[H3K27 acetylation promotes lncRNA OIP5-AS1 transcription and induces apoptosis of nasal epithelial cells in allergic rhinitis through up-regulation of TLR4].

Objective To investigate the effect of lysine 27 residue of histone H3 (H3K27) acetylation modification on the transcriptional promotion of long noncoding RNA OPA interacting protein 5-antisense RNA 1 (lncRNA OIP5-AS1) and apoptosis of nasal epithelial cells (NECs) in allergic rhinitis (AR) via regulating Toll-like receptor 4 (TLR4). Methods Interleukin-13 (IL-13) was used to treat NECs to establish an AR cell model. Real-time quantitative PCR was utilized to detect the expressions of OIP5-AS1 and TLR4 in nasal mucosal tissues of AR patients and in the in vitro cell model. The concentrations of macrophage colony-stimulating factor (GM-CSF), eotaxin-1, and mucin 5AC (MUC5AC) were detected by ELISA. The apoptosis of NECs was determined by terminal deoxynucleotidyl transferase mediated dUTP-biotin nick end labeling (TUNEL). A dual-luciferase report experiment was carried out to verify the relationship between OIP5-AS1 and TLR4. Chromatin immunoprecipitation (ChIP) assay was performed to verify H3K27 acetylation of histones in the OIP5-AS1 promoter region. Results Compared with healthy controls and untreated NECs, OIP5-AS1 and TLR4 were both up-regulated in nasal mucosal tissues from AR patients and IL-13-stimulated NECs. Knockdown of OIP5-AS1 decreased the level of TLR4 in IL-13-treated NECs, while overexpression of OIP5-AS1 increased the level of TLR4. Inhibition of OIP5-AS1 reduced the apoptosis rate, and inhibited the secretion of GM-CSF, eotaxin-1, and MUC5AC from IL-13-treated NECs, while overexpression of TLR4 partially reversed the effects of OIP5-AS1 knockdown on NEC apoptosis and the secretion of GM-CSF, eotaxin-1, and MUC5AC. In addition, H3K27 acetylation was markedly enriched in the promoter region of OIP5-AS1, and H3K27 acetylation promoted the expression of OIP5-AS1 in IL-13-treated NECs. Conclusion H3K27 acetylation promotes OIP5-AS1 transcription and induces NEC apoptosis in AR via upregulation of TLR4.

Nanoparticles for efficient drug delivery and drug resistance in glioma: New perspectives.

Gliomas are the most common primary tumors of the central nervous system, with glioblastoma multiforme (GBM) having the highest incidence, and their therapeutic efficacy depends primarily on the extent of surgical resection and the efficacy of postoperative chemotherapy. The role of the intracranial blood-brain barrier and the occurrence of the drug-resistant gene O6-methylguanine-DNA methyltransferase have greatly limited the efficacy of chemotherapeutic agents in patients with GBM and made it difficult to achieve the expected clinical response. In recent years, the rapid development of nanotechnology has brought new hope for the treatment of tumors. Nanoparticles (NPs) have shown great potential in tumor therapy due to their unique properties such as light, heat, electromagnetic effects, and passive targeting. Furthermore, NPs can effectively load chemotherapeutic drugs, significantly reduce the side effects of chemotherapeutic drugs, and improve chemotherapeutic efficacy, showing great potential in the chemotherapy of glioma. In this article, we reviewed the mechanisms of glioma drug resistance, the physicochemical properties of NPs, and recent advances in NPs in glioma chemotherapy resistance. We aimed to provide new perspectives on the clinical treatment of glioma.

One-Step Multi-View Clustering With Diverse Representation.

Multi-View clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views. Although tremendous progress has been made recently, most existing methods undergo high complexity, preventing them from being applied to large-scale tasks. Multi-View clustering via matrix factorization is a representative to address this issue. However, most of them map the data matrices into a fixed dimension, limiting the model's expressiveness. Moreover, a range of methods suffers from a two-step process, i.e., multimodal learning and the subsequent k -means, inevitably causing a suboptimal clustering result. In light of this, we propose a one-step multi-view clustering with diverse representation (OMVCDR) method, which incorporates multi-view learning and k -means into a unified framework. Specifically, we first project original data matrices into various latent spaces to attain comprehensive information and auto-weight them in a self-supervised manner. Then, we directly use the information matrices under diverse dimensions to obtain consensus discrete clustering labels. The unified work of representation learning and clustering boosts the quality of the final results. Furthermore, we develop an efficient optimization algorithm with proven convergence to solve the resultant problem. Comprehensive experiments on various datasets demonstrate the promising clustering performance of our proposed method. The code is publicly available at https://github.com/wanxinhang/OMVCDR.

Fast Continual Multi-View Clustering With Incomplete Views.

Multi-view clustering (MVC) has attracted broad attention due to its capacity to exploit consistent and complementary information across views. This paper focuses on a challenging issue in MVC called the incomplete continual data problem (ICDP). Specifically, most existing algorithms assume that views are available in advance and overlook the scenarios where data observations of views are accumulated over time. Due to privacy considerations or memory limitations, previous views cannot be stored in these situations. Some works have proposed ways to handle this problem, but all of them fail to address incomplete views. Such an incomplete continual data problem (ICDP) in MVC is difficult to solve since incomplete information with continual data increases the difficulty of extracting consistent and complementary knowledge among views. We propose Fast Continual Multi-View Clustering with Incomplete Views (FCMVC-IV) to address this issue. Specifically, the method maintains a scalable consensus coefficient matrix and updates its knowledge with the incoming incomplete view rather than storing and recomputing all the data matrices. Considering that the given views are incomplete, the newly collected view might contain samples that have yet to appear; two indicator matrices and a rotation matrix are developed to match matrices with different dimensions. In addition, we design a three-step iterative algorithm to solve the resultant problem with linear complexity and proven convergence. Comprehensive experiments conducted on various datasets demonstrate the superiority of FCMVC-IV over the competing approaches. The code is publicly available at https://github.com/wanxinhang/FCMVC-IV.

On the Consistency and Large-Scale Extension of Multiple Kernel Clustering.

Existing multiple kernel clustering (MKC) algorithms have two ubiquitous problems. From the theoretical perspective, most MKC algorithms lack sufficient theoretical analysis, especially the consistency of learned parameters, such as the kernel weights. From the practical perspective, the high complexity makes MKC unable to handle large-scale datasets. This paper tries to address the above two issues. We first make a consistency analysis of an influential MKC method named Simple Multiple Kernel k-Means (SimpleMKKM). Specifically, suppose that ∧γn are the kernel weights learned by SimpleMKKM from the training samples. We also define the expected version of SimpleMKKM and denote its solution as γ*. We establish an upper bound of ||∧γn-γ*||∞ in the order of ~O(1/√n), where n is the sample number. Based on this result, we also derive its excess clustering risk calculated by a standard clustering loss function. For the large-scale extension, we replace the eigen decomposition of SimpleMKKM with singular value decomposition (SVD). Consequently, the complexity can be decreased to O(n) such that SimpleMKKM can be implemented on large-scale datasets. We then deduce several theoretical results to verify the approximation ability of the proposed SVD-based method. The results of comprehensive experiments demonstrate the superiority of the proposed method. The code is publicly available at https://github.com/weixuan-liang/SVD-based-SimpleMKKM.

B7-H3 suppresses CD8+ T cell immunologic function through reprogramming glycolytic metabolism.

Malignant neoplasms pose a formidable threat to human well-being. Prior studies have documented the extensive expression of B7 homolog 3 (B7-H3 or CD276) across various tumors, affecting glucose metabolism. Yet, the link between metabolic modulation and immune responses remains largely unexplored. Our study reveals a significant association between B7-H3 expression and advanced tumor stages, lymph node metastasis, and tumor location in oral squamous cell carcinoma (OSCC). We further elucidate B7-H3's role in mediating glucose competition between cancer cells and CD8+ T cells. Through co-culturing tumor cells with flow cytometry-sorted CD8+ T cells, we measured glucose uptake and lactate secretion in both cell types. Additionally, we assessed interferon-gamma (IFN-γ) release and the immune and exhaustion status of CD8+ T cells. Our findings indicate that B7-H3 enhances glycolysis in OSCC and malignant melanoma, while simultaneously inhibiting CD8+ T cell glycolysis. Silencing B7-H3 led to increased IFN-γ secretion in co-cultures, highlighting its significant role in modulating CD8+ T cell functions within the tumor microenvironment and its impact on tumorigenicity. We also demonstrate that glycolysis inhibition can be mitigated by exogenous glucose supplementation. Mechanistically, our study suggests B7-H3's influence on metabolism might be mediated through the phosphoinositide3-kinase (PI3K)/ protein kinase B (Akt)/ mammalian target of rapamycin (mTOR) signaling pathway. This research unveils how B7-H3 affects immune functions via metabolic reprogramming.

Efficient C-N coupling for urea electrosynthesis on defective Co3O4 with dual-functional sites.

Urea electrosynthesis under ambient conditions is emerging as a promising alternative to conventional synthetic protocols. However, the weak binding of reactants/intermediates on the catalyst surface induces multiple competing pathways, hindering efficient urea production. Herein, we report the synthesis of defective Co3O4 catalysts that integrate dual-functional sites for urea production from CO2 and nitrite. Regulating the reactant adsorption capacity on defective Co3O4 catalysts can efficiently control the competing reaction pathways. The urea yield rate of 3361 mg h-1 gcat-1 was achieved with a corresponding faradaic efficiency (FE) of 26.3% and 100% carbon selectivity at a potential of -0.7 V vs. the reversible hydrogen electrode. Both experimental and theoretical investigations reveal that the introduction of oxygen vacancies efficiently triggers the formation of well-matched adsorption/activation sites, optimizing the adsorption of reactants/intermediates while decreasing the C-N coupling reaction energy. This work offers new insights into the development of dual-functional catalysts based on non-noble transition metal oxides with oxygen vacancies, enabling the efficient electrosynthesis of essential C-N fine chemicals.

Identifying the Key Role of Plutella xylostella General Odorant Binding Protein 2 in Perceiving a Larval Attractant, (E,E)-2,6-Farnesol.

There is currently a lack of effective olfaction-based techniques to control diamondback moth (DBM) larvae. Identifying behaviorally active odorants for DBM larvae and exploring their recognition mechanisms can provide insights into olfaction-based larval control strategies. Through the two-choice assay, (E,E)-2,6-farnesol (farnesol) was identified as a compound exhibiting significant attractant activity toward DBM larvae, achieving an attraction index of 0.48 ± 0.13. PxylGOBP1 and PxylGOBP2, highly expressed in the antennae of DBM larvae, both showed high affinity toward farnesol. RNAi technology was used to knock down PxylGOBP1 and PxylGOBP2, revealing that the attraction of DBM larvae to farnesol nearly vanished following the knockdown of PxylGOBP2, indicating its critical role in recognizing farnesol. Further investigation into the PxylGOBP2-farnesol interaction revealed the importance of residues like Thr9, Trp37, and Phe118 in PxylGOBP2's binding to farnesol. This research is significant for unveiling the olfactory mechanisms of DBM larvae and developing larval behavior regulation techniques.

Jasmonate signaling pathway confers salt tolerance through a NUCLEAR FACTOR-Y trimeric transcription factor complex in Arabidopsis.

Jasmonate (JA) is a well-known phytohormone essential for plant response to biotic stress. Recently, a crucial role of JA signaling in salt resistance has been highlighted; however, the specific regulatory mechanism remains largely unknown. In this study, we found that the NUCLEAR FACTOR-Y (NF-Y) subunits NF-YA1, NF-YB2, and NF-YC9 form a trimeric complex that positively regulates the expression of salinity-responsive genes, whereas JASMONATE-ZIM DOMAIN protein 8 (JAZ8) directly interacts with three subunits and acts as the key repressor to suppress both the assembly of the NF-YA1-YB2-YC9 trimeric complex and the transcriptional activation activity of the complex. When plants encounter high salinity, JA levels are elevated and perceived by the CORONATINE INSENSITIVE (COI) 1 receptor, leading to the degradation of JAZ8 via the 26S proteasome pathway, thereby releasing the activity of the NF-YA1-YB2-YC9 complex, initiating the activation of salinity-responsive genes, such as MYB75, and thus enhancing the salinity tolerance of plants.

Key Contributions of the Overexpressed Plutella xylostella Sigma Glutathione S-Transferase 1 Gene (PxGSTs1) in the Resistance Evolution to Multiple Insecticides.

The overexpression of insect detoxification enzymes is a typical adaptive evolutionary strategy for insects to cope with insecticide pressure. In this study, we identified a glutathione S-transferase (GST) gene, PxGSTs1, that exhibited pronounced expression in the field-resistant population of Plutella xylostella. By using RNAi (RNA interference), the transgenic fly models, and quantitative real-time polymerase chain reaction (RT-qPCR) methods, we confirmed that the augmented expression of PxGSTs1 mediates the resistance of P. xylostella to various types of insecticides, including chlorantraniliprole, novaluron, λ-cyhalothrin, and abamectin. PxGSTs1 was found to bolster insecticide resistance in two ways: direct detoxification and enhancing antioxidative defenses. In addition, our findings demonstrated that pxy-miR-8528a exerts a pivotal influence on forming insecticide resistance in P. xylostella by downregulating PxGSTs1 expression. In summary, we elucidated the multifaceted molecular and biochemical underpinnings of PxGSTs1-driven insecticide resistance in P. xylostella. Our results provide a new perspective for understanding the insecticide resistance mechanism of P. xylostella.

YBX1 Underwent Phase Separation into Stress Granules Stimulated by Ionizing Radiation.

Stress granules (SGs) are formed through liquid-liquid phase separation (LLPS), in response to external stimuli. YBX1, an integral component of SGs, plays a crucial role in tumor progression and cellular stress response. This study aims to elucidate the mechanisms and specific biological implications of YBX1 in SG formation, along with the identification of key regions and interacting proteins. Our observations indicate that YBX1 rapidly undergoes liquid-liquid phase separation, leading to SG formation in response to 8 Gy X-ray irradiation within 1 h, with SGs reverting to their original state after 5 h. There was a potential interaction between ATXN2L and YBX1, persisting YBX1 within the SGs. Our data suggested a potential interaction between ATXN2L and YBX1, and it remained associated with YBX1 within the SGs. Furthermore, our subsequent studies demonstrate that targeting ATXN2L can diminish the recruitment of YBX1 to stress granules (SGs), consequently enhancing the radiosensitivity of HeLa cells.

High-efficiency removal of pyrethroids using a redesigned odorant binding protein.

Pyrethroids are ubiquitously present in environmental media and threaten both the ecosystem and human health. To explore effective ways to remove pyrethroids from the environment, an odorant binding protein (OBP) with affinity for various pyrethroids was investigated. Initially, the target OBP, Spodoptera littoralis pheromone binding protein 1 (SlitPBP1), underwent redesign to enhance its affinity for pyrethroids. The modified SlitPBP1E97ND106E demonstrated a substantially increased affinity for deltamethrin (DeltaM), with a dissociation constant of 0.77 ± 0.17 µM. The affinity of SlitPBP1E97ND106E for other pyrethroids also increased to varying extents. Consequently, SlitPBP1E97ND106E displayed a markedly enhanced capability to adsorb and remove pyrethroids. When exposed to free SlitPBP1E97ND106E in solution, the reduction in DeltaM surged from 16.78 ± 0.32% to 97.51 ± 0.56%. SlitPBP1E97ND106E was immobilized by coupling the protein to Ni2+-NTA agarose resin. Liquid chromatography results attested to the superior efficacy of immobilized SlitPBP1E97ND106E in removing pyrethroids, especially DeltaM. No significant differences in pyrethroid removal were detected across various water samples. Our findings introduce a potent tool for pyrethroid removal. A wider range of OBPs can similarly be optimized and applied to remove organic pollutants, including but not limited to pesticides.

A biosensor for D-2-hydroxyglutarate in frozen sections and intraoperative assessment of IDH mutation status.

The oncometabolite D-2-hydroxyglutarate (D-2-HG) has emerged as a valuable biomarker in tumors with isocitrate dehydrogenase (IDH) mutations. Efficient detection methods are required and rapid intraoperative determination of D-2-HG remains a huge challenge. Herein, D-2-HG dehydrogenase from Achromobacter xylosoxidans (AX-D2HGDH) was found to have high substrate specificity. AX-D2HGDH dehydrogenizes D-2-HG and reduces flavin adenine dinucleotide (FAD) bound to the enzyme. Interestingly, the dye resazurin can be taken as another substrate to restore FAD. AX-D2HGDH thus catalyzes a bisubstrate and biproduct reaction: the dehydrogenation of D-2-HG to 2-ketoglutarate and simultaneous reduction of non-fluorescent resazurin to highly fluorescent resorufin. According to steady-state analysis, a ping-pong bi-bi mechanism has been concluded. The Km values for resazurin and D-2-HG were determined as 0.56 µM and 10.93 µM, respectively, suggesting high affinity to both substrates. On the basis, taking AX-D2HGDH and resazurin as recognition and fluorescence transducing element, a D-2-HG biosensor (HGAXR) has been constructed. HGAXR exhibits high sensitivity, accuracy and specificity for D-2-HG in different biological samples. With the aid of HGAXR and the matched low-cost palm-size detecting device, D-2-HG levels in frozen sections of resected brain tumor tissues can be measured in a direct, simple and accurate manner with a fast detection (1-3 min). As the technique of frozen section is familiar to surgeons and pathologists, HGAXR and the portable device can be easily integrated into the current workflow, having potential to provide rapid intraoperative pathology for IDH mutation status and guide decision-making during surgery.

Switching between C2+ Products and CH4 in CO2 Electrolysis by Tuning the Composition and Structure of Rare-Earth/Copper Catalysts.

Rational regulation of the reaction pathway to produce the desired products is one of the most significant challenges in the electrochemical CO2 reduction reaction (CO2RR). Herein, we designed a series of rare-earth Cu catalysts with mixed phases. It was found that the products could be switched from C2+ to CH4 by tuning the composition and structure of the catalysts. Particularly at the Cu/Sm atomic ratio of 9/1 (Cu9Sm1-Ox), the Faradaic efficiency (FE) for C2+ products (FEC2+) could reach 81% at 700 mA cm-2 with negligible CH4. However, the FE of CH4 (FECH4) was 65% at 500 mA cm-2 over Cu1Sm9-Ox (Cu/Sm = 1/9), and the FEC2+ was extremely low. Experiments and theoretical studies indicated that the stable CuSm2O4 phase existed in all the catalysts within the Cu/Sm range of 9/1 to 1/9. At a high Cu content, the catalyst was composed of CuSm2O4 and Cu phases. The small amount of Sm could enhance the binding strength of *CO and facilitate C-C coupling. Conversely, at a high Sm content, the catalyst was composed of CuSm2O4 and Sm2O3 phases. Sm could effectively stabilize bivalent Cu and enrich proton donors, lowering the reaction energy of *CO for deep hydrogenation to generate CH4. In both pathways, the stable CuSm2O4 phase could cooperate with the Cu or Sm2O3 phases, which induced the formation of different microenvironments to generate different products. This strategy also had commonality with other Cu-rare-earth (La, Pr, and Eu) catalysts to boost the CO2RR for C2+ or CH4 production.

Machine-Learning-Assisted Optimization of a Single-Atom Coordination Environment for Accelerated Fenton Catalysis.

Machine learning (ML) algorithms will be enablers in revolutionizing traditional methods of materials optimization. Here, we broaden the use of ML to assist the construction of Fenton-like single-atom catalysts (SACs) by developing a methodology including model building, training, and prediction. Our approach can efficiently extract synthesis parameters that exert a substantial influence on Fenton activity and accurately predict the phenol degradation rate k of SACs with a mean error of ±0.018 min-1. The extended synthesis window with accelerated learning enables the realization that the heating temperatures during SAC synthesis significantly influence the Fe-N coordination number, which ultimately dictates their performance. Through ML-guided optimization, a highly efficient SAC dominated by Fe-N5 sites with exceptional Fenton activity (k = 0.158 min-1) is identified. Our work provides an example for ML-assisted optimization of single-atom coordination environments and illuminates the feasibility of ML in accelerating the development of high-performance catalysts.

High-Rate CO2 Electrolysis to Formic Acid over a Wide Potential Window: An Electrocatalyst Comprised of Indium Nanoparticles on Chitosan-Derived Graphene.

Realizing industrial-scale production of HCOOH from the CO2 reduction reaction (CO2 RR) is very important, but the current density as well as the electrochemical potential window are still limited to date. Herein, we achieved this by integration of chemical adsorption and electrocatalytic capabilities for the CO2 RR via anchoring In nanoparticles (NPs) on biomass-derived substrates to create In/X-C (X=N, P, B) bifunctional active centers. The In NPs/chitosan-derived N-doped defective graphene (In/N-dG) catalyst had outstanding performance for the CO2 RR with a nearly 100 % Faradaic efficiency (FE) of HCOOH across a wide potential window. Particularly, at 1.2 A ⋅ cm-2 high current density, the FE of HCOOH was as high as 96.0 %, and the reduction potential was as low as -1.17 V vs RHE. When using a membrane electrode assembly (MEA), a pure HCOOH solution could be obtained at the cathode without further separation and purification. The FE of HCOOH was still up to 93.3 % at 0.52 A ⋅ cm-2 , and the HCOOH production rate could reach 9.051 mmol ⋅ h-1 ⋅ cm-2 . Our results suggested that the defects and multilayer structure in In/N-dG could not only enhance CO2 chemical adsorption capability, but also trigger the formation of an electron-rich catalytic environment around In sites to promote the generation of HCOOH.

Construction of 3D copper-chitosan-gas diffusion layer electrode for highly efficient CO2 electrolysis to C2+ alcohols.

High-rate electrolysis of CO2 to C2+ alcohols is of particular interest, but the performance remains far from the desired values to be economically feasible. Coupling gas diffusion electrode (GDE) and 3D nanostructured catalysts may improve the efficiency in a flow cell of CO2 electrolysis. Herein, we propose a route to prepare 3D Cu-chitosan (CS)-GDL electrode. The CS acts as a "transition layer" between Cu catalyst and the GDL. The highly interconnected network induces growth of 3D Cu film, and the as-prepared integrated structure facilitates rapid electrons transport and mitigates mass diffusion limitations in the electrolysis. At optimum conditions, the C2+ Faradaic efficiency (FE) can reach 88.2% with a current density (geometrically normalized) as high as 900 mA cm-2 at the potential of -0.87 V vs. reversible hydrogen electrode (RHE), of which the C2+ alcohols selectivity is 51.4% with a partial current density of 462.6 mA cm-2, which is very efficient for C2+ alcohols production. Experimental and theoretical study indicates that CS induces growth of 3D hexagonal prismatic Cu microrods with abundant Cu (111)/Cu (200) crystal faces, which are favorable for the alcohol pathway. Our work represents a novel example to design efficient GDEs for electrocatalytic CO2 reduction (CO2RR).

Hydrogen Radical-Induced Electrocatalytic N2 Reduction at a Low Potential.

Realizing efficient hydrogenation of N2 molecules in the electrocatalytic nitrogen reduction reaction (NRR) is crucial in achieving high activity at a low potential because it theoretically requires a higher equilibrium potential than other steps. Analogous to metal hydride complexes for N2 reduction, achieving this step by chemical hydrogenation can weaken the potential dependence of the initial hydrogenation process. However, this strategy is rarely reported in the electrocatalytic NRR, and the catalytic mechanism remains ambiguous and lacks experimental evidence. Here, we show a highly efficient electrocatalyst (ruthenium single atoms anchored on graphdiyne/graphene sandwich structures) with a hydrogen radical-transferring mechanism, in which graphdiyne (GDY) generates hydrogen radicals (H•), which can effectively activate N2 to generate NNH radicals (•NNH). A dual-active site is constructed to suppress competing hydrogen evolution, where hydrogen preferentially adsorbs on GDY and Ru single atoms serve as the adsorption site of •NNH to promote further hydrogenation of NH3 synthesis. As a result, high activity and selectivity are obtained simultaneously at -0.1 V versus a reversible hydrogen electrode. Our findings illustrate a novel hydrogen transfer mechanism that can greatly reduce the potential and maintain the high activity and selectivity in NRR and provide powerful guidelines for the design concept of electrocatalysts.