Microalgal-bacteria consortium provide self-aeration through photosynthetic oxygen production, offering an energy-efficient strategy for wastewater treatment. However, biofilm regulation in practical application is still limited by various factors, with acyl-homoserine lactones (AHLs) serving as key regulatory elements. This study established a microalgal-bacteria biofilm reactor (MBBfR) to investigate the regulatory mechanisms of AHLs-mediated quorum sensing under varying nitrogen (N) source. Results showed that a machine learning (ML) model successfully predicted the total suspended solids (TSS, R2 = 0.951), specific oxygen consumption rate (SOCR, R2 = 0.873) and oxygen generation rate (SOGR, R2 = 0.977) using extracellular polymeric substances (EPS) and AHLs as predictors. N source and C/N ratio altered AHLs concentrations and their associations with microbial community composition, with C6-HSL identified as the predominant AHLs. Nitrate facilitated the rapid formation of high quality and density biofilms, and enhanced signaling molecules secretion, thereby improving N removal efficiency. With high N stress and low C/N ratio, AHLs significantly induced EPS production, maintained microbial metabolic and photosynthetic activities, and promoted microalgal viability and proliferation. This signaling mechanism contributed to sustaining MBBfR stability and function. The findings provide a theoretical foundation and practical guidance for optimizing MBBfR-based wastewater treatment, enabling precise and convenient biofilm functionality regulation.
Pesticides, especially eco-friendly biopesticides, are indispensable for crops protection and food security. In this work, we propose a scoring framework to screen or repurpose existing organic compounds, natural products and pharmaceutical drugs as sustainable agrochemicals. This scoring strategy adopts one-class support vector machine (OCSVM) as base learner to capture the patterns of pharmacophore-relevant fragments of organic compounds exhibiting pesticidal effects, in which only pesticides are required as training data, and the uncertainty and error-proneness of conventional sampling of negative training data are eliminated. T-SNE demonstrates that Morgan fingerprint is effective to transform molecule-level heterogeneity into fragment-level homogeneity to comply the requirement of OCSVM cohesive space. Furthermoe, Morgan bits are explained via SHapley Additive exPlanations (SHAP) in terms of significance, and are visualized via reverse engineering to bridge the gap between abstract representation of local chemical environments and intuitive structural fragments. Hierarchical clustering analyses show that two molecules exhibiting close substructural/fragmental similarities tend to be physically proximal in the inlier subspace, justifying the metric validity and explainability of OCSVM hyperplane distances as pesticide-likeness scores. K-fold cross validation (k = 5) shows that the OCSVM model achieves 4% novelty or outlier rate with the empirically and heuristically determined hyperparameters ([Formula: see text]). External test on 38 independent biopesticides shows that the OCSVM model recognises 92% of external biopesticides, equivalent to or better than two binary supervised classifiers (82.05% and 81.57%). Further model sanity check demonstrates that the OCSVM model has the competitive potentials of screening natural products molecules or fragments as eco-friendly biopesticides.
Metal nanoparticles, such as gold nanoparticles (AuNPs), are widely used as biosensing materials. In previous studies, dark-field microscopy (DFM) has been utilised to examine target-induced AuNP aggregation for molecular sensing. The intensity of scattering light of each spot observed by DFM was analysed at the single-cluster level for sensitive molecular detection. However, changes in the intensity and colour of AuNP aggregates were not significant when the inter-particle distance was large because of the insufficient effect of the surface plasmon resonance, suggesting difficulty in the sensitive detection of large molecules such as proteins. In this study, we developed a machine learning-based method to distinguish target-induced dimers from monomers by DFM, given large inter-particle distance, using two types of nanoparticles with different spot colours. When the two types of nanoparticles form a dimer (heterodimer), observation of a new spot colour derived from the heterodimer could be expected. As a proof-of-concept study, Protein A-modified silver nanoparticles and BSA-modified gold nanourchins were used to detect anti-BSA antibody; in the presence of the target, heterodimer was formed. The colours of individual spots observed by DFM at the single-cluster level were utilised for machine learning-based classification, and spots derived from heterodimers were identified for molecular detection. Our results demonstrate that the heterodimer formation increased in a target concentration-dependent manner. Furthermore, scattered lights from non-specific aggregates and impurities such as dust can be discriminated by this method. This assay is expected to be applicable to the detection of large molecules, such as proteins.
Cardiovascular disorders and neurodegenerative diseases are among the leading causes of death worldwide, with oxidative stress being a prominent etiological factor in their development. Lowering chronic oxidative stress has been proposed as a strategy for improving or treating these conditions. The body does this naturally through the KEAP1:NRF2 pathway in healthy individuals. Inhibiting the KEAP1 regulatory protein releases the NRF2 transcription factor, leading to the biosynthesis of the antioxidant proteins. Novel molecules that activate this pathway have recently been proposed as potential mechanisms to halt diseases driven by oxidative stress, but this approach has not yet reached clinical translation. To advance this approach to drug development, there is a strong need to rapidly identify KEAP1-specific molecules. Incorporating machine-learning tools into the drug development process reduces the risk of failure. Hence, this study presents a quantitative structure-activity relationship-based machine-learning model that can predict the potential of novel KEAP1 inhibitors before synthesis and biological evaluation. To achieve this goal, molecular fingerprints of KEAP1 inhibitors retrieved from ChEMBL and BindingDB were generated by using PaDEL, Mordred, and RDKit. Subsequently, these fingerprints were screened using a novel composite-score-based feature selection method, and the resulting features were then used to train 30 models. Their performances were rigorously evaluated and ranked using the coefficient of determination (R 2), root-mean-square error (RMSE), and mean absolute error (MAE). The best model was further validated using the concordance correlation coefficient (CCC), external validation (Q F1 2, Q F2 2, and Q F3 2), k-fold testing, and y-scrambling. On completion of the study, CatBoost demonstrated the highest predictive power with strong R 2 values for the test (0.8373) and training (0.9548) sets. A significant improvement was observed in CCC, external validation, cross-validation, and the y-scrambling test. This model was finally deployed as a Web server, which is freely available to researchers.
Poly(ADP-ribose) or PAR regulates multiple aspects of cell biology, both as an independent signaling molecule and as a modification on biomolecules. As a posttranslational modification, PAR can modulate the biochemical properties of target proteins. Isolated free PAR molecules function in cellular signaling. This chapter describes two methods to isolate and purify free PAR and protein-linked PAR from biochemical reactions, one using chemical fractionation and another using physical separation. A method to isolate free PAR and protein-linked PAR from human cells is also presented. These methods allow monitoring of free PAR and protein-linked PAR levels under different biochemical conditions or in response to different cellular stimuli.
Reactive oxygen species (ROS) are key signaling molecules in plant responses against biotic stresses, however their regulation must be controlled to prevent cellular damage. WRKY transcription factors (TFs) play role in regulating biotic stresses via oxidative stress-related signaling molecules; however, specific functions of WRKY62 and WRKY63 in oxidative stress signaling remain poorly understood. The paper aims to investigate the physiological and molecular function of WRKY62 and WRKY63 TFs in Arabidopsis thaliana under biotic stress induced by oligogalacturonides (OGs). In this study, photosynthetic performance, ROS accumulation, activities of antioxidants and selected defense-associated transcriptional responses were evaluated in wrky62 and wrky 63 mutants along with wild-type (WT) plants of A. thaliana following OGs treatment. In silico studies depicted conserved WRKYGQK motif; while phylogenetic analysis formed two clades of WRKY TFs with orthologs. Promoter analysis showed that they contain environment-related, hormone-related and light-responsive elements. Treatment of 200 μM OGs to WT and wrky mutant plants increased proline, ROS, malondialdehyde accumulation, and increased activities of enzymatic and non-enzymatic antioxidants, with lesser increase in wrky mutants. These results support with their predicted regulatory roles in stress signaling. Furthermore, expression analysis showed upregulation of wall-associated kinase 1 and respiratory burst oxidase homolog D genes in WT plants compared to wrky mutants. These findings suggest that WRKY62 and WRKY63 are strongly associated with OGs-induced redox homeostasis and antioxidant responses under the tested experimental conditions and could serve as possible targets for enhancing stress tolerance in plants.
We have authored a compelling and timely review that highlights the transformative potential of high-content imaging for discovering small molecules (SMs). SMs are crucial for advancing drug potency, selectivity, and precision, and their assessment through specialized cellular assays accelerates drug development. High-content imaging (HCI), which seamlessly integrates automated microscopy with sophisticated image analysis powered by artificial intelligence (AI), is revolutionizing cell-based assays by enabling a comprehensive evaluation of cellular parameters following drug treatment. The rich multivariate data generated by HCI from multiplexed assays unlock unprecedented insights into the cellular and subcellular effects of lead compounds, making it an indispensable tool in modern drug discovery.
This study aimed to elucidate the causal relationships between serum metabolites and infertility in both men and women, and to identify key metabolic biomarkers. This study employed a two-sample Mendelian randomization design, with circulating plasma metabolite genome-wide association study data as an exposure factor and FinnGen Consortium R10 genome-wide association study data for infertility in men and women as an outcome. The causal relation between plasma metabolites and infertility in men and women was assessed using five methods: inverse variance weighted, Egger regression, weighted median, maximum likelihood estimation, and simple mode. This analysis identified 17 and 10 metabolites positively and negatively associated with infertility in women, respectively. Similarly, 22 and 30 metabolites were positively and negatively associated with infertility in men, respectively. Galactonate and glycerate levels were identified as risk factors for infertility in both men and women. In addition, sphingomyelin exerts protective effects against infertility in both men and women. Metabolic pathway analysis revealed enrichment of critical metabolic pathways related to infertility. This study identified several circulating metabolic biomarkers associated with infertility. These biomarkers can be used for the screening and prevention of infertility. In addition, they could be employed as candidate molecules for future mechanistic exploration and drug-targeting studies.
Achieving uniform and high-quality perovskite crystallization across large substrates is a prerequisite for transitioning perovskite solar cells (PSCs) from laboratory scale to industrial production. Herein, we systematically regulate perovskite crystallization kinetics by using polyfluoroarene molecules featured with distinct electronic effects. We demonstrate that 3,4,5-trifluorobenzonitrile (TFBN) containing a strong electron-withdrawing cyano group creates an electron-deficient conjugation system. This configuration enhances anion-π interactions with I- and forms strong coordination with Pb2+ framework. Synergizing with intermolecular hydrogen bonding, TFBN increases the effective nucleation barrier, converting rapid nucleation into a controlled, uniform growth process. Consequently, this multidimensional regulation yields pinhole-free perovskite films with significantly suppressed non-radiative recombination. The TFBN-optimized small area device (aperture area: 0.09 cm2) achieves a champion power conversion efficiency (PCE) of 27.01% along with a low non-radiative voltage loss of only 55 mV. Furthermore, we demonstrate excellent scalability, achieving PCEs of 25.56% for 1 cm2 (aperture area) cells, 24.51% for 14.63 cm2 (active area) modules, and 21.05% for 58.51 cm2 (active area) modules, respectively. Moreover, the resulting devices exhibit improved long-term operational stability under maximum power point tracking and thermal stress. Overall, this synergistic regulation approach provides molecular-level design principles for scalable fabrication of efficient and durable perovskite photovoltaics.
Ortho-C-H alkenylation of aromatic ketones is a key transformation for bioactive molecules and functional materials, but all existing methods rely on scarce noble metals, hindering industrial applications. Herein, we report the first manganese-catalyzed ortho-C-H alkenylation of aromatic ketones with commercial acrylates. Using MnBr-(CO)5 as the precatalyst, the reaction proceeds under mild aqueous conditions with broad substrate scope, scalable to 10 mmol, and allows tunable mono/bis-alkenylation via acrylate stoichiometry. Mechanistic studies support an electrophilic C-H metalation pathway, and the products can be readily derivatized to isoquinoline frameworks. This work provides a sustainable alternative and enables manganese catalysis with weakly coordinating directing groups.
Translating in situ dynamic changes of key signaling molecules into actionable clinical readouts remains a formidable challenge for noninvasive diagnostics. Here, focusing on reactive oxygen species (ROS) as pivotal signaling mediators, we developed defect-programmed DNA origami ROS sensors (DOSs) for portable urinalysis of localized oxidative stress. Using triangular DNA origami (DO) nanostructures as two-dimensional synthetic soft crystals, we programmed the number of discontinuity defects between adjacent staple strands and established a positive correlation between defect number and ROS-triggered degradation kinetics. To transform this programmable degradation into a diagnostic function, we then engineered DOSs via orthogonal assembly of targeting and signaling modules onto DO. In a murine model of acute liver injury (ALI), DOSs selectively accumulated in the liver and underwent ROS-triggered fragmentation into renal-clearable debris, converting hepatic ROS levels into quantifiable urinary signals. Notably, this transformation efficiency depended positively on defect number in DOSs, enabling portable urinalysis that detected ALI onset at least 4 h earlier than conventional alanine aminotransferase (ALT) testing, with a maximum area under the curve of 0.94. This defect-engineering strategy establishes a generalizable platform for early, noninvasive diagnosis of ROS-related diseases.
Significant attention is paid to improving the capacity and stability of n-type organic electrode materials (OEMs), but the operating voltage is habitually ignored and is commonly low (<0.8 V vs. Zn2+/Zn), which significantly restricts the energy density of aqueous zinc-organic batteries. Herein, we clarify the working potential-related reduction process of OEMs and propose a universal strategy by regulating the ionic potential (φ) of carrier ions to adjust the redox potential of OEMs. Theoretical simulations and comprehensive experiments suggest that the coordination energy dominates the reduction potential, and high φ can significantly enhance the binding ability of the ion-coordination step during the reduction of OEMs, thereby improving their reduction potential. In particular, high-φ Al3+ with an ultralow coordination energy, as well as the reduced desolvation energy barrier of Zn2+, enable the discharge voltage of the novel polymer cathode (namely PNSBQ@rGO) to be improved from 0.9 to 1.1 V. Additionally, the Al3+ remarkably destroys the hydrogen-bond network among water molecules owing to its strong coordination ability and lowers the freezing point from -5.6 °C to -49.5 °C. Thus, Zn‖PNSBQ@rGO batteries using the 1 M Zn(OTf)2 + 0.5 M Al(OTf)3 electrolyte exhibit a high output voltage, superior cycling stability, and impressive low-temperature performance (-40 °C).
Ceramides (Cer) are lipid signaling molecules regulating cell proliferation, differentiation, senescence and apoptosis, whose metabolic disturbance disrupts organismal homeostasis. Accumulating evidence links ceramide imbalance to psychiatric and neurodegenerative diseases, yet few systematic reviews summarize subtype-specific ceramide functions. This review outlines ceramide anabolic and catabolic pathways, compares functional distinctions among ceramides with distinct acyl chain lengths, describes substrate preferences of ceramide metabolic enzymes, and introduces ceramide detection techniques. On this basis, we discuss regulatory roles and underlying molecular mechanisms of ceramides in schizophrenia, Alzheimer's disease, epilepsy, depression, bipolar disorder and anxiety disorders, identify disease-specific effects of distinct ceramide subtypes, and provide theoretical evidence for lipid-targeted pharmacological research on psychiatric disorders.
Planting altitude is a critical environmental factor shaping the flavor quality of Coffea arabica. However, how altitude influences flavor through microbial dynamics and chemical transformations during wet processing remains poorly understood. This study systematically investigates microbial diversity during wet processing and the corresponding changes in non-volatile and volatile compounds in coffee beans from four elevations: 1000 m (A1), 1200 m (A2), 1400 m (A3), and 1600 m (A4). The results showed that fermentation significantly altered the dominant bacterial genera from Sphingomonas and Pleomorphomonas (before fermentation) to Weissella and Lactobacillus (after fermentation), while Cladosporium and Fusarium remained the dominant fungal genera. Notably, non-volatile compounds in de-pulping coffee beans and volatile compounds of roasted coffee beans showed significant differences. Among them, 57 (1000 m), 218 (1200 m), 133 (1400 m), and 173 (1600 m) differentially changed non-volatile compounds (DCn-VCs) belonging to lipids and lipid-like molecules, organic acids and derivatives, organoheterocyclic compounds, organic oxygen, phenylpropanoids and polyketides, benzenoids, and other classes were identified. The cupping score of roasted coffee beans increased with increasing planting altitude, and A4 exhibited a long-lasting aftertaste, mellow and full body with high sweetness, nutty, and flowery with 42 (A4 vs. A1), 27 (A4 vs. A2), and 26 (A4 vs. A3) volatile compounds showing significant variation across altitudes. Therefore, planting altitude significantly influences coffee flavor by reshaping microbial diversity and chemical composition during wet processing. High-altitude cultivation promotes desirable sensory characteristics, highlighting its potential for producing specialty-grade coffee.
V gamma 9 V delta 2 (Vγ9Vδ2) T cells, the predominant γδ T cell population in human peripheral blood, uniquely recognize nonpeptidic phosphoantigens (pAgs) independent of major histocompatibility complex molecules. This sensing mechanism relies on intracellular pAg accumulation, which triggers heteromeric cooperation between transmembrane butyrophilin 3A1 (BTN3A1) and butyrophilin 2A1 (BTN2A1) receptors to activate the γδ T cell receptor. This review synthesizes current knowledge of Vγ9Vδ2 T cell immunobiology, focusing on responses to Plasmodium falciparum and Toxoplasma gondii. We examine how these cells detect parasite- or host-derived pAgs to drive rapid cytotoxicity and interferon gamma production. Understanding these sensing mechanisms offers novel insights for harnessing γδ T cells in antiparasitic therapies and vaccine design.
Despite hyposmia being a dominant non-motor manifestation of Parkinson's disease (PD), its underlying driving mechanisms are poorly defined. The redox protein Thioredoxin-1 (Trx-1) offers neuroprotection against various insults; however, its potential involvement in the neural proliferation in the subventricular zone (SVZ) and neural differentiation in the olfactory bulb (OB) related to MPTP-induced olfactory dysfunction have not been previously established. Our research demonstrates that when Trx-1 is downregulated in the substantia nigra pars compacta (SNpc), MPTP-triggered olfactory deficits are significantly intensified. A key anatomical discovery in our study is the existence of projections from the SNpc to the SVZ. We established that the MPTP-driven death of SNpc dopaminergic (DAergic) neurons correlates with decreased dopamine D1 receptor (D1R) levels in the SVZ, an effect that is magnified by the loss of Trx-1. Alongside D1R reductions, MPTP suppressed a cascade of SVZ signaling molecules (phosphorylated PKA, Wnt3a, β-catenin, Pax6, cyclin D1, and CDK4), with Trx-1 deficiency causing even steeper declines. Furthermore, Trx-1 knockdown hindered the generation of immature neurons and disrupted DAergic neuronal differentiation within the OB. Collectively, our findings suggest that reduced Trx-1 expression in the SNpc may contribute to PD-related olfactory deficits, potentially via inhibiting SVZ neural proliferation, decreasing immature and mature neuron populations, and disrupted differentiation of OB immature neurons. By accelerating MPTP-triggered degeneration of DAergic neurons in the SNpc, Trx-1 downregulation reduces the SVZ of DAergic input. This disruption impairs D1R-mediated the neural proliferation in the SVZ, as well as the maturation and differentiation of immature neurons in OB, ultimately driving the progression of olfactory dysfunction in a PD mouse model.
This study focused on understanding the diffusion of sodium and magnesium ions in a sulfonated poly-(ether ether ketone) cation exchange membrane as a function of water content and ionic composition. For that aim, molecular dynamics simulations were used to represent a conductive environment involving the polymer's functional chains, water molecules and ions. Mean squared displacement and self-diffusion coefficients revealed subdiffusive behavior, indicating that ion mobility is limited by the membrane's structure, leading to slower-than-normal diffusion rates. Magnesium ions exhibited self-diffusion coefficients 2 orders of magnitude lower than sodium ions and established stronger interactions with membrane functional groups than monovalent ions, affecting properties like porosity and tortuosity. The water content and polymer structure were critical in conforming hydrophilic networks that support ion mobility and diffusion. This research gives valuable insights into the transport of divalent ions through cation exchange membranes, which is relevant to applications such as electrodialysis and reverse electrodialysis.
To validate moderate chemical aggregation of flame-retardant groups as an effective strategy for obtaining advanced flame-retardant molecules, moderate-aggregated linear oligomer B-PDHQ (trimer-dominant) and high-aggregated crosslinked macromolecule C-PDHQ were synthesized via the polymerization between the 10-(2,5-Dihydroxyphenyl)-10H-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO-HQ) monomer and formaldehyde. By propagating linear polymerization aggregation of phenolated phosphaphenanthrene (PDOPO) groups, the moderate-aggregated B-PDHQ exhibits outstanding superiority in combustibility suppression efficiency and toughness enhancement effectiveness of epoxy thermoset (EP), compared with the highly aggregated C-PDHQ and non-aggregated monomer DOPO-HQ. Especially, 3%B-PDHQ/EP passed UL94 V-0 rating, 3%DOPO-HQ/EP only passed UL94 V-2 rating, while 3%C-PDHQ/EP failed to pass any rating of UL94 vertical burning test. Furthermore, moderate-aggregated B-PDHQ also showed leading efficiency in the limited oxygen index, combustion heat inhibition, smoke emission reduction, and char formation enhancement of EP. The behavior of linear oligomer B-PDHQ that triggers the flame-retardant groups aggregation effect was revealed from the condensed-phased char-forming behavior, char layer morphology investigation, and the gas-phased thermal decomposition volatile tracing. In addition, the moderate polymerization aggregation of PDOPO groups in linear oligomer B-PDHQ still enable EP matrix with a higher glass transition temperature and impact toughness. The superiority of moderate-aggregated B-PDHQ provides a practical and efficient route for designing and manufacturing high-performance reactive flame-retardant molecules.
Acute Hepatopancreatic Necrosis Disease (AHPND) is one of the main culprits for massive shrimp mortality in the aquaculture industry. AHPND is caused by Vibrio parahaemolyticus (VPAHPND) containing plasmids for the PirA and PirB binary toxins. These toxins cause sloughing and cell death in the hepatopancreatic tubules as well as inflammation in the shrimp gastrointestinal tract. Nevertheless, changes in the pro- and anti-inflammatory signaling molecules have yet to be reported. Here, we examined changes in eicosanoid biosynthesis in the Litopenaeus vannamei gastrointestinal tract infected with VPAHPND isolate 5HP. At 24 h post-infection, cyclooxygenase enzyme levels increased in the hepatopancreases of VPAHPND-infected shrimp. The transcription levels of cytosolic phospholipase A2 were elevated in the intestines of VPAHPND-infected shrimp compared with control shrimp. Although the eicosanoid and PUFA profiles were differentially altered in shrimp hepatopancreas, stomach, and intestines, the levels of prostaglandin F2α were elevated, whereas the levels of 5-hydroxyeicosapentaenoic acid and 5-hydroxyeicosatetraenoic acid were suppressed in all three organs of VPAHPND-infected shrimp. These findings suggest that eicosanoid changes may serve as biomarkers for shrimp gastrointestinal health. Moreover, they indicate the conserved nature of the eicosanoid biosynthesis pathway in the host-pathogen response to bacterial infection in crustaceans.
Aromatic interactions organize molecules into ordered supramolecular architectures, while peptides form functional soft materials through hydrogen bonding and water-mediated assembly. In peptide-based systems, strong aromatic stacking is typically achieved by terminal capping, whereas terminally uncapped peptides organize water through polar end groups but rarely form highly ordered materials. Here we show that a π-extended aromatic unit can be integrated into a terminally uncapped peptide to create a class of supramolecular hydrogels with structural order. A pyrene-modified dipeptide hierarchically assembles into monodisperse helical nanofibers and self-healing hydrogels. Cryo-electron microscopy resolves the nanofibers at near-atomic precision (1.7 Å), revealing tightly packed protofilaments, continuous ordered water channels, and a unidirectional dipole extending along the fiber. These results demonstrate how reinforced aromatic stacking, polar interactions, and cooperative water organization can be orchestrated to generate emergent electrostatics and mechanical resilience, bridging conjugated materials and biomolecular matter, enabling functional soft materials inaccessible to either domain alone.