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bioRxiv2026

The mood stabilizer lithium alters behaviour and physiology via the gut brain axis.

Lithium, introduced 75 years ago by John Cade1, remains the most effective mood stabilizer for bipolar disorder2. Lithium is proposed to modulate an array of cellular pathways, many ubiquitous to all cells, with pleiotropic roles unlinked to bipolar disorder or lithium responsiveness in genome-wide association studies3,4. These mechanisms cannot explain lithiums specific effects on mood and behaviour. We demonstrate that lithiums primary action is in the periphery, not in the brain itself. Lithium acts in the gut to trigger behavioural and physiological changes, akin to those associated with a torpor-like state, that protect individuals from ingested toxins. Lithium activates gastrointestinal enterochromaffin (EC) cells via their Trpm2 cation channels to modulate afferent vagal and area postrema inputs to the brain. Eliminating these inputs by focal brain lesions eliminates lithiums effects, as does ablation of EC cells or their Trpm2 expression. Lithiums Trpm2-dependent activation of EC cells also occurs in human gut tissue, providing translational relevance for our discovery. These findings challenge the prevailing perception that lithium acts directly on the brain. Via a previously unsuspected gut-brain pathway, lithium engages brain circuitry that reduces arousal and interaction with the external world, therapeutic goals in the manic phase of bipolar disorder.

neuroscience↗

bioRxiv2026

Elucidating redox-driven inhibition of methanogenesis by an artificial quinone in Methanosarcina barkeri: Integrated proteomic and physiological evidence

Methanogenesis is a crucial component of Earths carbon cycle and a source of methane for biofuel production. The presence of higher energy electron acceptors, such as iron(III) oxides and quinones, is believed to significantly impact methanogenesis. This study investigated the physiological and proteomic responses of the type I Methanosarcina, M. barkeri, to the artificial quinone 9,10-anthraquinone-2,7-disulfonate disodium (2,7-AQDS), using H2/CO2 as substrates. Our findings revealed that during 2,7-AQDS reduction, cellular growth ceased. The lack of energy conservation was associated with direct inhibition of both methanogenesis and CO2 utilization, corroborated by a significant downregulation of the enzymes involved in this metabolic pathway. Furthermore, the significant upregulation of specific subunits of the reversible Ech hydrogenase suggests that this enzyme redirects electrons from H2 towards the most energetically favorable reaction (2,7-AQDS reduction), rather than the reduction of ferredoxin, which is a highly energy-demanding process, essential for initiating the CO2 reduction pathway. Additionally, it is conceivable that Ech homologues in other hydrogenotrophic methanogens also participate in the reduction of higher energy-yielding electron acceptors. These findings provide novel insights into how quinones, particularly in their oxidized state, directly impact methanogenesis, thereby influencing both artificial and natural methanogenic environments.

microbiology↗

bioRxiv2026

The Signal in the Noise: Hierarchy and Robustness of Physiological Audience Alignment during Narrative Media

Communication research traditionally prioritizes receiver variance, often overlooking how a single message induces commonality across distinct systems. Drawing on classical theory, we propose that effective messages entrain the biological rhythms of the audience. In a large-scale experiment (N=198) monitoring eye-tracking, heart rate, and electrodermal activity during a film, we mapped this alignment. Results reveal a processing hierarchy: alignment was strongest for information acquisition (gaze) but also extended to downstream autonomic regulation. Crucially, a temporal manipulation confirmed this state is content-locked: alignment vanished when the narrative sequence was altered but was restored upon computational unscrambling of the physiological time series. This confirms that responses carry a temporal fingerprint of the content. We conclude that messages function as alignment devices, reducing individual noise to create a network of receivers exhibiting shared processing states, thus providing a materially grounded, signal-based definition of the audience.

animal behavior and cognition↗

bioRxiv2026

Inferring population-level physiologically based model parameters for sleep in infancy and young childhood

The sleep patterns of infants and young children differ from adult sleep patterns, with longer duration and multiple bouts per 24 hours. There is also considerable heterogeneity in infant sleep, both between individuals and within individuals across development. While mathematical models have been used to understand the mechanisms that regulate adult sleep, the development of sleep from infancy through early childhood has remained largely unexplored. Here we used an established mathematical model for adult sleep regulation to investigate how the underlying mechanisms of sleep mature from ages 1 month to 5 years, and identify a basis for inter-individual differences at each age. Using a Bayesian approach to estimate joint distributions of model parameters at different ages, we found that: (i) decreases in the rate of accumulation of sleep homeostatic pressure captured the reduction in sleep duration across development; while (ii) increases in the time scale of the sleep homeostatic drive captured the consolidation of sleep into fewer sleep bouts per day across development. In terms of inter-individual differences, we found larger spread in the parameters within the earlier stages of infancy (<1 year). The center and boundaries of the joint distributions evolved with age through the parameter space, converging towards previously described adult parameter values. A bifurcation analysis of the homeostatic time scale parameter revealed that progressive consolidation of sleep occurs through abrupt loss of bouts one at a time, punctuated by narrow intervals of non-entrained (>24-h) cycles. These results establish plausible, population-level trajectories in physiological parameters underpinning maturation of sleep regulation through infancy and early childhood.

neuroscience↗

bioRxiv2026

Glycolytic Specialization Shapes Neuronal Physiology and Function in vivo

Neurons perform diverse functions that impose distinct energetic demands, but how energy-metabolic pathways are matched to these functions in vivo remains unknown. Here we show that two functionally divergent sister chemosensory neurons in C. elegans, ASEL and ASER, exhibit asymmetric glycolytic flux, with ASER exhibiting high and ASEL having low levels of glycolysis. Metabolic imaging, metabolic network modeling, and electrophysiology measurements reveal that ASERs elevated glycolysis supports a hyperpolarized resting potential, low input resistance, and rapid repolarization that enable a distinct functional role compared to ASEL. Impairing glycolysis collapses these electrophysiological specializations without abolishing neuronal excitability, and selectively disrupts ASERs calcium responses while leaving ASEL largely unaffected. These findings demonstrate that neuron-specific glycolytic programs shape core biophysical properties and are required for functional identity in vivo, establishing metabolism as an active determinant of neuronal physiology.

neuroscience↗

bioRxiv2026

A New Sparse Bayesian Quantile Neural Network-based Approach and Its Application to Discover Physiological Sweet Spots in the Canadian Longitudinal Study on Aging

Identifying physiological sweet spots (optimal ranges for homeostasis) is essential for precision medicine. However, traditional statistical methods often rely on globally linear or locally jagged models that struggle to capture the smooth, non-linear nature of biological regulation in high-dimensional data. We present the Quantile Feature Selection Network (Q-FSNet), a neural network-based framework that integrates quantile regression, feature selection, and uncertainty estimation to identify biomarkers with sweet spots. Unlike traditional methods, Q-FSNet learns continuous response curves without requiring pre-specified number of change points. We further introduce Quantile Dirichlet Network (Q-DirichNet), a fully Bayesian extension that utilizes Dirichlet priors to automate feature shrinkage. Using data from the Canadian Longitudinal Study on Aging, we identified 25 metabolites with distinct homeostatic ranges for which biological age acceleration is minimized. The metabolites with sweet spots for biological aging include some derived from diet or produced by the gut microbiome; this highlights their potential for knowledge translation and public health impact. Our results, corroborated by existing literature, demonstrate that these sparse neural network-based methods offer a scalable and interpretable tool for discovering metabolic signatures of healthy aging vs. dysregulation in large-scale omics research.

bioinformatics↗

bioRxiv2026

Interoceptive autonomic regulation in typical development and autism spectrum disorder: A computational model integrating multiple physiological systems

BackgroundInteroceptive cardiovascular signals, including heart rate (HR) and blood pressure (BP), arise from coordinated sympathetic (SNS) and parasympathetic (PSNS) regulation and contribute to affective and cognitive processes. Although atypical autonomic nervous system (ANS) modulation has been reported in autism spectrum disorder (ASD), the dynamical structure underlying branch-specific coordination remains insufficiently characterized. ObjectiveTo estimate latent ANS regulatory structure in typically developing (TD) and ASD individuals using a computational modeling framework. MethodsA closed-loop computational model integrating cardiovascular, respiratory, and autonomic dynamics was developed. ANS regulation was formalized using three autonomic control modes (coupled reciprocal, coupled nonreciprocal, and uncoupled) and parameterized by the relative activity of SNS and PSNS branches. HR and BP responses to the head-up tilt (HUT) test were simulated, and regulatory surfaces were compared with empirical HR and BP data from TD and ASD groups. Additional simulations under normal respiration, deep respiration, and absence of respiration evaluated mean arterial pressure (MAP) regulation across varying SNS-PSNS activity combinations. ResultsTD individuals exhibited differentiated SNS-PSNS coordination patterns across control modes, whereas ASD individuals showed convergence of relative SNS-PSNS activity. In TD, HR and BP distributions under coupled reciprocal mode were most consistent with expected physiological responses, characterized by high SNS and low PSNS activity during postural challenge. In ASD, empirical data extended toward regions of relatively higher PSNS weighting. Incorporation of deep respiration enhanced MAP reduction during BP recovery, particularly under over-elevated SNS activity. ConclusionThis study provides a mechanistic, state-space characterization of autonomic coordination in TD and ASD populations, enabling inference of latent autonomic regulation from measurable interoceptive phenotypes and identifying respiration as a model-based regulatory lever that augments cardiovascular stabilization.

neuroscience↗

bioRxiv2026

Physiologically realistic gamma activity produced in silico by weakening the PING attractor state

Network oscillations in the gamma (30-80 Hz) frequency range are implicated in many vital neurological functions. The seminal Pyramidal Interneuron Network Gamma (PING) mechanism produces gamma oscillations in silico with dynamics associated with a strongly attracting limit cycle. However, this activity is stronger and more stable than what is observed during in vivo gamma oscillations, which are transient and driven by sparse spiking activity. Here we describe multiple biophysically motivated changes degenerately driving PING-motivated networks to produce more realistic gamma activity. Increased heterogeneity, synapse-like noise, and a depolarized chloride reversal potential each destabilize the attractor represented by PING-driven activity: minor alterations disrupt the unrealistic organization of idealized PING oscillations, while larger changes entirely prevent this overly-synchronous network activity. This allows new dynamics to emerge: spontaneous and transient increases in gamma-band oscillatory power with excitatory cells only weakly entrained to the population rhythm. These features, better approximating the in vivo reality, mirror a systems oscillatory return to a stable focus following noise-induced perturbations. In fact, dynamical systems with both a stable focus and limit cycle are associated with Hopf bifurcations, which have been characterized in models of reciprocally connected excitatory and inhibitory neuronal populations as required for PING. We therefore propose a revised explanation for physiologically-realistic gamma activity that retains the key elements of the PING mechanism--strong reciprocal connection between excitatory and inhibitory neurons--but where biophysical phenomena bias the system towards damped oscillations around a weakly stable focus rather than a stable limit cycle. Significance StatementThe mechanisms underlying network oscillations at gamma frequencies have long been a focus of computational neuroscience. This research has produced idealized mechanisms yielding highly synchronous, active, and stable oscillations; however, in vivo gamma rhythms are transient with sparse neuronal spiking. Here, we illustrate minimal, biophysically relevant adjustments to established Pyramidal Interneuron Network Gamma (PING) networks that bridge a major divide between these traditional in silico systems and the experimental reality. Uncorrelated noisy input and a depolarized chloride reversal potential disrupt stereotyped PING rhythms and promote more realistic transient increased gamma power in network activity. This important step forward in the modeling of gamma oscillations illustrates how including biophysical detail can promote more realistic activity in computational systems.

neuroscience↗

bioRxiv2026

Ontogenetic consequences of developmental temperature in amphibians: simultaneous gains in heat tolerance and cumulative costs to stress physiology

Heat tolerance is critical for ectotherms facing environmental temperature variability, yet how it varies across life stages, and whether trade-offs occur between temperature-induced developmental plasticity and heat tolerance, remain unclear, particularly in organisms undergoing metamorphosis which represent 95% of all animal species. We examined how early-life thermal conditions shape growth, development, survival, acclimation capacity, heat tolerance, and energy allocation across ontogeny in the African clawed frog (Xenopus laevis), reared at six constant temperatures (17-32{degrees}C). We tested whether higher developmental temperatures generate trade-offs between accelerated growth and heat tolerance, and the consequences for post-metamorphic resilience to extreme heat. Rearing at 32{degrees}C was lethal before metamorphosis. At non-lethal warm temperatures (17-29{degrees}C), larvae and juveniles simultaneously accelerated development, maintained growth, and enhanced heat tolerance. However, juveniles reared at 29{degrees}C showed reduced survival, elevated corticosterone responses to acute stress, and diminished acclimation capacity, indicating increased energetic demands and constrained metabolic flexibility. These findings show that amphibians can integrate developmental plasticity with plastic adjustments in heat tolerance, but that such strategies incur cumulative physiological costs. By adopting an across-life-stage approach, our study highlights energy-allocation constraints that may limit population persistence under climate warming in species with complex life cycles.

ecology↗

bioRxiv2026

Decoupling CAR-T Expansion, Conversion, and Decay Timing: Physiologically Aligned Semi-Mechanistic Modeling with Smooth Gating and a Cauchy Likelihood Residual Model

Chimeric antigen receptor (CAR) T-cell therapies exhibit complex cellular kinetics characterized by rapid expansion, contraction, and long-term persistence, often with substantial inter-individual variability, frequent below-quantification (BLQ) observations, and occasional influential data points. These features can destabilize inference under Gaussian residual assumptions and motivate robust, likelihood-based approaches that can jointly accommodate outliers and censoring. In prior work, we showed that combining Students t residuals with likelihood-based BLQ handling (M3 censoring) improves robustness in CAR-T cellular kinetics modeling; however, implementing Students t censoring likelihoods is not straightforward in all modeling platforms because the Students t cumulative distribution function (CDF) lacks a simple elementary closed-form. Here, we evaluated the Cauchy residual likelihood as a practical, heavy-tailed alternative to Students t that provides closed-form expressions for both the probability density function (PDF) and CDF. In a two-compartment IV pharmacokinetic simulation with terminal-phase outlier contamination, Cauchy residuals preserved stable parameter recovery comparable to Students t while avoiding the bias and instability observed under Normal residuals. In a real-data integrated CAR-T cellular kinetics application fitted with full Bayesian inference and likelihood-based BLQ handling, replacing Students t with Cauchy yielded highly concordant posterior inference and similar subject-level predictions. We further extended a semi-mechanistic CAR-T cellular kinetics framework by replacing piecewise switching with smooth S-shaped time-varying rate functions and allowing process-specific transition times. Full Bayesian posterior summaries supported asynchronous transition timing, including earlier conversion relative to expansion and later decay-related transitions. Collectively, these results support Cauchy likelihoods as an implementation-friendly robust option and demonstrate that smooth, process-specific transition modeling can enhance physiological plausibility for CAR-T kinetics.

pharmacology and toxicology↗

bioRxiv2026

Physiologic variation in sperm miRNAs tune embryonic gene regulatory programs and developmental outcomes

Small RNAs delivered by sperm can transmit environmentally regulated, epigenetically inherited phenotypes to offspring, yet the mechanisms by which modest changes in sperm microRNA abundance overcome dilution within the much larger egg to influence embryonic development remain unresolved. Here, we show that physiologically relevant variation in individual sperm miRNAs is sufficient to quantitatively program embryonic gene expression and developmental outcomes. Using parthenogenetic and fertilized embryos, we show that as few as 200 molecules of miR-200c-3p or miR-465c-3p induces reproducible, dose-dependent gene expression responses across defined developmental windows. Parthenogenetic embryos faithfully recapitulate early miRNA-driven gene expression changes observed in fertilized embryos, validating their use for isolating early regulatory mechanisms. We further developed AGO2-REMORA, an RNA adenosine base editor fused to Argonaute2 to map miRNA-mRNA interactions in embryos, revealing that early mRNA repression reflects direct miRNA targeting, while transcriptional changes at later stages arise as secondary consequences of these initial interactions. Furthermore, we show that modest elevation of miR-200c-3p during early development is sufficient to induce transcriptional alterations through early development and produce craniofacial phenotypes in late-stage embryos, recapitulating features of fetal alcohol syndrome associated with paternal alcohol consumption. Together, these findings establish a generalizable framework by which small perturbations in sperm miRNA content quantitatively modulate early gene regulatory programs, triggering cascades that persist throughout development and influence offspring phenotype.

developmental biology↗

bioRxiv2026

Introduction to Single-cell Physiologically-Based Pharmacokinetic (scPBPK) Models

The current investigation introduces single-cell physiologically-based pharmacokinetic (scPBPK) models to gain insight into drug disposition at the cellular scale. The transition from standard PBPK (sPBPK) models to scPBPK models required depiction of expression-dependent (ED) processes, such as drug metabolism or membrane transport. ED processes utilize weighting functions - a defined or data-driven distribution -that yield heterogeneity in individual cell kinetics. Two scPBPK model examples are provided, one involving a drug (AZD1775) subject to 3 ED blood-brain barrier transport processes, and another drug (midazolam) with a single ED process of metabolism by hepatocytes. For both examples, the weighting function for each ED process was defined by a negative binomial distribution that is often used in scRNAseq analytics. The AZD1775 model simulations indicated a large degree of single cell drug concentration heterogeneity, whereas those for midazolam did not, due to high membrane transport relative to metabolism. scPBPK models offer a means to probe cellular pharmacokinetics compatible with modern omic technologies and may be extended to pharmacodynamic models. TeaserThe modeling framework to predict drug concentrations in single cells is presented.

pharmacology and toxicology↗

bioRxiv2026

A biatrial digital twin integrating electrophysiology, mechanics, and circulation: from physiology to atrial fibrillation

Atrial electromechanics plays a key role in cardiac function by regulating ventricular filling and global hemodynamics, yet remains challenging to model consistently across scales. In this work, a multiscale atrial digital twin for simulations of normal and pathological atrial function is presented, formulated as an electromechanical framework for biatrial simulations that couples three-dimensional atrial electrophysiology and mechanics with a closed-loop zero-dimensional circulatory model. The framework is calibrated on a patient-specific biatrial anatomy to reproduce physiological regional activation times, atrial volumes, ejection fractions, and pressure-volume loop characteristics. The simulations capture all atrial functional phases throughout the cardiac cycle, including realistic figure-eight pressure-volume loops, an aspect hard to achieve in computational studies. A systematic sensitivity analysis quantifies the influence of active contraction, passive stiffness, boundary conditions, and circulatory parameters on atrial function. Finally, application to a pathological scenario through induced persistent atrial fibrillation demonstrates how electrophysiological remodelling propagates across scales, leading to loss of effective atrial contraction, altered atrioventricular flow patterns, and a clinically relevant reduction in cardiac output. Overall, this multiphysics and multiscale framework provides a robust platform to investigate how atrial electrical alterations drive mechanical and hemodynamic alterations in both healthy and pathological conditions.

bioengineering↗

bioRxiv2026

Behavioral, Physiological, and Transcriptional Mechanisms of Memory in a Synthetic Living Construct

Synthetic living constructs, which lack the long histories of selection in ecological contexts that shape behaviors of conventional organisms, offer an important complement to traditional studies of learning. Could novel biobots exhibit sensing and memory of experiences? Here, we investigated the effects of chemical stimuli on basal Xenobots - autonomously motile entities derived from Xenopus embryonic ectodermal explants (with no additional sculpting or bioengineering). We quantified and characterized the coordinated ciliary activity that generates fluid flow fields guiding the trajectory of Xenobot motion. We also show distinct and specific changes in Xenobot behavior after brief exposure to Xenopus embryonic cell extract and to ATP. These two experiences produced distinct, long-term, stimulus-specific memories, detectable through both transcriptional and physiological signatures. Exposure to specific environmental stimuli induced alterations in the spatiotemporal patterns of calcium signaling across Xenobots. Together, these data lay a foundation for characterizing the capabilities of synthetic cellular collectives to sense and discriminate among stimuli, as well as store functional information in a non-neural context. Understanding behavioral competencies in novel, non-neural systems have broad implications across evolutionary biology, behavioral science, bioengineering, and bio/hybrid robotics.

synthetic biology↗

bioRxiv2026

Physiological responses of submerged freshwater macrophytes to multiple stressors

O_LISubmerged macrophytes are central to freshwater ecosystems functioning but are declining globally under multiple anthropogenic stressors. We aimed to identify general patterns in physiological responses and interaction types, and to assess whether a mechanistic understanding of stressor interactions can be developed from published evidence. C_LIO_LIWe systematically reviewed 12,858 records, identified 172 relevant papers, and extracted effect sizes from 124 experiments included in the meta-analysis. C_LIO_LIMost studies examined combinations of nutrient enrichment, shading, toxic trace metals, warming, and emerging contaminants such as PFAS and microplastics, typically under simplified 2 x 2 factorial laboratory designs. Additive effects dominated (50%), while synergistic interactions were relatively infrequent (14%). Antagonistic interactions often reflected dominance of a single stressor or compensatory responses, whereas synergisms were most frequent with metals combined with co-stressors enhancing bioavailability. C_LIO_LIOur synthesis suggests that accumulated stressors cause negative, but not necessarily amplified, responses, although the limited number of experiments testing more than two stressors means synergistic effects may be underestimated. We propose Stuckenia pectinata as a model organism because of its cosmopolitan distribution, experimental tractability, and available genomic resources, and argue that expanding stressor complexity, duration, and taxonomic breadth will strengthen predictions of macrophyte responses and inform freshwater conservation under global change. C_LI

plant biology↗

bioRxiv2026

Physiological, Histological, and Cognitive Characterization of a Macaque Model of Presbycusis

Age-related hearing loss (ARHL), or presbycusis, is one of the most prevalent sensory deficits in older adults and has been increasingly implicated in cognitive decline and dementia. This study characterizes ARHL in a rhesus macaque model by combining histological, physiological, and cognitive assessments. Aged macaques exhibited progressive cochlear degeneration, with marked outer hair cell loss at mid-to-high frequencies, elevated auditory thresholds, reduced distortion product otoacoustic emissions, and impaired auditory brainstem responses including amplitude reduction, latency prolongation, and diminished temporal precision. Despite modest reductions in inner hair cell ribbon synapse counts, hypertrophic changes were observed. These auditory deficits correlated with subtle impairments in visual working memory, as measured by a delayed match-to-sample task, underscoring a potential sensory-cognitive link. By capturing cross-domain aging markers in a translationally relevant primate model, this work lays a foundation for mechanistic studies and therapeutic interventions targeting both hearing and cognition in aging populations.

neuroscience↗

bioRxiv2026

A Machine Learning Approach for Physiological Role Prediction in Protein Contact Networks: a large-scale analysis on the human proteome

Proteins are fundamental macromolecules involved in virtually all biological processes. Their physiological roles are tightly linked to their three-dimensional structure, which can be naturally abstracted as Protein Contact Networks (PCNs), i.e., graphs where residues are nodes and edges encode spatial proximity. This representation enables the application of graph machine learning to address the protein functional annotation gap at proteome scale. In this work, protein function prediction is studied on the majority of the human proteome, focusing on enzymatic activity and enzyme class assignment as well-defined and biologically meaningful targets. A large-scale supervised analysis was conducted on PCNs derived from experimentally resolved human protein structures. Multiple graph-based learning paradigms were systematically compared under a unified evaluation protocol, including handcrafted graph embeddings, kernel methods, and end-to-end Graph Neural Networks (GNNs). Feature engineering approaches included (i) spectral density embeddings of the normalized graph Laplacian and (ii) higher-order topological representations based on simplicial complexes, with optional INDVAL-based feature selection. These representations were paired with linear, ensemble, and kernel classifiers, while GNNs were trained directly on raw PCNs exploiting a diverse set of message-passing strategies. Two tasks were considered: binary classification of enzymatic versus non-enzymatic proteins and multiclass prediction of first-level Enzyme Commission (EC) classes. Performance was assessed using repeated stratified splits to ensure robust and variance-aware evaluation. In the binary enzymatic classification task, the Jaccard-based graph kernel achieved the best performance with an adjusted balanced accuracy of 0.90, closely followed by GNNs trained end-to-end on PCNs. In the multiclass EC prediction task, GNNs demonstrated superior discriminative power, reaching an adjusted balanced accuracy of 0.92 and outperforming all explicit embedding and kernel-based approaches. Overall, results indicate that EC class prediction is intrinsically more complex than binary enzymatic discrimination and benefits from the higher expressivity of deep message-passing architectures. The findings demonstrate that graph-based representations of protein structure support competitive functional prediction at proteome scale, with classical kernel methods and modern GNNs offering complementary strengths in terms of accuracy, inference-time applicability, and flexibility.

bioinformatics↗

bioRxiv2026

Physiological architecture and evolutionary origins of cellular adaptability

The capacity to adapt to complex environments ( adaptability) is a defining property of cells. Yet, its relationship across single-cell physiology, population-level responses, and evolutionary timescales remains unclear. We performed single-cell transcriptional profiling of budding yeast across 20 complex environments before and after long-term selection for increased osmotolerance. In ancestral populations, transcriptional responses organized into a reproducible hierarchy where adaptation to certain cues took precedence over others. This hierarchy mechanistically originated within individual cells through contingent regulation of translation initiation. Evolution for >3,000 generations under osmotic stress increased osmotolerance while reordering this hierarchy, selectively deprioritizing osmotic stress as an organizing axis of adaptation. The derived strain exhibited impaired integration of stress responses, defective translational coordination, and reduced fitness outside the selected condition. Together, these findings illustrate that cellular adaptability is an evolved architecture whose form is set by the history of selection. One Sentence SummaryCells adapt to complex environments via a prioritization hierarchy; this hierarchy is shaped by the frequency and magnitude of environmental fluctuations in their evolutionary history.

cell biology↗