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Effective pyroptosis induction is a promising approach to potentiate cancer immunotherapy. However, the actual efficacy of the present pyroptosis inducers can be weakened by successive biological barriers. Here, a cascaded pH‐activated supramolecular nanoprodrug (PDNP) with a stepwise size shrinkage property is developed as a pyroptosis inducer to boost antitumor immune response. PDNPs comprise multiple poly(ethylene glycol) (PEG) and doxorubicin (DOX) drug–polymer hybrid repeating blocks conjugated by ultra‐pH‐sensitive benzoic imine (bzi) and hydrazone (hyd) bonds. The PEG units endow its “stealth” property and ensure sufficient tumor accumulation. A sharp switch in particle size and detachment of PEG shielding can be triggered by the acidic extracellular pH to achieve deep intratumor penetration. Following endocytosis, second‐stage size switching can be initiated by more acidic endolysosomes, and PDNPs disassociate into ultrasmall cargo to ensure accurate intracellular delivery. The cascaded pH activation of PDNPs can effectively elicit gasdermin E (GSDME)‐mediated pyroptosis to enhance the immunological response. In combination with anti‐PD‐1 antibody, PDNPs can amplify tumor suppression and extend the survival of mice, which suggests a powerful immune adjuvant and pave the way for high‐efficiency immune checkpoint blockade therapy. A cascaded pH‐activated supramolecular nanoprodrug (PDNP) with mutistage size shrinkage property is developed to combat the successive drug delivery barriers. The size‐transformable PDNPs can precise intracellular delivery drug for effectively eliciting pyroptosis and augmenting antitumor immune response, favorable for boosting checkpoint blockade‐based immunotherapy.

Diosgenin is a spiroketal steroidal natural product extracted from plants and used as the single most important precursor for the world steroid hormone industry. The sporadic occurrences of diosgenin in distantly related plants imply possible independent biosynthetic origins. The characteristic 5,6-spiroketal moiety in diosgenin is reminiscent of the spiroketal moiety present in anthelmintic avermectins isolated from actinomycete bacteria. How plants gained the ability to biosynthesize spiroketal natural products is unknown. Here, we report the diosgenin-biosynthetic pathways in himalayan paris ( Paris polyphylla ), a monocot medicinal plant with hemostatic and antibacterial properties, and fenugreek ( Trigonella foenum–graecum ), an eudicot culinary herb plant commonly used as a galactagogue. Both plants have independently recruited pairs of cytochromes P450 that catalyze oxidative 5,6-spiroketalization of cholesterol to produce diosgenin, with evolutionary progenitors traced to conserved phytohormone metabolism. This study paves the way for engineering the production of diosgenin and derived analogs in heterologous hosts. Diosgenin is a spiroketal natural product that is used as a precursor in the industrial synthesis of steroids. Here, the authors identified key cytochrome P450 enzymes responsible for the conversion of cholesterol to diosgenin from two phylogenetical distinct diosgenin-producing plant species.

Sirtuin 3 (SIRT3) is a potential therapeutic target for cardiovascular, metabolic, and other aging-related diseases. In this study, we investigated the role of SIRT3 in diabetic cardiomyopathy (DCM). Mice were injected with streptozotocin (STZ, 60 mg/kg, ip) to induce diabetes mellitus. Our proteomics analysis revealed that SIRT3 expression in the myocardium of diabetic mice was lower than that of control mice, as subsequently confirmed by real-time PCR and Western blotting. To explore the role of SIRT3 in DCM, SIRT3-knockout mice and 129S1/SvImJ wild-type mice were injected with STZ. We found that diabetic mice with SIRT3 deficiency exhibited aggravated cardiac dysfunction, increased lactate dehydrogenase (LDH) level in the serum, decreased adenosine triphosphate (ATP) level in the myocardium, exacerbated myocardial injury, and promoted myocardial reactive oxygen species (ROS) accumulation. Neonatal rat cardiomyocytes were transfected with SIRT3 siRNA, then exposed to high glucose (HG, 25.5 mM). We found that downregulation of SIRT3 further increased LDH release, decreased ATP level, suppressed the mitochondrial membrane potential, and elevated oxidative stress in HG-treated cardiomyocytes. SIRT3 deficiency further raised expression of necroptosis-related proteins including receptor-interacting protein kinase 1 (RIPK1), RIPK3, and cleaved caspase 3, and upregulated the expression of inflammation-related proteins including NLR family pyrin domain-containing protein 3 (NLRP3), caspase 1 p20, and interleukin-1β both in vitro and in vivo. Collectively, SIRT3 deficiency aggravated hyperglycemia-induced mitochondrial damage, increased ROS accumulation, promoted necroptosis, possibly activated the NLRP3 inflammasome, and ultimately exacerbated DCM in the mice. These results suggest that SIRT3 can be a molecular intervention target for the prevention and treatment of DCM.

PurposeDespite its flourishing development since first proposed, job crafting literature has provided limited insights into why people craft their jobs. This study theoretically develops a two-dimensional integrative framework for the motives of job crafting, including orientation (self-oriented vs work-oriented vs other-oriented) and self-determination (autonomous vs introjected vs external) dimensions. We further investigate the specific motives of job crafting from actor and observer perspectives.Design/methodology/approachWe conducted two critical-incident recall surveys among 120 and 100 employees from varied sectors and organizations, who responded from the actor and observer perspective respectively. 395 and 299 valid open-ended responses were then collected and coded following the steps for content analysis.FindingsDrawing from the proposed two-dimensional theoretical framework, we identified 16 specific job crafting motives from actor and observer perspectives.Practical implicationsOur findings remind managers to pay attention to employees' motives of job crafting and take appropriate managerial actions according to their varied motives.Originality/valueBy incorporating job crafting from the motivation literature and identifying diversified motives that drive employees to engage in job crafting, this qualitative study contributes to both the job crafting literature and the broader application of self-determination theory in the field of organizational behavior.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.

Orthologous proteins of species adapted to different temperatures exhibit differences in stability and function that are interpreted to reflect adaptive variation in structural “flexibility.” However, quantifying flexibility and comparing flexibility across proteins has remained a challenge. To address this issue, we examined temperature effects on cytosolic malate dehydrogenase (cMDH) orthologs from differently thermally adapted congeners of five genera of marine molluscs whose field body temperatures span a range of ∼60 °C. We describe consistent patterns of convergent evolution in adaptation of function [temperature effects on K M of cofactor (NADH)] and structural stability (rate of heat denaturation of activity). To determine how these differences depend on flexibilities of overall structure and of regions known to be important in binding and catalysis, we performed molecular dynamics simulation (MDS) analyses. MDS analyses revealed a significant negative correlation between adaptation temperature and heat-induced increase of backbone atom movements [root mean square deviation (rmsd) of main-chain atoms]. Root mean square fluctuations (RMSFs) of movement by individual amino acid residues varied across the sequence in a qualitatively similar pattern among orthologs. Regions of sequence involved in ligand binding and catalysis—termed mobile regions 1 and 2 (MR1 and MR2), respectively—showed the largest values for RMSF. Heat-induced changes in RMSF values across the sequence and, importantly, in MR1 and MR2 were greatest in cold-adapted species. MDS methods are shown to provide powerful tools for examining adaptation of enzymes by providing a quantitative index of protein flexibility and identifying sequence regions where adaptive change in flexibility occurs.

This work grew out of rapid developments of topology optimization approaches and emerging industry trends of “3D printing” techniques, the latter bridging to a large extent the gap between innovative design and advanced manufacturing. In the present work, we first make an application-oriented review of topology optimization approaches in an attempt to illustrate their efficacy in the design of high-performance structures. Subsequently, a broad panorama of additive manufacturing is provided with a particular interest in its application in the automotive and the aerospace sectors. Taking an aerospace bracket as an example, we further go through an entire procedure from topology optimization design to additive manufacturing, then to performance verification. In the interest of cultivating a long-term partnership upon this combination, we finally examine, in face of present and near future, limitations of additive manufacturing in the loss of geometric accuracy and performance deterioration, and provide a roadmap for future work.

Thin-walled shell structures are widely used in aeronautical and aerospace engineering. This paper develops an effective B-spline parameterization method for stiffener layout optimization of shell structures. Height variables are defined by B-spline control parameters to characterize the stiffener layout reinforcing the shell structure. A continuous height field is subsequently generated via B-spline and basis functions. In view of possible curvatures of shell structures, the height field is projected from parametric space onto the shell structure by means of the parametric mapping. In this work, the finite element method is adopted with the solid-shell coupling method used for structural analysis. Pseudo-densities associated with solid elements are determined based on the B-spline parameterization and Heaviside function. Several numerical examples are dealt with to demonstrate the proposed method. Compared with the standard density-based method, the proposed method produces checkerboard-free design results with a clear layout and naturally avoids overhang stiffeners.

The protective role of sodium glucose cotransporter 2 (SGLT2) inhibitors in renal outcomes has been revealed by large cardiovascular outcome trials among patients with type 2 diabetes. However, the effect of SGLT2 inhibitors on lupus nephritis (LN) and its underlying mechanisms remain unknown. We applied empagliflozin treatment to lupus-prone MRL/ mice to explore the renal protective potential of SGLT2 inhibitors. An SGLT2 knockout monoclonal podocyte cell line was generated using the CRISPR/Cas9 system to examine the cellular and molecular mechanisms. In MRL/ mice treated with empagliflozin, the levels of mouse anti-dsDNA IgG-specific antibodies, serum creatinine and proteinuria were markedly decreased. For renal pathology assessment, both the glomerular and tubulointerstitial damages were lessened by administration of empagliflozin. The levels of SGLT2 expression were increased and colocalised with decreased synaptopodin in the renal biopsy samples from patients with LN and MRL/ mice with nephritis. The SGLT2 inhibitor empagliflozin could alleviated podocyte injury by attenuating inflammation and enhanced autophagy by reducing mTORC1 activity. Nine patients with LN treated with SGLT2 inhibitors with more than 2 months of follow-up showed that the use of SGLT2 inhibitors was associated with a significant decrease in proteinuria from 29.6% to 96.3%. Moreover, the estimated glomerular filtration rate (eGFR) was relatively stable during the treatment with SGLT2 inhibitors. This study confirmed the renoprotective effect of SGLT2 inhibitors in lupus mice, providing more evidence for non-immunosuppressive therapies to improve renal function in classic autoimmune kidney diseases such as LN.