Biology subjects

Shimizu, T. S.

Publications and source records attributed to Shimizu, T. S..

9 recordsLinked to original sources

bioRxiv2026

Growth-dependent sensory bet-hedging enhances collective navigation

Phenotypic heterogeneity in microbes can be a double-edged sword -- boosting individual survival in uncertain environments, but potentially compromising beneficial population coordination. Dissecting how microbes resolve this tradeoff is challenging because interactions span multiple scales--from molecular interactions to single-cell behavior, population dynamics, and environmental feedback. Here, we address this question for the Escherichia coli chemotaxis system, which implements both individual motile explorations and collective resource exploitations using the same cellular machinery. We quantify the heterogeneity of individual sensory and behavioral phenotypes, as well as the abundance of key signaling proteins during growth in various environments, and test their impact on population-scale collective migration. We identify growth rate as a key environment-dependent physiological variable governing not only the mean but also the variance of sensory phenotypes. Remarkably, rather than hindering population coordination, we find that sensory diversity benefits collective chemotactic navigation in uncertain environments. Strong heterogeneity of expressed phenotypes enhances readiness to multiple sensory cues, and the required population coordination is achieved by phenotypic filtering of that diversity by the collective behavior itself. These results reveal a sensory bet-hedging strategy for collective navigation during population growth: diversity in sensitivity to nutrients currently being consumed is reduced to promote focused exploitation, while it is increased for nutrients not yet encountered to enhance exploration for new growth opportunities.

biophysics↗

bioRxiv2025

Carbon-phosphorous exchange rate constrains growth of arbuscular mycorrhizal fungal networks

Symbiotic nutrient exchange between arbuscular mycorrhizal (AM) fungi and their host plants varies widely depending on their physical, chemical, and biological environment. Yet dissecting this context dependency remains challenging because we lack methods for tracking nutrients such as carbon (C) and phosphorus (P). Here, we developed a new approach to quantitatively estimate C and P fluxes in the AM symbiosis from comprehensive network morphology quantification, achieved by robotic imaging and machine learning based on roughly 100 million hyphal shape measurements. We found that rates of C transfer from the plant and P transfer from the fungus were, on average, related proportionally to one another. This ratio was nearly invariant across AM fungal strains despite contrasting growth phenotypes, but was strongly affected by plant host genotype. Fungal phenotype distributions were bounded by a Pareto front with a shape favoring specialization in an exploration-exploitation trade-off. This means AM fungi can be fast range expanders or fast resource extractors, but not both. Manipulating the C/P exchange rate by swapping the plant host genotype shifted this Pareto front, indicating that the exchange rate constrains possible AM fungal growth strategies. We show by mathematical modeling how AM fungal growth at fixed exchange rate leads to qualitatively different symbiotic outcomes depending on fungal traits and nutrient availability.

biophysics↗

bioRxiv2024

Non-genetic adaptation by collective migration

Cell populations must adjust their phenotypic composition to adapt to changing environments. One adaptation strategy is to maintain distinct phenotypic subsets within the population and to modulate their relative abundances via gene regulation. Another strategy involves genetic mutations, which can be augmented by stress-response pathways. Here, we studied how a migrating bacterial population regulates its phenotypic distribution to traverse diverse environments. We generated isogenic Escherichia coli populations with varying distributions of swimming behaviors and observed their phenotype distributions during migration in liquid and porous environments. We found that the migrating populations became enriched with high-performing swimming phenotypes in each environment, allowing the populations to adapt without requiring mutations or gene regulation. This adaptation is dynamic and rapid, reversing in a few doubling times when migration ceases. By measuring the chemoreceptor abundance distributions during migration towards different attractants, we demonstrated that adaptation acts on multiple chemotaxis-related traits simultaneously. These measurements are consistent with a general mechanism in which adaptation results from a balance between cell growth generating diversity and collective migration eliminating under-performing phenotypes. Thus, collective migration enables cell populations with continuous, multi-dimensional phenotypes to flexibly and rapidly adapt their phenotypic composition to diverse environmental conditions. Significance statementConventional cell adaptation mechanisms, like gene regulation and stochastic phenotypic switching, act swiftly but are limited to a few traits, while mutation-driven adaptations unfold slowly. By quantifying phenotypic diversity during bacterial collective migration, we discovered an adaptation mechanism that rapidly and reversibly adjusts multiple traits simultaneously. By balancing the generation of diversity through growth with the loss of phenotypes unable to keep up, this process tunes the phenotypic composition of migrating populations to the environments they traverse, without gene regulation or mutations. Given the prevalence of collective migration in microbes, cancers, and embryonic development, non-genetic adaptation through collective migration may be a universal mechanism for populations to navigate diverse environments, offering insights into broader applications across various fields.

systems biology↗

bioRxiv2023

Sensory diversity and precise adaptation enable independent bet-hedging strategies for multiple signals at the same time

In uncertain environments, phenotypic diversity can be advantageous for survival. However, as the environmental uncertainty decreases, the relative advantage of having diverse phenotypes decreases. Here, we show how populations of E. coli integrate multiple chemical signals to adjust sensory diversity in response to changes in the prevalence of each ligand in the environment. Measuring kinase activity in single cells, we quantified the sensitivity distribution to various chemoattractants in different mixtures of background stimuli. We found that when ligands bind uncompetitively, the population tunes sensory diversity to each signal independently, decreasing diversity when the signal ambient concentration increases. However, amongst competitive ligands the population can only decrease sensory diversity one ligand at a time. Mathematical modeling suggests that sensory diversity tuning benefits E. coli populations by modulating how many cells are committed to tracking each signal proportionally as their prevalence changes.

systems biology↗

bioRxiv2022

Near-critical tuning of cooperativity revealed by spontaneous switching in a protein signalling array

Dynamic properties of allosteric complexes are crucial for cellular information processing. However, direct observations of allosteric switches have been limited to compact molecular assemblies. Here, we report in vivo FRET measurements of spontaneous discrete-level fluctuations in the activity of the Escherichia coli chemosensory array -- an extensive membrane-associated assembly comprising thousands of molecules. Finite-size scaling analysis of the temporal statistics by a two-dimensional conformational spread model revealed nearest-neighbor coupling strengths within 3% of the Ising second-order phase transition, indicating that chemosensory arrays are poised at criticality. Our analysis yields estimates for the intrinsic timescale of conformational changes (~ 10 ms) of allosteric units, and identifies near-critical tuning as a design principle for balancing the inherent tradeoff between response amplitude and response speed in higher-order signaling assemblies. One-setence summaryIn vivo measurements of protein signaling array fluctuations reveal an allosteric system poised at criticality.

biophysics↗

bioRxiv2022

Optimal inference of molecular interactions in live FRET imaging

Intensity-based live-cell fluorescence resonance energy transfer (FRET) imaging converts otherwise unobservable molecular interactions inside cells into fluorescence time-series signals. However, inferring the degree of molecular interactions from these observables is challenging, due to experimental complications such as spectral crosstalk, photobleaching, and measurement noise. Conventional methods solve this inverse problem through algebraic manipulations of the observables, but such manipulations inevitably accumulate measurement noise, limiting the scope of FRET analysis. Here, we introduce a Bayesian inference framework, B-FRET, which estimates molecular interactions from FRET data in a statistically optimal manner. B-FRET requires no additional measurements beyond those routinely conducted in standard 3-cube FRET imaging methods, and yet, by using the information contained in the data more efficiently, dramatically improves the signal-to-noise ratio (SNR). We validate B-FRET using simulated data, and then apply it to FRET data measured from single bacterial cells, a system with notoriously low SNR, to reveal signaling dynamics that are otherwise hidden in noise.

biophysics↗

bioRxiv2020

Functional modules from variable genes: Leveraging percolation to analyze noisy, high-dimensional data

While measurement advances now allow extensive surveys of gene activity (large numbers of genes across many samples), interpretation of these data is often confounded by noise — expression counts can differ strongly across samples due to variation of both biological and experimental origin. Complimentary to perturbation approaches, we extract functionally related groups of genes by analyzing the standing variation within a sampled population. To distinguish biologically meaningful patterns from uninterpretable noise, we focus on correlated variation and develop a novel density-based clustering approach that takes advantage of a percolation transition generically arising in random, uncorrelated data. We apply our approach to two contrasting RNA sequencing data sets that sample individual variation — across single cells of fission yeast and whole animals of C. elegans worms — and demonstrate robust applicability and versatility in revealing correlated gene clusters of diverse biological origin, including cell cycle phase, development/reproduction, tissue-specific functions, and feeding history. Our technique exploits generic features of noisy high-dimensional data and is applicable, beyond gene expression, to feature-rich data that sample population-level variability in the presence of noise.Significance Statement Gene expression largely determines the fate of each cell and ultimately the development and behavior of the whole organism. Whereas most of our knowledge on gene regulatory networks has been obtained from perturbation experiments (e.g. manipulating environmental conditions, genotype, or other physiological variables), here we develop an alternative approach based on the analysis of naturally occurring variations across individuals within a population. Using both single-cell and whole-animal RNA sequencing data, we demonstrate how a rich set of co-regulated gene modules can be uncovered from transcriptomic variability of individuals within unperturbed populations. To robustly extract interpretable clusters from the strong noise background, we devise a novel, versatile clustering approach based on network theory. With a foundation in the generic behavior of random networks near their percolation critical point, our method is broadly applicable, beyond gene expression, to any noisy, high-dimensional data that sample variation across individuals within a population.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2019

Modeling the ballistic-to-diffusive transition in nematode motility reveals variation in exploratory behavior across species

A quantitative understanding of organism-level behavior requires predictive models that can capture the richness of behavioral phenotypes, yet are simple enough to connect with underlying mechanistic processes. Here we investigate the motile behavior of nematodes at the level of their translational motion on surfaces driven by undulatory propulsion. We broadly sample the nematode behavioral repertoire by measuring motile trajectories of the canonical lab strain C. elegans N2 as well as wild strains and distant species. We focus on trajectory dynamics over timescales spanning the transition from ballistic (straight) to diffusive (random) movement and find that salient features of the motility statistics are captured by a random walk model with independent dynamics in the speed, bearing and reversal events. We show that the model parameters vary among species in a correlated, low-dimensional manner suggestive of a common mode of behavioral control and a trade-off between exploration and exploitation. The distribution of phenotypes along this primary mode of variation reveals that not only the mean but also the variance varies considerably across strains, suggesting that these nematode lineages employ contrasting \"bet-hedging\" strategies for foraging.

biophysics↗

bioRxiv2017

Generation And Attenuation Of Variability In A Bacterial Signalling Network Revealed By Single-Cell FRET

We present in vivo single-cell FRET measurements in the Escherichia coli chemotaxis system that reveal pervasive signaling variability, both across cells in isogenic populations and within individual cells over time. We quantify cell-to-cell variability of adaptation, ligand response, as well as steady-state output level, and analyze the role of network design in shaping this diversity from gene expression noise. In the absence of changes in gene expression, we find that single cells demonstrate strong temporal fluctuations. We provide evidence that such signaling noise can arise from at least two sources: (i) stochastic activities of adaptation enzymes, and (ii) receptor-kinase dynamics in the absence of adaptation. We demonstrate that under certain conditions, (ii) can generate giant fluctuations that drive signaling activity of the entire cell into a stochastic two-state switching regime. Our findings underscore the importance of molecular noise, arising not only in gene expression but also in protein networks.

systems biology↗