Browse Articles

Origin of class B J-domain proteins involved in amyloid transactions

J-domain protein (JDP) chaperones function widely in proteostasis. Notably, eukaryotic class B JDPs of the cytosol/nucleus prevent assembly or drive disassembly of amyloid aggregates known to cause neurodegenerative diseases, yet their evolutionary origin is not known. Members of the most ubiquitous class B subgroup, canonical B (B C ) JDPs, lack the signature zinc finger domain (ZnF) of the more prevalent class A JDPs, while having other key features in common. Our phylogenetic analysis revealed that B C JDPs evolved more than once from class A duplicates, losing their ZnF. The cytonuclear B C s emerged at the base of eukaryotes. Cytonuclear class B’ (i.e., B’ (ST) ) JDPs that have a substrate binding domain of unknown origin, distinct from that of As and B C s, emerged from a B C duplication at the base of metazoans and subsequently multiplied by duplications. The origin of B’ (ST) s, which are capable of suppressing formation of amyloid aggregates, predated the emergence of disease-causing amyloidogenic proteins. Using ancestral protein resurrection, we tested when cytonuclear Bs evolved their amyloid related functions. We found that their common ancestor with As, AncAB that has a ZnF does not efficiently facilitate disassembly of amyloid fibrils, while AncB, which lacks a ZnF, does. Overall, our findings are consistent with the idea that, though the ZnF of class A JDPs is important for some roles, its loss allowed evolution of novel functions, as illustrated by the ability of B C and B’ (ST) JDPs to control amyloid aggregate levels.

<scp>ADAR-GPT</scp> : A continually fine-tuned language model for predicting A-to-I RNA editing sites

Adenosine-to-inosine (A-to-I) RNA editing by ADAR enzymes shapes transcript fate and underpins emerging RNA editing therapeutics, yet predicting which adenosines are edited remains difficult. We introduce ADAR-GPT, a model-agnostic fine-tuning framework that adapts a GPT-class language model to classify editing at candidate sites using sequence context in standardized 201 nt windows with the target adenosine explicitly marked. We train and evaluate on GTEx liver data ( n = 131 samples) at a clinically relevant 15% editing threshold, using a two-stage continual fine-tuning approach where lower thresholds serve as curriculum data to progressively sharpen decision boundaries. Using sequence data, ADAR-GPT demonstrates competitive or superior performance when benchmarked against established computational approaches, including convolutional and foundation model architectures, achieving a better balance of recall, precision, and specificity alongside stronger operating-curve metrics. The approach is reproducible and portable across GPT backbones without architectural changes. Beyond accurate site classification, ADAR-GPT provides practical adenosine scoring to prioritize experimental targets and inform guide RNA design, with a framework adaptable to new datasets and model architectures.

Supersaturation-engineered wafer-scale growth of anisotropic 2D organic crystals for uniform polarimetric sensing

Organic single crystals endowed with intrinsic anisotropy hold great promise for the realization of miniaturized, polarization-sensitive photodetectors. However, conventional dip-coating approaches struggle to produce wafer-scale single-crystalline films, as the supersaturation conditions optimized for nucleation invariably conflict with those required for crystal growth, leading to fragmented domains and compromised device performance. Here, we introduce an in situ seeded dip coating (SDC) strategy that overcomes this limitation by engineering supersaturation to temporally decouple nucleation from crystal growth. This enables the scalable production of ultrathin two-dimensional molecular crystal (2DMC) films with exceptional uniformity across wafer dimensions. Organic field-effect transistor arrays fabricated from these 2DMCs exhibit high average charge carrier mobility (14.5 cm 2 V −1 s −1 ) and spatial homogeneity, evidenced by an ultralow mobility coefficient of variation (CV) of 4.5%. These arrays demonstrate robust polarization sensitivity with an average dichroic ratio (DR) of 2.2 and a low DR CV of 8.6%. Capitalizing on this uniformity, we constructed dual-device pixel units capable of full-range 180° polarization angle identification, enabling direct application in polarization-encoded optical encryption. The SDC technique provides a scalable pathway for practical high-performance organic polarization-sensitive optoelectronics.

Phase-transition-like behaviors of sequence-selective dynamic bonds

Sequence-selective dynamic bonds (SSDBs) are ubiquitous in nature and man-made systems as diverse as DNA, proteins, and synthetic sequence-defined oligomers. The specific and dynamic nature of this class of bonds enables encoded linkers to program the self-assembly of a broad range of building blocks. However, the possible collective behavior emerging from multiple SSDBs in programmable self-assembly remains elusive, due to experimental challenges. Here, we analyze the thermodynamic properties and kinetic pathways of SSDB hybridization through simulations supported by analytical theories, treating widely applicable cases of sequence-selective interactions. Our results reveal that the hybridization of SSDB linkers with certain rotational freedom can result in phase-transition-like behavior, which dictates the stability and the spontaneous transitions of typical states. In particular, there exists a metastable intermediate that plays a critical role in the thermally active hybridization facilitated by entropy, in contrast to normal dynamic bonds. We demonstrate that such a unique characteristic of thermodynamics causes stepwise kinetics of SSDB hybridization and hence anomalous diffusion of particles functionalized with SSDB linkers. Our work suggests that the presence of collective effect and phase-transition-like behavior may well be the crucial feature that guides larger scale ordering and dynamics in diverse systems of multiple SSDBs.

Blood-borne sphingosine 1-phosphate maintains vascular resistance, blood pressure, and cardiac function in mice

Sphingosine 1-phosphate (S1P) is a bioactive lipid that circulates in plasma bound to high-density lipoproteins (HDL) and albumin. Circulating S1P levels correlate positively with systolic blood pressure (BP) in hypertension and negatively with severity in septic shock and with left ventricular function in heart disease. In mice, isolated deficiency in HDL-S1P and endothelial cell S1P receptor (R)-1 both trigger hypertension, supporting an essential role for HDL-S1P in endothelial function. Physiological roles of albumin-S1P and myocyte S1PRs in the cardiovascular system remain incompletely defined. We report that mice lacking all circulating S1P pools display hypotension and lack of BP increase with age, which contrasts with HDL-S1P deficiency and suggests an essential role for albumin-S1P in cardiovascular homeostasis. Although cardiac output was preserved in a basal state, left ventricular systolic function and contractile reserve were reduced in the absence of circulating S1P. Cardiac function and BP were partially or fully normalized by transfusion of erythrocytes capable of S1P production. Hypotension was accompanied by reduced peripheral resistance, and albumin-S1P, but not S1P complexed to an HDL-like chaperone, dose-dependently increased vascular resistance in isolated perfused kidneys via S1PR3 and S1PR2. Epistatic analysis supported a critical role for S1PR3 in S1P-dependent BP maintenance and pointed to a distinct origin of the cardiac phenotype. We thus uncover an essential role for circulating S1P in maintaining BP and left ventricular systolic function in mice. Our results also highlight distinct functions for the pools of S1P bound to HDL and to albumin, carrying both diagnostic and therapeutic implications.

Correction for Freitas et al., Impact of baleen whales on ocean primary production across space and time

Self-healing for the long haul: In situ automation delivers century-scale fracture recovery in structural composites

Nature’s structural composites, such as bone and wood, achieve mechanical performance through hierarchical multimaterial design. Though, their real vantage lies in the exceptional ability to repeatedly heal after damage. Synthetic fiber-reinforced polymer (FRP) composites also leverage material hierarchy via fibrous reinforcement encapsulated within a polymer matrix, maximizing stiffness and strength. However, the layered architecture of laminated FRP composites makes them vulnerable to interlaminar delamination—debonding of fibers from the matrix—which significantly compromises structural integrity. Recently, we introduced a self-healing strategy via in situ heating, where soft yet tough thermoplastic inclusions achieve interlaminar fracture recovery via polymer chain re-entanglement, i.e., thermal remending. Here, in our latest embodiment, by automating in situ thermo-mechanical experiments, we achieve an order-of-magnitude enhancement in self-healing repeatability—reaching an unprecedented 1,000 cycles. Healing begins at 175% and slowly declines to 60% of the mode-I fracture resistance of a plain (nonhealing) composite, revealing unique chemo-physical mechanisms that govern this behavior. Both fiber-debris accumulation in the molten poly(ethylene-co-methacrylic acid) (EMAA) healing agent, and waning interfacial chemical reactions between the EMAA and epoxy matrix, contribute. A Weibull distribution capturing this complex fracture recovery predicts an asymptotic healing limit above 40%, suggesting sustained repair is possible. Translating these newfound thermal remending results into real-world context, a modest quarterly self-healing schedule could maintain interlaminar fracture repair of FRP composites for over 125 y—well beyond the typical design life of many modern structures including aircraft and wind turbines. Thus, this latest self-healing paradigm effectively eliminates delamination as a failure mode.

A pothole-filling strategy for selective targeting of rCUG-repeats associated with myotonic dystrophy type 1

We present an alternative approach to conventional small-molecule and antisense strategies for selectively targeting expanded CUG-RNA repeats associated with Myotonic Dystrophy type 1. Our alternatively designed nucleic acid ligands uniquely integrate advantageous features from both existing methods: They are compact (only three units in length), structurally resembling small molecules, yet recognize RNA targets through directional hydrogen-bonding similar to antisense oligonucleotides. Notably, these ligands exhibit greater specificity and selectivity than either approach alone. This enhanced specificity results from their bifacial recognition mechanism, wherein mismatches on one binding interface are reciprocally mirrored on the complementary face. Additionally, their short length significantly amplifies specificity, as even a single mismatch substantially reduces the overall binding free energy, effectively minimizing off-target interactions. Unlike conventional oligonucleotides, these ligands avoid binding single-stranded RNA and only recognize defined hairpin motifs via a “pothole-filling” mechanism. This method amplifies recognition specificity and selectivity, circumventing the thermodynamic penalties associated with RNA unfolding. This proof-of-concept study thus lays a foundation for developing versatile nucleic acid ligands capable of selectively targeting not only pathogenic CUG-RNA repeats in Myotonic Dystrophy type 1 but also other disease-associated triplet-repeat expansions prevalent in various neuromuscular disorders.

Distinct PlzC mechanisms integrate chemotaxis and c-di-GMP signaling to regulate <i>Vibrio cholerae</i> motility and biofilm formation

Bacterial motility and biofilm formation are essential for the adaptation and survival of Vibrio cholerae , the causative agent of cholera. In many bacterial species, the second messenger c-di-GMP regulates these processes through PilZ domain proteins, however the downstream mechanisms have remained poorly defined. Here, we identify the PilZ domain protein PlzC as a key positive regulator of motility and biofilm formation through distinct mechanisms. A suppressor screen for mutants restoring Δ plzC migration identified downstream regulators, including CheX, a CheY-3 phosphatase. Genetic and phenotypic analyses revealed that PlzC promotes motility by modulating CheX, thereby influencing the frequency of direction changes during swimming. Notably, the role of PlzC in motility regulation is independent of CheZ, another CheY-3 phosphatase, demonstrating that among the two CheY-3 phosphatases, only CheX is under PlzC control. This distinction suggests that CheY-3-P levels are regulated by at least two separate signaling pathways, one of which operates through PlzC. Despite its function in motility, CheX is not required for PlzC-mediated biofilm regulation, indicating pathway specificity. PlzC regulates biofilm formation through a mechanism that involves c-di-GMP binding, separating it from its CheX-dependent motility regulation. Together, our findings establish PlzC as a central regulator linking CheX-mediated motility and c-di-GMP signaling, thereby impacting motility and biofilm formation in V. cholerae .

Catalysts and inhibitors of critical transitions in ecological systems

Ecological systems can experience sudden and often irreversible regime shifts, also known as critical transitions, with major consequences such as desertification, locust outbreaks, and coral reef collapse. Anticipating these shifts is a central challenge, particularly under accelerating climate change. Although early warning signals of critical transitions have been widely studied, the mechanisms that drive or prevent them remain less well understood. Here, we develop a theoretical framework based on time-delayed dynamics that allows us to identify processes acting as catalysts or inhibitors of critical transitions in ecological systems. We show that a composite measure combining time-delayed species interactions with species abundances is a key modulator of critical transitions. Beyond the critical point, systems exhibit persistent abundance oscillations, substantially increasing the risk of large-scale destabilization and species extinctions. Additionally, we show that a high diversity of species interaction types can act as a buffer of critical transitions. Instead, strong species self-regulation effects can act as catalysts of such transitions, contrary to common expectations. We illustrate the framework with empirical data from microbial systems. Together, these results provide a formal platform for exploring and understanding the drivers of critical transitions in complex living systems.

Spatially resolved multiomics reveals the self-enforcing property of the leading-edge multicellular ecosystem of head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) involves aggressive invasion at the tumor–host interface, particularly at the leading edge. However, the mechanisms sustaining this invasive front remain unclear. Here, we performed spatially resolved multiomics profiling to characterize the leading-edge multicellular ecosystem (LEMCE) of HNSCC. We identified a set of twelve autocrine ligands, including TGFB1, ICAM1, and TNC, that support a stable invasive transcriptional state. Impaired fatty acid (FA) degradation in this region enhances autocrine ligands and amplifies proinvasive gene expression. Spatial single-cell analysis revealed that the specific resident cells in the LEMCE, which exhibited increased expression of autocrine ligands and impaired FA degradation, participated in a fibroblast–macrophage–T cell interaction circuit involving MMP1 + fibroblasts and C1QC + /SPP1 + macrophages, followed by interactions between C1QC + macrophages and cytotoxic T cells. These interactions may contribute to the structural organization and immunosuppressive features of the LEMCE. Therapeutically, targeting this niche via a combination of autocrine cytokine blockade, FA metabolic restoration, and PD-1 immune checkpoint inhibition suppressed invasion, reduced metastasis, and prolonged survival in mouse models. Our findings define the LEMCE as a self-reinforcing invasive and immunosuppressive niche and highlight its potential as a targetable vulnerability in HNSCC.

Mutation rate variability in viral populations: Implications for lethal mutagenesis

Lethal mutagenesis is a strategy to achieve viral extinction by drugging viral mutation rates beyond an extinction threshold. Accurate estimation of the extinction threshold is critical, as elevating viral mutation rates near, but not past this threshold increases the likelihood of mutations that could result in drug resistance, vaccine escape, or increased pathogenesis. Traditional models of lethal mutagenesis rely on the Poisson distribution, which assumes a uniform mutation rate across individuals. Yet, RNA viruses like influenza A virus (IAV) can have varied mutation rates due to mutations in the polymerase complex. This variability suggests that lethal mutagenesis models incorporating mutation rate diversity, such as ones using the gamma-Poisson distribution, may be more accurate for RNA viruses. Poisson models assume count data have equal mean and variance, while gamma-Poisson counts are overdispersed (variance greater than mean). Here, we provide experimental data showing that IAV mutations are overdispersed, indicating that the gamma-Poisson distribution is more appropriate for modeling IAV mutations. Modeling of lethal mutagenesis using the gamma-Poisson distribution reveals that the degree of overdispersion is critical in determining survival or extinction. Increased overdispersion shifts the extinction threshold higher, indicating that Poisson-based models have underestimated the mutation rate required to achieve viral extinction and avoid viral escape or accelerated evolution. Furthermore, time to extinction in simulated populations is significantly longer with gamma-Poisson-based models than Poisson-based. This investigation of how mutation rate variability affects lethal mutagenesis will directly impact antiviral drug design and strategy, thus advancing efforts to combat virus outbreaks and future pandemics.

Static and dynamic rough energy landscapes can lead to identical diffusivity

Molecules in dense environments, such as biological cells, are subjected to forces that fluctuate both in time and in space. While spatial fluctuations are captured by Lifson-Jackson-Zwanzig’s model of “diffusion in a rough potential,” and temporal fluctuations are often viewed as leading to additional friction effects, a unified view where the environment fluctuates both in time and in space is currently lacking. Here, we introduce a discrete-state model of a landscape fluctuating both in time and in space. Importantly, the model accounts for the reciprocal interaction of the diffusing particle with the landscape, which alters the landscape dynamics. As a result we find, surprisingly, that many features of the observable dynamics do not depend on the temporal fluctuation timescales and are already captured by the model of diffusion in a rough potential, even though this assumes a static energy landscape. Using this model, we reevaluate results of several experimental studies of protein dynamics and propose more accurate bounds on the inferred energetic roughness scales, which account for landscape dynamics.

Engineered and decellularized human cartilage graft exhibits intrinsic immunosuppressive properties and full skeletal repair capacity

Tissue engineering strategies predominantly consist of the autologous generation of living substitutes capable of restoring damaged body parts. Persisting challenges with patient-specific approaches include inconsistent performance, high costs, and delayed graft availability. Toward developing a one-for-all solution, a more attractive paradigm lies in the exploitation of dedicated cell lines for the fabrication of human tissue grafts. Following decellularization, this new class of biomaterials relies on the sole extracellular matrix and embedded growth factors instructing endogenous repair. This conceptual approach was previously validated using a custom mesenchymal cell line for the manufacturing of human cartilage, exhibiting remarkable osteoinductive capacity following lyophilization. Key missing criteria to envision clinical translation include proper decellularization as well as stringent assessment of both immunogenicity and regenerative performance. Here, we report the engineering and subsequent decellularization of human cartilage tissue with minimal matrix impairment. Ectopic evaluation in immunocompetent (IC) and immunocompromised animals reveals preservation of osteoinductivity predicted by macrophage kinetic of polarization. By establishing in vitro human allogeneic coculture models, we evidenced the immunosuppressive properties of cell-free human cartilages, controlling macrophage and dendritic cell maturation as well as T cell activation. Finally, regenerative performance was stringently assessed in an IC rat orthotopic model whereby decellularized human cartilage grafts achieved morphological and mechanical restoration of all critical-sized femoral defects. Taken together, our study provides robust safety and efficacy prerequisites prompting a first-in-human trial for engineered and decellularized human tissue grafts.

A chemogenetic approach for temporal and cell-specific activation of endogenous GPCRs in vivo

Cell-specific regulation of endogenous G protein–coupled receptors (GPCRs) is crucial for understanding their roles in physiological processes. We present chemogenetic tools using shield-1-dependent irreversible protein switches to regulate peptide agonist activity. To demonstrate this platform, we engineered chemogenetically regulated pituitary adenylate cyclase activating polypeptide (cPACAP), which exhibited &gt;15-fold chemical-dependent regulation of endogenous receptor activity. In vivo application of cPACAP allowed neuronal activation via the endogenous receptor for PACAP, engaging neural circuits that control respiratory and feeding behaviors. By integrating cPACAP with transgenic mice, we selectively activated endogenous PACAP receptor signaling in hypocretin-expressing neurons of the lateral hypothalamic area (LHA), revealing its role in regulating sighing, a stress-related physiological output. We further extended this design to chemogenetically regulate the parathyroid hormone receptor and corticotropin-releasing factor peptide receptor activity. Using a common small molecule, these chemogenetic tools enable temporally regulated peptidergic activation of endogenous GPCRs in targeted cell populations, facilitating the study of their function.

Mesoscale imaging of the human cerebellum reveals converging regional specialization of its morphology, vasculature, and cytoarchitecture

The human cerebellar cortex, despite containing the majority of the brain’s neurons, remains poorly characterized in vivo due to its extreme folding and thin laminar architecture. Here, we present a high-resolution imaging framework based on parallel-transmit, motion-corrected, ultra-high-field (7T) MRI paired with an automated, anatomically faithful cerebellar segmentation pipeline. This enables detailed quantification of cerebellar morphology and vascular architecture at scales previously inaccessible in living humans, as validated with postmortem data. Applying this framework, we uncover consistent interlobular heterogeneity in both cortical thickness and vascularization. These spatial gradients relate to differences in the granular layer from 3D-histology data. Our findings suggest that the cerebellar granular layer may act as a structural determinant of vascular density, linking anatomical features with metabolic load. These findings align cerebellar structure–function relationships with similar findings in the neocortex, where morphology reflects functional specialization and energy consumption. Our results also suggest that the in vivo imaging biomarkers we derive may offer broad avenues to monitor cerebellar involvement in neurological diseases such as multiple sclerosis, where granular layer pathology is prominent. Our study provides both insights into cerebellar organization and a toolset for studying cerebellar contributions to cognition and pathology.

Mechanisms of the viscosity decrease and increase of aqueous CsCl

Aqueous salt solutions occur in many aspects of chemistry, biology, and geology. Increasing the concentration of most aqueous salt solutions increases the viscosities. In contrast, adding CsCl to water initially decreases the viscosity, but at moderate concentration further addition increases it. While this phenomenon is well known, the molecular mechanisms for the reduction and increase have not been elucidated. We used ultrafast optical heterodyne–detected optical Kerr effect (OHD-OKE) and IR pump–probe experiments, as well as density functional theory to investigate the impact of Cs + ions on water dynamics, interactions, and structure. OHD-OKE experiments demonstrated that the dynamics of the water hydrogen bond (H-bond) network underpin the viscosity of CsCl solutions. Transient IR spectra of HOD in H 2 O interacting with Cs + showed a significant blue shift, a hallmark of hydrogen bonds weaker than those of pure water. Due to its low charge density, Cs + is distinct from high charge density cations, e.g., Na + and Li + , which have been observed to strengthen water hydrogen bonds and drive a large, monotonic increase in viscosity with concentration. The results showed that water hydrogen bonds in the Cs + second solvation shell are weaker than typical water–water hydrogen bonds, and these weak hydrogen bonds give rise to faster collective structural dynamics, leading to reduced viscosity. However, at sufficiently high salt concentrations, the low number of water molecules per ion pair leads to water clusters. Water confined in small clusters slows H-bond rearrangement, leading to an increase in viscosity.

Correction for Srivastava et al., Emergent neuronal mechanisms mediating covert attention in convolutional neural networks

Erk5-mediated microglial ferroptosis drives ischemic white matter damage via the Nfatc4–Clptm1l axis

Ischemic white matter damage is a significant pathological feature of chronic cerebral hypoperfusion, leading to cognitive impairments. However, the underlying molecular mechanisms remain poorly understood. In this study, we identify a causal association between genetically predicted extracellular signal-regulated kinase 5 (ERK5) expression and higher white matter hyperintensity volume through druggable target screening, suggesting its potential as a therapeutic target for white matter damage. Using different animal models of white matter damage, we show that Erk5 expression is significantly upregulated in microglia following both ischemic and demyelinating injury, correlating with the severity of white matter damage. Mechanistically, Erk5 exacerbates white matter damage by promoting microglial ferroptosis through the phosphorylation of nuclear factor of activated T-cells, cytoplasmic 4 (Nfatc4), which subsequently activates the expression of cleft lip and palate transmembrane protein 1-like protein (Clptm1l), a lipid scramblase involved in ferroptosis. Pharmacological and genetic inhibition of Erk5 in microglia effectively mitigates oxidative stress, lipid peroxidation, and ferroptosis, leading to a reduction in white matter damage and improved cognitive function. These findings underscore the potential of targeting the Erk5–Nfatc4–Clptm1l axis as a therapeutic strategy for ischemic white matter damage. Our study offers valuable insights into the molecular pathways driving white matter damage and provides a framework for the clinical translation of Erk5 inhibitors in the treatment of ischemic white matter damage.

Biological causes and impacts of rugged tree landscapes in phylodynamic inference

Phylodynamic analysis has been instrumental in elucidating epidemiological and evolutionary dynamics of pathogens. Bayesian phylodynamics integrates out phylogenetic uncertainty, which is typically substantial in phylodynamic datasets due to limited genetic diversity. Phylodynamic inference does not, however, scale with modern datasets, partly due to difficulties in traversing tree space. Here, we characterize tree space and landscape in phylodynamic inference and assess its impacts on analysis difficulty and key biological estimates. By running extensive Bayesian analyses of 15 classic large phylodynamic datasets and carefully analyzing the posterior samples, we find that the posterior tree landscape is diffuse yet rugged, leading to widespread tree sampling problems that usually stem from sequences in a small part of the tree. We develop clade-specific diagnostics to show that a few sequences—including putative recombinants and recurrent mutants—frequently drive the ruggedness and sampling problems, although existing data-quality tests show limited power to detect them. The sampling problems can significantly impact phylodynamic inferences or distort major biological conclusions; the impact is usually stronger on “local” estimates (e.g., introduction history) associated with particular clades than on “global” parameters (e.g., demographic trajectory) governed by general tree shape. We evaluate existing Markov chain Monte Carlo diagnostics and diagnostics developed here, and offer strategies for optimizing phylodynamic analysis settings and mitigating sampling problem impacts. Our findings highlight the need and directions to develop efficient traversal over rugged tree landscapes, ultimately advancing scalable and reliable phylodynamics.