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Genetic assimilation and accommodation shape adaptation to heat stress in a splash pool copepod

Understanding how organisms respond to variable environments is becoming increasingly important in our rapidly changing world. Beyond genetic adaptation, plastic responses to the environment can alter phenotypes and fitness, ultimately driving evolution. However, the interaction between plasticity and adaptation during environmental change is complex and hard to measure in natural systems. Here, we used two populations of Tigriopus californicus copepods, a thermally tolerant southern population and a thermally sensitive northern population, to conduct a fully factorial split brood experiment where we exposed animals as larvae and adults to either a sublethal heat stress or control (no heat treatment) before measuring heat tolerance and gene expression patterns. We found that increased thermal tolerance across populations came at the expense of physiological plasticity and evolved through higher baseline expression of heat stress response genes across environmental contexts as well as increased gene expression plasticity in response to heat stress. In the thermally sensitive northern population, developmental exposure to heat stress led to higher adult tolerance and lower physiological plasticity underpinned by higher gene expression plasticity. Importantly, we found that the same set of genes were largely responsible for both the evolved higher tolerance in the southern population and the developmentally induced tolerance in the northern population suggesting that in this system, a shared molecular response contributes to acclimation and adaptation across both populations. These results link existing physiological plasticity with long-term evolutionary responses providing insight into how these populations will adapt and respond to future environmental change. SIGNIFICANCEUnderstanding plastic and evolutionary responses to dynamic environments is critical to anticipating species vulnerability to climate change. In this study, we compared gene expression and physiological responses to heat stress across two populations of a marine copepod that differ in thermal tolerance to investigate mechanisms of adaptation. We found evidence for plasticity-led evolution in this system, with the same set of genes contributing to long-term evolutionary changes across populations and to short-term physiological adjustments within populations. Our results suggest that populations with a reservoir of plasticity have a greater potential to evolve as the climate continues to warm, but that there may be a limit to this adaptive capacity.

evolutionary biology↗

Artificial Soil (ArtSoil): recreating soil conditions in synthetic plant growth media

Controlled plant growth in laboratories can be achieved by cultivating plants in sterile or axenic conditions on pre-defined synthetic growth media typically supplemented with sugar. In nature, plants do not receive exogenous sugar supply, form symbiosis with microbes, and plant growth is influenced by soil edaphic factors. Thus, physiological and multi-omic analyses from plants grown on synthetic media will differ from those of soil-grown plants due to the influence of sucrose, and the lack of influence from microbiota and soil edaphic factors on plant growth. The rapid advancements in spatial-omics call for accurate characterization of plants grown in conditions similar to soil. To address the issue, we developed Artificial Soil (ArtSoil), a growth medium containing essential nutrients for plant growth, and aqueous soil extract (ASE) to maintain soil microbiomes and edaphic factors, simultaneously eliminating the need for sugar supplementation in the medium. We compared Arabidopsis thaliana grown on conventional media to ArtSoil under various growth conditions, and showed that complex soil microbiota in ArtSoil promote plant growth without physiological side effects induced by sucrose. We demonstrate an application for ArtSoil in single-cell transcriptomics and report microbiota-induced cell-type specificity in immune and nitrogen signaling. We tested ArtSoil with six types of ASEs to present the potential of ArtSoil in decoupling the effects of nutrients from microbiota in plant growth. We conclude that ArtSoil recapitulates the soil environment compared to conventional media, hence, enabling physiologically relevant plant growth. Significance statementLaboratory plant growth commonly relies on sugar-supplemented media and sterile growth conditions, which introduces physiological artifacts and fails to capture the natural influences of soil microbiota and edaphic factors, leading to analyses that diverge from native conditions. We developed Artificial Soil (ArtSoil), a medium containing aqueous soil extract that supports microbiota and sugar-independent growth, and demonstrate its utility in recapitulating native soil properties and enabling physiologically relevant plant development in vitro.

plant biology↗

A biphasic metabolism-β-lactamase production landscape governs the inoculum effect in β-lactamase-expressing Escherichia coli.

The inoculum effect (IE) reduces antibiotic efficacy in vitro and in clinical settings, yet remains difficult to predict. IE limits antibiotic efficacy but remains unpredictable, particularly in resistant bacteria, where resistance mechanisms and bacterial physiology interact in poorly understood ways. Using Escherichia coli expressing the NDM-1 {beta}-lactamase and clinical isolates, we quantified metabolism, growth rate, {beta}-lactamase expression, and IE across diverse growth environments. Across enzyme classes, antibiotics, and clinical isolates, we find that IE is governed by a conserved, biphasic dependence on metabolism normalized by growth rate. Mathematical modeling shows that this behavior reflects a tradeoff between {beta}lactamasemediated protection and metabolismpotentiated antibiotic lethality. These findings establish a predictive, physiology-based framework for IE in resistant bacteria and explain why resistance determinants alone fail to predict treatment outcomes across environments, including the clinic. Significance StatementAntibiotic efficacy depends on both bacterial resistance and physiology, yet these factors are rarely integrated when predicting treatment outcomes. One important consequence of their interaction is the inoculum effect (IE), in which antibiotic efficacy depends on the density of a resistant bacterial population. Here, we show that IE in {beta}lactamase-expressing bacteria is governed by a conserved physiological tradeoff between metabolism and growth. Across growth environments, antibiotics, inocula, and clinical isolates, IE is strongest at intermediate metabolic states, reflecting a balance between resistancemediated protection and metabolismpotentiated antibiotic lethality. This framework helps explain why resistance determinants alone are insufficient to account for IE and underscores the role of bacterial physiology in shaping antibiotic responses.

microbiology↗

The SI compartment model describes embolism spreading in networks of vessels and bordered pits in angiosperm xylem

Plant xylem consists of a network of interconnected vessels, through which water is transported under negative pressure. Filling of vessels with air, or embolism, disturbs this transport process and, in extreme cases, leads to tree mortality. Despite this significance, embolism propagation dynamics are still poorly understood, primarily because xylem is opaque to direct observation. Furthermore, existing models of embolism spreading build excessively on physiological and anatomical parameters, and many misrepresent the inter-vessel pit membrane as a 2D surface. Here, we first extend these physiological models by implementing the pit membrane as a 3D object. Then, we introduce a susceptible-infected (SI) model, a simple stochastic model for tracking spreading through a population, for embolism propagation. After correctly fitting the spreading probability, our SI model reproduces vulnerability curves produced by both the physiological model and empirical data, highlighting that the SI model can address embolism spreading dynamics in plant species, for which detailed physiological data are not available. Furthermore, relating the SI model to the physiological one allows interpreting embolism spreading as a directed percolation process. Elucidating the exact mapping between directed percolation and embolism spreading will likely yield new fundamental insights into the relationships between xylem network architecture and embolism dynamics.

plant biology↗

Universal phylogenetic inertia in body temperature evolution across endothermic and ectothermic tetrapods.

Species must adapt to persist in a changing world. As global temperatures rise, how species adapt and respond to thermal shifts is crucial for anticipating global patterns of biodiversity change. Land vertebrates can be divided into two major thermoregulatory strategies, endothermy and ectothermy. One might hypothesize that, given their reputation as being "cold blooded," ectotherms are thermal generalists, capable of operating across a greater range of body temperatures than endotherms and exhibit greater plasticity and evolvability in body temperature. However, a wide variety of traits and ecologies could modulate responses of thermal physiology to environmental change. Here, we employ macroevolutionary models to estimate the rate of adaptation of thermal physiology across squamates, mammals, and birds in the context of their ecology, physiology, and changing climatic conditions and whether there are fundamental differences in how the three clades respond to their environments. We find stronger relationships between squamates body temperature and their environment than in birds and mammals, significant effects of diel activity (nocturnal and diurnal) on body temperature evolution in all clades, and no effect of aquatic/terrestrial habits and rumination on the evolution of body temperature in mammals. Most surprisingly, our findings suggest shared limits on the evolution of thermal physiology across ectothermic and endothermic groups that argue for universal constraints on the rate of evolution in thermal physiology while explaining disparate patterns of body temperature and niche evolution across groups.

evolutionary biology↗

Phage display-mediated immuno-PCR to detect low-abundance secreted proteins in Drosophila

Circulating hormones, that mediate communications across organs to maintain physiological balance, are commonly detected and quantified using enzyme-linked immunosorbent assays (ELISAs). However, while ELISA is well-suited for organisms where sample blood can be readily obtained, its application is considerably more challenging in smaller organisms, particularly Drosophila, which has gained widespread use in recent years for physiological studies. Here, we present sensitive phage display-mediated immuno-PCR (PD-iPCR) to detect Drosophila hemolymph proteins via two approaches: 1) by identifying high-affinity nanobodies through phage-display library screening and subsequent affinity maturation and 2) by generating a knock-in fly line producing secreted proteins tagged with tandem NanoTags composed of VHH05 and 127D01. Using these approaches, we successfully established PD-iPCR to detect insulin-binding ImpL2 protein in fly hemolymph. Notably, the tandem NanoTag-based sandwich PD-iPCR enabled highly sensitive detection of tagged antigens, allowing us to quantify elevated ImpL2 levels in the hemolymph of starved flies and those bearing Yki-induced gut tumors. Collectively, our results demonstrate that PD-iPCR enables detection of endogenous, low-abundance circulating hormones in Drosophila, providing a powerful tool for studying interorgan communication. Significance statementHormones and other secreted factors orchestrate organism-wide physiology, yet their routine quantification in Drosophila has been limited by the limited volume of hemolymph available for assays like enzyme-linked immunosorbent assay (ELISA). Here, we present phage display-mediated immuno-PCR (PD-iPCR) as a novel sensitive platform for quantifying secreted proteins in flies in vivo. Using ImpL2 as an example, we successfully detected nanomolar level of circulating ImpL2 and monitored its physiological changes during starvation and tumorigenesis using PD-iPCR. This approach can be readily expanded to multiplexed quantification of secreted proteins in vivo by leveraging the multiple available nanobodies and the vast collection of epitope-tagged Drosophila lines. This work opens the door to systematic endocrine phenotyping across developmental stages and diverse physiological conditions.

genetics↗

Flux modelling analysis reveals the metabolic impact of cryptic plasmids and environmental conditions in probiotic Escherichia coli Nissle 1917

Escherichia coli Nissle 1917 (EcN) is a well-characterized Gram-negative probiotic distinguished by its unique, strain-specific physiology. Genome-scale metabolic models (GEMs) are powerful tools for elucidating metabolic traits and predicting genotype-phenotype relationships. Although several EcN GEMs have been published, none have explicitly represented its probiotic physiology. Here, we present a manually curated GEM of EcN that, for the first time, incorporates the energetic costs associated with its cryptic plasmids. Inclusion of a plasmid-specific module improved biomass yield predictions and overall model accuracy, providing a more physiologically realistic representation of EcN metabolism. Using COBRA methodologies and possibilistic metabolic flux analysis, this model and previous EcN reconstructions were systematically compared to evaluate the trade-off between model complexity and predictive performance. The analysis revealed that increased structural detail does not necessarily enhance quantitative accuracy and that predictive reliability depends on both computational methodology and model context. Metabolomic profiling under gut-like anaerobic conditions further showed that EcN exhibits a distinctive metabolic phenotype, characterized by elevated amino acid consumption and enhanced short-chain fatty acid production. These findings highlight the unique probiotic physiology of EcN and demonstrate the utility of metabolic modeling for reproducing and exploring such traits. Overall, this study provides a quantitatively reliable and physiologically relevant framework for modeling E. coli Nissle 1917 and related commensal bacteria, supporting advances in probiotic engineering, synthetic biology, and bioprocess design. Graphical Abstract SummaryThis study presents a manually curated genome-scale model of Escherichia coli Nissle 1917 that accounts for the metabolic cost of its cryptic plasmids. Through systematic comparison with previous reconstructions and validation against fluxomics datasets, the models improved accuracy in predicting growth and fluxes. Simulations and experiments under gut-like conditions provide new insights into EcNs unique probiotic traits. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/688048v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@6c8c98org.highwire.dtl.DTLVardef@8263e3org.highwire.dtl.DTLVardef@6c04c3org.highwire.dtl.DTLVardef@1acb811_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI is the first method that integrates thermodynamics together with relative gene-expression and metabolomic data as constraints for FBA. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was also able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.\n\nAuthor SummaryThe recent advances in omics technologies have provided us with an unprecedented abundance of data spanning genomes, global gene expression, and metabolomes. Though these advancements in high-throughput data collection offer an excellent opportunity for a more thorough understanding of metabolic capacities of a wide range of species, they have caused a considerable gap between \"data generation\" and \"data integration.\" reconstructed model to predict the observed physiology, e.g., growth phase through omics data integration. In this study, we present a new method named REMI (Relative Expression and Metabolomic Integrations) that enables the co-integration of gene expression, metabolomics and thermodynamics data as constraints in genome-scale models. This not only allows the better understanding of how different phenotypes originate from a given genotype but also aid to understanding the interactions between different types of omics data.

systems biology↗

Whole Genome Detection of Sequence and Structural Polymorphism in Six Diverse Horses

The domesticated horse has played a unique role in human history, serving not just as a source of animal protein, but also as a catalyst for long-distance migration and military conquest. As a result, the horse developed unique physiological adaptations to meet the demands of both their climatic environment and their relationship with man. Completed in 2009, the first domesticated horse reference genome assembly (EquCab 2.0) produced most of the publicly available genetic variations annotations in this species. Yet, there are around 400 geographically and physiologically diverse breeds of horse. To enrich the current collection of genetic variants in the horse, we sequenced whole genomes from six horses of six different breeds: an American Miniature, a Percheron, an Arabian, a Mangalarga Marchador, a Native Mongolian Chakouyi, and a Tennessee Walking Horse. Aside from extreme contrasts in body size, these breeds originate from diverse global locations and each possess unique adaptive physiology. A total of 1.3 billion reads were generated for the six horses with coverage between 15x to 24x per horse. After applying rigorous filtration, we identified and functionally annotated 8,128,658 Single Nucleotide Polymorphisms (SNPs), and 830,370 Insertions/Deletions (INDELs), as well as novel Copy Number Variations (CNVs) and Structural Variations (SVs). Our results revealed putatively functional variants including genes associated with size variation like ANKRD1 and HMGA1 in the very large Percheron and the ZFAT gene in the American Miniature horse. We detected a copy number gain in the Latherin gene that may be the result of evolutionary selection for thermoregulation by sweating, an important component of athleticism and heat tolerance. The newly discovered variants were formatted into user-friendly browser tracks and will provide a foundational database for future studies of the genetic underpinnings of diverse phenotypes within the horse. Author SummaryThe domesticated horse played a unique role in human history, serving not just as a source of dietary animal protein, but also as a catalyst for long-distance migration and military conquest. As a result, the horse developed unique physiological adaptations to meet the demands of both their climatic environment and their relationship with man. Although the completion of the horse reference genome yielded the discovery of many genetic variants, the remarkable diversity across breeds of horse calls for additional effort in quantification of the breadth of genetic polymorphism within this unique species. Here, we present genome re-sequencing and variant detection analysis for six horses belonging to geographically and physiologically diverse breeds. We identified and annotated not just single nucleotide polymorphisms (SNPs), but also large insertions and deletions (INDELs), copy number variations (CNVs) and structural variations (SVs). Our results illustrate novel sources of polymorphism and highlight potentially impactful variations for phenotypes of body size and conformation. We also detected a copy number gain in the Latherin gene that could be the result of an evolutionary selection for thermoregulation through sweating. Our newly discovered variants were formatted into easy-to-use tracks that can be easily accessed by researchers around the globe.

genomics↗

COALIA: a computational model of human EEG for consciousness research

Understanding the origin of the main physiological processes involved in consciousness is a major challenge of contemporary neuroscience, with crucial implications for the study of Disorders of Consciousness (DOC). The difficulties in achieving this task include the considerable quantity of experimental data in this field, along with the non-intuitive, nonlinear nature of neuronal dynamics. One possibility of integrating the main results from the experimental literature into a cohesive framework, while accounting for nonlinear brain dynamics, is the use of physiologically-inspired computational models. In this study, we present a physiologically-grounded computational model, attempting to account for the main micro-circuits identified in the human cortex, while including the specificities of each neuronal type. More specifically, the model accounts for thalamo-cortical (vertical) regulation of cortico-cortical (horizontal) connectivity, which is a central mechanism for brain information integration and processing. The distinct neuronal assemblies communicate through feedforward and feedback excitatory and inhibitory synaptic connections implemented in a template brain accounting for long-range connectome. The EEG generated by this physiologically-based simulated brain is validated through comparison with brain rhythms recorded in humans in two states of consciousness (wakefulness, sleep). Using the model, it is possible to reproduce the local disynaptic disinhibition of basket cells (fast GABAergic inhibition) and glutamatergic pyramidal neurons through long-range activation of VIP interneurons that induced inhibition of SST interneurons. The model (COALIA) predicts that the strength and dynamics of the thalamic output on the cortex control the local and long-range cortical processing of information. Furthermore, the model reproduces and explains clinical results regarding the complexity of transcranial magnetic stimulation TMS-evoked EEG responses in DOC patients and healthy volunteers, through a modulation of thalamo-cortical connectivity that governs the level of cortico-cortical communication. This new model provides a quantitative framework to accelerate the study of the physiological mechanisms involved in the emergence, maintenance and disruption (sleep, anesthesia, DOC) of consciousness.

neuroscience↗

A machine learning approach to identifying objectivebiomarkers of anxiety and stress

Anxiety and stress-related disorders are highly prevalent and debilitating conditions that impose an enormous burden on society. Sensitive measurements that can enable early diagnosis could mitigate suffering and potentially prevent onset of these conditions. Self-reports, however, are intrusive and vulnerable to biases that can conceal the true internal state. Physiological responses, on the other hand, manifest spontaneously and can be monitored continuously, providing potential objective biomarkers for anxiety and stress. Recent studies have shown that algorithms trained on physiological measurements can predict stress states with high accuracy. Whether these predictive algorithms generalize to untested situations and participants, however, remains unclear. Further, whether biomarkers of momentary stress indicate trait anxiety - a vulnerability foreshadowing development of anxiety and mood disorders - remains unknown. To address these gaps, we monitored skin conductance, heart rate, heart rate variability and EEG in 39 participants experiencing physical and social stress and compared these measures to non-stressful periods of talking, rest, and playing a simple video game. Self-report measures were obtained periodically throughout the experiment. A support vector machine trained on physiological measurements identified stress conditions with ~96% accuracy. A decision tree that optimally combined physiological and self-report measures identified individuals with high trait anxiety with ~84% accuracy. Individuals with high trait anxiety also displayed high baseline state anxiety but a muted physiological response to acute stressors. Overall, these results demonstrate the potential for using machine learning tools to identify objective biomarkers useful for diagnosing and monitoring mental health conditions like anxiety and depression.

neuroscience↗

Reported estimates of human airway dimensions are inconsistent across studies

RationaleRespiratory diseases are a source of immense socioeconomic burden globally. In silico approaches can predict changes in human lung function due to disease or response to therapy. By stratifying patient-specific response a priori, these models can enable clinical-scale deployment of precision medicine strategies. Key to this is developing accurate organ geometries on which the models can be simulated. However, we lack analyses assessing the clinical applicability of reported airway dimension estimates. ObjectiveTo investigate physiologically-/anatomically-relevant airway dimension estimates and evaluate consistency across reported literature. MethodsWe conducted a systematic review of 37 published datasets. Airway wall thickness estimates were mined for healthy subjects and patients, and standardised to the Horsfield order airway generations. We simulated dynamic lung function to quantitatively assess their physiological relevance. We created an online database to make all datasets available to the research community. Measurements and Main ResultsReported human airway wall thickness estimates are inconsistent across studies. K-means clustering divided estimates for healthy subjects and patients into three and four clusters, respectively. Only one of the clusters in each category yielded anatomically-relevant estimates. Pressure-volume curves generated to assess physiological relevance also showed that only one cluster in each category exhibited plausible physiology. Principal Component Analysis weakly implicated imaging modalities to explain this inconsistency. ConclusionsReported airway dimension estimates are inconsistent and lack standardisation. To support future modelling efforts, we report physiologically-relevant estimates and introduce an open-access airway-dimension database to help standardise geometric inputs and quantify how measurement variability propagates to functional predictions.

biophysics↗

Phage Portal Proteins Suppress Bacterial Stringent Response to Promote Infection

Bacteria restrict viral replication not only through dedicated defense systems but also by entering global physiological states that limit cellular resources. How phages counter such host-imposed physiological barriers remains poorly understood. The stringent response (SR), mediated by the alarmones ppGpp and pppGpp, induces a growth-restrictive state that can act as a barrier to bacteriophage infection. Here we show that elevated alarmone levels delay T7-mediated host lysis, whereas (p)ppGpp-deficient cells are hypersensitive to infection, establishing alarmone signaling as a physiological constraint on phage replication. A systematic functional screen identifies the essential capsid portal protein Gp8 as a viral factor genetically linked to SR-dependent host physiology. Gp8 directly binds the alarmone synthetases RelA and SpoT, selectively inhibiting their synthetase activities in vitro and suppressing alarmone accumulation in vivo. Phages carrying interaction-defective portal mutations exhibit impaired replication, delayed lysis, and sustained (p)ppGpp elevation during infection, defects that are alleviated in SR-deficient hosts. Structural and mutational analyses reveal electrostatically mediated interfaces required for targeting RelA and SpoT. Portal proteins from diverse coliphages share similar structural features, interact with stringent-response enzymes, and display SR-linked phenotypes, indicating a broadly conserved viral strategy. Together, these findings identify phage portal proteins as a previously unrecognized class of viral counter-defense factors that target a central bacterial stress signaling pathway, revealing that essential structural virion components can moonlight as regulators of host stress physiology.

microbiology↗

Heart rate variability as a candidate correlate of susceptibility to ASMR and music-induced frisson: an exploratory pilot study

BackgroundAutonomous sensory meridian response (ASMR) and music-induced frisson are sensory-affective phenomena characterized by tingling, chills, and pronounced emotional responses. Previous research has mainly focused on physiological changes during these experiences, whereas much less is known about whether baseline physiological state is associated with subsequent susceptibility. ObjectiveTo examine whether baseline autonomic flexibility, indexed primarily by heart rate variability (HRV), is associated with later ASMR/frisson responsiveness. Resting EEG measures were included as secondary exploratory markers. MethodsFifteen participants were recruited by convenience sampling; after artifact-based exclusion, 10 participants were included in the analyses. A 5-minute resting baseline EEG and ECG was recorded prior to stimulus presentation. Participants were then exposed to auditory and audiovisual ASMR stimuli, classical music excerpts, and a control stimulus, and reported whether they had experienced ASMR-typical sensations or frisson. Main analyses examined associations between baseline physiological parameters and a combined response-positive outcome. Exploratory analyses included participant-level correlations, comparisons between susceptible and non-susceptible participants, and stimulus-specific effect sizes. ResultsHRV-related measures showed the clearest and most consistent pattern of association with responsiveness. Higher baseline total HRV power was associated with a greater number of response-positive stimuli (r = 0.756, p = 0.011), with similar positive associations for high-frequency HRV (HF; r = 0.672, p = 0.033) and baseline heart rate slope (r = 0.751, p = 0.012). Stimulus-specific analyses likewise showed the most consistent positive baseline effects for total HRV power, with HF and heart rate slope pointing in the same direction. Frontal alpha asymmetry (FAA) was negatively associated with responsiveness ({rho} = -0.862, p = 0.001), but EEG findings overall were less consistent than the HRV-related pattern and are best interpreted as secondary exploratory observations. ConclusionsIn this exploratory pilot sample, baseline HRV, particularly total HRV power, showed the most coherent physiological association with susceptibility to ASMR and music-induced frisson. The findings are consistent with the possibility that these experiences depend not only on stimulus properties, but also on pre-existing physiological state. Given the small sample and exploratory design, the results should be interpreted as hypothesis-generating and require replication in larger confirmatory studies.

neuroscience↗

Evolution of ion channels in the water-to-land transition of vertebrates

The transition of vertebrates from aquatic to terrestrial environments represents one of the most profound evolutionary events in their history, involving extensive physiological and morphological innovations. Key adaptations included the transformation of fins into limbs with digits to enable efficient terrestrial locomotion, the ability to perceive novel environmental stimuli, and the emergence of reproductive strategies suited to life on land, processes in which ion channels played fundamental roles. Accordingly, understanding the genetic basis of vertebrate terrestrialization requires investigating the evolution of this group of membrane proteins. Our analyses reveal that the proportion of ion channel genes is highly conserved, representing approximately 1.4% to 1.6% of total protein-coding genes in most lineages, with a notable increase to [~]1.9% in teleost fishes. Our natural selection analyses revealed an overrepresentation of specific ion channel gene families, including TRP, RyR, HTR3, and HCN. We identified 29 ion channel genes showing signatures of positive selection, many of which are associated with key physiological functions such as nociception and thermosensation. We also detected an elevated rate of gene turnover in the common ancestor of terrestrial vertebrates, indicative of substantial genomic remodeling through gene gain and loss. Together, these findings suggest that, despite overall conservation in the proportions of ion channel genes, specific gene families underwent changes that were likely critical to meeting the physiological demands of terrestrial life. These results provide a foundation for future comparative and functional studies aimed at elucidating the molecular mechanisms underlying major environmental transitions. SignificanceThe transition from water to land was one of the most important events in vertebrate evolution, requiring animals to detect new environmental cues, sense temperature, coordinate movement, and cope with unfamiliar pathogens and physical stresses. Because ion channels control many of these physiological processes, they provide an ideal system for investigating the molecular basis of vertebrate terrestrialization. By tracing their evolutionary history across vertebrates, we show that many ion channels underlying these functions underwent adaptive evolution during the transition from water to land. Our findings suggest that adaptive changes in ion channels helped vertebrates acquire the physiological capabilities needed to survive and diversify on land, providing new insight into the molecular basis of one of the greatest phenotypic transformations in vertebrate evolution.

evolutionary biology↗

EMPAC: A Multimodal Dataset for Bridging Affective and Cognitive Empathy

Empathy, a key element of social interaction, involves both cognitive and affective processes and is commonly investigated through measures such as empathic accuracy and affective physiological synchrony. While physiological synchrony offers a continuous measure of affective processes, empathic accuracy typically relies on discrete self-reports, leaving their temporal relationship largely unexplored. Advancing this line of research requires datasets that integrate time-continuous self-reports with physiological signals, yet such datasets--particularly those focusing on the empathizee--remain limited. To fill this gap, we present EMPAC (Empathy Measurement: Physiological, Affective, and Cognitive), a multimodal dataset constructed. To create empathy-eliciting stimuli, professional actors performed emotionally intense, pseudo-autobiographical narratives while their physiological signals (e.g., ECG, EDA) and continuous self-reported emotional states were recorded. We then conducted two observer experiments using these video recordings. In Experiment 1, to validate the stimuli as empathy-eliciting materials, observers continuously rated emotional intensity without being informed of the specific emotion portrayed, following the protocol of previous studies on time-series empathic accuracy. Yet this approach sometimes revealed a gap between the emotion category portrayed by the target and that perceived by the observers. In Experiment 2, we introduced a revised procedure in which the target emotion category was disclosed prior to viewing, revealing that specifying the target emotion led to a different relationship between individual empathy traits and empathic accuracy than observed in Experiment 1. EMPAC thus provides a rich, temporally aligned resource for investigating empathy dynamics in naturalistic settings and for evaluating methodological variations in empathic accuracy paradigms.

neuroscience↗

A Deep Dive into the Cognitive Soundscape of Flow: Finding Your Groove

Flow state, characterized by optimal engagement and performance, represents a key concept in understanding human performance and cognitive resource allocation. Grounded in Csikszentmihalyis and Sherrys flow theory and the Limited Capacity Model of Motivated Mediated Message Processing (LC4MP), this study investigated physiological and neural correlates of flow state during a simulated driving task under different music conditions and difficulty levels. Using a 2 x 3 factorial design with 20 participants, this study examined self-selected versus non-self-selected music across three difficulty levels, testing the relationship between task switching, cognitive resource allocation, and flow state. Physiological measures included heart rate and EEG (alpha/theta power) using a 4-channel Muse 2 headband, alongside a self-report measure of flow experience. Hierarchical linear modeling revealed significant physiological changes during self-selected music: heart rate decreased ({beta} = -5.15, p < .001), while alpha ({beta} = 5829.77, p < .001) and theta power ({beta} = 7637.24, p < .001) increased. Task difficulty also showed significant effects, with heart rate decreasing during hard ({beta} = -6.70, p < .001) and moderate ({beta} = -3.40, p = .001) conditions. In particular, while physiological measures showed robust changes, the self-reported flow state did not reach significance. Task switching rates showed significant decreases during self-selected music ({beta} = -0.86, p < .001) and hard difficulty ({beta} = -0.61, p < .001), supporting the LC4MP frameworks predictions regarding cognitive resource allocation. These findings demonstrate how task switching and cognitive resource allocation relate to flow state induction. The results highlight the importance of multimodal measurement approaches and demonstrate that personal relevance through music selection and task difficulty significantly influence physiological and neural responses during performance. Future research should employ more comprehensive measurement approaches to better capture the complexity of flow-related neural activity and its relationship to task switching and cognitive resource allocation.

animal behavior and cognition↗

Shared book reading promotes experience-dependent autonomic synchrony in parent-preterm infant dyads

Preterm birth is associated with alterations in early caregiver-infant regulation, with potential consequences for socio-emotional and physiological development. However, the mechanisms through which early interactional experience shapes these processes remain unclear. Here, we tested whether a structured dyadic intervention could modify co-regulatory dynamics across physiological, behavioral, and relational levels. Fifty-four 7-month-old preterm infants and their parents were assigned to either a shared book reading intervention (n = 22) or an active control condition based on a shared building activity (n = 32) and compared with 39 full-term infants. The intervention consisted of an 8-week program of shared book reading, designed to structure parent-infant interaction. Physiological synchrony was assessed at the dyadic level, alongside infants autonomic regulation and cardiovascular signal complexity. Behavioral engagement and parental attachment representations were also evaluated. Results showed that mother-infant physiological synchrony emerged selectively within the interactional context trained by the intervention and only in the intervention group. This context-specific synchrony was accompanied by modulation of vagal activity and increased cardiovascular complexity in preterm infants, consistent with enhanced flexibility of autonomic control. At the behavioral and relational levels, intervention infants showed increased initiating joint attention, while parents reported higher secure attachment. These findings support a model of experience-dependent early synchrony, in which repeated dyadic interaction through shared book reading shapes the coupling between interpersonal coordination and individual physiological regulation. By linking synchrony, autonomic flexibility, and social engagement, this study identifies a mechanism through which early caregiving experience can organize developmental trajectories following prematurity.

developmental biology↗