Journal: bioRxiv

Article Title: Computational modeling establishes mechanotransduction as a potent entrainment cue for the mammalian circadian clock

doi: 10.1101/2023.10.09.561563

Figure Lengend Snippet: Markov chain Monte Carlo (MCMC) was run for 10,000 iterations with 60 walkers. After discarding the first 5,000 iterations as burn-in, posterior distributions were estimated using kernel density estimation in MATLAB, with a bandwidth of 5% of the maximum value of a given parameter included in the prior distribution.

Article Snippet: We conducted a global parametric sensitivity analysis using the UQLab framework in MATLAB [ ].

Techniques:

Journal: bioRxiv

Article Title: Computational modeling establishes mechanotransduction as a potent entrainment cue for the mammalian circadian clock

doi: 10.1101/2023.10.09.561563

Figure Lengend Snippet: A) Kymograph depicting oscillations in nuclear BMAL1 for 200 model cells on a soft (0.1 kPa) substrate. Cells exhibit consistent circadian oscillations, while variability is observed at the level of the population. B) Kymograph depicting oscillations in nuclear BMAL1 for 200 model cells on glass (10 GPa). Most cells show consistent, lower magnitude oscillations; in some cases, oscillations are weaker or nonexistent. C-D) Distribution of oscillation period (C) and circadian power fraction (D) for cell populations on different substrate stiffnesses. Central points and error bars in violin plots denote the median and interquartile range. Compact letter display is used to denote statistical significance, where groups sharing a letter are statistically similar according to ANOVA followed by Tukey’s post hoc test with a significance threshold of p = 0.05. Violin plots were generated using Violinplot in MATLAB

Article Snippet: We conducted a global parametric sensitivity analysis using the UQLab framework in MATLAB [ ].

Techniques: Generated

Journal: bioRxiv

Article Title: Computational modeling establishes mechanotransduction as a potent entrainment cue for the mammalian circadian clock

doi: 10.1101/2023.10.09.561563

Figure Lengend Snippet: Correlation between circadian power fraction and nuclear to cytosolic ratios (N/C) of YAP/TAZ or MRTF. Populations of cells were subjected to the treatments from and circadian power fraction was plotted against YAP/TAZ N/C (A) and MRTF N/C (B). Data points denote median values and error bars denote the range from the 40th to the 60th percentile for 500 model cells. In each case, Pearson’s correlation coefficient ( r ) was assessed for the correlation between median power fraction and median N/C on the log scale. r and the p -value associated with the null hypothesis, r = 0, are included on each graph. To generate distinguishable colors for each condition, we used the linspecer tool in MATLAB .

Article Snippet: We conducted a global parametric sensitivity analysis using the UQLab framework in MATLAB [ ].

Techniques:

Journal: bioRxiv

Article Title: Computational modeling establishes mechanotransduction as a potent entrainment cue for the mammalian circadian clock

doi: 10.1101/2023.10.09.561563

Figure Lengend Snippet: Reducing the circadian power fraction for cells on a soft substrate changes the correlations between circadian power fraction and YAP/TAZ N/C (A) or the MRTF N/C (B). In this case, we altered the power fraction for cells on a soft substrate by averaging that of cells on a soft substrate and cells on a stiff substrate. Pearson’s correlation coefficient (between median YAP/TAZ or MRTF N/C and median circadian power fraction, both on the log scale), r , and the p -value associated with the null hypothesis, r = 0, are included on each graph. Data points denote median and error bars denote the range from the 40th to the 60th percentile for 500 model cells. To generate distinguishable colors for each condition, we used the linspecer tool in MATLAB .

Article Snippet: We conducted a global parametric sensitivity analysis using the UQLab framework in MATLAB [ ].

Techniques:

Journal: bioRxiv

Article Title: Computational modeling establishes mechanotransduction as a potent entrainment cue for the mammalian circadian clock

doi: 10.1101/2023.10.09.561563

Figure Lengend Snippet: A-B) Nuclear concentrations of YAP/TAZ (A) and MRTF (B) compared across wild type and both mutant populations on 30 kPa substrate. Overexpression of 5SA-YAP results in increased nuclear YAP/TAZ but not MRTF, whereas a mutation in LMNA causes elevations in both nuclear YAP/TAZ and MRTF. C) Kymograph depicting the dynamics of nuclear BMAL1 for a wild type population, YAP mutant population, and lamin A mutant population, all on 30 kPa substrates. Each plot shows 200 cells over 5 days; oscillations are visibly weaker for either mutation. D) Circadian power fraction compared across wild type and mutant cells on 30 kPa substrates. Mutations in YAP or LMNA each cause significant reductions in the power fraction. E) Rescue of normal circadian oscillations in mutant cells via reduction of substrate stiffness. The circadian power fraction for YAP mutant cells on a 0.3 kPa substrate, or LMNA mutant cells on a 3 kPa substrate, are statistically similar to that of wild type cells on a stiff (30 kPa) substrate. Central points and error bars in violin plots denote the median and interquartile range for populations of 500 cells each. Compact letter display is used to denote statistical significance, where groups sharing a letter are statistically similar according to ANOVA followed by Tukey’s post hoc test with a significance threshold of p = 0.05. Violin plots were generated using Violinplot in MATLAB . F) Summary of the causal flow from cell mechanotransduction to changes in the circadian clock. Upper colored boxes denote disruptions to mechanotransduction explored at different points in this paper, either due to cytoskeleton-targeting drugs or mutations in YAP or LMNA. The lower box depicts the rescue of normal circadian oscillations in mutant cells via a reduction in substrate stiffness. Dashed arrows indicate possible feedback from the circadian clock to cell and tissue mechanics.

Article Snippet: We conducted a global parametric sensitivity analysis using the UQLab framework in MATLAB [ ].

Techniques: Mutagenesis, Over Expression, Generated