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Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation.

Chiang M, Cinquin A, Paz A, Meeds E, Price CA, Welling M, Cinquin O - BMC Biol. (2015)

Bottom Line: Computational simulations of mutation accumulation characterize a tradeoff between fast development and low mutation accumulation, and show that slow-cycling stem cells allow for an advantageous compromise to be reached.Experimental measurements of cell cycle lengths derived using a new, quantitative technique are consistent with these predictions.Our findings shed light both on design principles that underlie the role of stem cells in delaying aging and on evolutionary forces that shape stem-cell gene regulatory networks.

View Article: PubMed Central - PubMed

Affiliation: Department of Developmental & Cell Biology, University of California, Irvine, California, USA.

ABSTRACT

Background: Stem cells are thought to play a critical role in minimizing the accumulation of mutations, but it is not clear which strategies they follow to fulfill that performance objective. Slow cycling of stem cells provides a simple strategy that can minimize cell pedigree depth and thereby minimize the accumulation of replication-dependent mutations. Although the power of this strategy was recognized early on, a quantitative assessment of whether and how it is employed by biological systems is missing.

Results: Here we address this problem using a simple self-renewing organ - the C. elegans gonad - whose overall organization is shared with many self-renewing organs. Computational simulations of mutation accumulation characterize a tradeoff between fast development and low mutation accumulation, and show that slow-cycling stem cells allow for an advantageous compromise to be reached. This compromise is such that worm germ-line stem cells should cycle more slowly than their differentiating counterparts, but only by a modest amount. Experimental measurements of cell cycle lengths derived using a new, quantitative technique are consistent with these predictions.

Conclusions: Our findings shed light both on design principles that underlie the role of stem cells in delaying aging and on evolutionary forces that shape stem-cell gene regulatory networks.

No MeSH data available.


Quantitative cell cycle models that allow for a cell cycle gradient across the MZ provide a good fit to experimental data, and show ~1.5-fold slower cycling of stem cells. a DNA content histograms of EdU-positive cells derived from best-fit simulations of cell cycling to L4 + 1 day experimental data (black) overlaid with the same experimental data (blue), at 0 h, 3 h, and 5 h (full overlay shown in Additional file 2: Figure S1). Experimental data were derived from a total of n = 157 gonadal arms. b Fractions of EdU-labeled mitoses derived from L4 + 1 day experimental data (“Exp” row) or from best-fit simulations (“Sim” row; full overlay shown in Additional file 2: Figure S1). c, d Best-fit cell cycle parameters show faster cell cycling at the proximal end of the MMZ (y-axis) than at the distal DMMZ (x-axis) both at L4 (c) and L4 + 1 day (d), and both when fitting DNA content histograms (DEMD; green) or fractions of labeled mitoses (FLM; blue). Each dot on the graph corresponds to a bootstrap sample; ellipses contain 95 % of bootstrap samples and are located off the diagonal, which corresponds to equal cell cycle speeds across the distal–proximal axis. Jitter was added to bootstrap samples to aid visualization (see Additional file 2: Figure S2 for display without jitter). e, f Distal cells have longer G2 than proximal cells. Stacked bars show the length of each cell cycle phase along the distal–proximal axis, as computed using best-fit parameters. Note that absolute cell cycle lengths cannot be directly derived from Fig. 3b
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Fig4: Quantitative cell cycle models that allow for a cell cycle gradient across the MZ provide a good fit to experimental data, and show ~1.5-fold slower cycling of stem cells. a DNA content histograms of EdU-positive cells derived from best-fit simulations of cell cycling to L4 + 1 day experimental data (black) overlaid with the same experimental data (blue), at 0 h, 3 h, and 5 h (full overlay shown in Additional file 2: Figure S1). Experimental data were derived from a total of n = 157 gonadal arms. b Fractions of EdU-labeled mitoses derived from L4 + 1 day experimental data (“Exp” row) or from best-fit simulations (“Sim” row; full overlay shown in Additional file 2: Figure S1). c, d Best-fit cell cycle parameters show faster cell cycling at the proximal end of the MMZ (y-axis) than at the distal DMMZ (x-axis) both at L4 (c) and L4 + 1 day (d), and both when fitting DNA content histograms (DEMD; green) or fractions of labeled mitoses (FLM; blue). Each dot on the graph corresponds to a bootstrap sample; ellipses contain 95 % of bootstrap samples and are located off the diagonal, which corresponds to equal cell cycle speeds across the distal–proximal axis. Jitter was added to bootstrap samples to aid visualization (see Additional file 2: Figure S2 for display without jitter). e, f Distal cells have longer G2 than proximal cells. Stacked bars show the length of each cell cycle phase along the distal–proximal axis, as computed using best-fit parameters. Note that absolute cell cycle lengths cannot be directly derived from Fig. 3b

Mentions: We next quantified the differences in cell cycle speed between the DMMZ and MMZ regions. This quantification must account for the fact that cells from the DMMZ feed into the MMZ, minimizing the apparent differences between these regions; the MMZ thus cannot be analyzed independently. We therefore fitted experimentally derived DNA content histograms and FLMs to simulations of germ-cell cycling that assumed a linear gradient of cell cycle lengths spanning those regions (see “Methods” for details). These simulations were identical to those used for pedigree-depth optimization in terms of rules for cell division and ensuing cell displacements, but different in that they were initiated with a pre-filled MZ instead of a single progenitor, only covered the period of time corresponding to the cell cycle experiment analyzed (at most 8 h), did not allow for changes in cell cycle length parameters over that period, and kept track of progression through the G1, S, G2, and M phases of the cycle. The best-fit simulation data provided a close fit to the experimental data (Fig. 4a, b and Additional file 2: Figure S1), supporting the validity of our model. The average ratio of cell cycle speeds between the distal DMMZ and proximal MMZ was 1.50 (95 % bootstrapped CI = 1.26–1.67) and 1.53 (95 % bootstrapped CI = 1.20–1.90) at the L4 and L4 + 1 day stages, respectively (Fig. 4c, d, Additional file 2: Table S6). Importantly, this result is supported by two independent analysis techniques: one based on the FLM, which has been used before without distinguishing between subpopulations along the distal-proximal axis [45], and the new technique we report based on DNA content histograms (DNA earth mover’s distance or DEMD) that makes use of all cells instead of only rare M-phase cells (see overlap in Fig. 4c, d). FLM-based analysis of the proximal MZ (PMZ) suggests a flat cell cycle profile for cells that have not left the mitotic cycle (Additional file 2: Figure S3, Additional file 2: Table S7). Therefore, our experimental analysis verified the theoretical prediction that an approximately twofold cell cycle speed gradient should exist along the distal–proximal axis.


Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation.

Chiang M, Cinquin A, Paz A, Meeds E, Price CA, Welling M, Cinquin O - BMC Biol. (2015)

Quantitative cell cycle models that allow for a cell cycle gradient across the MZ provide a good fit to experimental data, and show ~1.5-fold slower cycling of stem cells. a DNA content histograms of EdU-positive cells derived from best-fit simulations of cell cycling to L4 + 1 day experimental data (black) overlaid with the same experimental data (blue), at 0 h, 3 h, and 5 h (full overlay shown in Additional file 2: Figure S1). Experimental data were derived from a total of n = 157 gonadal arms. b Fractions of EdU-labeled mitoses derived from L4 + 1 day experimental data (“Exp” row) or from best-fit simulations (“Sim” row; full overlay shown in Additional file 2: Figure S1). c, d Best-fit cell cycle parameters show faster cell cycling at the proximal end of the MMZ (y-axis) than at the distal DMMZ (x-axis) both at L4 (c) and L4 + 1 day (d), and both when fitting DNA content histograms (DEMD; green) or fractions of labeled mitoses (FLM; blue). Each dot on the graph corresponds to a bootstrap sample; ellipses contain 95 % of bootstrap samples and are located off the diagonal, which corresponds to equal cell cycle speeds across the distal–proximal axis. Jitter was added to bootstrap samples to aid visualization (see Additional file 2: Figure S2 for display without jitter). e, f Distal cells have longer G2 than proximal cells. Stacked bars show the length of each cell cycle phase along the distal–proximal axis, as computed using best-fit parameters. Note that absolute cell cycle lengths cannot be directly derived from Fig. 3b
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Fig4: Quantitative cell cycle models that allow for a cell cycle gradient across the MZ provide a good fit to experimental data, and show ~1.5-fold slower cycling of stem cells. a DNA content histograms of EdU-positive cells derived from best-fit simulations of cell cycling to L4 + 1 day experimental data (black) overlaid with the same experimental data (blue), at 0 h, 3 h, and 5 h (full overlay shown in Additional file 2: Figure S1). Experimental data were derived from a total of n = 157 gonadal arms. b Fractions of EdU-labeled mitoses derived from L4 + 1 day experimental data (“Exp” row) or from best-fit simulations (“Sim” row; full overlay shown in Additional file 2: Figure S1). c, d Best-fit cell cycle parameters show faster cell cycling at the proximal end of the MMZ (y-axis) than at the distal DMMZ (x-axis) both at L4 (c) and L4 + 1 day (d), and both when fitting DNA content histograms (DEMD; green) or fractions of labeled mitoses (FLM; blue). Each dot on the graph corresponds to a bootstrap sample; ellipses contain 95 % of bootstrap samples and are located off the diagonal, which corresponds to equal cell cycle speeds across the distal–proximal axis. Jitter was added to bootstrap samples to aid visualization (see Additional file 2: Figure S2 for display without jitter). e, f Distal cells have longer G2 than proximal cells. Stacked bars show the length of each cell cycle phase along the distal–proximal axis, as computed using best-fit parameters. Note that absolute cell cycle lengths cannot be directly derived from Fig. 3b
Mentions: We next quantified the differences in cell cycle speed between the DMMZ and MMZ regions. This quantification must account for the fact that cells from the DMMZ feed into the MMZ, minimizing the apparent differences between these regions; the MMZ thus cannot be analyzed independently. We therefore fitted experimentally derived DNA content histograms and FLMs to simulations of germ-cell cycling that assumed a linear gradient of cell cycle lengths spanning those regions (see “Methods” for details). These simulations were identical to those used for pedigree-depth optimization in terms of rules for cell division and ensuing cell displacements, but different in that they were initiated with a pre-filled MZ instead of a single progenitor, only covered the period of time corresponding to the cell cycle experiment analyzed (at most 8 h), did not allow for changes in cell cycle length parameters over that period, and kept track of progression through the G1, S, G2, and M phases of the cycle. The best-fit simulation data provided a close fit to the experimental data (Fig. 4a, b and Additional file 2: Figure S1), supporting the validity of our model. The average ratio of cell cycle speeds between the distal DMMZ and proximal MMZ was 1.50 (95 % bootstrapped CI = 1.26–1.67) and 1.53 (95 % bootstrapped CI = 1.20–1.90) at the L4 and L4 + 1 day stages, respectively (Fig. 4c, d, Additional file 2: Table S6). Importantly, this result is supported by two independent analysis techniques: one based on the FLM, which has been used before without distinguishing between subpopulations along the distal-proximal axis [45], and the new technique we report based on DNA content histograms (DNA earth mover’s distance or DEMD) that makes use of all cells instead of only rare M-phase cells (see overlap in Fig. 4c, d). FLM-based analysis of the proximal MZ (PMZ) suggests a flat cell cycle profile for cells that have not left the mitotic cycle (Additional file 2: Figure S3, Additional file 2: Table S7). Therefore, our experimental analysis verified the theoretical prediction that an approximately twofold cell cycle speed gradient should exist along the distal–proximal axis.

Bottom Line: Computational simulations of mutation accumulation characterize a tradeoff between fast development and low mutation accumulation, and show that slow-cycling stem cells allow for an advantageous compromise to be reached.Experimental measurements of cell cycle lengths derived using a new, quantitative technique are consistent with these predictions.Our findings shed light both on design principles that underlie the role of stem cells in delaying aging and on evolutionary forces that shape stem-cell gene regulatory networks.

View Article: PubMed Central - PubMed

Affiliation: Department of Developmental & Cell Biology, University of California, Irvine, California, USA.

ABSTRACT

Background: Stem cells are thought to play a critical role in minimizing the accumulation of mutations, but it is not clear which strategies they follow to fulfill that performance objective. Slow cycling of stem cells provides a simple strategy that can minimize cell pedigree depth and thereby minimize the accumulation of replication-dependent mutations. Although the power of this strategy was recognized early on, a quantitative assessment of whether and how it is employed by biological systems is missing.

Results: Here we address this problem using a simple self-renewing organ - the C. elegans gonad - whose overall organization is shared with many self-renewing organs. Computational simulations of mutation accumulation characterize a tradeoff between fast development and low mutation accumulation, and show that slow-cycling stem cells allow for an advantageous compromise to be reached. This compromise is such that worm germ-line stem cells should cycle more slowly than their differentiating counterparts, but only by a modest amount. Experimental measurements of cell cycle lengths derived using a new, quantitative technique are consistent with these predictions.

Conclusions: Our findings shed light both on design principles that underlie the role of stem cells in delaying aging and on evolutionary forces that shape stem-cell gene regulatory networks.

No MeSH data available.