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Fragile X syndrome (FXS) is the most common inherited form of intellectual disability and the leading monogenic cause of autism. The condition stems from loss of fragile X mental retardation protein (FMRP), which regulates a wide range of ion channels via translational control, protein–protein interactions and second messenger pathways. Rapidly increasing evidence demonstrates that loss of FMRP leads to numerous ion channel dysfunctions (that is, channelopathies), which in turn contribute significantly to FXS pathophysiology. Consistent with this, pharmacological or genetic interventions that target dysregulated ion channels effectively restore neuronal excitability, synaptic function and behavioural phenotypes in FXS animal models. Recent studies further support a role for direct and rapid FMRP–channel interactions in regulating ion channel function. This Review lays out the current state of knowledge in the field regarding channelopathies and the pathogenesis of FXS, including promising therapeutic implications.Ion channel dysfunctions contribute significantly to fragile X pathophysiology. In this Review, Deng and Klyachko discuss the mechanisms underlying the effects of these channelopathies in fragile X syndrome, and the therapeutic potential of pharmacological interventions that target ion channels.

Peripheral sensory neurons regenerate their axon after nerve injury to enable functional recovery. Intrinsic mechanisms operating in sensory neurons are known to regulate nerve repair, but whether satellite glial cells (SGC), which completely envelop the neuronal soma, contribute to nerve regeneration remains unexplored. Using a single cell RNAseq approach, we reveal that SGC are distinct from Schwann cells and share similarities with astrocytes. Nerve injury elicits changes in the expression of genes related to fatty acid synthesis and peroxisome proliferator-activated receptor (PPARα) signaling. Conditional deletion of fatty acid synthase ( Fasn ) in SGC impairs axon regeneration. The PPARα agonist fenofibrate rescues the impaired axon regeneration in mice lacking Fasn in SGC. These results indicate that PPARα activity downstream of FASN in SGC contributes to promote axon regeneration in adult peripheral nerves and highlight that the sensory neuron and its surrounding glial coat form a functional unit that orchestrates nerve repair. The contribution of satellite glia to peripheral nerve regeneration is unclear. Here, the authors show that satellite glia are transcriptionally distinct from Schwann cells, share similarities with astrocytes, and, upon injury, they contribute to axon regeneration via Fasn-PPARα signalling pathway.

Cancer treatment by immune checkpoint blockade (ICB) can bring long-lasting clinical benefits, but only a fraction of patients respond to treatment. To predict ICB response, we developed TIDE, a computational method to model two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high infiltration of cytotoxic T lymphocytes (CTL) and the prevention of T cell infiltration in tumors with low CTL level. We identified signatures of T cell dysfunction from large tumor cohorts by testing how the expression of each gene in tumors interacts with the CTL infiltration level to influence patient survival. We also modeled factors that exclude T cell infiltration into tumors using expression signatures from immunosuppressive cells. Using this framework and pre-treatment RNA-Seq or NanoString tumor expression profiles, TIDE predicted the outcome of melanoma patients treated with first-line anti-PD1 or anti-CTLA4 more accurately than other biomarkers such as PD-L1 level and mutation load. TIDE also revealed new candidate ICB resistance regulators, such as SERPINB9 , demonstrating utility for immunotherapy research. An algorithm-selected gene signature focused on tumor immune evasion and suppression predicts response to immune checkpoint blockade in melanoma, exceeding the accuracy of current clinical biomarkers.

The RNA-guided CRISPR-associated (Cas) nucleases are versatile tools for genome editing in various organisms. The large sizes of the commonly used Cas9 and Cas12a nucleases restrict their flexibility in therapeutic applications that use the cargo-size-limited adeno-associated virus delivery vehicle. More compact systems would thus offer more therapeutic options and functionality for this field. Here, we report a miniature class 2 type V-F CRISPR-Cas genome-editing system from Acidibacillus sulfuroxidans (AsCas12f1, 422 amino acids). AsCas12f1 is an RNA-guided endonuclease that recognizes 5′ T-rich protospacer adjacent motifs and creates staggered double-stranded breaks to target DNA. We show that AsCas12f1 functions as an effective genome-editing tool in both bacteria and human cells using various delivery methods, including plasmid, ribonucleoprotein and adeno-associated virus. The small size of AsCas12f1 offers advantages for cellular delivery, and characterizations of AsCas12f1 may facilitate engineering more compact genome-manipulation technologies. Miniature CRISPR-AsCas12f1 has been biochemically characterized and engineered as an effective genome-editing tool for bacteria and human cells.

Jamming is an athermal transition between flowing and rigid states in amorphous systems such as granular matter, colloidal suspensions, complex fluids and cells. The jamming transition seems to display mixed aspects of a first-order transition, evidenced by a discontinuity in the coordination number, and a second-order transition, indicated by power-law scalings and diverging lengths. Here we demonstrate that jamming is a first-order transition with quenched disorder in cyclically sheared systems with quasistatic deformations, in two and three dimensions. Based on scaling analyses, we show that fluctuations of the jamming density in finite-sized systems have important consequences on the finite-size effects of various quantities, resulting in a square relationship between disconnected and connected susceptibilities, a key signature of the first-order transition with quenched disorder. This study puts the jamming transition into the category of a broad class of transitions in disordered systems where sample-to-sample fluctuations dominate over thermal fluctuations, suggesting that the nature and behavior of the jamming transition might be better understood within the developed theoretical framework of the athermally driven random-field Ising model. Jamming in amorphous systems is an athermal transition characterized by mixed first-order and second-order aspects. Authors demonstrate that jamming is a first-order transition with quenched disorder, revealing significant finite-size effects and a square relationship between disconnected and connected susceptibilities in cyclically sheared systems.

Topological optical states exhibit unique immunity to defects, rendering them ideal for photonic applications. A powerful class of such states is based on time-reversal symmetry breaking of the optical response. However, existing proposals either involve sophisticated and bulky structural designs or can only operate in the microwave regime. Here we show a theoretical demonstration for highly confined topologically protected optical states to be realized at infrared frequencies in a simple two-dimensional (2D) material structure—a periodically patterned graphene monolayer—subject to a magnetic field of only 2 tesla. In our graphene honeycomb superlattice structures, plasmons exhibit substantial nonreciprocal behavior at the superlattice junctions under moderate static magnetic fields, leading to the emergence of topologically protected edge states and localized bulk modes. This approach is simple and robust for realizing topologically nontrivial optical states in 2D atomic layers, and could pave the way for building fast, nanoscale, defect-immune photonic devices. Current proposals suitable for experimental realization of topologically protected optical states rely on complicated structures or only operate in the microwave regime. Here, Pan et al. propose topological Dirac plasmons to be realized at infrared frequencies in a periodically patterned graphene monolayer, subject to a magnetic field of only 2 Tesla.

MICA and MICB proteins can be expressed on tumors and act as “kill me” signals to the immune system. But tumors often disguise themselves by shedding these proteins, which prevents specialized natural killer (NK) cells from recognizing and destroying the cancer. Ferrari de Andrade et al. engineered antibodies directed against the site responsible for the proteolytic shedding of MICA and MICB (see the Perspective by Cerwenka and Lanier). The approach effectively locked MICA and MICB onto tumors so that NK cells could spot them for elimination. The antibodies exhibited preclinical efficacy in multiple tumor models, including humanized melanoma. Furthermore, the strategy reduced lung cancer metastasis after NK cell–mediated tumor lysis. Science , this issue p. 1537 ; see also p. 1460 Natural killer cell–mediated antitumor response is restored when tumors are prevented from sloughing off surface markers. MICA and MICB are expressed by many human cancers as a result of cellular stress, and can tag cells for elimination by cytotoxic lymphocytes through natural killer group 2D (NKG2D) receptor activation. However, tumors evade this immune recognition pathway through proteolytic shedding of MICA and MICB proteins. We rationally designed antibodies targeting the MICA α3 domain, the site of proteolytic shedding, and found that these antibodies prevented loss of cell surface MICA and MICB by human cancer cells. These antibodies inhibited tumor growth in multiple fully immunocompetent mouse models and reduced human melanoma metastases in a humanized mouse model. Antitumor immunity was mediated mainly by natural killer (NK) cells through activation of NKG2D and CD16 Fc receptors. This approach prevents the loss of important immunostimulatory ligands by human cancers and reactivates antitumor immunity.

Lung cancer remains to be the leading cause of cancer-related mortality worldwide. Finding new noninvasive biomarkers for lung cancer is still a significant clinical challenge. Exosomes are membrane-bound, nano-sized vesicles that are released by various living cells. Studies on exosomal proteomics may provide clues for developing clinical assays. In this study, we performed semi-quantitative proteomic analysis of proteins that were purified from exosomes of NCI-H838 non-small cell lung cancer cell line, with total cellular membrane proteins as control. In the exosomes, LC-MS/MS by data-independent analysis mode identified 3235 proteins. THBS1, ANXA6, HIST1H4A, COL18A1, MDK, SRGN, ENO1, TUBA4A, SLC3A2, GPI, MIF, MUC1, TALDO1, SLC7A5, ICAM1, HSP90AA1, G6PD, and LRP1 were found to be expressed in exosomes at more than 5-fold higher level as compared to total cellular membrane proteins. A well-known cancer biomarker, MUC1, is expressed at 8.98-fold higher in exosomes than total cellular membrane proteins. Subsequent analysis of plasma exosomes from non-small cell lung cancer (NSCLC) patients by a commercial electrochemiluminescence immunoassay showed that exosomal MUC1 level is 1.5-fold higher than healthy individuals (mean value 1.55 ± 0.16 versus mean value 1.05 ± 0.06, p = 0.0213). In contrast, no significant difference of MUC1 level was found between NSCLC patients and healthy individuals′ plasma (mean value 5.48 ± 0.65 versus mean value 4.16 ± 0.49). These results suggest that certain proteins, such as MUC1, are selectively enriched in the exosome compartment. The mechanisms for their preferential localization and their biological roles remain to be studied.

Intelligent tire technology significantly enhances vehicle performance and driving safety by integrating sensors and electronics within the tire to facilitate the real-time monitoring of tire–road interactions. However, its testing and validation face challenges due to the absence of integrated bench and road testing frameworks. This paper introduces a novel, comprehensive testing system designed to support the full lifecycle development of intelligent tire technologies across both laboratory and real-world driving scenarios, focusing on accelerometer and strain-based sensing. Featuring a modular, distributed architecture, the system integrates an instrumented wheel equipped with multiple embedded tire sensors and a wheel force transducer (WFT), as well as vehicle motion and driving behavior sensors. A robust data acquisition platform based on NI CompactRIO supports multiple-channel high-precision sensing, with sampling rates of up to 50 kHz. The system ensures that data performance aligns with diverse intelligent tire sensing principles, supports a wide range of test parameters, and meets the distinct needs of each development stage. The testing system was applied and validated in a tire vertical load estimation study, which systematically explored and validated estimation methods using multiple accelerometers and PVDF sensors, compared sensor characteristics and estimation performance under different installation positions and sensor types, and culminated in a product-level assessment in road conditions. The experimental results confirmed the higher accuracy of accelerometers in vertical load estimation, validated the developed estimation algorithms and the intelligent tire product, and demonstrated the functionality and performance of the testing system. This work provides a versatile and reliable platform for advancing intelligent tire technologies, supporting both future research and industrial applications.

Accurate vertical load measurement through intelligent tire technology is crucial for vehicle stability, handling, and safety. Existing studies have mainly focused on modeling and bench experiments, overlooking a detailed comparative analysis of real sensor performance and validation under actual driving conditions. This study addresses this gap by performing sensor comparisons and extensive real-road validation to ensure the accuracy and reliability of the proposed methods. First, finite element modeling (FEM) is used to assess the feasibility of accelerometer and strain-based sensors for vertical load prediction. High-precision bench tests quantitatively compare the performance of multiple triaxial Integrated Electronics Piezoelectric (IEPE) accelerometers and Polyvinylidene Fluoride (PVDF) sensors, identifying accelerometers as the superior choice due to their better stability and linearity. Vertical load prediction algorithms are developed using Support Vector Machine (SVM) and linear regression, considering variables like contact length, vehicle speed, and tire pressure. The algorithms are validated under real-road conditions using high-performance instruments across constant speed, acceleration, braking, and cornering, and a self-designed compact Intelligent Tire Test Unit (ITTU) is deployed for product-level implementation, confirming its effectiveness in real-world driving scenarios. The findings provide a validated framework for accurate vertical load estimation and real-time tire parameter prediction, offering practical insights for improving intelligent tire technology in dynamic driving conditions.