TORC1 and PKA activity towards ribosome biogenesis oscillates in synchrony with the budding yeast cell cycle

Recent studies have revealed that the growth rate of budding yeast and mammalian cells varies during the cell cycle. By linking a multitude of signals to cell growth, the highly conserved Target of Rapamycin Complex 1 (TORC1) and Protein Kinase A (PKA) pathways are prime candidates for mediating the dynamic coupling between growth and division. However, measurements of TORC1 and PKA activity during the cell cycle are still lacking. Following the localization dynamics of two TORC1 and PKA targets via time-lapse microscopy in hundreds of yeast cells, we found that the activity of these pathways towards ribosome biogenesis fluctuates in synchrony with the cell cycle even under constant external conditions. Mutations of upstream TORC1 and PKA regulators suggested that internal metabolic signals partially mediate these activity changes. Our study reveals a new aspect of TORC1 and PKA signaling, which will be important for understanding growth regulation during the cell cycle

Modeling dragonfly population data with a Bayesian bivariate geometric mixed-effects model

We develop a generalized linear mixed model (GLMM) for bivariate count responses for statistically analyzing dragonfly population data from the Northern Netherlands. The populations of the threatened dragonfly species Aeshna viridis were counted in the years 2015–2018 at 17 different locations (ponds and ditches). Two different widely applied population size measures were used to quantify the population sizes, namely the number of found exoskeletons (‘exuviae’) and the number of spotted egg-laying females were counted. Since both measures (responses) led to many zero counts but also feature very large counts, our GLMM model builds on a zero-inflated bivariate geometric (ZIBGe) distribution, for which we show that it can be easily parameterized in terms of a correlation parameter and its two marginal medians. We model the medians with linear combinations of fixed (environmental covariates) and random (location-specific intercepts) effects. Modeling the medians yields a decreased sensitivity to overly large counts; in particular, in light of growing marginal zero inflation rates. Because of the relatively small sample size (n = 114) we follow a Bayesian modeling approach and use Metropolis-Hastings Markov Chain Monte Carlo (MCMC) simulations for generating posterior samples