Metabolic regulation of homologous recombination repair by MRE11 lactylation.

Lactylation is a lactate-induced post-translational modification best known for its roles in epigenetic regulation. Herein, we demonstrate that MRE11, a crucial homologous recombination (HR) protein, is lactylated at K673 by the CBP acetyltransferase in response to DNA damage and dependent on ATM phosphorylation of the latter. MRE11 lactylation promotes its binding to DNA, facilitating DNA end resection and HR. Inhibition of CBP or LDH downregulated MRE11 lactylation, impaired HR, and enhanced chemosensitivity of tumor cells in patient-derived xenograft and organoid models. A cell-penetrating peptide that specifically blocks MRE11 lactylation inhibited HR and sensitized cancer cells to cisplatin and PARPi. These findings unveil lactylation as a key regulator of HR, providing fresh insights into the ways in which cellular metabolism is linked to DSB repair. They also imply that the Warburg effect can confer chemoresistance through enhancing HR and suggest a potential therapeutic strategy of targeting MRE11 lactylation to mitigate the effects.

High-quality semiconductor fibres via mechanical design.

Recent breakthroughs in fibre technology have enabled the assembly of functional materials with intimate interfaces into a single fibre with specific geometries1-11, delivering diverse functionalities over a large area, for example, serving as sensors, actuators, energy harvesting and storage, display, and healthcare apparatus12-17. As semiconductors are the critical component that governs device performance, the selection, control and engineering of semiconductors inside fibres are the key pathways to enabling high-performance functional fibres. However, owing to stress development and capillary instability in the high-yield fibre thermal drawing, both cracks and deformations in the semiconductor cores considerably affect the performance of these fibres. Here we report a mechanical design to achieve ultralong, fracture-free and perturbation-free semiconductor fibres, guided by a study on stress development and capillary instability at three stages of the fibre formation: the viscous flow, the core crystallization and the subsequent cooling stage. Then, the exposed semiconductor wires can be integrated into a single flexible fibre with well-defined interfaces with metal electrodes, thereby achieving optoelectronic fibres and large-scale optoelectronic fabrics. This work provides fundamental insights into extreme mechanics and fluid dynamics with geometries that are inaccessible in traditional platforms, essentially addressing the increasing demand for flexible and wearable optoelectronics.

Experimental observation of repulsively bound magnons.

Stable composite objects, such as hadrons, nuclei, atoms, molecules and superconducting pairs, formed by attractive forces are ubiquitous in nature. By contrast, composite objects stabilized by means of repulsive forces were long thought to be theoretical constructions owing to their fragility in naturally occurring systems. Surprisingly, the formation of bound atom pairs by strong repulsive interactions has been demonstrated experimentally in optical lattices1. Despite this success, repulsively bound particle pairs were believed to have no analogue in condensed matter owing to strong decay channels. Here we present spectroscopic signatures of repulsively bound three-magnon states and bound magnon pairs in the Ising-like chain antiferromagnet BaCo2V2O8. In large transverse fields, below the quantum critical point, we identify repulsively bound magnon states by comparing terahertz spectroscopy measurements to theoretical results for the Heisenberg-Ising chain antiferromagnet, a paradigmatic quantum many-body model2-5. Our experimental results show that these high-energy, repulsively bound magnon states are well separated from continua, exhibit notable dynamical responses and, despite dissipation, are sufficiently long-lived to be identified. As the transport properties in spin chains can be altered by magnon bound states, we envision that such states could serve as resources for magnonics-based quantum information processing technologies6-8.

An extra-erythrocyte role of haemoglobin body in chondrocyte hypoxia adaption.

Although haemoglobin is a known carrier of oxygen in erythrocytes that functions to transport oxygen over a long range, its physiological roles outside erythrocytes are largely elusive1,2. Here we found that chondrocytes produced massive amounts of haemoglobin to form eosin-positive bodies in their cytoplasm. The haemoglobin body (Hedy) is a membraneless condensate characterized by phase separation. Production of haemoglobin in chondrocytes is controlled by hypoxia and is dependent on KLF1 rather than the HIF1/2α pathway. Deletion of haemoglobin in chondrocytes leads to Hedy loss along with severe hypoxia, enhanced glycolysis and extensive cell death in the centre of cartilaginous tissue, which is attributed to the loss of the Hedy-controlled oxygen supply under hypoxic conditions. These results demonstrate an extra-erythrocyte role of haemoglobin in chondrocytes, and uncover a heretofore unrecognized mechanism in which chondrocytes survive a hypoxic environment through Hedy.

Spatial cell fate manipulation of human pluripotent stem cells by controlling the microenvironment using photocurable hydrogel.

Human pluripotent stem cells (hPSCs) dynamically respond to their chemical and physical microenvironment, dictating their behavior. However, conventional in vitro studies predominantly employ plastic culture wares, which offer a simplified representation of the in vivo microenvironment. Emerging evidence underscores the pivotal role of mechanical and topological cues in hPSC differentiation and maintenance. In this study, we cultured hPSCs on hydrogel substrates with spatially controlled stiffness. The use of culture substrates that enable precise manipulation of spatial mechanical properties holds promise for better mimicking in vivo conditions and advancing tissue engineering techniques. We designed a photocurable polyethylene glycol-polyvinyl alcohol (PVA-PEG) hydrogel, allowing the spatial control of surface stiffness and geometry at a micrometer scale. This versatile hydrogel can be functionalized with various extracellular matrix proteins. Laminin 511-functionalized PVA-PEG gel effectively supports the growth and differentiation of hPSCs. Moreover, by spatially modulating the stiffness of the patterned gel, we achieved spatially selective cell differentiation, resulting in the generation of intricate patterned structures.

Immune surveillance and therapy of lymphomas driven by Epstein-Barr virus protein LMP1 in a mouse model.

B cells infected by Epstein-Barr virus (EBV), a transforming virus endemic in humans, are rapidly cleared by the immune system, but some cells harboring the virus persist for life. Under conditions of immunosuppression, EBV can spread from these cells and cause life-threatening pathologies. We have generated mice expressing the transforming EBV latent membrane protein 1 (LMP1), mimicking a constitutively active CD40 coreceptor, specifically in B cells. Like human EBV-infected cells, LMP1+ B cells were efficiently eliminated by T cells, and breaking immune surveillance resulted in rapid, fatal lymphoproliferation and lymphomagenesis. The lymphoma cells expressed ligands for a natural killer (NK) cell receptor, NKG2D, and could be targeted by an NKG2D-Fc fusion protein. These experiments indicate a central role for LMP1 in the surveillance and transformation of EBV-infected B cells in vivo, establish a preclinical model for B cell lymphomagenesis in immunosuppressed patients, and validate a new therapeutic approach.

Mechanism of EBV inducing anti-tumour immunity and its therapeutic use.

Tumour-associated antigens (TAAs) comprise a large set of non-mutated cellular antigens recognized by T cells in human and murine cancers. Their potential as targets for immunotherapy has been explored for more than two decades1, yet the origins of TAA-specific T cells remain unclear. While tumour cells may be an important source of TAAs for T cell priming2, several recent studies suggest that infection with some viruses, including Epstein-Barr virus and influenza virus can elicit T cell responses against abnormally expressed cellular antigens that function as TAAs3,4. However, the cellular and molecular basis of such responses remains undefined. Here we show that expression of the Epstein-Barr virus signalling protein LMP1 in B cells provokes T cell responses to multiple TAAs. LMP1 signalling leads to overexpression of many cellular antigens previously shown to be TAAs, their presentation on major histocompatibility complex classes I (MHC-I) and II (MHC-II) (mainly through the endogenous pathway) and the upregulation of costimulatory ligands CD70 and OX40L, thereby inducing potent cytotoxic CD4+ and CD8+ T cell responses. These findings delineate a mechanism of infection-induced anti-tumour immunity. Furthermore, by ectopically expressing LMP1 in tumour B cells from patients with cancer and thereby enabling them to prime T cells, we develop a general approach for rapid production of autologous cytotoxic CD4+ T cells against a wide range of endogenous tumour antigens, such as TAAs and neoantigens, for treating B cell malignancies. This work stresses the need to revisit classical concepts concerning viral and tumour immunity, which will be critical to fully understand the impact of common infections on human health and to improve the rational design of immune approaches to treatment of cancers.

Acetylation of PHF5A Modulates Stress Responses and Colorectal Carcinogenesis through Alternative Splicing-Mediated Upregulation of KDM3A.

Alternative pre-mRNA-splicing-induced post-transcriptional gene expression regulation is one of the pathways for tumors maintaining proliferation rates accompanying the malignant phenotype under stress. Here, we uncover a list of hyperacetylated proteins in the context of acutely reduced Acetyl-CoA levels under nutrient starvation. PHF5A, a component of U2 snRNPs, can be acetylated at lysine 29 in response to multiple cellular stresses, which is dependent on p300. PHF5A acetylation strengthens the interaction among U2 snRNPs and affects global pre-mRNA splicing pattern and extensive gene expression. PHF5A hyperacetylation-induced alternative splicing stabilizes KDM3A mRNA and promotes its protein expression. Pathologically, PHF5A K29 hyperacetylation and KDM3A upregulation axis are correlated with poor prognosis of colon cancer. Our findings uncover a mechanism of an anti-stress pathway through which acetylation on PHF5A promotes the cancer cells' capacity for stress resistance and consequently contributes to colon carcinogenesis.

Oxygenate-induced structural evolution of high-entropy electrocatalysts for multifunctional alcohol electrooxidation integrated with hydrogen production.

High-entropy compounds have been emerging as promising candidates for electrolysis, yet their controllable electrosynthesis strategy remains a formidable challenge because of the ambiguous ionic interaction and codeposition mechanism. Herein, we report a oxygenates directionally induced electrodeposition strategy to construct high-entropy materials with amorphous features, on which the structural evolution from high-entropy phosphide to oxide is confirmed by introducing vanadate, thus realizing the simultaneous optimization of composition and structure. The representative P-CoNiMnWVOx shows excellent bifunctional catalytic performance toward alkaline hydrogen evolution reaction and ethanol oxidation reaction (EOR), with small potentials of -168 mV and 1.38 V at 100 mA cm-2, respectively. In situ spectroscopy illustrates that the electrochemical reconstruction of P-CoNiMnWVOx induces abundant Co-O species as the main catalytic active species for EOR and follows the conversion pathway of the C2 product. Theoretical calculations reveal the optimized electronic structure and adsorption free energy of reaction intermediates on P-CoNiMnWVOx, thereby resulting in a facilitated kinetic process. A membrane-free electrolyzer delivers both high Faradaic efficiencies of acetate and H2 over 95% and superior stability at100 mA cm-2 during 120 h electrolysis. In addition, the unique composition and structural advantages endow P-CoNiMnWVOx with multifunctional catalytic activity and realize multipathway electrosynthesis of formate-coupled hydrogen production.

How plants manage pathogen infection.

To combat microbial pathogens, plants have evolved specific immune responses that can be divided into three essential steps: microbial recognition by immune receptors, signal transduction within plant cells, and immune execution directly suppressing pathogens. During the past three decades, many plant immune receptors and signaling components and their mode of action have been revealed, markedly advancing our understanding of the first two steps. Activation of immune signaling results in physical and chemical actions that actually stop pathogen infection. Nevertheless, this third step of plant immunity is under explored. In addition to immune execution by plants, recent evidence suggests that the plant microbiota, which is considered an additional layer of the plant immune system, also plays a critical role in direct pathogen suppression. In this review, we summarize the current understanding of how plant immunity as well as microbiota control pathogen growth and behavior and highlight outstanding questions that need to be answered.

Gram-scale bottom-up flash graphene synthesis.

Most bulk-scale graphene is produced by a top-down approach, exfoliating graphite, which often requires large amounts of solvent with high-energy mixing, shearing, sonication or electrochemical treatment1-3. Although chemical oxidation of graphite to graphene oxide promotes exfoliation, it requires harsh oxidants and leaves the graphene with a defective perforated structure after the subsequent reduction step3,4. Bottom-up synthesis of high-quality graphene is often restricted to ultrasmall amounts if performed by chemical vapour deposition or advanced synthetic organic methods, or it provides a defect-ridden structure if carried out in bulk solution4-6. Here we show that flash Joule heating of inexpensive carbon sources-such as coal, petroleum coke, biochar, carbon black, discarded food, rubber tyres and mixed plastic waste-can afford gram-scale quantities of graphene in less than one second. The product, named flash graphene (FG) after the process used to produce it, shows turbostratic arrangement (that is, little order) between the stacked graphene layers. FG synthesis uses no furnace and no solvents or reactive gases. Yields depend on the carbon content of the source; when using a high-carbon source, such as carbon black, anthracitic coal or calcined coke, yields can range from 80 to 90 per cent with carbon purity greater than 99 per cent. No purification steps are necessary. Raman spectroscopy analysis shows a low-intensity or absent D band for FG, indicating that FG has among the lowest defect concentrations reported so far for graphene, and confirms the turbostratic stacking of FG, which is clearly distinguished from turbostratic graphite. The disordered orientation of FG layers facilitates its rapid exfoliation upon mixing during composite formation. The electric energy cost for FG synthesis is only about 7.2 kilojoules per gram, which could render FG suitable for use in bulk composites of plastic, metals, plywood, concrete and other building materials.

Comparison of copper and graphene-assembled films in 5G wireless communication and THz electromagnetic-interference shielding.

Since first developed, the conducting materials in wireless communication and electromagnetic interference (EMI) shielding devices have been primarily made of metal-based structures. Here, we present a graphene-assembled film (GAF) that can be used to replace copper in such practical electronics. The GAF-based antennas present strong anticorrosive behavior. The GAF ultra-wideband antenna covers the frequency range of 3.7 GHz to 67 GHz with the bandwidth (BW) of 63.3 GHz, which exceed ~110% than the copper foil-based antenna. The GAF Fifth Generation (5G) antenna array features a wider BW and lower sidelobe level compared with that of copper antennas. EMI shielding effectiveness (SE) of GAF also outperforms copper, reaching up to 127 dB in the frequency range of 2.6 GHz to 0.32 THz, with a SE per unit thickness of 6,966 dB/mm. We also confirm that GAF metamaterials exhibit promising frequency selection characteristics and angular stability as flexible frequency selective surfaces.

Operando probing and adjusting of the complicated electrode process of multivalent metals at extreme temperature.

Nearly half of the elements in the periodic table are extracted, refined, or plated using electrodeposition in high-temperature melts. However, operando observations and tuning of the electrodeposition process during realistic electrolysis operations are extremely difficult due to severe reaction conditions and complicated electrolytic cell, which makes the improvement of the process very blind and inefficient. Here, we developed a multipurpose operando high-temperature electrochemical instrument that combines operando Raman microspectroscopy analysis, optical microscopy imaging, and a tunable magnetic field. Subsequently, the electrodeposition of Ti-which is a typical polyvalent metal and generally shows a very complex electrode process-was used to verify the stability of the instrument. The complex multistep cathodic process of Ti in the molten salt at 823 K was systematically analyzed by a multidimensional operando analysis strategy involving multiple experimental studies, theoretical calculations, etc. The regulatory effect and its corresponding scale-span mechanism of the magnetic field on the electrodeposition process of Ti were also elucidated, which would be inaccessible with existing experimental techniques and is significant for the real-time and rational optimization of the process. Overall, this work established a powerful and universal methodology for in-depth analysis of high-temperature electrochemistry.

Precise arrangement of metal atoms at the interface by a thermal printing strategy.

The kinetics and pathway of most catalyzed reactions depend on the existence of interface, which makes the precise construction of highly active single-atom sites at the reaction interface a desirable goal. Herein, we propose a thermal printing strategy that not only arranges metal atoms at the silica and carbon layer interface but also stabilizes them by strong coordination. Just like the typesetting of Chinese characters on paper, this method relies on the controlled migration of movable nanoparticles between two contact substrates and the simultaneous emission of atoms from the nanoparticle surface at high temperatures. Observed by in situ transmission electron microscopy, a single Fe3O4 nanoparticle migrates from the core of a SiO2 sphere to the surface like a droplet at high temperatures, moves along the interface of SiO2 and the coated carbon layer, and releases metal atoms until it disappears completely. These detached atoms are then in situ trapped by nitrogen and sulfur defects in the carbon layer to generate Fe single-atom sites, exhibiting excellent activity for oxygen reduction reaction. Also, sites' densities can be regulated by controlling the size of Fe3O4 nanoparticle between the two surfaces. More importantly, this strategy is applicable to synthesize Mn, Co, Pt, Pd, Au single-atom sites, which provide a general route to arrange single-atom sites at the interface of different supports for various applications.

A minimum functional form of the Escherichia coli BAM complex constituted by BamADE assembles outer membrane proteins in vitro.

The biogenesis of outer membrane proteins is mediated by the ß-barrel assembly machinery (BAM), which is a heteropentomeric complex composed of five proteins named BamA-E in Escherichia coli. Despite great progress in the BAM structural analysis, the molecular details of BAM-mediated processes as well as the exact function of each BAM component during OMP assembly are still not fully understood. To enable a distinguishment of the function of each BAM component, it is the aim of the present work to examine and identify the effective minimum form of the E. coli BAM complex by use of a well-defined reconstitution strategy based on a previously developed versatile assay. Our data demonstrate that BamADE is the core BAM component and constitutes a minimum functional form for OMP assembly in E. coli, which can be stimulated by BamB and BamC. While BamB and BamC have a redundant function based on the minimum form, both together seem to cooperate with each other to substitute for the function of the missing BamD or BamE. Moreover, the BamAE470K mutant also requires the function of BamD and BamE to assemble OMPs in vitro, which vice verse suggests that BamADE are the effective minimum functional form of the E. coli BAM complex.

Low molecular weight heparin promotes the PPAR pathway by protecting the glycocalyx of cells to delay the progression of diabetic nephropathy.

Diabetic nephropathy (DN) is one of the most important comorbidities for diabetic patients, which is the main factor leading to end-stage renal disease. Heparin analogs can delay the progression of DN, but the mechanism is not fully understood. In this study, we found that low molecular weight heparin therapy significantly upregulated some downstream proteins of the peroxisome proliferator-activated receptor (PPAR) signaling pathway by label-free quantification of the mouse kidney proteome. Through cell model verification, low molecular weight heparin can protect the heparan sulfate of renal tubular epithelial cells from being degraded by heparanase that is highly expressed in a high-glucose environment, enhance the endocytic recruitment of fatty acid-binding protein 1, a coactivator of the PPAR pathway, and then regulate the activation level of intracellular PPAR. In addition, we have elucidated for the first time the molecular mechanism of heparan sulfate and fatty acid-binding protein 1 interaction. These findings provide new insights into understanding the role of heparin in the pathogenesis of DN and developing corresponding treatments.

InDEP: an interpretable machine learning approach to predict cancer driver genes from multi-omics data.

Cancer driver genes are critical in driving tumor cell growth, and precisely identifying these genes is crucial in advancing our understanding of cancer pathogenesis and developing targeted cancer drugs. Despite the current methods for discovering cancer driver genes that mainly rely on integrating multi-omics data, many existing models are overly complex, and it is difficult to interpret the results accurately. This study aims to address this issue by introducing InDEP, an interpretable machine learning framework based on cascade forests. InDEP is designed with easy-to-interpret features, cascade forests based on decision trees and a KernelSHAP module that enables fine-grained post-hoc interpretation. Integrating multi-omics data, InDEP can identify essential features of classified driver genes at both the gene and cancer-type levels. The framework accurately identifies driver genes, discovers new patterns that make genes as driver genes and refines the cancer driver gene catalog. In comparison with state-of-the-art methods, InDEP proved to be more accurate on the test set and identified reliable candidate driver genes. Mutational features were the primary drivers for InDEP's identifying driver genes, with other omics features also contributing. At the gene level, the framework concluded that substitution-type mutations were the main reason most genes were identified as driver genes. InDEP's ability to identify reliable candidate driver genes opens up new avenues for precision oncology and discovering new biomedical knowledge. This framework can help advance cancer research by providing an interpretable method for identifying cancer driver genes and their contribution to cancer pathogenesis, facilitating the development of targeted cancer drugs.

From multi-omics data to the cancer druggable gene discovery: a novel machine learning-based approach.

The development of targeted drugs allows precision medicine in cancer treatment and optimal targeted therapies. Accurate identification of cancer druggable genes helps strengthen the understanding of targeted cancer therapy and promotes precise cancer treatment. However, rare cancer-druggable genes have been found due to the multi-omics data's diversity and complexity. This study proposes deep forest for cancer druggable genes discovery (DF-CAGE), a novel machine learning-based method for cancer-druggable gene discovery. DF-CAGE integrated the somatic mutations, copy number variants, DNA methylation and RNA-Seq data across ˜10 000 TCGA profiles to identify the landscape of the cancer-druggable genes. We found that DF-CAGE discovers the commonalities of currently known cancer-druggable genes from the perspective of multi-omics data and achieved excellent performance on OncoKB, Target and Drugbank data sets. Among the ˜20 000 protein-coding genes, DF-CAGE pinpointed 465 potential cancer-druggable genes. We found that the candidate cancer druggable genes (CDG) are clinically meaningful and divided the CDG into known, reliable and potential gene sets. Finally, we analyzed the omics data's contribution to identifying druggable genes. We found that DF-CAGE reports druggable genes mainly based on the copy number variations (CNVs) data, the gene rearrangements and the mutation rates in the population. These findings may enlighten the future study and development of new drugs.

Cooperation of structural motifs controls drug selectivity in cyclin-dependent kinases: an advanced theoretical analysis.

Understanding drug selectivity mechanism is a long-standing issue for helping design drugs with high specificity. Designing drugs targeting cyclin-dependent kinases (CDKs) with high selectivity is challenging because of their highly conserved binding pockets. To reveal the underlying general selectivity mechanism, we carried out comprehensive analyses from both the thermodynamics and kinetics points of view on a representative CDK12 inhibitor. To fully capture the binding features of the drug-target recognition process, we proposed to use kinetic residue energy analysis (KREA) in conjunction with the community network analysis (CNA) to reveal the underlying cooperation effect between individual residues/protein motifs to the binding/dissociating process of the ligand. The general mechanism of drug selectivity in CDKs can be summarized as that the difference of structural cooperation between the ligand and the protein motifs leads to the difference of the energetic contribution of the key residues to the ligand. The proposed mechanisms may be prevalent in drug selectivity issues, and the insights may help design new strategies to overcome/attenuate the drug selectivity associated problems.

Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors.

Machine learning including modern deep learning models has been extensively used in drug design and screening. However, reliable prediction of molecular properties is still challenging when exploring out-of-domain regimes, even for deep neural networks. Therefore, it is important to understand the uncertainty of model predictions, especially when the predictions are used to guide further experiments. In this study, we explored the utility and effectiveness of evidential uncertainty in compound screening. The evidential Graphormer model was proposed for uncertainty-guided discovery of KDM1A/LSD1 inhibitors. The benchmarking results illustrated that (i) Graphormer exhibited comparative predictive power to state-of-the-art models, and (ii) evidential regression enabled well-ranked uncertainty estimates and calibrated predictions. Subsequently, we leveraged time-splitting on the curated KDM1A/LSD1 dataset to simulate out-of-distribution predictions. The retrospective virtual screening showed that the evidential uncertainties helped reduce false positives among the top-acquired compounds and thus enabled higher experimental validation rates. The trained model was then used to virtually screen an independent in-house compound set. The top 50 compounds ranked by two different ranking strategies were experimentally validated, respectively. In general, our study highlighted the importance to understand the uncertainty in prediction, which can be recognized as an interpretable dimension to model predictions.