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Genome dilution by cell growth drives starvation-like proteome remodeling in mammalian and yeast cells

Nature Structural & Molecular Biology volume 31, pages 1859–1871 (2024)Cite this article

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Abstract

Cell size is tightly controlled in healthy tissues and single-celled organisms, but it remains unclear how cell size influences physiology. Increasing cell size was recently shown to remodel the proteomes of cultured human cells, demonstrating that large and small cells of the same type can be compositionally different. In the present study, we utilize the natural heterogeneity of hepatocyte ploidy and yeast genetics to establish that the ploidy-to-cell size ratio is a highly conserved determinant of proteome composition. In both mammalian and yeast cells, genome dilution by cell growth elicits a starvation-like phenotype, suggesting that growth in large cells is restricted by genome concentration in a manner that mimics a limiting nutrient. Moreover, genome dilution explains some proteomic changes ascribed to yeast aging. Overall, our data indicate that genome concentration drives changes in cell composition independently of external environmental cues.

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Fig. 1: Ploidy-to-cell size ratio, rather than cell size itself, drives conserved changes in mammalian proteome composition.
Fig. 2: Size-dependent changes in proteome composition are conserved from yeast to human cells.
Fig. 3: Transcriptional and post-transcriptional mechanisms underlie the size-scaling behavior of individual yeast proteins.
Fig. 4: Increasing cell size activates the general stress response.
Fig. 5: The large size of old yeast explains some age-associated phenotypes.
Fig. 6: Increasing cell size elicits a starvation-like phenotype in mammalian and yeast cells.
Fig. 7: External environment has a large effect on proteome composition, but only a small effect on how the proteome changes with cell size.
Fig. 8: Cell size is an intrinsic determinant of cell composition in growing cells.

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Data availability

The UniProt proteome database (https://www.uniprot.org) was used as a reference to search mass spectral data. The data used to generate all the figures are provided as supplementary tables. All raw proteomics datafiles are available on PRIDE (accession no. PXD052786). All raw mRNA-seq files are available on the Gene Expression Omnibus (accession no. GSE269091).

Code availability

The code used to process proteomics data and calculate the protein slope value is available at https://github.com/mikechucklanz/Yeast-proteomics-processing-and-slope-calculation (ref. 84).

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Acknowledgements

We thank K. Schmoller, and members of J.M.S.’s and J.E.E.’s labs for feedback on the manuscript and helpful discussions. This work was generally supported by the National Institutes of Health (NIH; through grant no. R35 GM134858 to J.M.S.) and the Chan Zuckerberg Biohub San Francisco (to J.M.S., Investigator Award, MCL collaborative postdoctoral fellowship). The NIH grant (no. R01 DK128578 to J.M.S.) supported the work on hepatocytes.

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Authors and Affiliations

  1. Department of Biology, Stanford University, Stanford, CA, USA

    Michael C. Lanz, Shuyuan Zhang, Matthew P. Swaffer, Luisa Hernández Götz, Jacob Kim & Jan M. Skotheim

  2. Chan Zuckerberg Biohub San Francisco, Stanford University, Stanford, CA, USA

    Michael C. Lanz, Frank McCarthy, Joshua E. Elias & Jan M. Skotheim

  3. Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA

    Inbal Ziv, Jacob Kim & Daniel F. Jarosz

  4. Department of Developmental Biology, Stanford University, Stanford, CA, USA

    Daniel F. Jarosz

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  1. Michael C. Lanz
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Contributions

M.C.L. designed and carried out yeast-related experiments, except for Fig. 7d (performed by L.H.) and Fig. 5 (performed by D.F.J. and I.Z.). M.C.L. prepared samples for MS analysis and acquired MS data. M.C.L. and F.M. maintained the performance of the mass spectrometers. M.C.L. performed all data analyses. S.Z. derived the hepatocyte primary cells and FACS-isolated the cell populations measured in Fig. 1. M.P.S. and M.C.L. performed mRNA-seq for Fig. 3. M.P.S. constructed the SILAC yeast strains. J.K. calculated the surface:volume ratio for diploid and haploid yeast strains. M.C.L. and J.M.S. wrote the paper. J.M.S. and J.E.E. supervised the study.

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Correspondence to Michael C. Lanz or Jan M. Skotheim.

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Nature Structural & Molecular Biology thanks Bruce Futcher and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Dimitris Typas, in collaboration with the Nature Structural & Molecular Biology team.

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Extended data

Extended Data Fig. 1 Supplement for Fig. 1.

(a) Stained liver section illustrating the heterogeneity of hepatocyte ploidy. Mononucleated cells are diploid and binucleated cells are tetraploid. See methods for detailed description of primary cell isolation and culturing. (b) FACS scheme to isolate G1 phase primary hepatocyte cells of different sizes and ploidy. A fluorescent FUCCI cell cycle reporter was used to identify G1 phase cells. After gating for G1 cells, 2n and 4n G1 cells were differentiated using a DNA stain. Diploid and tetraploid G1 cells were separated by size using the side scatter parameter. (c) Example images of post-sorted primary hepatocytes stained with Hoechst. The mononucleated cell is diploid and the binucleated cell is tetraploid. Sorted populations were lysed and peptides were quantified using MS3-TMT proteomics (Fig. 1; methods).

Extended Data Fig. 2 Supplement for Fig. 2.

(a) Budding yeast of different sizes were metabolically labeled in cell culture and subjected to proteomic analysis. Strategies to separate cells by size are described in Fig. 2 and Extended Data Fig. 3. See methods for processing steps prior to mass spec data acquisition. (b) After cell lysis, protein concentrations were quantified. An equal amount of protein from small-, medium-, or large-cell size populations (each possessing a unique metabolic SILAC label) were mixed. Loading error from the mixing process was normalized as described in the methods. Rather than normalize L/H and L/M SILAC ratios separately, we normalize all three channels together so that the values in our dataset represent relative changes across all cell sizes. Data normalization from a poorly mixed hypothetical example experiment is depicted. (c) For each individual peptide triplet, we determined the fraction of the triplet’s total ion intensity present in each SILAC channel. The distributions of these fractions were then adjusted by the median (see methods for a complete description of the normalization process). Data from the hypothetical example in (b) is depicted. (d) Peptide slope values are calculated from a linear regression of the relative ion intensity in each SILAC channel and mean cell size. Mean cell size was determined by Coulter counter prior to mixing and lysis. The mean squared error was used to track the linear fit of each peptide triplet regression. (e) Correlation of peptide slopes calculated from replicate experiments before and after applying a filter for mean squared error (MSE). 146,492 unique peptide measurements were identified in two replicate experiments (“G1 arrest time” from Fig. 2). A unique peptide measurement is defined by the peptide sequence, modification state, charge state, and pre-fractionation fraction number. Loosely filtering peptide triplets by MSE increased the correlation between biological replicate experiments.

Extended Data Fig. 3 Supplement for Fig. 2.

(a) Cell cycle distribution and steady-state growth rate is unaffected by the different SILAC labels. Cell size mutants have doubling times similar to wild type (~90 min) when growing asynchronously in synthetic complete media with 2% glucose. (b) Growth behavior before and after G1 arrest in different SILAC media. The curved trajectory indicates a declining specific growth rate ~200 minutes after washout. Measurements of volume and optical density were collected in parallel on the same cultures. Y-axis is relative to 135 min, which is when ~95% of cells are in G1 phase. (c) The attainment of differentially sized cells was confirmed using a Coulter counter. Color gradients correspond to replicate cultures grown with light, medium, and heavy SILAC labels. (d) PC1 vs PC2 plotted for both orthogonal experimental systems. (e) Illustration of the protein slope calculation from relative changes in peptide concentration and cell size for both orthogonal experimental systems. See methods for a detailed explanation of the protein slope calculation. (f) To determine if protein abundance predicts size-scaling behavior, the individual proteins were grouped into abundance quartiles (depicted in the left plot). We only considered the ion intensity from the “small” cell SILAC channel in each of three G1 arrest time replicate experiments. The right plot depicts the distribution of mean slope values (G1 arrest time, n=3) for the proteins in each abundance quartile. Box region represents the median and interquartile range (IQR). Tails extend to 1.5x the IQR. (g) Correlation of protein slopes calculated from the two orthogonal experimental systems. Each slope value is the mean of three replicates for both systems. For the left panel, the mean volume measurements from the cell size mutants (C) were used to calculate the protein slope. For the right panel, the mean volume measurements from (C) were weighted by the relative DNA content shown in (A). Blue dots are x-binned data and error bars represent the 99% confidence interval. The “adjusted” plot is shown in Fig. 2. (h) Heatmap depicting the relative concentrations of ~4000 budding yeast proteins in the G1 arrest time experiment shown in (c) and (d). Proteins are ordered from top to bottom by descending protein slope value.

Extended Data Fig. 4 Supplement for Fig. 2.

(a) Experimental scheme to test whether SILAC-related mutations or culture density affects size-dependent proteome changes. The indicated samples were labeled with 10-plex TMT. A protein slope value was calculated in a manner similar to Fig. 2d. Here, TMT-MS3 reporter ions were used to quantify relative changes in protein concentration rather than MS1 SILAC triplet ions. (b) Cell size distributions determined by a Coulter counter. Time points of the G1 arrest time course are differentially colored. (c) During the G1 arrest time course, culture flasks were repeatedly diluted with pre-warmed media to maintain a constant culture density. (d) Principal component analysis of the proteome measurements of the G1 arrested cells. The 1st principal component is plotted against mean cell volume. Dot size represents mean cell volume. Colors represent the replicate experiments in the different strain backgrounds depicted in (a). (e) Correlation of protein slope values calculated in the arg4Δlys1Δ and ARG4 LYS1 strain backgrounds from (a). Each slope was calculated from a single replicate experiment. Blue dots are x-binned data and error bars represent the 99% confidence interval. r value denotes the Pearson correlation coefficient. (f) Correlation of protein slope values calculated using TMT with the protein slope measurements calculated using SILAC (G1 arrest time, Fig. 2). TMT-calculated slope is the mean of the two axes plotted in (e). Blue dots are x-binned data and error bars represent the 99% confidence interval. r value denotes the Pearson correlation coefficient.

Extended Data Fig. 5 Supplement for Fig. 4.

(a) Heatmap of relative protein concentration changes across different cell sizes. Each size is represented by two replicate columns. Size mutants correspond to asynchronous whi5Δ, WT, and cln3Δ cultures. G1 arrest time corresponds to four time points after G1 arrest taken at 1-hour intervals (using the genetic systems described in Fig. 2). (b and c) Coulter counter measurements of (b) MSN2 MSN4 and (c) msn2Δmsn4Δ G1-arrested cells. Biological replicate experiments are differentially shaded. (d) Proteins whose expression is dependent on msn2Δmsn4Δ are denoted in blue. Proteins highlighted in blue are the same set depicted in Fig. 4d. Msn2/4-dependency was defined as decrease in concentration of > 1.6-fold. (e) Fig. 4d is re-plotted here for reference. The same genes identified in (d) as Msn2/4-dependent are shown in blue. (f) Relative surface area (that is, surface area-to-volume ratio) for individual yeast cells was calculated from wide-field images of asynchronously proliferating cells. Yeast mothers and buds are assumed to be prolate ellipsoids. By measuring the polar (major axis) and equatorial (minor axis) radii, the surface area S and volume V of each ellipsoid was calculated (see methods). (g) Relative surface area (μm2 / μm3) measurements for individual cells of the indicated strain background. Average surface area-to-volume ratio for each strain is marked by the dashed horizontal line. (h) Coulter counter measurements of the indicated strains. Biological replicate experiments are differentially shaded. SILAC channels were swapped for replicate experiments to maximize measurement accuracy. (i) Correlation of protein slope values (G1 arrest time) with the concentration ratios calculated from the binary comparison of the indicated strains. Protein slope values are the average from three replicate experiments (see Fig. 2). Each concentration ratio is the average of two SILAC label-swapped replicate experiments. Core histone proteins are shown in red. Blue dots are x-binned data and error bars represent the 99% confidence interval. r value denotes Pearson correlation coefficient.

Extended Data Fig. 6 Supplement for Fig. 5.

(a) Correlation of log2 (old / young) concentration ratios derived from young and old budding yeast mothers and their daughter cells. Each concentration ratio is the average from three replicate experiments. Blue dots are x-binned data and error bars represent the 99% confidence interval. (b) Relative concentrations of core histone proteins in young, middle-aged, and old yeast mothers. Each dot corresponds to 1 of 3 biological replicate experiments. Measurements of the daughters of young, middle-aged, and old mothers is depicted in Fig. 5. (c) Correlation of age-associated and size-dependent proteome changes. Log2 concentration ratio (old / young) is calculated from mother cells. Protein slope value is from the G1 arrest time experiment in Fig. 2. Measurements of the daughters of young, middle-aged, and old mothers is depicted in Fig. 5. Concentration ratios are the average from three replicate experiments. Blue dots are x-binned data and error bars represent the 99% confidence interval. r value denotes Pearson correlation coefficient. (d) Correlation grid containing size- and age-associated proteomics data. Protein slope values (Fig. 2), log2 (old / young) concentration ratios for daughters (Fig. 5) and mothers (b), were cross correlated with similar measurements from the literature. Numbers and colors denote Pearson correlation value for indicated datasets.

Extended Data Fig. 7 Supplement for Fig. 6.

(a) Schematic outlining a phosphoproteomic approach to determine whether changes in cell signaling coincide with size-dependent proteome remodeling. (b) The attainment of differentially-sized cells was confirmed using a Coulter counter. Color gradients correspond to replicate cultures grown with light, medium, and heavy SILAC labels. (c) Correlation of phosphopeptide ratios calculated from two of the three label-swapped replicate experiments. Each phosphopeptide measurement is colored based on the size-scaling behavior of the protein that harbors the phosphorylation event (that is, its protein slope). The correlation plot illustrates that the main source of variance in the phosphoproteomic data (Large / Small) is changes in the concentrations of the proteins harboring the phosphorylation sites. (d) Large changes in cell cycle-related signaling between asynchronous and arrested yeast. Cumulative distribution of all phosphopeptide ratios (gray) and phosphopeptides that harbor CDK1-dependent phosphorylation events (blue)75. (e) Phosphopeptide ratios (Large / Small) were corrected using the protein slope value (Fig. 2d). This correction reduces the variance in the phosphoproteome data set. Cumulative distribution of all phosphopeptide ratios before (red) and after (gray) a correction is applied to mitigate protein-level change in concentration. The gray distribution is depicted in Fig. 6f.

Extended Data Fig. 8 Supplement for Fig. 7.

(a) Proteome comparison of budding yeast utilizing fermentable or non-fermentable carbon sources. Four label-swapping biological replicate experiments were initially performed. Two of these experiments were then pre-fractionated for deeper proteome analysis. The media mixtures used in the comparison were synthetic complete with 2% dextrose (Glucose) and synthetic complete with 1% ethanol and 2% glycerol (Ethanol/glycerol). (b) Principal component analysis of all 4 biological replicate experiments. The same plot is shown twice. Coloring highlights the source of variance for PC1 and PC2. (c) Protein abundance does not influence the degree to which a protein’s concentration changes between growth conditions. For each individual protein, a crude estimation of copy number (summed peptide intensity) was used to bin the proteome by abundance quartiles. One of the two replicate experiments was used to calculate summed ion intensity for each protein. Box region represents the median and IQR. Tails extend to 1.5x the IQR. (d) Correlation of SILAC ratios for every unique peptide measurement shared between two biological replicate experiments. A unique peptide measurement is defined by the peptide sequence, modification state, charge state, and fraction number. To filter out peptide measurements that were contaminated by analytical interference, peptides that produced a reciprocal measurement between SILAC-swapped replicates were excluded (bottom plot). (e) G1 arrest time course (as described in Fig. 2) was performed in synthetic complete media supplemented 1% ethanol and 2% glycerol as a carbon source. The length of the G1 arrest time course was extended due to the slower growth rate in ethanol/glycerol media. Size separation was confirmed using a Coulter counter. Light-, medium-, and heavy-labeled yeast are differentially shaded. (f) Principal component analysis of the proteome measurements on small-, medium-, and large-sized cells grown in ethanol/glycerol media. The 1st principal component is plotted against mean cell volume. Dot size represents mean cell volume. Colors represent the SILAC label. SILAC labeling orientation for small, medium, and large cells was swapped for three replicate experiments. All three replicate experiments are plotted together.

Extended Data Table 1 Strains list
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Supplementary Table 1

Proteome measurements of primary mouse hepatocytes of different sizes and ploidy, related to Fig. 1.

Supplementary Table 2

Orthology-matched human, mouse, and yeast proteins and their corresponding proteome measurements, related to Figs. 1g and 2h–j.

Supplementary Table 3

Protein slope values derived from all yeast experiments. Includes the gene set used for all ESR-related analyses, related to Figs. 2, 4 and 7.

Supplementary Table 4

All significant GO annotations and their corresponding 2D annotation enrichment scores, related to Figs. 2g,j and 7f.

Supplementary Table 5

The mRNA slope values and corresponding protein slope values. It also includes all input data for the linear regression models, related to Fig. 3.

Supplementary Table 6

Input data for stress-associated correlation grid (Fig. 4a), TMT-16plex analysis of size mutants and G1 arrest time experiments combined (Fig. 4c) and proteomic analysis of msn2Δmsn4Δ mutant strains (Fig. 4d). All are related to Fig. 4.

Supplementary Table 7

Label-swapped SILAC proteomics comparison of wild-type haploids, diploids and size mutant strains, related to Fig. 4f.

Supplementary Table 8

Proteome measurements of young, middle-aged and old budding yeast, related to Fig. 5.

Supplementary Table 9

Proteome measurements of yeast and human cells acutely treated with rapamycin, related to Fig. 6c.

Supplementary Table 10

Phosphoproteome measurements of small and large budding yeast, related to Fig. 6d–f.

Supplementary Table 11

Proteome measurements of yeast growing asynchronously in medium containing either glucose or ethanol/glycerol as the sole carbon source, related to Fig. 7a–c.

Supplementary Table 12

Summary table for all proteomics experiments.

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Lanz, M.C., Zhang, S., Swaffer, M.P. et al. Genome dilution by cell growth drives starvation-like proteome remodeling in mammalian and yeast cells. Nat Struct Mol Biol 31, 1859–1871 (2024). https://doi.org/10.1038/s41594-024-01353-z

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