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The ParABS system orchestrates chromosome segregation in many bacterial species. The centromere-like parS sites serve as nucleation points for the initial binding of the ParB protein. Subsequent diffusion on adjacent, non-specific DNA regions (spreading) in the presence of CTP and binding of more ParB molecules along with DNA looping via ParB-ParB interactions bring distal parts of the chromosome into proximity. ParB interaction with the ParA-ATPase motor protein, then, drives genomic segregation. It has been shown that in some bacterial species, the ParB-parS complex undergoes phase separation into a condensate. However, the physico-chemical properties of such condensates and their response to forces, such as those they may face in the cell, have not yet been characterized. Performing turbidity measurements in the presence of CTP and various concentrations of DNA and physiologically relevant mono and divalent salt It was shown that Mg2+ facilitates, while K+ concentrations higher than ~20 mM disfavors, condensate formation. Microrheology measurements showed that condensates of ParB and DNA including parS sites (ParB-parS DNA) in the presence of CTP, are viscoelastic with a viscosity at Troom of ~5 Pa·s and able to quickly respond to deformations with a network relaxation time of 0.1 s. Additionally, fluorescence combined with force spectroscopy showed that mechanical disruption of ParB-DNA condensates in the presence of CTP requires ~ 5-7.5 pN of tension in the DNA, which is lower than the force required to stall a molecular motor such as RNA polymerase, but higher than the force required for the relocation of chromosomes and plasmids during segregation. These results support the idea that ParB-parS condensates dynamically rearrange at the molecular level while maintaining the cohesion necessary to sustain the drag force of segregation without interfering with genomic transactions. This physical mechanism could be the basis for the critical role of ParB-parS condensates in organizing and partitioning bacterial chromosomes.

Organophosphate esters (OPEs) are emerging environmental pollutants with potential estrogenic activity. However, long-term OPE exposure has also been linked to an increased risk of type 2 diabetes mellitus (T2DM), though the molecular mechanisms remain unclear. α-glucosidase plays a critical role in carbohydrate digestion, making it a key target for T2DM. In this study, two typical aromatic organophosphate esters (AOPEs), triphenyl phosphate (TPHP) and tricresyl phosphate (TCP), were selected to investigate how they affect the structure and function of α-glucosidase. Enzyme activity assays revealed that both TPHP and TCP activated α-glucosidase. TPHP exhibited a clear dose-dependent activation, whereas TCP showed no such concentration dependency. The formation of both complexes was driven by hydrophobic interactions. TCP exhibited a higher binding affinity than TPHP, which may be attributed to the distinct structural and electronic properties revealed by density functional theory (DFT) calculations. Computational simulations confirmed that both compounds form stable complexes with α-glucosidase, inducing only subtle local perturbations without significantly altering the overall enzyme fold. This conclusion is supported by stable Rg values, minor CD spectral changes, and a single energy minimum observed in FEL analysis. Furthermore, alanine scanning mutagenesis identified PHE157 and PHE177 as common critical residues for TPHP and TCP binding to α-glucosidase, with energy contributions exceeding 1.5 kcal/mol. Together, these findings provide molecular-level insights into how TPHP and TCP affect the structure and function of α-glucosidase, offering a potential mechanistic explanation for their glucose metabolism-disrupting effects.

Accurate prediction of apple fruit maturity date is essential for optimizing harvest timing, fruit quality and market value under climate change. However, process-based crop models often show limited performance when extrapolated across large spatial scales, whereas machine learning models lack physiological interpretability. To address these limitations, this study has developed a hybrid framework integrating the process-based STICS model with machine learning approaches across China's apple planting regions. Phenological observations from 24 sites and meteorological data from 250 stations during 1991-2020 were used to calibrate and evaluate six machine learning models. Among them, the random forest (RF) model achieved the best performance [coefficient of determination (R2)&#x2009;>&#x2009;0.65, root mean square error (RMSE)&#x2009;<&#x2009;8.1&#x2009;days]. A hybrid approach was implemented by incorporating STICS-simulated maturity dates as input features into the machine learning models, enabling the capture of residual non-linear relationships between maturity dates of apple fruit and climatic and geographic variables. This integration further improved prediction accuracy (R2&#x2009;>&#x2009;0.71, RMSE&#x2009;<&#x2009;7.5&#x2009;days), reducing errors by over 50% compared to the standalone STICS model. Spatially, the average maturity date was 282.5&#x2009;&#xb1;&#x2009;7.0&#x2009;DOY (i.e. day of year), with the latest maturity in the Yellow River region and the earliest in the Southwest highlands. Temporally, maturity dates advanced slightly at 0.1&#x2009;days&#x2009;decade-1, with substantial regional variability. SHAP (i.e. Shapley Additive exPlanations) analysis identified chilling requirement, elevation and STICS-simulated maturity date as dominant drivers. The hybrid STICS+RF framework effectively combines mechanistic understanding with data-driven learning, improving prediction accuracy and interpretability of apple fruit maturity date at regional scales. This approach provides a robust tool for optimizing harvest management and adapting apple production systems to climate change. &#xa9; 2026 Society of Chemical Industry.

Heart failure and chronic liver disease account for substantial morbidity and mortality worldwide. Both conditions share common risk factors and a bidirectional pathophysiology, and the coexistence of both conditions is expected to increase over time. Management of coexisting heart failure and liver disease is challenged by the under-representation of participants with liver disease in landmark heart failure clinical trials, impaired hemodynamics at advanced stages of liver disease, altered drug metabolism, and higher risk of adverse events than portended by either condition alone. Moreover, diagnostic pitfalls might be encountered in relation to assessing the primary etiologies driving the disease process, estimating the degree of liver fibrosis, and differentiating primary liver disease from heart failure-related liver congestion particularly, given the complex interplay between sinusoidal pressure, congestion, and structural fibrosis. Cardiovascular-hepatic cross-thematic research, clinical education, and health care services could optimize management and patient outcomes. The purpose of the current review is to (i) highlight the growing epidemiology of concurrent heart failure-liver disease; (ii) provide diagnostic clues for liver disease and an approach for interpreting liver marker abnormalities amongst heart failure patients; (iii) describe the main therapeutic strategies in real-world clinical settings; and (iv) discuss current gaps in knowledge and future directions. This update on the framework of the heart failure-liver disease overlap phenomenon can inform clinical care policies and facilitate novel research in the field.

Adaptive immunity relies on T-cell receptor (TCR) recognition of peptides presented by the major histocompatibility complex (pMHC). Accurate prediction of TCR:pMHC binding pairs from sequence data remains a longstanding challenge in computational immunology, limiting the development of precision immunotherapies like cancer vaccines and adoptive cell therapies. Here, we present enFoldX ( en semble of Fold ed comple X es), a structure-based approach leveraging biophysical characterization of AlphaFold3-generated ensembles to classify TCR:pMHC sequence pairs as cognate versus non-cognate. Unlike previous methods reliant on only sequence data or a single, static predicted structure, enFoldX extracts features from an entire generated ensemble with a custom focus on the biophysical binding interface. Our model distinguishes T cell reactivity between peptides differing by a single amino acid substitution, the resolution required for cancer neoantigens, and generalizes to unseen peptides, MHCs, and TCRs, a major objective for artificial intelligence (AI) in immunology. Our performance on these crucial tasks demonstrates that diverse, structural sampling of biophysical interactions over an ensemble is fundamental for accurate AI-driven binding predictions and offers lessons for efficient future data generation to improve models. Our findings therefore offer a scalable framework to accelerate therapeutic binder design, and we provide access to a publicly available code repository.

Reliability of AlphaFold2 predictions is mainly assessed using the predicted Local Distance Difference Test (pLDDT). For model organisms, 30-40% of residues fall into the low-confidence pLDDT range. Moreover, pLDDT sometimes fails to flag physically implausible structures. This raises two questions: can more robust reliability indicators be identified, and do unreliable predictions share common structural or biophysical features? We characterize protein structures through histograms of per-residue neighbor counts, and use the Wasserstein principal component analysis to define the arity map, and lightweight and informative 2D embedding of proteins in a dataset. Using AlphaFold-DB, we show that the arity map reveals three structurally and biophysically distinct populations (well-folded proteins, intrinsically disordered proteins, and physically implausible predictions). We also use our packing based encoding at the residue level to define abstraqt (Arity-Based STRuctural Arrangement Quality assessmenT), a per-residue scoring function complementing the pLDDT, assigning low scores to hallucinated helices and distorted beta strands while correctly scoring native-like predictions. The code to compute arity maps and rerun the analyses is available within the Structural Bioinformatics Library. See: AlphaFold analysis, and also Documentation, Applications, Installation guide.

The E3 ubiquitin ligase Nedd4-1 is a structurally complex, multidomain enzyme that plays a crucial role in maintaining proteostasis and regulating the cellular stress response. Nedd4-1's complex regulatory mechanism involves both intermolecular interactions (with upstream E2 conjugating enzymes and substrates) and intramolecular interactions that govern its function. Additionally, Nedd4-1 has received increased attention recently due to a small-molecule N-arylbenzimidazole 2 (NAB2) that prevents proteotoxicity and restores organelle trafficking associated with synucleopathies in a Nedd4-1-dependent manner. To study the enzymology of Nedd4-1, it is essential to employ recombinant Nedd4-1 in its native and untagged form to ensure high-fidelity biophysical characterization. In this study, we develop an efficient and optimized methodology for enhanced expression and purification of untagged active Nedd4-1 from E. coli over three affinity column steps. This strategy employs a nine-residue poly-histidine tag, glutathione S-transferase, and proteolytic cleavage with the TEV fusion protease, His6-MBP-uTEV3, providing 3.9 mg per liter of culture of high-purity (&#x2265;95%), active, stable, and storable untagged Nedd4-1. Additionally, the NAB2-Nedd4-1 interaction was re-evaluated using the untagged enzyme via two orthogonal techniques: microscale thermophoresis (MST) and surface plasmon resonance (SPR). Steady-state SPR analysis (&#x3c7;2 = 0.543) estimated a Kdapp of 169 &#x3bc;M. While binding was constrained by the solubility limits of NAB2, these results suggest that the previously reported nanomolar affinity may be an overestimate resulting from affinity tag interference or immobilization-induced artifacts. This data highlights the potential importance of this method for accessing high-purity, stable untagged Nedd4-1 for biophysical characterization and mechanistic enzymology.

Pentacyclic triterpenoids are diverse class of plant-derived bioactive scaffolds, but their potential to resensitize resistant Gram-negative pathogens to antibiotics remains underexplored. Polymyxins are last-line drugs for multidrug-resistant (MDR) Gram-negative infections, yet the global dissemination of mcr genes and chromosomal mutations in pmrAB/phoPQ compromise their efficacy. This study aimed to evaluate the antibacterial potential of the natural triterpenoid celastrol (CEL, derived from Tripterygium wilfordii). We specifically investigated its ability to function as a polymyxin adjuvant to potentiate polymyxin activity against MDR Gram-negative pathogens and elucidated the underlying molecular mechanism. Antibacterial activity and potentiation efficacy with polymyxins were assessed in vitro by MIC determination, checkerboard assays, and time-kill analyses against clinical isolates and engineered resistant strains. To elucidate the precise mechanism, biophysical target validations, comprising drug affinity responsive target stability (DARTS), biolayer interferometry (BLI), limited proteolysis-MS (LiP-MS), molecular dynamics (MD) simulations, and FabI enzymatic assays, were systematically coupled with cellular and omics analyses, such as intracellular accumulation, genetic validation, metabolic rescue, lipidomics, and qPCR. Therapeutic efficacy was tested in murine peritonitis and cutaneous abscess models. CEL effectively potentiated polymyxin activity and resensitized Escherichia coli and Klebsiella pneumoniae harboring mcr-1 or pmrAB/phoPQ mutations to polymyxins. Mechanistically, biophysical and structural analyses demonstrated that CEL directly binds to and inhibits the enoyl-ACP reductase FabI. Furthermore, genetic validations and metabolic rescue assays confirmed that FabI-mediated type II fatty acid synthesis (FASII) pathway disruption triggers perturbation of lipid homeostasis. In vivo assays confirmed that the combination therapy significantly reduced bacterial burden in infected mice. Our findings demonstrate that the plant-derived pentacyclic triterpenoid CEL targets FabI and functions as an effective polymyxin adjuvant. By inhibiting the FASII pathway, CEL potentiates polymyxin activity and resensitizes multidrug-resistant Gram-negative pathogens to polymyxins, highlighting a promising phytopharmacological strategy to restore the clinical utility of last-resort antibiotics against MDR infections.

Virally-derived ribosomal skipping 2A peptides are a popular tool for protein co-expression. Despite their use in over 9,000 publications, the biochemical and biophysical properties underlying the skipping mechanism remain largely unexplored. We identified 4,218 2A-like peptides originating from non-viral organisms. We developed and utilized the Trifluorescent Reporter fluorescent tool for high-throughput multiplexable analysis of ribosomal skipping, and tested 3,271 2A-like peptide sequences. We identified peptides that skipped, failed to skip, and skipped but failed to restart translation, in addition to peptides that induced a reduction in protein abundance. Peptides that skipped and induced reductions in protein abundance largely originated from eukaryotes. A poly-leucine stretch in an alpha-helix N-terminal to the conserved GDxExNPGP motif drove both skipping and the reduction in protein abundance. Analysis of the native eukaryotic protein contexts revealed that reduction may be harnessed as an expression regulator. The high-throughput approach used in this work greatly expands the functional knowledge of what biophysical and biochemical characteristics lead to ribosomal skipping, including an apparent latent eukaryotic 'leucine stall-helix' motif.

Cholesterol biosynthesis is among the best-characterized metabolic pathways in biology, yet a fundamental question remains unresolved: why does this pathway generate more than twenty enzymatic reactions and numerous structurally distinct intermediates if cholesterol is its major biological end product? Over the past several decades, biochemical, genetic, pharmacological, biophysical, and lipidomic studies have progressively revealed that many sterol intermediates are not merely transient precursors. Instead, they possess distinct biophysical, signaling, and oxidative properties that contribute directly to cellular physiology and disease. However, these discoveries have largely been interpreted within separate biological and experimental contexts, including inherited disorders of cholesterol biosynthesis, membrane biology, nuclear receptor signaling, oxysterol metabolism, and pharmacological inhibition of distal sterol enzymes. Here, we propose that sterol flux rewiring provides an integrative framework that connects these independent observations into a unified view of cholesterol metabolism. In this framework, biological responses emerge from dynamic redistribution of metabolic flux, generating distinct sterol states characterized by specific membrane properties, signaling activities, oxidative potentials, and downstream metabolic outputs rather than by the accumulation of individual metabolites alone. This perspective explains how changes in sterol composition reshape membrane organization, oxidative diversification, and interconnected signaling networks, including the epoxycholestanoid pathway. It also provides a coherent framework for understanding how alterations in cholesterol metabolism contribute to development, immunity, neurobiology, ageing, regeneration, and cancer, while highlighting new opportunities for therapeutic strategies aimed at reprogramming sterol-state organization rather than simply inhibiting cholesterol synthesis.

The extracellular matrix (ECM) plays a pivotal role in shaping tumor behavior by providing biochemical and biophysical cues to cancer cells. Traditional 2D culture systems fail to recapitulate this complexity, and in vivo systems do not readily allow for interrogation of how individual ECM components influence tumor cell behavior. Here, we introduce a fully synthetic, tunable three-dimensional (3D) hydrogel that mimics a soft tissue tumor microenvironment (TME) to enable mechanistic studies of ECM-tumor interactions. The hydrogel features a proteolytically degradable poly(ethylene glycol) base network functionalized with integrin-binding peptides derived from ECM components collagen I and fibronectin. To model metastatic ECM and further tune the hydrogel, we incorporated a tenascin-C (TNC)-derived peptide. To study the impact of these tunable ECM parameters on cancer cell behavior, we encapsulated Ewing sarcoma (EwS) cells within the hydrogels. EwS is an aggressive bone and soft tissue tumor that commonly metastasizes to lung. Our studies demonstrated matrix-dependent growth and phenotypic variation of EwS cells. Specifically, the TNC peptide drove divergent tumor behaviors and induced cell state changes consistent with alterations induced by the native TNC protein. To facilitate downstream functional assays, the hydrogel incorporates sortase-degradable crosslinkers that enable non-perturbative recovery of encapsulated cells. This platform provides a reductionist and reproducible model for studying the ECM's role in cancer cell biology while addressing long-standing challenges in PEG hydrogel degradation and cell retrieval. Collectively, this work establishes a biomaterials-based framework to dissect EwS tumor-ECM interactions in a controllable 3D microenvironment. STATEMENT OF SIGNIFICANCE: Engineered biomaterials to model Ewing sarcoma (EwS) have been previously employed to study metastasis to the bone. While important efforts, platforms to study EwS metastasis in lung - the most common site of metastasis - have not been developed. Here, we introduce a user programmable biomaterial designed to mimic the environment of soft tissue metastases. The platform features several attributes: 1) it is mechanically matched to lung tissue; 2) it is readily functionalized with extracellular matrix protein-derived peptides (e.g., Tenascin-C); 3) encapsulated cells can be retrieved in a "biologically invisible" manner via sortase-mediated gel degradation, enabling expanded downstream analysis. This platform provides a powerful, reproducible tool to dissect tumor behavior and identify new targets for cancer therapy.

Preeclampsia (PE) remains a leading cause of maternal and perinatal morbidity. Women of advanced maternal age (AMA,&#x2009;&#x2265;&#x2009;35 years) constitute a rapidly expanding high-risk subpopulation in China. Existing first-trimester prediction models were derived in general obstetric populations and often rely on specialized biophysical or proprietary biomarker assays, with limited validation in AMA women. This multicenter retrospective study enrolled 2,582 AMA pregnant women from three tertiary centers in southern China, partitioned into a training cohort (n&#x2009;=&#x2009;1,327), an internal validation cohort (n&#x2009;=&#x2009;569), and two external validation cohorts (n&#x2009;=&#x2009;399 and 287). Predictors were selected using LASSO and multivariable logistic regression and assembled into a nomogram. Performance was evaluated by area under the receiver operating characteristic curve (AUC), calibration, Brier scores, decision curve analysis, and clinical impact curves. Seven independent predictors were retained: pre-pregnancy BMI, parity, mode of conception, uric acid, white blood cell count, red blood cell count, and hemoglobin. The nomogram achieved AUCs of 0.819, 0.795, 0.787, and 0.756 across the four cohorts, with close calibration and favorable net benefit. Performance was preserved when women with chronic hypertension were included and across early-onset and late-onset PE subgroups. Notably, maternal age itself was not discriminative within the AMA stratum (P&#x2009;=&#x2009;0.626). This nomogram relying exclusively on routine clinical and laboratory parameters provides an accessible tool for individualized early-pregnancy PE risk assessment and risk-stratified antenatal management in AMA women.

The advent of single-molecule nanopore sequencing established a powerful platform for modern genomics by using static biological pores to report the translocation of canonical nucleic acids, enabling rapid, accessible nucleic acid analysis. However, extending this strategy to single-molecule proteomics has stalled against a fundamental biophysical bottleneck. Current efforts in nanopore proteomics attempt to retrofit these static, spatial "caliper" biological nanopores (e.g., &#x3b1;-hemolysin, MspA, CsgG, aerolysin) for protein sequencing despite the immense steric, charge, and conformational heterogeneity of proteins. Unlike the chemically uniform, polyanionic phosphodiester backbone of DNA, the proteome contains isosteric and isobaric variants that confound purely volumetric measurements made by static pores. To address this bottleneck, we propose the application of dynamical translocases - naturally evolved, protein-handling nanomachines (e.g., the anthrax toxin protective antigen). Unlike static pores that rely on passive diffusion, dynamical translocases employ target-docking clamp architectures that achieve low nanomolar sensitivity. Active-site conformational dynamics generate high-dimensional kinetic fingerprints that enable molecular discrimination during translocation. By coupling dynamical translocases with Physics-Informed Machine Learning (PIML), we demonstrate that amino-acid side-chain-dependent thermodynamic friction can be mathematically decoded, enabling >90% accurate classification of chemically distinct amino acid classes and doing so label-free without the artificial DNA-handles required by legacy platforms.

Neuroendocrine cells communicate with other cells by releasing neurotransmitters or hormones by exocytosis, which involves SNARE-mediated fusion between secretory vesicles and the plasma membranes of the secreting cells. In neurons two plasma membrane SNARE proteins, Syntaxin-1a and SNAP25, join with the vesicle membrane SNARE protein Synaptobrevin-2 to form a four-helix bundle, which drives membrane fusion. The assembly of these SNAREs, which is highly orchestrated in cells, has been intensely studied in solution using fragments of the SNARE proteins without their transmembrane domains or lipid anchors. However, in cell and model membranes, Syntaxin and SNAP25 are known to oligomerize and cluster, and little is known about how clustering affects their incorporation into SNARE complexes. In cells, the SM protein Munc18 has been implicated in aiding secretory vesicle docking and facilitating SNARE complex assembly through its interactions with Syntaxin. To understand how Munc18 orchestrates SNARE complex assembly on membranes, we employed protein reconstitution in model membranes as well as biochemical and biophysical assays to show that lipid-dependent oligomerization of Syntaxin affects Munc18-Syntaxin binding and SNAP25 insertion into the plasma membrane acceptor SNARE complex. We showcase the consequences of the different modes of Munc18-Syntaxin and SNAP25 interaction on Syntaxin's oligomerization and orientation relative to the membrane surface, as well as on docking and fusion of purified insulin granules. We also determined low-resolution structures by cryoEM in nanodiscs and on the surface of proteoliposomes of membrane-bound assembly states of Munc18/Syntaxin and Munc18/Syntaxin/SNAP25 complexes.

The aging brain exhibits a decline in the regenerative populations of neural stem cells (NSCs). While mechanisms that restore old NSC function have started to be identified, the role of lipids-especially complex lipids-in NSC aging remains largely unclear. Using lipidomic profiling by mass spectrometry, we identify age-related changes in complex lipids in quiescent NSCs in vitro and in vivo. Moreover, several polyunsaturated fatty acids increase across lipid classes in quiescent NSCs during aging. Using spatial lipidomics, we find that some of the changes in complex lipids are also observed in situ. Several age-related changes in complex lipids and side chain composition are occurring at the plasma membrane, as revealed by lipidomic profiling of isolated plasma membrane vesicles. Experimentally, we show that aging is accompanied by a decrease in plasma membrane order, a key membrane biophysical property, in old quiescent NSCs in vitro and in vivo. To determine the functional role of plasma membrane lipids in aging NSCs, we performed genetic and supplementation studies. Knocking out the phospholipid acyltransferase MBOAT2 exacerbates age-related lipidomic changes in old quiescent NSCs and impedes their ability to activate. Mboat2 overexpression reverses age-related lipidomic changes in old quiescent NSCs and boosts their ability to activate in vitro and in vivo. Moreover, supplementation of plasma membrane lipids from young NSCs improves the ability of old quiescent NSCs to activate. Our work could lead to lipid-based strategies for restoring the regenerative potential of NSCs, which has important implications for countering brain decline during aging.

The dynamics of calcium ions (Ca 2+ ) in skeletal muscles link electrochemical activation and contractile force generation. Recent experimental data suggest that store-operated Ca 2+ entry (SOCE), the process of extracellular Ca 2+ influx upon depletion of Ca 2+ from the sarcoplasmic reticulum (SR), helps delay the onset of muscle fatigue during exercise. We hypothesize that SOCE regulates force generation during prolonged muscle activity by allowing for sustained Ca 2+ release from the SR. We test this hypothesis with a quantitative biophysical model that simulates the biochemical events of muscle contraction, from depolarization at the T-tubules to Ca 2+ release from the SR to Ca 2+ binding and force generation throughout the myoplasm. We also consider the balance between Ca 2+ removal from the myoplasm and SOCE through the T-tubule membrane, along with mitochondrial uptake of free Ca 2+ and phosphate. We use the model to test the effects of SOCE inhibition on force production. The magnitude of myoplasmic Ca 2+ and force are lower in SOCE knockout cells, especially when SOCE reduction is combined with impaired uptake of phosphate by mitochondria. We then test the effects of SOCE during resistance exercise or high-intensity interval training. These simulations predict a context-dependent relationship between force generation and SOCE - increased SOCE is associated with greater force production during resistance exercise, but worsens the effects of fatigue in certain cases of high-intensity training. Such SOCE-induced fatigue is attributed to phosphate accumulation in the myoplasm and can be mitigated by increased rates of mitochondrial phosphate uptake. Store-operated calcium entry (SOCE) provides a mechanism for calcium ion (Ca 2+ ) influx following depletion of Ca 2+ from intracellular stores such as the sarcoplasmic reticulum (SR). Recent experiments suggest that SOCE is an important modulator of contractile force generation in skeletal muscle. Here, we develop a computational model of Ca 2+ handling in the myoplasm, SR, and mitochondria and the resulting effects on force generation in skeletal muscle fibers to examine the role of SOCE during extended periods of activity. Our model predicts that increasing SOCE leads to enhanced force over periods of repeated stimuli during resistance exercise due to sustained Ca 2+ release. Our simulations show a complex relationship between SOCE and force production during high-intensity interval training, with exacerbated phosphate accumulation in the myoplasm leading to force reduction for very high levels of SOCE. This effect can be mitigated by enhanced mitochondrial phosphate uptake. Emmet Francis is a K99/R00 awardee in the Rangamani Lab at UC San Diego whose research explores the intersection between cell signaling and mechanics. His doctoral research in the Heinrich Lab at UC Davis examined the role of calcium bursts in neutrophil chemotaxis and phagocytosis. More recently, he has used spatial modeling approaches to shed light on the role of nanoscale membrane curvature and nuclear deformation in YAP/TAZ mechanotransduction. In his own research lab, he plans to use both experiments and computational models to probe the mechanisms of bidirectional mechanotransduction in neutrophils. This study uses systems modeling to demonstrate a role for SOCE in sustained force generation during exercise. SOCE leads to two competing effects on contractile force in myofibers - increased crossbridge cycling due to elevated myoplasmic Ca 2+ enhances force, whereas increased accumulation of myoplasmic phosphate (due to increased ATP hydrolysis) can lead to force reduction (fatigue). The tradeoff between these two effects is modulated by phosphate uptake into mitochondria via the phosphate carrier PiC. Figure created in BioRender.

The lateral spacing between filaments in crosslinked cytoskeletal bundles is a critical yet poorly understood physical parameter that affects force generation, transport, and bundle architecture. We develop a biophysical model of crosslinkers and crosslinking motors on filament pairs. Motor/crosslinker binding sets the filament spacing, which in turn biases which motors/crosslinkers can bind. Crosslinking motors generate pulling forces that bring antiparallel filaments closer together, while non-motor crosslinkers with a preferred binding angle exert repulsive torques that maintain larger spacing. We demonstrate these principles in a model of the fission yeast anaphase mitotic spindle midzone, where microtubules form a square array with nearest-neighbor spacing 2-5 times smaller than the length of crosslinking proteins. Our model shows that motor-crosslinker interactions alone are sufficient to drive self-organization into this experimentally observed geometry. Furthermore, the feedback between geometry and binding creates strong indirect cooperativity, because crosslinkers establish spacing that favors binding of similar-length proteins, leading to history-dependent states that persist long after individual protein binding equilibration. This feedback mechanism in which crosslinkers control geometry and geometry controls crosslinker binding should operate in any multi-crosslinker-filament system and represents a general self-organizing principle for cytoskeletal networks.

In North America, climate change is projected to increase wildfire activity, leading to more frequent megafires that will likely overwhelm current regeneration processes, threatening the resilience of boreal forests and the services that they provide. The devastating 2023 wildfire season exposes the magnitude of the challenge facing North America, including eastern regions. Post-fire reforestation needs can far exceed current capacity, which relies almost exclusively on manual tree planting. Our review addresses urgent needs to increase reforestation capacity to restore vast burned landscapes that are experiencing regeneration failures. We first provide an overview of current challenges and promising reforestation methods for tree planting and direct seeding. Challenges include addressing regeneration failure risks and limited road access, and strengthening reforestation value chains amid workforce shortages. We discuss the suitability of traditional and innovative reforestation methods, considering these challenges, to situate them within a broader restoration strategy. We show that using multiple methods expands reforestation capacity by providing more options for reforesting sites with a wider range of biophysical conditions. However, robust evidence on method-specific outcomes across different timescales is still needed to understand their effectiveness for post-fire restoration. Future research should further assess the cost-benefits of these methods and improve our ability to predict and mitigate regeneration failures across varying forest landscapes to support efficient restoration efforts. Our work provides a foundation for transferring current knowledge into strategic reforestation planning and execution. It is relevant to communities, scientists, governments and managers seeking to adapt regeneration silviculture to changing fire regimes.

Red blood cells (RBCs) are widely utilized as natural drug delivery systems due to their unique mechanical properties, which enable prolonged circulation and physiological function. This study examines a biophysical strategy that employs shear-responsive RBC (sr-RBC) carriers to mechanically release drugs at stenotic arteries. Intracellularly loaded sr-RBCs exhibited shear-dependent cargo release when perfused through stenotic arterial models, while no release was observed in unconstricted vessels. Notably, the sheared sr-RBCs retained their membrane integrity and CD47 expression while maintaining their ability to traverse microvascular networks without release when perfused through a microfluidic model simulating pulmonary capillaries. Multiscale modelling of RBC suspension fluid dynamics demonstrates a strong positive correlation between the sr-RBC drug release and the overall RBC membrane stretching induced by the high shear rates at various stenotic percentages. Sr-RBCs and mechanosensitive cell carriers may enable new avenues for treating life-threatening arterial constrictions and other vascular diseases.