pyruvate kinase

Hk1Hk2

Gpi1

Pfkl

Pgam1

Pfkm

AldoaAldobAldoc

Tpi1

Gapdh

Pgk1

Eno1Eno3

Pkm2

Acetyl-CoAPyruvate dehydrogenase

Pdhb

LactateLactate dehydrogenase

Gck Glucokinase

Glucose sensing

Pklr

ADP-dependent glucokinaseAdpgk

2,3-bisphosphoglycerate mutase

2,3-Bisphospho

glycerate

Bpgm

Malate dehydrogenase

Mdh1Mdh2

TCA cycle

citrate

isocitrate

a-ketoglutarate

succinyl-CoAsuccinate

fumarate

malate

oxaloacetate

Glutamine

CoA

dehydrogenase

a-ketoglutarate

Ogdh

DlatPdha1

Glucokinase regulatory proteinGckr

glutamateoxaloacetate

aspartate

Glutaminase

Glutamate oxaloacetate transaminase

Got2Got1GlsGls2

Citrate synthase

Cs

Aconitase

Aco1Aco2

Fumarate hydratase

Fh1

2,3-bisphosphoglycerate phosphatase

Bpgm

Glut1/Slc2a1 Glut2/Slc2a2Glut5/Slc2a5 Glut8/Slc2a8

Glucose transporters

Pgam1

Sdha SdhbSdhc Sdhd

Idh1 Idh2 Idh3aIdh3b Idh3g

Glutaminolysis

0.8 - 1.0

0.6 - 0.8

0.4 - 0.6

0.2 - 0.4

0.0 - 0.2

0.0 - -0.2

-0.2 - -0.4

-0.4 - -0.6

-0.6 - -0.8

-0.8 - -1.0

Gene expressioncorrelation with nuclearradius

Ldha Ldhb

Isocitrate

dehydrogenase

Suclg1Suclg2Sucla2

Pentose phosphate

Serine synthesis

18

16

6-Phosphogluconate

13

12

15

14

Glyceraldehyde-3PDihydroxyacetone-P

Glycerol

13

12

Pyruvate

Lactate

Glycerol synthesis

Succinate

Malate

Fumarate

10.8

11.8

Citrate/Isocitrate

16

14

Phosphoserine

14

15

15

17

18

16

21

19

20

19

15

16

20

17

Glucose-6-phosphate

Bisphosphoglycerate

10.5

11.5

Fructose-6-phosphate

Glycolysis

TCA cycle

Branchingpathways

A

log2 ion count

log2 ion count

log2 ion count

log2 ion count

log2 ion count

log2 ion count

log2 ion count

log2 ion count

log2 ion count

log2 ion count

log2 ion count

log2 ion count

log2 ion count

log2 ion count

4

2

-2

-4

0

Glycogen

Glucose

Glucose-6-phosphate

Glyceraldehyde-3P

Bisphosphoglycerate

Lactate

Pyruvate

6-Phosphogluconate

Glycerol

Phosphoserine

Citrate/Isocitrate

Succinate

Fumarate

Malate

GlycolysisBranching

pathwaysTCA cycle

Relative metabolite change

Cdk1 knock-out

enhances

metabolite change

Cdk1 knock-out

suppresses/reverses

metabolite change

No cell size effect

Relative signal of

mtDNA/genomic DNA

Cdk1Flox/Flox

0.0

0.5

1.0

1.5

2.0

Cdk1Liv-/-

Pre Post Post Post PostPre Pre Pre

Flox/Flox PrePH

Flox/Flox PostPH

Liv-/- PrePH

Liv-/- PostPH

30

20

10

0

Gene expression (RNAseq)

Dna2

Rrm2b

Peo1Tk2

Polg

Dnaja3

Polg2

B

C

DGlycogen

20

12

16

log2 ion count

Cdk1fl/fl PrePH

Cdk1fl/fl PostPH

Cdk1Liv-/- PrePH

Cdk1Liv-/- PostPH

Sample order

Number

12

8

4

0

Area (AU)

0

4

8

Cristae

0

8

16

Density (AU)

0

1

2

Cdk1fl/fl PrePH

Cdk1fl/fl PostPH

Cdk1Liv-/- PrePH

Cdk1Liv-/- PostPH

Sample order

E

10), mitochondrial area (n=47-‐65 mitochondria), number of mitochondrial cristae

(n=44-‐65) and mitochondrial electron density (n=47-‐65). Data is mean ± SD. (B)

RNAseq expression values for genes involved in mitochondrial DNA replication in the liver

expression data. (C) Mitochondrial DNA to genomic DNA ratio as measured by quantitative

PCR from the liver samples (n=3). (D) Detailed map of glycolysis and TCA cycle with

branching biosynthesis pathways. Enzymes involved in each step are indicated next to the

metabolites in the pathway map. Positive and negative gene expression correlations with cell

size are in blue and red, respectively. Metabolite levels in liver samples are shown with box

plots. (E) Relative metabolite changes in CdkLiv-/- mice compared to Cdk1Flox/Flox control

animals. Fold changes for postPH vs. prePH were calculated and these compared between

Cdk1Liv-/- and control mice. Cdk1 knockout enhances metabolite changes in later stages of

glycolysis with an increase in metabolite levels going to serine and glycerol synthesis. The

sample order is the same in all plots (Flox/Flox prePH, Flox/Flox postPH, Liv-/- prePH and

Liv-/- postPH).

Figure S4, related to figure 4. Effects of mitochondrial targeting on proliferation and

histone acetylation. (A) HeLa cell numbers (red line) and cell size (blue line) was analysed

BA C

0

40

80

120

100

110

120

0 0.5 1 1.5 2 2.5

Azide (mM)

Cell number (% control)


0 25 50 75 100

Hela

Mdivi-1 (µM)

0

40

80

120

100

110

120

130


Cell number (% control) 200

100

0

Control Azide

electricalcurrentexclusion

Flowcytometry

Cell size

(% control)

200

100

0

Control AzideTotal protein/cell

(% control)

D

U2OS

Oxygen consumption

(pmol/min)

WT Rho0


90

100

110

120 600

450

300

150

0

0 40 80 120 160

Control Rotenone Antimycin A NaN3 Oligomycin

Time (min)

Oxygen consumption

(pmol/min)

0

100

200

300Inhibitorinjection

acetyl-coenzyme A

Mevalonate

pathway

Fatty acid

synthesis

pyruvate OAA citrate

citrateOAApyruvate

acetyl-coenzyme A

Mitochondria

Cytosol

Histone

acetylation

Pcx Cs

Aclymalate

Mdh1Me1

Brp44l Slc25A1

Control SLC25A1si1 si2

U2OSHeLa

90

120

110

100


H3H2A/H2BH4

Ac-Lys

H3-K9Ac

H4-K8Ac

H2B-K5Ac

Cdk1flox Cdk1Liv-/-PrePH PostPH PostPHPrePH

100

50

0 Ac-Lys

(% control) 100

50

0

Ac-Lys

(% control)

Ac-Lys

H3-K9Ac

H4-K8Ac

H2B-K5Ac

CoxIV

Control Rotenone

50

70

90

110 Cell size

Control

Control

Control

MB-3 CPTH2 C646

50 100

250

5 25 50 0.5

2.5

5

% Control

Cell number

Control Mdivi-1LipidMix - -+ +

Hela150

100

50

0

% control

U2OS150

100

50

0

% control

Cell size Cell number

Control Mdivi-1LipidMix - -+ +

E F G

H I J

K L

p<0.01

p<0.01

p<0.001

p<0.001

p<0.01

p<0.01

as a function of Mdivi-1 concentration (n=3, 48h). (B) U2OS cell numbers (red line) and cell

size (blue line) was analysed as a function of sodium azide concentration (n=3, 48h). (C)

Comparison of cell size measurement of control and azide treated chicken DT40 cells using

Casy TT (electrical current exclusion) and flow cytometry forward scatter methods. 1mM

azide was used for experiment (n=3, 48h). (D) Sodium azide (1mM) increases cell size by

production of more protein. U2OS were trypsinised and analysed by flow cytometry and

protein concentrations were measured by Coomassie method and normalised for cell size

(n=4, t-test). (E) Cell size (blue bars) and oxygen consumption (red line) of wild type (WT)

and Rho0 U2OS cells (n=4, t-test). (F) Validation of OxPhos inhibitor function using oxygen

consumption measurement (Seahorse assay). Inhibitors were injected at 50 minutes time

point. For concentrations used, see Table S5 (n=3-5). (G) Schematic of metabolite transport

between mitochondria and cytoplasm for lipid synthesis and histone acetylation. Expression

of the enzymes and transporters marked with red correlate negatively with cell size. (H)

Citrate transporter SLC25A1 knockdown by RNAi using two independent siRNAs in HeLa

and U2OS cell (n=3, 48h). p-value is <0.01 for all SLC25A1 treatments compared to control

siRNA (t-test). (I) Decrease in histone acetylation in vivo. Mouse liver sample histones were

purified using acid extraction, run on SDS-PAGE and stained with Coomassie (lowest panel).

Levels of histone acetylation were analysed by Western blotting using total acetylated lysine

(Ac-Lys) or individual acetylation sites. Quantification of the total Ac-Lys signal is shown in

the bottom panel. (J) U2OS cells were treated with 6 µM rotenone for 48 h. Histone

acetylation was analysed by Western blot from total cellular lysates using total acetylated

lysine (Ac-Lys) or individual acetylation sites. Total Ac-Lys levels were quantified and

plotted (blue bars). CoxIV was used as loading control. (K) U2OS cell size (blue line) and

numbers (red line) after histone acetylation inhibitor treatments. Data shown is mean with

standard deviation (n=3, 28h). MB-3 is a Gcn5 inhibitor, CPTH2 inhibitor modulates the

Gcn5 network, and C646 is a competitive inhibitor of p300/CBP. (L) Rescue of Mdivi-1

induced cell size increase by 50 µl/ml LipidMix in U2OS (upper panel, 72h) and HeLa cells

(lower panel, 44h) (n=3, t-test). Data shown in all panels is mean and standard deviation.

B

D E F

2

1

0

-1

-2

Pparg expression mRNA

counts per million (log2)


PrePH PostPH PrePH PostPH

20

10

0

-5mRNA counts per

million (log2)

Pparg

Ranked gene expression

C

-0.3

0.3

0.6

0

Correlation with nuclear size

STAT

PPAR

E2F

CSDARIDMBD

ETS

TSC22

IRF

TF family rank

A

19

20

p<0.01

oleic acid

log2 ion count


ctrl SREBF1 SREBF2

130

120

110

100

90

Cell size (% control) 130

120

110

100

90

Control

SREBF1

SREBF2

SREBF1+2

Srebf1 Srebf2

Gtf2i

Nr1h2 Mlxipl

Atf6

Stat5b

Irf6

Zfp467Irf3

Stat1

Rxrg

Rxra

Hnf4aRxrb

Nr2f6

Ppara

SREBP1

SREBP2

SREBP1+2

SREBP1

SREBP2

SREBP1+2

10% FBS1% lipid-free

FBS

fl-SREBP2

m-SREBP2

Actin

Control

Control

120

110

100

90

hTERT-RPE

Cell size

(% control)

Srebf1

Srebf2

Scap

Insig1

Insig2

300

200

100

0

Cdk1Flox/Flox PrePH

Cdk1Liv-/- PostPH

RNAseq

reads/million

G

H

I


PrePH PostPH PrePH PostPH

Ergosterol

PE 34:2

PI 34:1

PC 34:2

PI 32:1

PC 32:2

PE 32:2

IPC 44:0:4

MIP2C 44:0:4

MIPC 44:0:4

PA 34:2

PA 34:1

lysoPC 18:1

lysoPC 16:1

PE 34:1

lysoPE 18:1

PA 32:1

PE 32:1 0

2

4

6

8

10

12

14

16

J

BY4741 (haploid a)

BY4742 (haploid

BY4743 (diploid)

Mean values (mol%/total lipids)

p<0.001p<0.001

p<0.001

Figure S5, related to figure 5. Cell size is associated with changes in lipid biosynthesis.

(A) Analysis of transcription factor family expression correlation with cell size in mouse

liver. Mean correlations for each family with three or more genes were calculated. (B) A

network of lipogenic transcription factors correlates negatively with cell size. All

transcription factors with cell size correlation <-0.3 were analysed for connectivity using

STRING database. The resulting network of 17 out of these 55 transcription factors which are

connected to each other is shown. (C) Expression of SREBPs and SREBP processing proteins

is strongly downregulated as measured by liver RNAseq. Mean and standard deviations for

the smallest and largest cells are shown. (D) Quantification of hTERT-RPE cell size changes

by targeting SREBP1 and 2 in normal culture medium with 10% FBS or two days in normal

medium followed by 24h in 1% delipidated FBS (n=3, 72h) to emulate the conditions used by

[27]. Cell size was quantified by flow cytometry. Both full length (fl-SREBP2) and mature

(m-SREBP2) forms of SREBP2 are detected by Western blotting. β-actin was used as

loading control. The cell size difference between control siRNA groups under both conditions

is statistically significant (p-value <0.001, t-test) as are the differences between SREBP

siRNA treatments to the control siRNA (p<0.001 in all, t-test). (E) siRNA inhibition of

SREBFs increases cell size in a dose dependent manner. U2OS cells were transfected with

SREBF1 and SREBF2 siRNAs that were used at 6.3, 12.5, and 25 nM concentration and

adjusting total siRNA concentration to 25 nM using the control siRNA (n=3, 48h). Compared

to control siRNA p-value is <0.001 with all siRNA concentrations (t-test). (F) Redundant

action of SREBP1 and 2 on cell size. U2OS cells were transfected with 12.5 nM of SREBP1,

SREBP2 or both in combination adjusting total siRNA concentration to 25 nM using the

control siRNA (n=4, 48h, t-test). (G) Cdk1Liv-/- mice display increase in oleate, a marker for

fatty liver disease. Statistical significance was measured by ANOVA followed by Tukey's

test. (H) PPARγ [Pparg], another biomarker for fatty liver, expression is not induced as

shown by RNAseq and (I) PPARγ does not increase in response to cell size changes in liver

gene expression data. We conclude based on lack of PPARγ induction that the mice do not

have fatty liver disease. Data shown in all panels is mean and standard deviation. (J)

Comparison of lipid profiles in haploid (smaller) and diploid (larger) yeast cells. Lipidomic

data is from Klose et al. [28]. Profiles for most abundant lipid species in haploid and diploid

cells are plotted and names of the lipid species which appear to be differentially regulated in

haploid and diploid cells are indicated with red. Two of the three most abundant

phospholipids phosphatidylethanolamine (PE 34:2) and phosphatidylcholine (PC 34:2)) are

increased in diploid cells versus haploid cells. Inositolphosphorylceramide (IPC44:0:4) and

phosphatidic acid (PA 34:1) are less abundant in diploid cells. This data suggests that

individual lipid species respond differentially to cell size changes. For naming conventions

see original article [28].

Figure S6, related to figure 6. Cell size, lipid synthesis and mitochondrial functionality

are coupled. (A) Inhibition of SREBP maturation by fatostatin in HepG2 (left, 72h) and

hTERT-RPE cells (right, 96h) increases cell size (n=3). (B) Rescue of simvastatin (7.5 µM)

effect on cell size and cell proliferation by mevalonolactone (5 mM) in HepG2 cells (n=3,

72h, t-test). (C) Reciprocal effects of the lipid mix on U2OS cell size and cell number in

LipidMix (µl/ml)

0.02

0.01

0

Mitotracker red/cell size (AU)

Fatostatin

Control

0.019

0.020

0.021

0.022

Control

SREBF1

SREBF2

HepG2

40

60

80

100

120

90

100

110

120

130

0 5 10 25

Fatostatin (uM)



A

E

C

D

Mitotracker red/cell size (AU)

hTERT-RPE

0

40

80

120

90

100

110

120

0 5 10 15 20 25



Fatostatin (µM)

88

92

96

100

104

70

90

110

130

0 20 40 60 80



40

60

80

100

120

% of control

B

Control

Simvastatin

Mevalonolactone

Simvastatin and

mevalonolactone

Cell size Cell number

p<0.01

p<0.01p<0.01

U2OS

p<0.01

p<0.001

normal FBS containing medium (n=3). In normal medium, lipotoxicity is observed with high

doses. (D) U2OS cells were treated with or without 20 µM fatostatin. Mitochondrial

membrane potential was measured with MitoTracker Red and normalised to cell size (n=3,

54h, t-test). (E) U2OS cells were treated with control, SREBP1 or SREBP2 RNAi.

Mitochondrial membrane potential was measured with MitoTracker Red and normalised to

cell size (n=3, 50h, t-test). Data shown in all panels is mean ± SD.

SUPPLEMENTAL EXPERIMENTAL PROCEDURES Mouse model

The generation of the Cdk1 conditional mice has been described previously [S1]. For the

RNAseq and metabolomics analyses, Cdk1Flox/Flox and Cdk1Liv-/- mice were used before and

96 hours after partial (70%) hepatectomy. All mice used were 14 weeks old females at the

time of the partial hepatectomy (PH) surgery. The liver was collected before and 96 hours

after PH, snap frozen, and stored at -80°C until RNA or metabolite extraction. Nuclear size

was calculated from Feulgen stained histological sections using Fiji image analysis software

(version 1.46a).

RNA sequencing

Total RNA from liver samples was purified using QIAzol Lysis Reagent (Qiagen) and

homogenization using Precellys homogenizer (Bertin Technologies). 15 µg total RNA was

fragmented for 3 min at 70°C in RNA fragmentation buffer (Ambion). Fragmentation was

terminated by cooling of the sample on ice and addition of EDTA to 17 mM final

concentration. RNA sequencing was performed by tag profiling essentially as described [S2].

The resulting RNAseq library was purified using SPRI beads and sequenced at Genotypic

Technology Pvt. Ltd, Bangalore, India, using 54 bp single end sequencing on Illumina GAIIx.

For Drosophila samples, total RNA from Kc167 cells treated with dsRED control and Pop2

RNAi in triplicates for four days was purified using Qiazol and processed as above. The

resulting library was sequenced at the Genepool, University of Edinburgh, using 50 bp single

end sequencing on Illumina HiSeq.

The sequencing reads were mapped to transcriptome using Bowtie version 0.12.5

[S3]. For mapping, we used the longest transcript in Ensembl database. Sequencing reads

matching to identical positions and having identical molecular identifier sequences were

merged and the numbers of unique template molecules were counted to get expression levels

[S2]. Differential gene expression was analysed using EdgeR package (mouse samples) or

DEseq (Drosophila samples) in R.

Metabolomics analysis

For each liver, three 50 mg samples were extracted with 1.5 ml cold 50:50% methanol/H2O

using TissueLyser homogenizer (Qiagen) at 2000 rpm for 5 min. 650 µl supernatant were

dried and resuspended in 1.5 ml H2O + 0.5 ml methanol and analysed in a 1:10 dilution. For

organic extraction, cell pellets from aqueous extraction were further homogenized in 1.5 ml

dichloromethane/methanol and 650 µl of the resulting supernatant were dried and

resuspended in 1 ml acetonitrile + 0.5 ml methanol. Samples were analysed on a 6550

Agilent QTOF mass spectrometer by untargeted flow injection analysis as described

previously [S4]. Profile spectra with high mass accuracy were recorded from 50 to 1000 m/z

in negative ionization mode.

For analysis, data from technical replicates (successive sample injections) was

averaged. For each sample, raw intensity data was median normalised using "robust quantile

normalization" function of preprocessCore package in R. This normalisation results in the

relative levels of individual metabolite to the total metabolite levels of the tissue and not the

metabolite levels per cell. Ions were annotated based on accurate mass comparison against

8492 human metabolites present in the Human Metabolome Database [S5] using 1 mDa mass

tolerance. The annotated metabolites were mapped on the human metabolic network derived

from the KEGG database using pathway projector [S6].

Correlation of gene and metabolite expression with nuclear size

The nuclear sizes were normalized relative to those of the Cdk1Flox/Flox mice before

hepatectomy. Pearson correlation co-efficients (r) were calculated using all samples for each

gene and metabolite. Gene ontology (GO) classifications for the genes were downloaded

from Ensembl database (version 62, April 2011) and used to calculate correlation histograms

for each cellular subcomponent. For Drosophila data, GO classifications were obtained from

Ensembl (version 73, September 2013).

Quantitative RT-PCR

Liver DNA was isolated and amplified in triplicates using primers

TAGAGGGACAAGTGGCGTTC and CGCTGAGCCAGTCAGTGT targeting 18S rDNA

sequences and mitochondrial DNA using primers

ACTTCTGCCAGCCTGACCCATAGCCA and ACGCGAATGGGCCGGCTGCGTAT

using Maxima SYBR Green qPCR Master Mix (ThermoFisher) with 0.1 µM ROX, 0.3 µM

primers. Melting curve analyses and gel electrophoresis indicated a single PCR product. The

ratio of the mitochondrial to genomic DNA values was calculated from the obtained

quantification cycle (Ct) values. For RNAseq validation, total RNA used for RNAseq was

reverse transcribed and amplified in triplicates with the gene-specific primer pairs (Table S6)

obtained from GETPrime qPCR primer database [S7]. qPCR was performed using Maxima

SYBR Green qPCR Master Mix. Results were analysed with R (version 2.14.0) package

HTqPCR (version 1.8.0) using the DCt method.

Cell culture and reagents

U2OS, HeLa, and HepG2 cells were cultured in DMEM containing 4.5g/l glucose, 10% FBS,

L-glutamine and penicillin and streptomycin. hTERT-RPE cells were grown in Advanced

DMEM/F-12 containing 4.5g/l glucose, 10% FBS, L-glutamine and penicillin and

streptomycin and the cells were supplemented with hygromycin B (50µg/ml). DT40 cells

were grown in RPMI with 10% FBS, 3% chicken serum, 1 mM β-mercaptoethanol, L-

glutamine and penicillin and streptomycin.

Delipidated FBS was prepared by extracting FBS once with 2:1 vol/vol mixture of

diisopropylether and n-butanol and a second extraction with diisopropylether followed by

extensive dialysis against PBS using 10 000 MWCO membrane. U2OSrho0 cells were

generated by incubating the cells with 0.1 µg/ml ethidium bromide for 3 weeks in the

presence of 50 µg/ml uridine in DMEM/10% FBS. Cell size and cell number measurements

were conducted using flow cytometry using Accuri C6 cytometer (Becton-Dickinson) or by

electrical current exclusion method (CASY TT, Roche).

Small molecules were obtained from Sigma-Aldrich, Tocris, Santa-Cruz and

Calbiochem. For small molecule data shown in Fig. 4A concentrations and solvents are

shown in Table S5. For rescue experiments using LipidMixture 1 (Sigma) cells were treated

with LipidMix simultaneously with chemical treatments or 24h after siRNA transfections.

RNAi was performed by transfecting with 25 nM siRNA with HiPerfect (Qiagen). The

siRNA sequences are shown in Table S6. Antibodies were used at their recommended

concentrations and detected using infrared-dye conjugated secondary antibodies and LICOR

Odyssey detection system.

For DNA content measurement, mammalian cells were stained with propidium iodide

as previously described [S8]. For Drosophila cells, DNA staining was performed using

Vybrant DyeCycle Orange live cell stain (Life Technologies) as per manufacture's

instructions. For measuring mitochondrial membrane potential, MitotrackerRed (Life

Technologies) was added to 150 nM final concentration for 40 min before the FACS assay

and cells were washed twice with PBS before flow cytometry analysis. Oxygen consumption

was measured using Seahorse XF24 instrument according to manufacturer's instructions.

Antibodies and Western Blotting

Histones were isolated using acid extraction and separated on 4-12% SDS-PAGE gels in

MES buffer (LifeTechnologies). For Western blots, the following antibodies were used:

Acetylated-Lysine #9441, Acetyl-Histone H3 (Lys9) (C5B11) #9649, Acetyl-Histone H2B

(Lys5) #2574, Acetyl-Histone H4 (Lys8) #2594, pAMPK #2535, CoxIV #4850, GAPDH

#5174, b-Actin (#4970) (all from Cell Signaling Technology). PGC-1a antibody (clone

4C1.3) was from Millipore. For analysis of OxPhos protein expression MitoProfile Total

OXPHOS Rodent WB Antibody Cocktail (ab110413, Abcam) was used. Antibodies were

used at their recommended concentrations and detected using infrared-dye conjugated

secondary antibodies and LICOR Odyssey detection system.

Electron microscopy

Liver pieces of 1 mm3 obtained before partial hepatectomy were immersed in 4%

paraformaldehyde, 2.5% glutaraldehyde in 0.1M phosphate buffer, pH 7.4 for 5 days at 4ºC

while the samples obtained after partial hepatectomy were fixed for 24 hours at 4ºC. Tissue

samples were rinsed in 0.1M phosphate buffer, pH 7.4 and then 0.1M Sodium Cacodylate

Trihydrate, pH 7.6 on ice. After fixation samples were exposed to Osmium fixative solution

[1% OsO4 + 1.5% K3Fe(CN)6 in 0.1M Sodium Cacodylate Trihydrate pH 7.6] for 1 hour at

RT. Samples were washed in dH20 and dehydrated through an ascending ethanol series (from

25% to 100% ethanol) and 100 % acetone. The infiltration was continued with resin in 100%

ethanol and then fresh resin overnight. Finally samples were embedded in Spurr’s fresh resin

and polymerized at 60ºC for 24 hours [S9]. Ultrathin sections were counterstained with 5%

uranyl acetate and Reynold's lead citrate and examined with a under JEM-1010 electron

microscope operated at 80 kV or JEM-2200FS at 100 kV. Images were analysed using

ImageJ.

Analysis of total phospholipids

Phospholipids were measured using a colorimetric method based on the formation of a

complex between phospholipids and ammonium ferrothiocyanate [S10]. Briefly, liver

samples (~20mg) were homogenised in 300µl PBS and 750µl of methanol and homogenates

were extracted with chloroform. Organic phase was allowed to react with FeCl3-thiocyanate

reagent followed by absorbance measurement at 488 nm from the organic phase. Liver

samples were analysed in triplicates.

Analysis of yeast lipidomics data

Haploid and diploid lipid profiles were obtained from supplementary file from Klose et al

[S11]. Individual lipid species were ranked by abundance in diploid cells and lipid levels in a

and a mating pairs were compared to these. Only most abundant lipid species are shown

(>1.4 mol%/total lipids). Statistical difference between yeast strains for individual lipids can

not be calculated as only means and standard deviations are available in the original

publication.

Statistics

The p-values for the obtained Pearson correlations were calculated from Student’s t-

distribution and were adjusted using Benjamini and Hochberg False Discovery Rate

correction. Statistical significances for the differential gene expression were directly obtained

using the EdgeR package (for mouse data) or DEseq package (for Drosophila data) in R. Cell

based assay data is presented as mean ± standard deviation using two-tailed t-test. All error

bars depict standard deviation. The whiskers in boxplots depict maximum and minimum

values excluding outliers which are indicated with circles. The significance of differential

correlation of gene expression levels with subcellular structures versus whole cell data was

done using Kolmogorov-Smirnov test. Significance of the individual metabolite level

changes between treatments was analysed by two-tailed t-test or ANOVA followed by

Tukey's HSD test as indicated in the figure legends. The 90% confidence intervals for linear

regression were calculated using predict function in R.

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S2. Kivioja, T., Vaharautio, A., Karlsson, K., Bonke, M., Enge, M., Linnarsson, S., and Taipale, J. (2012). Counting absolute numbers of molecules using unique molecular identifiers. Nature methods 9, 72-74.

S3. Langmead, B., Trapnell, C., Pop, M., and Salzberg, S.L. (2009). Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10, R25.

S4. Fuhrer, T., Heer, D., Begemann, B., and Zamboni, N. (2011). High-throughput, accurate mass metabolome profiling of cellular extracts by flow injection-time-of-flight mass spectrometry. Anal Chem 83, 7074-7080.

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S8. Bjorklund, M., Taipale, M., Varjosalo, M., Saharinen, J., Lahdenpera, J., and Taipale, J. (2006). Identification of pathways regulating cell size and cell-cycle progression by RNAi. Nature 439, 1009-1013.

S9. Wisse, E., Braet, F., Duimel, H., Vreuls, C., Koek, G., Olde Damink, S.W., van den Broek, M.A., De Geest, B., Dejong, C.H., Tateno, C., et al. (2010). Fixation methods for electron microscopy of human and other liver. World journal of gastroenterology : WJG 16, 2851-2866.

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Identification of Transcriptional and Metabolic Programs Related to Mammalian Cell Size

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