Multi-signal regulation of the GSK-3ß homolog Rim11 controls meiosis entry in budding yeast.

Starvation in diploid budding yeast cells triggers a cell-fate program culminating in meiosis and spore formation. Transcriptional activation of early meiotic genes (EMGs) hinges on the master regulator Ime1, its DNA-binding partner Ume6, and GSK-3ß kinase Rim11. Phosphorylation of Ume6 by Rim11 is required for EMG activation. We report here that Rim11 functions as the central signal integrator for controlling Ume6 phosphorylation and EMG transcription. In nutrient-rich conditions, PKA suppresses Rim11 levels, while TORC1 retains Rim11 in the cytoplasm. Inhibition of PKA and TORC1 induces Rim11 expression and nuclear localization. Remarkably, nuclear Rim11 is required, but not sufficient, for Rim11-dependent Ume6 phosphorylation. In addition, Ime1 is an anchor protein enabling Ume6 phosphorylation by Rim11. Subsequently, Ume6-Ime1 coactivator complexes form and induce EMG transcription. Our results demonstrate how various signaling inputs (PKA/TORC1/Ime1) converge through Rim11 to regulate EMG expression and meiosis initiation. We posit that the signaling-regulatory network elucidated here generates robustness in cell-fate control.

Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle.

The life cycle of biomedical and agriculturally relevant eukaryotic microorganisms involves complex transitions between proliferative and non-proliferative states such as dormancy, mating, meiosis, and cell division. New drugs, pesticides, and vaccines can be created by targeting specific life cycle stages of parasites and pathogens. However, defining the structure of a microbial life cycle often relies on partial observations that are theoretically assembled in an ideal life cycle path. To create a more quantitative approach to studying complete eukaryotic life cycles, we generated a deep learning-driven imaging framework to track microorganisms across sexually reproducing generations. Our approach combines microfluidic culturing, life cycle stage-specific segmentation of microscopy images using convolutional neural networks, and a novel cell tracking algorithm, FIEST, based on enhancing the overlap of single cell masks in consecutive images through deep learning video frame interpolation. As proof of principle, we used this approach to quantitatively image and compare cell growth and cell cycle regulation across the sexual life cycle of Saccharomyces cerevisiae. We developed a fluorescent reporter system based on a fluorescently labeled Whi5 protein, the yeast analog of mammalian Rb, and a new High-Cdk1 activity sensor, LiCHI, designed to report during DNA replication, mitosis, meiotic homologous recombination, meiosis I, and meiosis II. We found that cell growth preceded the exit from non-proliferative states such as mitotic G1, pre-meiotic G1, and the G0 spore state during germination. A decrease in the total cell concentration of Whi5 characterized the exit from non-proliferative states, which is consistent with a Whi5 dilution model. The nuclear accumulation of Whi5 was developmentally regulated, being at its highest during meiotic exit and spore formation. The temporal coordination of cell division and growth was not significantly different across three sexually reproducing generations. Our framework could be used to quantitatively characterize other single-cell eukaryotic life cycles that remain incompletely described. An off-the-shelf user interface Yeastvision provides free access to our image processing and single-cell tracking algorithms.