Biology subjects

Levin, M.

Publications and source records attributed to Levin, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Limb-localized induction of regeneration reshapes brain and amputation-site transcriptional states in adult Xenopus laevis

Limb regeneration requires coordinated tissue reconstruction and nervous system reintegration, yet whether regenerative induction alters brain responses to limb loss remains unclear. We analyzed transcriptomics from the paired brains and amputation sites of adult Xenopus laevis after hindlimb amputation with or without a 24 hour exposure to localized, pro-regenerative multi-drug treatment (MDT) delivered through a wearable bioreactor device. At 24 hours post treatment (24 HPT), MDT brains showed increased vascular and basement membrane transcripts and enrichment of oxidative phosphorylation and mTORC1 programs. In the limb, an exploratory integrated stress response (ISR) score increased at 11 and 24 HPT and returned to control levels by 7 days post-treatment (7 DPT), coinciding with induction of growth differentiation factor 15 (GDF15), a cytokine responsive to mitochondrial stress. We detected no corresponding increase in brain ISR score. At 7 DPT, MDT brains showed enrichment of immune, heme metabolism and biosynthetic programs, reduced synaptic transmission gene expression, and increased gdf15.L. Relative to an external uninjured reference, MDT and amputation only brains showed concordant changes, with treatment modifying selected programs. Cross-species comparative analyses identified partial overlap with an axolotl regenerative glutamatergic signature and separation from selected mammalian injury profiles. These findings reveal cross-tissue responses to regenerative induction and identify candidate mechanisms of brain body communication in biomedically engineered vertebrate limb regeneration.

neuroscience↗

bioRxiv2026

Goal directed closed-loop training of network bursts in cortical cultures produces improved performance and error correction over days

Dissociated neural cultures are living systems with no evolutionary history in their current form, which makes them a testbed for asking what goals neural tissue can learn to pursue once it is removed from its evolved body. It remains unclear which features of network activity such a culture can be trained to control, and whether that training is retained from one day to the next. We built an open-source closed-loop platform, the Holistic Agential Learning platform (HAL), that detects a network burst on a high-density microelectrode array, estimates its spatial origin in real time, and applies a minimal reinforcement rule. Here, a burst that originates off the goal side triggers global random stimulation that persists until the next burst. Over days of training, rat cortical cultures (n=10) increased the fraction of bursts that initiated on the goal side. This gain accrued between sessions, with little change within a session, a consolidation-like pattern that we interpret as a statement about timescale while the mechanism remains untested. The burst origin moved toward the goal side, while the burst peak and the repertoire of propagation directions were preserved. Trained cultures also recovered from incorrect states more reliably over days, while the rate at which they entered those states was unchanged, and this recovery effect was the most robust difference from unstimulated controls (n=6). Cultures that began on either side of chance converged to a similar level of performance. Damaged cultures (n=6) with comparable burst statistics showed neither the across-day gain nor the consolidation-like pattern. Training reweighted the activity patterns each culture already had, and we saw no new ones, which we read as a simple form of niche construction in which the tissue keeps a trace of its history that biases what it does next. These results identify burst initiation and error recovery as trainable variables in a novel living system, and they support training the tissue rather than engineering the machine.

bioengineering↗

bioRxiv2026

Computational Framework for Identifying Ion Channel Mutation-Compensating Interventions

We present an automated pipeline for finding therapeutic interventions in channelopathies. It starts from patch clamp recordings of the variant channel, searches over the pharmacologically accessible conductances, and says which currents must change and by how much. The intervention never touches the mutated channel: it compensates by modulating others. The feasibility of this approach is demonstrated through two computational strategies, each addressing a specific capability gap. First, we employ high-fidelity NEURON simulations combined with exploratory search algorithms to model two clinically described variants, and identify "stability windows" in which channel modulations restore the reference firing pattern. The compensating configurations are degenerate: 37 conductance triples fire the same number of action potentials at every one of the 35 injected-current levels, and the 10,780 triples reaching the best intervention value produce only six distinct firing patterns. Second, to address the computational cost of such detailed modeling, we implement a differentiable Hodgkin-Huxley model in PyTorch. Applied here to theoretical mutation screening, this approach trades granular detail for speed, treating the search as a continuous optimization problem that makes very large, non-local searches of the conductance parameter space affordable: the differentiable forward model evaluates 34,000-64,000 candidate parameter sets per second on a single consumer graphics card, against 11.9-16.9 per second for the non-differentiable pipeline on a 72-core node. The gain arises from forward-model throughput feeding a search rather than from the gradient itself, and it is what makes the 32,000,000-point survey of the solution topology reported here affordable. These computational predictions can inform practical drug development and high-throughput screening by naming the currents that must change and by how much.

bioinformatics↗

bioRxiv2026

A Closed-Loop Robot Scientist for Autonomous Biological Discovery

Biological systems respond to a wide range of physical inputs and an overarching goal of biological research is understanding how these signaling events coordinate cellular, tissue, and organism-level outcomes. Yet the vast combinatorial space of physical and chemical interventions that influence these processes remains largely unexplored. Thus, a search process is required that efficiently learns how inputs affect a biological target via a series of automatically generated interventions, and a robot scientist that can conduct them. To this end we here introduce the Multimodal Organismal Modulation Robot (MOMbot), a robot scientist that integrates four physical intervention modalities - chemical delivery, electrical field application, mechanical vibration, and thermal modulation -within a single hardware platform. MOMbots multimodal hardware is paired with high-resolution imaging and an online active learning algorithm that autonomously generates, executes, and refines interventions based on accumulated data. We validate the system across biological materials spanning three orders of magnitude in scale, including Xenopus laevis embryos, motile mucociliary organoids, and disaggregated ectodermal stem cells, to show the utility of this approach across diverse biological disciplines. Using these biological models, we demonstrate precise thermal control of embryonic developmental rate, automated chemical perturbation with behavioral readouts, programmable electrical field stimulation, and vibrational assembly of functional mucociliary organoids from loose cells. We also describe the active learning algorithm that autonomously concentrates interventions near regions of maximal outcome uncertainty, yielding sample-efficient discovery of an electrical duration-response relationship for motile organoids. These results establish MOMbot as a scalable robot/AI scientist team for biological discovery, enabling autonomous, multimodal physical experimentation and AI-driven exploration of complex biological systems.

developmental biology↗

bioRxiv2026

Membrane voltage and connexin expression work together to enhance tumor growth and metastasis in cancer

There is strong evidence of tumors manipulating their resting membrane potential (Vmem). While most fully-differentiated cells have a Vmem of roughly -70mV, tumor cells are generally depolarized, with Vmem {approx}-30mV, which more closely resembles the Vmem of stem cells. This is often believed to serve the purpose of accelerating the cell cycle and hence advantaging tumor proliferation. But when the tumor becomes invasive, its cells sometimes revert to a hyperpolarized Vmem with no obvious reason why. Separately, it is well accepted that solid tumors that are not yet invasive greatly underexpress connexins relative to healthy tissue; connexins, for our purpose, form gap junctions (GJs), small connecting tubes between nearby cells. Tumors that are invasive, by contrast, overexpress connexins. There is very little explanation for the paradox that connexins are first underexpressed and then overexpressed. However, it has long been known that Vmem electrically gates GJs; specifically, that homotypic GJs conduct best when the two cells they connect have a similar Vmem. Our in-silico model results explain this phenomenon, showing that when considered together, tumors' electrical and connexin-expression behaviors form a unified and effective strategy to control communication between the tumor and its healthy neighbor cells. This has implications for the emerging field of cancer bioelectrics, potentially leading to more precisely-targeted therapies.

cancer biology↗

bioRxiv2026

Living multicellular systems induce decodable spatial patterns in bacterial collectives

Living systems continuously modify their environments through chemical, mechanical, metabolic and bioelectrical activity. Whether a presence of a multicellular system can be encoded into the emergent spatial organization of another living collective in a distributed and decodable way is unknown. Here we show that motile Bacillus subtilis populations reorganize their spatial and ionic collective states in response to nearby Xenopus embryos and Xenobots. The bacteria in a liquid culture formed autonomous motility-dependent patterns that were redirected by living targets into attraction halos, which tracked target position at a distance. Extracellular levels of potassium amplified attraction, altered local potassium dynamics, and coupled target presence to global pattern complexity. Self-supervised machine learning further identified distributed bacterial spatial signatures predictive of Xenopus embryo vs. Xenobot presence at a distance from the target. Together, these findings suggest that bacterial collectives can encode information about the state of other biota in their environment, revealing a previously unrecognized form of inter-kingdom interaction between living morphogenetic systems.

systems biology↗

bioRxiv2026

A platform for automated training of mammalian cell physiology

Controlling cell physiology is difficult, not only because of cells complexity, but also their capacity for real-time adaptation to interventions, leading to challenges such as drug resistance and transgene silencing. Accumulating evidence suggests that this adaptivity resembles classical forms of learning defined in behavioral science. However, a lack of appropriate platforms has led to gaps in our understanding of cells capacity for adaptive problem-solving in physiological and transcriptional space. Here, we present a device, the Cell Trainer, capable of performing a wide variety of automated training experiments on non-neural mammalian cells, using timed drug pulses as the stimulus, and a mobile fluorescence microscope to capture images of responses, across replicate cultures. The Cell Trainer can operate in either an open-loop (feedforward) or closed-loop (feedback-controlled) mode, and our image analysis pipeline can report the behaviors of individual cells throughout each experiment and quantify population heterogeneity. We showcase the ability of the Cell Trainer to execute experimental protocols and perform single-cell analyses in both modes. We first demonstrate with a feedforward experiment in which myoblasts are repeatedly pulsed with dimethyl sulfoxide (DMSO) and their discrete calcium responses are analyzed, revealing sensitization-like dynamics. Next, we demonstrate a feedback control scheme wherein the fluorescence of a pH/voltage reporter in kidney cells is maintained below a threshold level with controlled pulses of acid. To accelerate research in the field of cell training, learning, and memory, we are openly sharing the Cell Trainer schematics and software with the research community. This platform provides a flexible tool for studying how cellular physiological states can be shaped by patterned stimulation and feedback control through approaches that work with the native adaptive competencies of cells.

bioengineering↗

bioRxiv2026

Trained Planaria Retain Memories Through Head Regeneration: A Model System for Insights into Non-Neural Memory and Neurodegenerative Diseases

Planaria exhibit remarkable regenerative ability, including the capacity to regrow complete heads and brains after decapitation. Here, we re-investigated whether regenerated planaria can preserve learned avoidance behavior, a phenomenon that has been reported previously but has been difficult to study due to unreliable experimental protocols. Using a light-to-food associative conditioning paradigm, planaria were trained to override their normal photophobic preference and then decapitated. Following a two-week regeneration period, behavioral responses to the conditioned stimulus were re-evaluated. Results indicated that the majority of regenerated planaria retained the learned response, supporting a model in which behavioral patterns can regenerate as well as anatomical patterns. By establishing a consistent, low-cost, and effective protocol for studying memory persistence through regeneration, such work may help inform future research on memory loss, resilience, and recovery in neurodegenerative diseases.

animal behavior and cognition↗

bioRxiv2026

Transcriptional Profiling of Planarian Regeneration Habituating to Physiological Stressor Reveals Individual and Collective Dynamics

Exposure to the potassium channel blocker barium chloride (BaCl2) causes head degeneration in Dugesia japonica flatworms, followed by regeneration of BaCl2-insensitive heads, offering a unique model for studying transcriptional resilience to novel stress. We performed RNA sequencing on individual planaria to investigate different transcriptional solutions to the BaCl2 challenge, and how regeneration history and social environment shape transcriptomic responses to BaCl2. We identified a robust transcriptional strategy and a potential sub-strategy for enabling BaCl2-insensitive head formation. Moreover, we observed pronounced transcriptional differences between untreated worms regenerating from tail fission fragments (tail-regenerated), and untreated full-sized worms that did not fission during the experiment (intact controls), highlighting the lasting impact of regeneration history. Relative to controls, tail-regenerated worms upregulated neurodevelopmental and morphogenetic programs, while downregulating mitochondrial transport and stress-response pathways. Relative to intact controls, BaCl2-exposed regenerates upregulated ion transport, metabolic, cell cycle, and inflammatory pathways, while downregulating neuronal signaling, ion homeostasis, morphogenesis, and tissue repair programs. Comparison of BaCl2-exposed isolated and BaCl2-exposed group-housed worms revealed minimal transcriptional divergence between social conditions. These findings underscore the complex interplay between regeneration, chemical stress, and social context in shaping gene expression.

molecular biology↗

bioRxiv2026

Bacterial histone-like proteins released during antibiotic treatment mediate vascular injury in meningococcal sepsis.

Vascular injury and coagulopathy are key drivers of mortality in bacterial sepsis. In Neisseria meningitidis infection, endothelial adhesion and thrombosis cause the characteristic petechial rash and, in the most severe cases, purpura fulminans. Although antibiotics rapidly kill bacteria, inflammation and vascular injury often persist or worsen after bacterial clearance, suggesting ongoing toxicity from released bacterial components. Here we identify bacterial histone-like proteins (HLPs), small positively charged DNA-binding proteins conserved across bacterial species, as previously unrecognized mediators of vascular damage. In vitro HLPs are released following antibiotic exposure, disrupting endothelial integrity. In patients with severe sepsis, they are detectable in plasma and tissue, colocalising with areas of vascular leak and coagulopathy. Non-anticoagulant heparins and anti-HLP antibodies neutralize HLP-induced endothelial disruption and toxicity in vitro and in vivo. These findings reveal HLPs as antibiotic-released bacterial toxins and suggest new therapeutic strategies to prevent vascular injury in sepsis. One sentence summaryAntibiotic treatment of meningococcal and other sepsis causative bacteria triggers the release of histone-like proteins (HLPs) that damage the endothelium and drive coagulopathy, but these effects can be neutralized by non-anticoagulant heparins or anti-HLP antibodies.

microbiology↗

bioRxiv2026

Multi-omics integration identifies a reproducible inflammatory host-response axis in pediatric sepsis

Sepsis is a major cause of morbidity and mortality in children, yet biological heterogeneity in host responses has limited progress toward targeted therapies and patient stratification. Multiomics integration can combine complementary molecular layers to identify coordinated disease programs not captured by individual assays. Here, we integrated genomic, bulk transcriptomic, proteomic and metabolomic data from blood samples of 22 children with culture-confirmed bacterial sepsis enrolled in the Swiss Pediatric Sepsis Study. Multi-Omics Factor Analysis identified a dominant host-response axis reflecting systemic inflammation. This axis was driven primarily by transcriptomic variation and supported by coordinated proteomic and metabolomic signals, including circulating inflammatory mediators and altered amino-acid metabolism. It was associated with C-reactive protein and a severity score proxy. Projection into an independent pediatric sepsis cohort (n = 22) reproduced the inflammatory and severity-related interpretation of this axis. Single-omic projections showed that the integrated signal could be approximated from individual layers, particularly transcriptomics. In three external pediatric whole-blood transcriptomic datasets, the RNA-derived projection separated septic shock from healthy controls and increased across clinical inflammatory syndromes. These findings define a reproducible inflammatory host-response axis in pediatric sepsis and support multi-omics-guided selection of molecular readouts suitable for clinical translation.

immunology↗

bioRxiv2026

Ionic Exposure History Shapes Inner Nuclear Membrane Voltage and Chromatin Texture Responses

While bioelectricity is increasingly recognized as an important regulator of cell function and morphogenesis, the field has almost exclusively focused on plasma membrane states. Voltage across the inner nuclear membrane (INM) has been proposed as a potential regulator of nuclear function, but how it responds to extracellular ionic perturbations and whether it relates to chromatin organization remain unclear. Here, we targeted the ratiometric genetically encoded voltage indicator ASAP3-R3 to SUN2-associated nuclear membranes in intact NRK cells and combined INM voltage measurements with Gray-Level Co-Occurrence Matrix (GLCM)-based chromatin texture analysis. Reporter localization was confirmed by fluorescence imaging and electron microscopy, and functional validation in isolated nuclei showed that sodium-potassium pump inhibition produced INM depolarization consistent with Goldman-Hodgkin-Katz (GHK)-based prediction. We then used our validated construct to determine the response of Vnuc and chromatin texture to changing ionic conditions via two exposure methods, gradual (ramped) exposure or direct application. In intact cells, ramping different sets of ionic solutions of decreasing sodium/increasing potassium, decreasing sodium, increasing potassium, or decreasing chloride induced INM hyperpolarization and coordinated changes in chromatin texture, including increased contrast and entropy, reduced homogeneity, and reduced nuclear area. These effects were strongly path-dependent, with nuclear responses shaped by the history and order of ionic exposure: sodium and potassium responses emerged most clearly during ramping exposure, whereas reducing chloride by direct exposure showed a more pronounced response profile. Direct changes in sodium exposure produced limited electrical and chromatin-texture effects, while direct potassium exposure altered chromatin texture and nuclear area without significantly changing VNuc. Importantly, shifting baseline chromatin state in either direction, through Trichostatin-A (TSA)-induced chromatin relaxation or sodium azide/2-deoxy-D-glucose-induced compaction, blunted ion-associated Vnuc and chromatin responses across sodium, potassium, and chloride conditions. Together, these findings identify the nucleus as a dynamic, ion-responsive electro-structural system in which INM voltage and chromatin organization are functionally coupled, and in which both ionic trajectory and pre-existing chromatin state shape the magnitude of the nuclear response.

cell biology↗

bioRxiv2026

Purinergic signaling disrupts emergent patterns of multicellular coordination in basal Xenobots.

The ability of self-organizing systems to display emergent, adaptive capabilities is a fundamental feature of biological life. Understanding the mechanisms by which cells co-ordinate at the micro-scale to produce macro-scale structures and behaviors is a fundamental problem in developmental biology. Moreover, it is an important goal of biomedicine to identify triggers that re-wire the physiological patterns of information flow and control signals. Here, we use a recently developed, synthetic biology platform known as basal Xenobots to explore how patterns of information flow and multi-cellular integration are regulated by chemical signaling pathways. Basal Xenobots are modified, organoid-like systems constructed from embryos, and have previously been shown to display complex patterns of information flows. In this study, we recorded calcium signals before and after exposing basal Xenobots to extra-cellular adenosine triphosphate (eATP), and used statistics from multivariate information theory to explore the differences in global patterns of information flow across the cells in each state. We found that eATP resulted in a dramatic reconfiguration of global information processing dynamics, characterized by a global decrease in multi-cellular co-ordination, reduced information transfer and integration, and a decrease in the global entropy rate of the basal Xenobots. These results provide evidence that purinergic signaling may play a key role in the regulation of multi-cellular self-organization, with implications for a variety of clinical disorders thought to involve aberrant purinergic signaling. These results also suggest the possibility that bioengineers may be able to "tune" the degree of self-organizing capacity in living systems via pharmacological intervention.

systems biology↗

bioRxiv2026

One Circuit, Many Flow Fields: Mechanistic Models of Single-Trial Neural Dynamics

A single neural circuit can exhibit qualitatively different dynamics across trials: one circuit, many flow fields. Standard models treat this trial-to-trial variability as noise around a fixed dynamical system or as discrete switches between regimes, yet neither captures how continuous internal-state variables, such as arousal or engagement, can gradually deform the circuits flow field. We propose that singletrial fitting can be reframed as inferring the low-dimensional control parameters that reshape a shared circuits flow field. We realize this with a low-rank recurrent network in which trial-specific static input biases act as bifurcation parameters: constant within a trial, they deform the flow field without directly driving activity over time. In a teacher-student setting, the model recovers the underlying dynamical system and its bifurcation structure from activity alone. Applied to large-scale recordings of mouse motor cortex during a delayed movement task, the model identifies a disengagement axis that separates engaged from disengaged trials and, when perturbed in silico, causally shifts the flow field between engaged and disengaged regimes. A generative extension reproduces the distribution of single-trial activity, and the inferred latent structure partially transfers across sessions and animals, suggesting shared low-dimensional structure across motor-cortical circuits. Together, these results reframe a methodological problem of fitting single-trial activity as a scientific opportunity: reading off the control parameters of the underlying dynamics, and connecting data-driven inference of neural dynamics to mechanistic theories of how a single circuit reuses its dynamics for flexible behavior.

neuroscience↗

bioRxiv2026

Homeostatic feedback model of energy metabolism with adaptive enzyme levels exhibits problem solving behavior

Metabolic networks are typically viewed as homeostatic systems that stabilize flux, energy charge, redox balance, and metabolite availability under perturbation. However, it remains unclear whether the same feedback architectures that support metabolic robustness can also generate learning-like, experience-dependent adaptation. Here, we develop a coarse-grained dynamical model of mammalian energy metabolism to test whether prior perturbation can improve future metabolic responses. The model represents core glucose, glutamine, fatty acid, and oxidative phosphorylation pathways as coupled ordinary differential equations with Michaelis-Menten-type fluxes, product-inhibition feedback, adaptive enzyme-capacity regulation, and explicit ATP costs for enzyme adjustment. Rather than aiming to reproduce quantitative fluxes for a specific cell type, the framework is designed to expose how metabolic feedback, regulatory cost, repeated perturbation, and environmental variability interact. We use this model to ask whether adaptive enzyme regulation enables improved recovery after repeated challenges, whether such effects depend on energetic control costs, and whether environmental variability broadens or constrains the set of reachable adaptive states. This approach provides a tractable way to investigate how homeostatic metabolic regulation may give rise to experience-dependent metabolic plasticity.

systems biology↗

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↗

bioRxiv2025

Ephaptic-Axonal Interactions Shape Radial Biases DuringNeural Self-Organization

Communication in the brain seems to not just be mediated by axonal action potential transmission resulting in post synaptic modulations, but also by electrical field effects of depolarizing neurons, i.e. ephaptic coupling. The significant role of ephaptic coupling in synchronizing neural ensembles has been conjectured to indirectly affect ontogenetic neural circuit development through sub threshold impacts on spike timing. We therefore hypothesized synchronously firing ensembles to emerge at spatial distances where ephaptic and axonal signals were most temporally correlated. To test our model of ephaptic axonal interactions during neural self-organization, we compared its predictions to developmental outcomes of cortical rat tissue on high-density multielectrode arrays in vitro. We observed a cosinusoidal variation of synchronous activity over radial distances that can be understood to result from the joint effects of ephaptic and axonal signals during gamma-band bursts. Recurrent amplification of this theorized interaction effect during ontogenetic differentiation appears to radially bias the spectral profile of neural activity in the spatial distribution of neural ensembles. While long- term plasticity has been conventionally attributed to synaptic action alone, we show that that the interaction effects with ephaptic waves deserve further investigation.

neuroscience↗

bioRxiv2025

Temperature modulates PFAS accumulation and energy allocation in sheepshead minnows

Climate warming and chemical pollution shape aquatic ecosystems, yet the physiological mechanisms underlying their combined effects remain unclear. We investigated how projected increases in mean summer surface water temperature alter per- and polyfluoroalkyl substances (PFAS) toxicokinetics and their effects on sheepshead minnows (Cyprinodon variegatus) physiological performance. Adult fish were chronically exposed to an environmentally relevant PFAS mixture (perfluorooctane sulfonate (PFOS) + perfluorooctanoate (PFOA)) under current and projected mean-temperature scenarios. Tissue PFAS concentrations, whole-organism metabolic rates, swimming performance, reproductive parameters, somatic indices were assessed. Temperature modified PFAS tissue concentrations in a compound- and tissue-specific manner, promoting PFOA redistribution to eggs. Metabolic responses were temperature-dependent: at 26 {degrees}C, higher tissue PFAS concentrations were associated with elevated standard and maximum metabolic rates (SMR and MMR), maintaining aerobic scope (AS). At 28.5 {degrees}C, SMR remained stable while MMR and AS declined with rising PFAS, indicating less oxygen for energetically demanding activities. Despite unchanged swimming and reproductive outputs, an increased hepatosomatic index with increasing tissue PFAS concentrations and altered PFAS distribution suggest detoxification costs. These findings indicate that increases in mean water temperature are likely to exacerbate contaminant stress, with consequences for coastal fish population resilience and offspring development. PFAS risk assessment should consider co-stressors under projected warming. SynopsisLimited research addresses how temperature affects PFAS toxicokinetics and toxicity. This study shows that warming reshapes tissue PFAS concentrations and distribution, and influences fish energy-allocation trade-offs.

physiology↗