Proceedings of the National Academy of Sciences

Individual differences in cognitive abilities have been linked to variability in cortical folding, a stable neuroanatomical scaffold largely established in utero. In the domain of reading, recent findings in small groups of typical readers suggest that a sulcal interruption (superficial annectant gyrus, gyral gap) in the left posterior occipital temporal sulcus (lhpOTS) predicts better reading skills, posing the lhpOTS as a potential early biomarker of reading difficulties. However, it remains unknown whether this relationship found in typical readers generalizes to the dyslexic population and whether the lhpOTS can serve as a biomarker for dyslexia or predict response to targeted instruction.To fill these gaps, we examine the patterns of the lhpOTS in 209 children, including children with dyslexia, from four independently-collected samples. In typical readers, we find that the relationship between the lhpOTS and reading skills is robust, replicating across binary and continuous quantifications of the sulcal interruption. However, lhpOTS patterns neither distinguish dyslexic children from typical readers nor do they predict response to intervention. Instead, targeted reading intervention drives long-term gains in reading skills that are equivalent irrespective of VOTC anatomy. Together, these findings distinguish neuroanatomical correlates of skilled reading from determinants of reading impairment and learning capacity and emphasize the importance of the educational environment in supporting reading acquisition for children with dyslexia. SIGNIFICANCE STATEMENTEarly predictors of dyslexia are important for understanding the etiology of reading difficulties and informing early intervention. One candidate biomarker for dyslexia is the left posterior occipital temporal sulcus (lhpOTS), a neuroanatomical feature established before birth. In typical readers, the presence of an interruption in the lhpOTS has been linked to better reading skills. Here, we examine this neuroanatomical feature in 209 children with and without dyslexia. While the lhpOTS reliably relates to reading skill in typical readers, it neither differentiates dyslexic from typical readers nor predicts response to intensive reading intervention. These results show that brain anatomy reflects reading proficiency but does not constrain learning and highlights the power of targeted intervention to support reading development.

A novel class of protein misfolding, involving changes in entanglement status, occurs across the cytosolic proteome of a bacterium and likely occurs in many other organisms. Here, we examine if this class of misfolding has measurable downstream consequences for protein homeostasis. Specifically, we test the hypothesis that proteins that misfold in this way are more likely to be degraded by the ubiquitin-proteosome system immediately after synthesis. We do this by cross-referencing protein structural information with ubiquitin mass spectrometry (Ubq-MS) data from human fibroblast cells. Ubq-MS identifies proteins that have been covalently modified with ubiquitin in a particular pattern and is a cellular signal for that protein to be degraded by the proteosome. We find that nascent proteins with native entanglements, which were previously shown to be twice as likely to misfold, are 93% (95% Confidence Interval: [44%, 160%]) more likely to be tagged with ubiquitin and targeted to the proteasome compared to proteins that do not contain such entanglements. Simulating the folding of these proteins using a coarse-grained model, we find that the ubiquitin-tagged proteins containing native entanglements are four times more likely to misfold than the non-ubiquitinated proteins that are devoid of entanglements. These results indicate that entanglement misfolding, primarily involving a failure to form native entanglements, leads to an increased likelihood that those proteins will be degraded in human cells. Finally, we estimate that approximately one-third of the globular proteome likely misfolds in this way but bypasses proteasomal degradation because their misfolded states are structurally similar to their native ensemble. These consequences for protein degradation are likely common across organisms as entanglement misfolding is inherent to the polymeric nature of proteins.

A recently discovered class of protein misfolding involving native entanglements could be a widespread mechanism by which loss-of-function diseases arise. Here, we test that hypothesis by examining if there is any statistical association between proteins predisposed to misfold in this way and a database of gene-disease relationships. We find that globular proteins containing non-covalent lasso entanglements (NCLEs) in their native structure, which are more prone to misfolding, are 61% more likely to be associated with disease, 68% more likely to harbor pathogenic missense mutations, and their misfolding-prone entangled regions are 64% more likely to harbor pathogenic missense mutations. Protein refolding simulations indicate that these disease associated, natively entangled proteins are 2.5-times more likely to misfold than comparable non-disease proteins that lack native NCLEs. These results indicate that native entanglement misfolding, especially in the presence of missense mutations, have the potential to contribute to a wide variety of diseases. More broadly, these findings open an entirely new space of therapeutic targets in which drugs are designed to avoid these misfolded states and increase the amount of folded, functional protein.

Cetacean dorsal fins are traditionally regarded as vertical stabilizers for yaw and roll, yet marked variation in fin area and position suggests that this function is not universal. We combined computational fluid dynamics simulations of five cetacean species with comparative analyses of dorsal fin and flipper morphology across 81 species to test whether variation in dorsal fin morphology reflects the evolution of hydrodynamic stabilization. Yaw-destabilizing moments were dominated by the trunk regardless of flipper posture, whereas dorsal fins were generally too small and too close to the center of rotation to provide substantial static yaw restoration. In contrast, dorsal fins influenced roll stability in concert with extended anhedral flippers. Although dorsal fins were likely present in the common ancestor of extant cetaceans, strong dorsal fin-mediated roll stabilization was largely restricted to oceanic bite-feeding delphinids, in which rapid evolutionary enlargement of the dorsal fin and persistently extended anhedral flippers likely enhanced roll stability. In most other lineages, roll stability could be maintained by flipper posture alone despite small dorsal fins. These results recast the cetacean dorsal fin not as a universal stabilizer, but as a lineage-specific roll stabilizing structure whose function emerges through mechanical coupling with the flippers.

Recent EEG studies of human quiet stance have identified beta-band event-related desynchronization (beta-ERD) and synchronization (beta-ERS; post-movement beta rebound) during the micro-fall and micro-recovery phases of postural sway, respectively. These modulations correlate with the activation and inactivation of calf muscle activity, supporting an intermittent control strategy that exploits the stable manifolds of the body at unstable equilibrium. However, the neural origin of this sway-related beta rhythmogenesis remains elusive. Here, we investigated this mechanism using a spiking neural network model of the cortico-basal ganglia-thalamic (CBGT) circuitry integrated with a physical inverted pendulum. In this model, sensory feedback is integrated into the striatum, while the motor cortex executes decisions between dorsiflexion and plantarflexion via competition between neuronal populations following drift-diffusion dynamics. In this framework, the decision time represents the control-off period of the intermittent controller. We found that the simulated EEG exhibited characteristic beta-ERD and beta-ERS only when cortico-striatal synaptic weights were functionally tuned to facilitate intermittent motor selection characterized by distinct decision times. Conversely, continuous control -characterized by immediate antagonistic actions without DT- failed to produce these beta modulations. Further analysis revealed that state-dependent sensory feedback and subthalamic nucleus (STN) activity are critical for generating these oscillations through closed-loop interactions. Our results suggest that CBGT-mediated rhythmic beta activity is a hallmark of intermittent motor selection, providing a computational link between basal ganglia dynamics, cortical oscillations, and postural stability. Significance StatementWhile beta-band EEG modulations correlate with postural stability in healthy individuals, their neural origins remain unclear. We present a novel spiking neural network model of the cortico- basal ganglia-thalamic (CBGT) loop that interacts with a physical inverted pendulum to achieve postural stability. This study provides the first computational evidence that sway-related beta modulations emerge specifically from intermittent control strategies, whereas continuous control--often observed in Parkinsons disease--abolishes these rhythms. By bridging cellular- level dynamics with behavioral stability, this study identifies beta oscillations as a functional biomarker of healthy intermittent motor selection. These findings provide a computational framework for understanding why postural impairments in clinical populations are associated with attenuated cortical beta dynamics.

Shenhar et al. (2026) report 50% "intrinsic" lifespan heritability after calibrating a one-component correlated-frailty survival model to Scandinavian twin lifespans. Their framework is mathematically coherent, but the intrinsic component is not identified if heritable, mortality-relevant extrinsic susceptibility is omitted at calibration. We show that one-component calibration absorbs omitted familial extrinsic structure into the intrinsic frailty scale parameter{sigma}{theta} , and that this variance absorption is visible through separate diagnostics (1) Variance absorption. Under misspecification,{sigma}{theta} is inflated by +22.1% (95% CI: 21.5-22.7%), corresponding to +49% inflation in [Formula]. Falconer h2 is downstream of calibration and inherits a +9.2 pp bias (95% CI: 8.7-9.7). The{sigma}{theta} inflation is model-general: +22% (GM), +18% (MGG), +14% (SR); any dependence summary that is strictly increasing in{sigma}{theta} inherits this inflation, so Falconer h2 is one affected downstream quantity among many (Corollary B3). (2) Structural fingerprint. In the joint twin survival surface S(t1, t2), misspecification produces systematic dependence errors (ISE 48x that of the recovery model). Conditional twin dependence is inflated at all ages, peaking at age 80 ({Delta}r = 0.048). (3) Specificity. The bias requires an omitted component that is both heritable and mortality-relevant. Three negative controls, a boundary check ({rho} = 0), and a two-component recovery refit ({sigma}{theta} restored to within -3.2%) establish specificity. ACE decomposition yields C {approx} 0 throughout: the omitted extrinsic component loads onto A (because it is shared 1.0/0.5 in MZ/DZ), so switching summary statistics does not restore identification. (4) Sensitivity and falsifiability. Over an empirically anchored regime ({sigma}{gamma} [isin] [0.30, 0.65],{rho} [isin] [0.20, 0.50]), Falconer bias ranges from +2.8 to +18.9 pp (mean 9 pp). If{rho} is sufficiently negative, the bias reverses sign in all three model families (Corollary B4). A full-likelihood robustness check shows that this upward pull is partly structural and partly estimator-specific: in the same misspecified one-component model, ML still inflates{sigma}{theta} (+3%), whereas matching only rMZ inflates it much more (+21%). These results do not resolve true intrinsic heritability but establish that Shenhars 50% estimate carries a structured, model-general upward bias originating in the fitted latent variance{sigma}{theta} .

Human sentence comprehension proceeds word-by-word, with prior research proposing two central sources of cognitive demand during incremental processing: forward-looking disambiguation of the incoming information stream, and backward-looking retrieval of information associated with previous words from working memory. Recent work has shown that Transformer-based language models successfully generate predictions about sentence processing load in human psycho- and neurolinguistic data by operationalizing disambiguation cost as next-token surprisal, and memory retrieval cost as normalized attention entropy (NAE). Such models, however, remain difficult to interpret as it is not well understood what factors play causally into the decision to assign a cost value to a given word in such artificial neural networks. Here, we present interpretable and cognitively grounded models of disambiguation and memory retrieval and evaluate their neural alignment and spatio-temporal correlates using human magnetoencephalography responses to naturalistic narrative speech. Multivariate temporal response function modeling demonstrates firstly that these human-bias-informed models fare equally well in accounting for observed human language processing data as their Transformer counterparts. This same modeling framework then suggests that surprisal and NAE temporally dissociate in the cortical language network -- surprisal being predictive of bilateral superior temporal gyrus and supramarginal gyrus activation [~]300-500 ms, and NAE being predictive of activity in the same regions, but later [~]750-850 ms. By demonstrating that interpretable neurocomputational models can achieve meaningful brain alignment while maintaining explanatory transparency, this work offers a methodological blueprint for bridging the gap between algorithmic theory and neural implementation.

T cell exhaustion is commonly viewed as a terminal differentiation state marked by irreversible loss of effector function during chronic infection and cancer. However, the rapid restoration of cytotoxic activity following PD-1 checkpoint blockade challenges this view, revealing a central paradox: T cells that appear functionally inert can regain effector function on timescales incompatible with de novo differentiation or extensive epigenetic reprogramming. To resolve this contradiction, we present a mathematical framework that explicitly decouples latent effector capacity from active effector output. We define latent effector capacity as a slow, history-dependent state variable representing preserved epigenetic accessibility and regulatory readiness at effector loci, distinct from instantaneous transcriptional activity. Within this framework, PD-1 signaling functions as a reversible, graded masking mechanism that suppresses effector realization without erasing latent capacity, thereby explaining the coexistence of preserved chromatin accessibility, rapid functional rebound, and heterogeneous responses to checkpoint blockade. Incorporating nonlinear self-maintenance of epigenetic programs together with checkpoint-dependent erosion of latent capacity reveals a bistable regime and a history-dependent point of no return, beyond which exhaustion becomes irreversible. Critically, the model demonstrates that PD-1 checkpoint blockade unmasks pre-existing effector potential but cannot recreate lost capacity, because therapeutic reversibility is governed by the prior dynamical stability of a latent epigenetic state rather than by instantaneous transcriptional output. This framework establishes a mathematical foundation for mechanistically predictive AI in PD-1 blockade therapy by identifying latent, history-dependent variables that can be inferred from epigenetic and transcriptional data to predict therapeutic responsiveness and irreversibility.

Clarifying the mechanisms underlying the emergence of consciousness remains a fundamental challenge in modern neuroscience. Integrated Information Theory (IIT) provides a mathematical framework derived from the phenomenological properties of consciousness as its axioms. IIT proposes that consciousness is identical to a systems intrinsic cause-effect information structure, quantified by integrated information {Phi}. While IIT predicts that the {Phi} of a neuronal system should decrease during the loss of consciousness, this hypothesis has remained untested at the neural circuit level. The present study provides empirical support for this IIT prediction. It was found that {Phi} within local circuits decreases during non-rapid-eye-movement (NREM) sleep compared to wakefulness and REM sleep, independent of cortical laminar structure or firing rates or regions. The reduction in {Phi} was particularly pronounced during off-periods, when neural activity is collectively suppressed. These results imply that consciousness is an information structure that cannot be reduced to the properties of individual system elements (such as firing rates), and that its collapse is fundamentally linked to the loss of consciousness. The findings provide critical empirical support for IIT as a mathematical theory aiming to explain conscious experiences. Significance StatementThis study bridges the gap between abstract mathematical theories of consciousness and high-resolution neurophysiology. According to Integrated Information Theory (IIT), conscious existence depends on a systems intrinsic cause-effect structure. By analyzing neural population activity, this study demonstrates that the transition from wakefulness to NREM sleep is characterized by a reduction in integrated information ({Phi}) within local circuits. This reduction is most pronounced during NREM off-periods, where causal integration is effectively severed, leading to a breakdown of the systems intrinsic information structure. These findings provide a neural foundation for IIT and suggest that consciousness is underpinned by specific, irreducible cause-effect structures within the brain.

TRPM2 is a Ca{superscript 2}-permeable cation channel activated by ADP-ribose (ADPR) and oxidative stress, yet the relative contributions of its two nucleotide-binding domains, MHR1/2 and NUDT9H, remain incompletely understood. Here, we quantitatively determine the affinities of the isolated human TRPM2 MHR1/2 and NUDT9H domains for ADPR, 2-deoxy-dADPR (dADPR), and 8-Br-cADPR using biophysical approaches. The MHR1/2 domain binds ADPR with high affinity (Kd {approx} 0.5 {micro}M), whereas the NUDT9H domain displays substantially lower affinity (Kd {approx} 192 {micro}M), revealing a difference of nearly three orders of magnitude. Mutational analysis demonstrates that alterations in MHR1/2 strongly affect ligand binding and channel activation, while mutations within NUDT9H that markedly reduce ligand affinity exert only modest effects on gating. In parallel, we quantify intracellular ADPR concentrations in resting and hydrogen peroxide-stimulated cells and find that they remain well below the affinity required for substantial NUDT9H occupancy. Together, our findings indicate that high-affinity binding to the MHR1/2 domain is sufficient to drive TRPM2 activation under physiological conditions, whereas the NUDT9H domain likely contributes to maintaining the structural integrity of the channel rather than directly mediating ligand-dependent activation. These results provide a quantitative framework for understanding ligand-dependent TRPM2 regulation in cells.

We study an unexpectedly fast decay of motility in dense suspensions of Escherichia coli bacteria supplied with excess glucose under anaerobic conditions. The decrease in swimming speed occurs on a timescale inversely proportional to the cell concentration, and is associated with the secretion of organic acids by the bacteria. We show that the decay is driven by the progressive accumulation of non-ionised organic acids in the medium, and develop a chemical kinetic model that successfully predicts the swimming speed variations over a range of conditions in the presence of these acids. We further measure the internal pH of E. coli cells exposed to organic acids, and find that the speed decay coincides with sharp declines in internal pH and metabolic rate. Our findings identify an additional layer of motility control that can arise in complex environments even when motility genes are expressed and energy sources are abundant. This mechanism is likely relevant for understanding bacterial motility in habitats such as the human gut, where high densities of bacteria and organic acids are common.

The stable hydrogen and carbon isotopic composition of methane is widely used to determine its sources. Methanogenic growth on methanol generates methane with significantly lower 13C/12C ratios relative to other substrates, which is often used as a marker for this metabolism in environmental samples. The biochemical basis for the unusual isotope effect associated with methanol growth is currently unknown. Here, we grew Methanosarcina acetivorans on methanol and measured the change in the carbon and hydrogen stable isotopic compositions of the methane. We coupled these results with an inverse modeling approach to calculate the kinetic isotopic effects (KIEs) of the rate-limiting step, catalyzed by the methanol-specific methyltransferase complex (MTA). Through this process, we estimate the carbon KIE of MTA (13{varepsilon}MTA) as -65.5 {per thousand} and the hydrogen KIE of MTA (2{varepsilon}MTA) as -56 {per thousand}. Next, we show that the 13{varepsilon}MTA contributes substantially to the large isotopic effect observed for methylotrophic methanogenesis on methanol. We also show that mutant strains that express only a single copy of the MTA complex (either MtaC1B1A1, MtaC2B2A1, or MtaC3B3A1) have 13{varepsilon}MTA and 2{varepsilon}MTA that are indistinguishable from the wild-type strain. Finally, based on a thermodynamic analysis, we propose that methanol activation by MTA will remain rate-limiting, even at low environmental methanol concentrations, and the large 13{varepsilon}MTA would be expressed in situ as well. ImportanceMethane is a potent greenhouse gas, and distinguishing between its biological sources is vital for modeling global carbon cycles. Methylotrophic methanogenesis produces methane with a uniquely depleted carbon isotope signature. However, the biochemical mechanisms driving this fractionation have remained unclear. In this study, we identify the methanol-specific methyltransferase (MTA) complex as the primary driver of these large carbon isotope effects. By utilizing Methanosarcina acetivorans mutants, we demonstrate that these effects are consistent across different MTA isozymes. Our results suggest these signatures are intrinsic to the enzyme complex and persist at low substrate concentrations. These findings provide a critical biochemical foundation for using stable isotopes to track microbial methane production in diverse natural ecosystems.

The mycobacterial cell envelope consists of multiple covalently linked layers that must be synthesized in a coordinated manner to maintain cell wall integrity. Despite the importance of this coordination, its molecular mechanisms remain poorly understood. PgfA (polar growth factor A) interacts with trehalose monomycolate lipids (TMMs) (1) and the TMM transporter MmpL3 (1, 2). PgfA promotes TMM transport in the periplasm and functions as an upstream regulator of polar growth. How TMM transport is linked to the expansion of the entire multi-layered cell wall is unclear. Here, we provide evidence that PgfA regulates peptidoglycan metabolism. We show that PgfA localization correlates with peptidoglycan metabolism and that PgfA can function as both an activator and inhibitor of peptidoglycan metabolism. We further explore the role of TMMs in polar growth and find evidence that periplasmic TMMs are a signaling molecule that may regulate polar peptidoglycan metabolism. Finally, we find an epistatic connection between PgfA overexpression and altered TMM levels that suggests that PgfA and TMMs work in the same pathway to regulate peptidoglycan metabolism. Our data are consistent with a model in which TMM-free PgfA inhibits peptidoglycan metabolism, while TMM-bound PgfA promotes polar peptidoglycan metabolism. This work identifies PgfA as a key protein that coordinates synthesis of the peptidoglycan and mycolic acid envelope layers. ImportanceThe mycobacterial cell envelope consists of multiple covalently linked layers whose synthesis must be coordinated to maintain cell integrity. Despite decades of research on individual envelope components, the molecular mechanisms coordinating synthesis of different layers remain poorly understood. Here, we identify PgfA as a key regulatory protein that coordinates peptidoglycan and mycolate synthesis in mycobacteria. PgfA has both inhibitory and stimulatory effects on peptidoglycan metabolism, depending on the context. Our findings suggest PgfA may act as a regulator that senses mycolate precursor availability and prevents envelope imbalance when these precursors are limiting. This work provides new insight into how mycobacteria coordinate the synthesis of their complex cell envelope, with implications for better understanding mycobacterial physiology and developing antimycobacterial therapeutics.

In 2016, the glycolytic Entner-Doudoroff (ED) pathway was reported in cyanobacteria and plants (1). The claim was based on the biochemical characterization of its key enzyme the 2-keto-3-deoxy-6-phosphogluconate (KDPG) aldolase EDA (ED aldolase), on protein sequence alignments, physiological data from cyanobacterial mutants, and the in vivo detection of an ED pathway specific metabolite (1). However, two enzymes 6-phoshogluconate (6PG) dehydratase (EDD) and EDA are unique to this route. A recent study suggests that EDD (Slr0452) from Synechocystis sp. PCC 6803 most likely encodes an enzyme involved exclusively in amino acid synthesis, indicating that a complete ED pathway would be missing (2). To answer the presence or absence of the ED pathway in Synechocystis, we conducted extended biochemical and physiological studies, revisited old data and resolved contradictions. These investigations reveal that Synechocystis lacks both an ED pathway and a glucose dehydrogenase/glucokinase (GDH/GK) bypass but contains a promiscuous aldolase EDA. EDA prefers KDPG as substrate but also decarboxylates oxaloacetate (OAA) and cleaves 2-keto-4-hydroxyglutarate (KHG). Synthesis of KDPG from pyruvate and glyceraldehyde 3-phosphate (GAP) is catalyzed with very low efficiency. These in vitro data suggest that EDA might be involved in the phosphoenolpyruvate (PEP)-pyruvate-OAA node and proline catabolism, which requires further clarification. The previous misconception was based on missing enzymatic characterizations, the oversight of a secondary mutation in a deletion strain, and an outdated view on carbohydrate fluxes. We conclude with a list of lessons and provide a solid foundation for future investigations into the role of EDA in cyanobacteria and other photoautotrophs. Significance statementThis study provides a retrospective on why, for many years, it was mistakenly assumed that the glycolytic Enter-Doudoroff (ED) pathway exists in the cyanobacterium Synechocystis sp. PCC 6803. It shows that the first enzyme of this pathway, ED dehydratase EDD, is absent, while the second enzyme, 2-keto-3-deoxy-6-phosphogluconate (KDPG) aldolase EDA, is present but is promiscuous, cleaving KDPG in addition to 2-keto-4-hydroxyglutarate (KHG) and decarboxylating oxaloacetate (OAA) in vitro. Finally, valuable lessons are drawn from prior misconceptions and experimental limitations. This study provides a solid foundation for future studies on the role of the ED aldolase in absence of the ED pathway in cyanobacteria and other photoautotrophs.

Bispecific T cell engagers and related immunotherapies are dosed using equilibrium binding models derived for well-mixed solution, yet therapeutic activity occurs at nanoscale membrane synapses with finite receptor copy numbers. Here we show that membrane confinement introduces geometry-dependent corrections to the landmark Douglass [6] ternary binding model, shifting formation half-points (TF50) by 2-10-fold at clinically relevant antigen densities. We present two complementary formulations--effective concentration and surface density--that preserve the Douglass framework while explicitly accounting for synapse geometry, surface topology, and the accessibility factor () of surface receptors. We further derive stochastic descriptions of trimer formation via the chemical master equation, demonstrating the recovery of the classical Ternary Binding Model equilibrium in appropriate limits. We illustrate the framework using the CD19-targeting BiTE blinatumomab as a case study. Accounting for microvillus-driven patchy close contact during immune surveillance yields a mechanistic explanation for why higher target antigen density can increase the dose required to achieve a fixed level of ternary formation: in the membrane-confined regime, excess target acts as a local antigen sink that sequesters drug and reduces the free fraction available for productive bridging. Rather than fitting to a single shift value, we emphasize the robust scaling and regime structure predicted by the theory (density-proportional behavior in the sink-dominated limit, and collapse toward affinity-limited behavior outside that limit). The generalized framework provides ready-to-use correction formulas and parameter-estimation guidance, establishing a rigorous foundation for antigen-density-aware dosing strategies in T cell engager pharmacology. Statement of SignificanceBispecific T cell engagers are currently interpreted largely through bulk-solution binding models, but at nanometer-scale immune synapses those models can miss a distinct, decision-relevant regime. We generalize the standard ternary binding model to membrane-confined synapses and relate whole-cell receptor counts to effective concentrations in the contact zone. In a blinatumomab case study, the framework explains a paradoxical observation: increasing target density can shift half-maximal ternary-complex formation to higher doses because abundant antigen acts as a local sink that sequesters drug. These results show when bulk affinity alone is insufficient for potency interpretation and support antigen-density-aware dosing and experiments that distinguish geometric from chemical control.

The pituitary gland operates as an organized signaling network in which endocrine cell populations coordinate hormone secretion, through homotypic and heterotypic interactions, yet the contribution of spontaneous intrinsic activity in shaping population-level dynamics remains poorly understood. Using geometric analysis of population trajectories -- including subspace alignment, manifold separation, and directed coupling metrics -- we identified two classes of spontaneous oscillatory signals associated with distinct cell populations exhibiting asymmetric geometric dominance and a reproducible temporal lag. Our results support that spontaneous activity generates a self-sustained oscillator exhibiting transient bistability, linked to increased physiological demand, with slow oscillations reflecting the properties of an excitatory resonator capable of self-oscillating dynamics without external drive. A low-rank recurrent neural network model recapitulated the empirical geometric landscape under three coupling conditions, confirming that directed population coupling underlies the observed coordination. These findings suggest that intrinsic population dynamics play a central role in coordinating pituitary secretion, with implications for understanding hormonal dysregulation in secretory adenomas and other pituitary disorders. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/716480v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@193954aorg.highwire.dtl.DTLVardef@2e4299org.highwire.dtl.DTLVardef@1165736org.highwire.dtl.DTLVardef@1b79cd5_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract.C_FLOATNO Structural and functional distinctions between homotypic and heterotypic interactions have been widely described in the pituitary endocrine system. However, whether functional differences in intrinsic calcium time-series dynamics are relevant to pulsatile hormone secretion remains unexplored. Here, we classify the spontaneous activity underlying both homotypic and heterotypic interactions and characterise their synchrony. We find that heterotypic interactions exhibit transient bistability, consistent with a Hopf-type oscillator regime, in which slow oscillations drive secretory output according to physiological demand. C_FIG

Response inhibition is a critical cognitive process that is not fully mature at the time of puberty but continues to improve during adolescence. To understand the neural basis of the maturation process, we obtained longitudinal behavioral, neurophysiological, and imaging data in macaque monkeys as they aged through adolescence. Behavioral performance in several variants of the antisaccade task improved markedly through this period. Neural activity in the prefrontal cortex generally increased, particularly when synchronized to the saccade generation. Trajectories of neural activity and cognitive performance were well predicted by maturation of long-distance white matter tracts connecting the frontal lobe with other brain areas. Our results link the maturation of response inhibition and prefrontal neural activity changes to white matter maturation.

Animals such as bees, ants, wasps, termites, and naked mole-rats live in colonies in which a single queen is the only female reproductive, an arrangement known as eusociality. Eusocial animals are known for their remarkably long lifespans. It has been argued that longevity becomes selected when queens are shielded from "external mortality". While such protection may contribute, we find a deeper reason: the eusocial reproduction strategy itself inherently creates selection for long lifespans. Lifespans typically reflect two processes: the baseline risk of death and the rate at which this risk increases with age. Each is a parameter in the Gompertz mortality equation. We show that the mathematical properties of eusocial reproduction lead to slowly-growing, older populations where selection acts more strongly on the rate at which risk increases than on the baseline risk. In addition, we show that channeling reproduction through a single female also selects for longevity, which we term the "queen effect". Thus, the dynamics of eusocial reproduction select for longer lifespan. More broadly, these results show that reproductive structure and population growth dynamics can fundamentally shape selection on lifespan, with implications outside eusocial systems as well.

Some proteins combine sequence features that are typical both for folded proteins and intrinsically disordered proteins (IDPs). The borderline properties of these so-called "marginal" IDPs render their conformational ensembles highly sensitive to the environmental changes, which may be important for their function. Here, we investigate the prokaryotic transcription factor Phd, which regulates the phd-doc toxin-antitoxin module through an allosteric mechanism involving disorder-order transition. Using an ensemble of biophysical techniques, we show that the protein is completely disordered at low ionic strength, whereas increasing salt concentration promotes its collapse into a partially ordered monomeric state, followed by the formation of a structured dimer. Using a thermodynamic model, we decipher the linkage between ionic strength, protein stability, oligomer state and degree of disorder. Via small-angle X-ray scattering we derive the structural ensemble of dimeric Phd, revealing a gradation of disorder as a function of salt. The sequence and biophysical properties of Phd position it at the boundary between macroscopically distinct conformational ensembles, representing a large pool of states capable of engaging in functional disorder-to-order interactions, enabling Phd to act as conformational rheostat. Together with previous crystallographic data, this charts the full spectrum of disorder-to-order states in the bacterial transcription factor and underscores the structural plasticity of IDPs with the marginal sequence properties. SIGNIFICANCEWhile globular proteins adopt stable three-dimensional structures, intrinsically disordered proteins (IDPs) remain flexible and dynamically sample partially ordered states. This behavior is largely determined by amino acid composition; however, some proteins lie at the borderline between order and disorder. Here, we focus on such a protein, the Phd transcription, and show how its conformation changes from completely disordered to fully ordered. These transitions are modulated by ionic strength and binding to macromolecules, including homodimerization. Using a thermodynamic model, we map the Phd conformational space, revealing a broad ensemble of states with varying degrees of disorder. The borderline amino acids properties enable Phd to function as a conformational rheostat, coupling functional interactions to the series of graded order-disorder transitions.

Ecological interactions can dramatically alter evolutionary outcomes in complex communities. Yet, the framework of population genetics largely neglects interactions from a species-rich community. Here, we bridge this gap by using dynamical mean-field theory to integrate community ecology into classical population genetics models. We show that ecological interactions result in emergent frequency-dependent selection between parents and mutants, characterized by a single parameter measuring the strength of ecological feedbacks. This result generalizes classical population genetics models to highly diverse communities and enables predictions of mutation outcomes in these eco-evolutionary settings. We derive an analytic expression for fixation probability that extends Kimuras formula and reveals that ecological interactions strongly suppress the fixation of moderately beneficial mutations. This suppression arises because frequency-dependent selection leads to prolonged coexistence between parent and mutant lineages, which acts as a barrier to fixation. The strength of these effects increases with effective population size and the number of open niches in the ecosystem. Our study establishes a framework for integrating ecological interactions into population genetics, showing that evolutionary outcomes can be predicted using simple models even in the presence of complex community feedbacks.