Distributed and dynamic intracellular organization of extracellular information

Abstract

Although cells respond specifically to environments, how environmental identity is encoded intracellularly is not understood. Here we study this organization of information in budding yeast by estimating the mutual information between environmental transitions and the dynamics of nuclear translocation for ten transcription factors. Our method of estimation is general, scalable, and based on decoding from single cells. The dynamics of the transcription factors are necessary to encode the highest amounts of extracellular information, and we show that information is transduced through two channels: generalists (Msn2/4, Tod6/Dot6, Maf1, and Sfp1) can encode the nature of multiple stresses but only if stress is high; specialists (Hog1, Yap1, and Mig1/2) encode one particular stress, but do so more quickly and for a wider range of magnitudes. Each transcription factor reports differently, and it is only their collective response that distinguishes between multiple environmental states. Changes in the dynamics of the localization of transcription factors thus constitute a precise, distributed internal representation of extracellular change, and we predict that such multi-dimensional representations are common in cellular decision-making. Significance StatementTo thrive in diverse environments, cells must represent extracellular change intracellularly despite stochastic biochemistry. Here we introduce a quantitative framework for investigating the organization of information within a cell. Combining single-cell measurements of intracellular dynamics with a new, scalable methodology for estimating mutual information between time-series and a discrete input, we demonstrate that extracellular information is encoded in the dynamics of the nuclear localization of transcription factors and that information is lost with alternative static statistics. Any one transcription factor is usually insufficient, but the collective dynamics of multiple transcription factors can represent complex extracellular change. We therefore show that a cell's internal representation of its environment can be both distributed across diverse proteins and dynamically encoded.

Age-dependent decline in stress response capacity revealed by proteins dynamics analysis

“…Cell nuclei or translation spots were identified by thresholding the m-Cherry or iRFP images. The mean value of the top 5 × 5 and 3 × 3 matrix pixels of m-Cherry and iRFP were used as the intensity of the cell cycle marker and mRNA indicator 42 .…”

Proteomic analysis of the S. cerevisiae response to the anticancer ruthenium complex KP1019

“…5A). As a ''generalist,'' Sfp1 responds to diverse stresses, including oxidative and osmotic stress, 111 both of which are caused by KP1019 ( Fig. 6 and Singh et al 31 ).…”

A Multicellular Network Mechanism for Temperature-Robust Food Sensing

“…We used decoding analysis (Dayan and Abbott, 2005) to quantify how well environmental conditions can be discriminated based on the corresponding distributions of lifespan or gene expression. This method has been successfully applied to gene expression, biochemical activities, and physiological responses (Entchev et al, 2015;Granados et al, 2018) and accommodates noisy responses and non-linear stimuli-response relationships. Using this approach (Figure 2A; see STAR Methods), we first infer the most likely stimulus for a given response, using prior knowledge of the response distributions under different stimuli.…”