Biolinguistic graph fusion model for circRNA-miRNA association prediction.

Emerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA-miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.

Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.

HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks.

Identifying new indications for drugs plays an essential role at many phases of drug research and development. Computational methods are regarded as an effective way to associate drugs with new indications. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering the biological knowledge of drugs and diseases, which are believed to be useful for improving the accuracy of drug repositioning. To this end, a novel heterogeneous information network (HIN) based model, namely HINGRL, is proposed to precisely identify new indications for drugs based on graph representation learning techniques. More specifically, HINGRL first constructs a HIN by integrating drug-disease, drug-protein and protein-disease biological networks with the biological knowledge of drugs and diseases. Then, different representation strategies are applied to learn the features of nodes in the HIN from the topological and biological perspectives. Finally, HINGRL adopts a Random Forest classifier to predict unknown drug-disease associations based on the integrated features of drugs and diseases obtained in the previous step. Experimental results demonstrate that HINGRL achieves the best performance on two real datasets when compared with state-of-the-art models. Besides, our case studies indicate that the simultaneous consideration of network topology and biological knowledge of drugs and diseases allows HINGRL to precisely predict drug-disease associations from a more comprehensive perspective. The promising performance of HINGRL also reveals that the utilization of rich heterogeneous information provides an alternative view for HINGRL to identify novel drug-disease associations especially for new diseases.

A geometric deep learning framework for drug repositioning over heterogeneous information networks.

Drug repositioning (DR) is a promising strategy to discover new indicators of approved drugs with artificial intelligence techniques, thus improving traditional drug discovery and development. However, most of DR computational methods fall short of taking into account the non-Euclidean nature of biomedical network data. To overcome this problem, a deep learning framework, namely DDAGDL, is proposed to predict drug-drug associations (DDAs) by using geometric deep learning (GDL) over heterogeneous information network (HIN). Incorporating complex biological information into the topological structure of HIN, DDAGDL effectively learns the smoothed representations of drugs and diseases with an attention mechanism. Experiment results demonstrate the superior performance of DDAGDL on three real-world datasets under 10-fold cross-validation when compared with state-of-the-art DR methods in terms of several evaluation metrics. Our case studies and molecular docking experiments indicate that DDAGDL is a promising DR tool that gains new insights into exploiting the geometric prior knowledge for improved efficacy.

A novel circRNA-miRNA association prediction model based on structural deep neural network embedding.

A large amount of clinical evidence began to mount, showing that circular ribonucleic acids (RNAs; circRNAs) perform a very important function in complex diseases by participating in transcription and translation regulation of microRNA (miRNA) target genes. However, with strict high-throughput techniques based on traditional biological experiments and the conditions and environment, the association between circRNA and miRNA can be discovered to be labor-intensive, expensive, time-consuming, and inefficient. In this paper, we proposed a novel computational model based on Word2vec, Structural Deep Network Embedding (SDNE), Convolutional Neural Network and Deep Neural Network, which predicts the potential circRNA-miRNA associations, called Word2vec, SDNE, Convolutional Neural Network and Deep Neural Network (WSCD). Specifically, the WSCD model extracts attribute feature and behaviour feature by word embedding and graph embedding algorithm, respectively, and ultimately feed them into a feature fusion model constructed by combining Convolutional Neural Network and Deep Neural Network to deduce potential circRNA-miRNA interactions. The proposed method is proved on dataset and obtained a prediction accuracy and an area under the receiver operating characteristic curve of 81.61% and 0.8898, respectively, which is shown to have much higher accuracy than the state-of-the-art models and classifier models in prediction. In addition, 23 miRNA-related circular RNAs (circRNAs) from the top 30 were confirmed in relevant experiences. In these works, all results represent that WSCD would be a helpful supplementary reliable method for predicting potential miRNA-circRNA associations compared to wet laboratory experiments.

iGRLCDA: identifying circRNA-disease association based on graph representation learning.

While the technologies of ribonucleic acid-sequence (RNA-seq) and transcript assembly analysis have continued to improve, a novel topology of RNA transcript was uncovered in the last decade and is called circular RNA (circRNA). Recently, researchers have revealed that they compete with messenger RNA (mRNA) and long noncoding for combining with microRNA in gene regulation. Therefore, circRNA was assumed to be associated with complex disease and discovering the relationship between them would contribute to medical research. However, the work of identifying the association between circRNA and disease in vitro takes a long time and usually without direction. During these years, more and more associations were verified by experiments. Hence, we proposed a computational method named identifying circRNA-disease association based on graph representation learning (iGRLCDA) for the prediction of the potential association of circRNA and disease, which utilized a deep learning model of graph convolution network (GCN) and graph factorization (GF). In detail, iGRLCDA first derived the hidden feature of known associations between circRNA and disease using the Gaussian interaction profile (GIP) kernel combined with disease semantic information to form a numeric descriptor. After that, it further used the deep learning model of GCN and GF to extract hidden features from the descriptor. Finally, the random forest classifier is introduced to identify the potential circRNA-disease association. The five-fold cross-validation of iGRLCDA shows strong competitiveness in comparison with other excellent prediction models at the gold standard data and achieved an average area under the receiver operating characteristic curve of 0.9289 and an area under the precision-recall curve of 0.9377. On reviewing the prediction results from the relevant literature, 22 of the top 30 predicted circRNA-disease associations were noted in recent published papers. These exceptional results make us believe that iGRLCDA can provide reliable circRNA-disease associations for medical research and reduce the blindness of wet-lab experiments.

iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network.

MOTIVATION: The task of predicting drug-target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from heterogeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction. RESULTS: In this work, an improved graph representation learning method, namely iGRLDTI, is proposed to address the above issue by better capturing more discriminative representations of drugs and targets in a latent feature space. Specifically, iGRLDTI first constructs an HBIN by integrating the biological knowledge of drugs and targets with their interactions. After that, it adopts a node-dependent local smoothing strategy to adaptively decide the propagation depth of each biomolecule in HBIN, thus significantly alleviating over-smoothing by enhancing the discriminative ability of feature representations of drugs and targets. Finally, a Gradient Boosting Decision Tree classifier is used by iGRLDTI to predict novel DTIs. Experimental results demonstrate that iGRLDTI yields better performance that several state-of-the-art computational methods on the benchmark dataset. Besides, our case study indicates that iGRLDTI can successfully identify novel DTIs with more distinguishable features of drugs and targets. AVAILABILITY AND IMPLEMENTATION: Python codes and dataset are available at https://github.com/stevejobws/iGRLDTI/.

Propofol improves survival in a murine model of sepsis via inhibiting Rab5a-mediated intracellular trafficking of TLR4.

BACKGROUND: Propofol is a widely used anesthetic and sedative, which has been reported to exert an anti-inflammatory effect. TLR4 plays a critical role in coordinating the immuno-inflammatory response during sepsis. Whether propofol can act as an immunomodulator through regulating TLR4 is still unclear. Given its potential as a sepsis therapy, we investigated the mechanisms underlying the immunomodulatory activity of propofol. METHODS: The effects of propofol on TLR4 and Rab5a (a master regulator involved in intracellular trafficking of immune factors) were investigated in macrophage (from Rab5a-/- and WT mice) following treatment with lipopolysaccharide (LPS) or cecal ligation and puncture (CLP) in vitro and in vivo, and peripheral blood monocyte from sepsis patients and healthy volunteers. RESULTS: We showed that propofol reduced membrane TLR4 expression on macrophages in vitro and in vivo. Rab5a participated in TLR4 intracellular trafficking and both Rab5a expression and the interaction between Rab5a and TLR4 were inhibited by propofol. We also showed Rab5a upregulation in peripheral blood monocytes of septic patients, accompanied by increased TLR4 expression on the cell surface. Propofol downregulated the expression of Rab5a and TLR4 in these cells. CONCLUSIONS: We demonstrated that Rab5a regulates intracellular trafficking of TLR4 and that propofol reduces membrane TLR4 expression on macrophages by targeting Rab5a. Our study not only reveals a novel mechanism for the immunomodulatory effect of propofol but also indicates that Rab5a may be a potential therapeutic target against sepsis.

Shenqifuzheng injection inhibits lactic acid-induced cisplatin resistance in NSCLC by affecting FBXO22/p53 axis through FOXO3.

BACKGROUND: Non-small cell lung cancer (NSCLC) accounts for 80% of lung cancers. Cisplatin (DDP)-based combination chemotherapy is the main treatment of NSCLC. Due to resistance to DDP, 5-year overall survival rate of NSCLC patients is very low. Shenqifuzheng injection (SQFZ) is essential for lung cancer progression. However, whether SQFZ plays a role in DDP resistance in NSCLC and its molecular mechanism remains unclear. METHODS: Levels of FOXO3, FBXO22 and p53 in NSCLC tissues and cells were assessed by RT-qPCR and Western blot. Cell proliferation and apoptosis were analyzed utilizing CCK-8, Colony formation and Flow cytometry assays. Lactate (LA) levels were tested via ELISA. ChIP and Dual luciferase reporter assays validated regulatory relationship between FOXO3 and FBXO22. Immunoprecipitation assay evaluated p53 ubiquitination levels. The subcutaneous tumor model of nude mice was constructed. TUNEL staining detected apoptosis in tissues, and IHC assessed expression of Ki67, FOXO3, FBXO22 and p53. RESULTS: FOXO3 was decreased, whereas LA and FBXO22 were increased in NSCLC patients. LA led to a higher DDP resistance in A549/DDP cells, while SQFZ reversed this effect by upregulating FOXO3. Furthermore, FBXO22 was a downstream effecter of FOXO3 and FBXO22 affected p53 ubiquitination to reverse the inhibitory effect of SQFZ. We next found SQFZ inhibited LA-induced DDP resistance in NSCLC via FOXO3/FBXO22/p53 axis. Finally, SQFZ regulated LA-mediated DDP resistance in NSCLC nude mice. CONCLUSION: SQFZ influences LA-induced DDP resistance in NSCLC via FOXO3/FBXO22/p53 pathway, providing a promising agent for NSCLC treatment.

Experimental Generation of Spin-Photon Entanglement in Silicon Carbide.

A solid-state approach for quantum networks is advantageous, as it allows the integration of nanophotonics to enhance the photon emission and the utilization of weakly coupled nuclear spins for long-lived storage. Silicon carbide, specifically point defects within it, shows great promise in this regard due to the easy of availability and well-established nanofabrication techniques. Despite of remarkable progresses made, achieving spin-photon entanglement remains a crucial aspect to be realized. In this Letter, we experimentally generate entanglement between a silicon vacancy defect in silicon carbide and a scattered single photon in the zero-phonon line. The spin state is measured by detecting photons scattered in the phonon sideband. The photonic qubit is encoded in the time-bin degree of freedom and measured using an unbalanced Mach-Zehnder interferometer. Photonic correlations not only reveal the quality of the entanglement but also verify the deterministic nature of the entanglement creation process. By harnessing two pairs of such spin-photon entanglement, it becomes straightforward to entangle remote quantum nodes at long distance.

Cooperative Rh/Achiral Phosphoric Acid-Enabled [3+3] Cycloannulation of Carbonyl Ylides with Quinone Monoimines: Synthesis of Benzofused Dioxabicyclo[3.2.1]octane Scaffolds.

A cooperative Rh/achiral phosphoric acid-enabled [3+3] cycloaddition of in situ-generated carbonyl ylides with quinone monoimines has been developed. With the ability to build up the molecular complexity rapidly and efficiently, this method furnishes highly functionalized oxa-bridged benzofused dioxabicyclo[3.2.1]octane scaffolds bearing two quaternary centers in good to excellent yields under mild conditions. Moreover, the utility of the current method was demonstrated by gram-scale synthesis and elaboration of the products into various functionalized oxa-bridged heterocycles.

Magnetoresistance properties in nickel-catalyzed, air-stable, uniform, and transfer-free graphene.

A transfer-free graphene with high magnetoresistance (MR) and air stability has been synthesized using nickel-catalyzed atmospheric pressure chemical vapor deposition. The Raman spectrum and Raman mapping reveal the monolayer structure of the transfer-free graphene, which has low defect density, high uniformity, and high coverage (>90%). The temperature-dependent (from 5 to 300 K) current-voltage (I-V) and resistance measurements are performed, showing the semiconductor properties of the transfer-free graphene. Moreover, the MR of the transfer-free graphene has been measured over a wide temperature range (5-300 K) under a magnetic field of 0 to 1 T. As a result of the Lorentz force dominating above 30 K, the transfer-free graphene exhibits positive MR values, reaching ∼8.7% at 300 K under a magnetic field (1 Tesla). On the other hand, MR values are negative below 30 K due to the predominance of the weak localization effect. Furthermore, the temperature-dependent MR values of transfer-free graphene are almost identical with and without a vacuum annealing process, indicating that there are low density of defects and impurities after graphene fabrication processes so as to apply in air-stable sensor applications. This study opens avenues to develop 2D nanomaterial-based sensors for commercial applications in future devices.

Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks.

Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.

Theoretical investigation of 2D/2D van der Waals SbPO4/BiOClxBr1-x heterojunctions for photocatalytic water splitting.

Bismuth halogenoxide (BiOX)-based heterojunctions have garnered considerable attention recently due to their potential to enhance photocatalytic performance. However, the predominant focus on II-type heterojunctions has posed challenges in achieving the requisite band edge positions for efficient water splitting. In this investigation, stable van der Waals SbPO4/BiOClxBr1-x heterojunctions were constructed theoretically by using density-functional theory (DFT). Our findings demonstrate that SbPO4 can modulate the formation of Z-scheme heterojunctions with BiOClxBr1-x. The structural properties of BiOX were preserved, while reaching excellent photocatalytic capabilities with high redox capacities. Further investigation unveiled that the band edge positions of the heterojunctions fully satisfy the oxidation-reduction potential of water. Moreover, these heterojunctions exhibit notable absorption efficiency in the visible range, with absorption increasing as x decreases. Our research provides valuable theoretical insights for the experimental synthesis of high-performance BiOX-based photocatalysts for water splitting, leveraging the unique properties of SbPO4. These insights contribute to the advancement of clean energy technology.

Schizophrenia in the genetic era: a review from development history, clinical features and genomic research approaches to insights of susceptibility genes.

Schizophrenia is a devastating neuropsychiatric disorder affecting 1% of the world population and ranks as one of the disorders providing the most severe burden for society. Schizophrenia etiology remains obscure involving multi-risk factors, such as genetic, environmental, nutritional, and developmental factors. Complex interactions of genetic and environmental factors have been implicated in the etiology of schizophrenia. This review provides an overview of the historical origins, pathophysiological mechanisms, diagnosis, clinical symptoms and corresponding treatment of schizophrenia. In addition, as schizophrenia is a polygenic, genetic disorder caused by the combined action of multiple micro-effective genes, we further detail several approaches, such as candidate gene association study (CGAS) and genome-wide association study (GWAS), which are commonly used in schizophrenia genomics studies. A number of GWASs about schizophrenia have been performed with the hope to identify novel, consistent and influential risk genetic factors. Finally, some schizophrenia susceptibility genes have been identified and reported in recent years and their biological functions are also listed. This review may serve as a summary of past research on schizophrenia genomics and susceptibility genes (NRG1, DISC1, RELN, BDNF, MSI2), which may point the way to future schizophrenia genetics research. In addition, depending on the above discovery of susceptibility genes and their exact function, the development and application of antipsychotic drugs will be promoted in the future.

Maintaining calcium homeostasis as a strategy to alleviate nephrotoxicity caused by evodiamine.

Evodiamine (EVO), the main active alkaloid in Evodia rutaecarpa, was shown to exert various pharmacological activities, especially anti-tumor. Currently, it is considered a potential anti-cancer drug due to its excellent anti-tumor activity, which unfortunately has adverse reactions, such as the risk of liver and kidney injury, when Evodia rutaecarpa containing EVO is used clinically. In the present study, we aim to clarify the potential toxic target organs and toxicity mechanism of EVO, an active monomer in Evodia rutaecarpa, and to develop mitigation strategies for its toxicity mechanism. Transcriptome analysis and related experiments showed that the PI3K/Akt pathway induced by calcium overload was an important step in EVO-induced apoptosis of renal cells. Specifically, intracellular calcium ions were increased, and mitochondrial calcium ions were decreased. In addition, EVO-induced calcium overload was associated with TRPV1 receptor activation. In vivo TRPV1 antagonist and calcium chelator effects were observed to significantly reduce body weight loss and renal damage in mice due to EVO toxicity. The potential nephrotoxicity of EVO was further confirmed by an in vivo test. In conclusion, TRPV1-mediated calcium overload-induced apoptosis is one of the mechanisms contributing to the nephrotoxicity of EVO due to its toxicity, whereas maintaining body calcium homeostasis is an effective measure to reduce toxicity. These studies suggest that the clinical use of EVO-containing herbal medicines should pay due attention to the changes in renal function of patients as well as the off-target effects of the drugs.

Effects and trend of steroid dosage reduction during cardiac surgery: A three-year retrospective analysis at a tertiary medical center.

BACKGROUND: Current adult cardiac surgery guidelines recommend against the routine use of prophylactic intravenous corticosteroids during cardiopulmonary bypass (CPB) due to concerns about myocardial injury, despite their potential to reduce postoperative atrial fibrillation. Traditionally, a high dose of 1,000 mg of methylprednisolone was used to attenuate the inflammatory response associated with CPB. Our institution aligned with guideline recommendations and gradually reduced methylprednisolone dosages; thus, we reevaluated the impact on postoperative clinical outcomes. METHODS: Our study reviewed 1341 cases from a total of 1680 adult cardiac surgeries performed between June 2019 and May 2022 after excluding cases with off-pump procedures, ventricular assist device implantations, heart transplants, and aortic surgeries requiring systemic circulatory arrest. The study timely sorted periods including a baseline data from 2018, and other three periods since 2019 to analyze the effects of three different methylprednisolone dosage: 0 mg, 500 mg, and 1000 mg. We assessed the annual trends in methylprednisolone administration and compared morbidity and mortality rates across the groups. RESULTS: We observed a significant decline in steroid use, with no-steroid surgeries increasing from 23% to 66.5% by period 3. Despite the decreased use of steroids, our study showed no increase in mortality, new-onset atrial fibrillation, acute kidney injury, cerebrovascular event and prolonged ventilation when compared to baseline data. Notably, less surgical site infection rate was observed in the no-steroid group. CONCLUSION: The data indicates that a reduction or discontinuation of steroids during CPB can be performed without compromising patient outcomes. This could support a transition towards a more conservative use of steroids in adult cardiac surgery, aligning with current guidelines, and potentially reducing certain postoperative complications.

Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks.

BACKGROUND: As an important task in bioinformatics, clustering analysis plays a critical role in understanding the functional mechanisms of many complex biological systems, which can be modeled as biological networks. The purpose of clustering analysis in biological networks is to identify functional modules of interest, but there is a lack of online clustering tools that visualize biological networks and provide in-depth biological analysis for discovered clusters. RESULTS: Here we present BioCAIV, a novel webserver dedicated to maximize its accessibility and applicability on the clustering analysis of biological networks. This, together with its user-friendly interface, assists biological researchers to perform an accurate clustering analysis for biological networks and identify functionally significant modules for further assessment. CONCLUSIONS: BioCAIV is an efficient clustering analysis webserver designed for a variety of biological networks. BioCAIV is freely available without registration requirements at http://bioinformatics.tianshanzw.cn:8888/BioCAIV/ .

Hyperconjugative Aromaticity-Based Circularly Polarized Luminescence Enhancement in Polyaurated Heterocycles.

Hyperconjugative aromaticity (HA) frequently appears in metalla-aromatics, but its effect on photophysical properties remains unexplored to date. Herein, we reveal two different HA scenarios in nearly isostructural triaurated indolium and benzofuranylium compounds. The biased HAs show a discernible effect on the spatial arrangement of metal atoms and thus tailor metal parentage in frontier orbitals and the HOMO-LUMO energy gap. Theoretical calculations and structural analyses demonstrate that HA not only influences the degree of electron delocalization over the trimetalated aromatic rings but also affects π-coordination of Au(I) and intercluster aurophilic interaction. Consequently, the triaurated benzofuranylium complex shows better photoluminescence performance (quantum yield up to 49.7%) over the indolium analogue. Furthermore, four pairs of axially chiral bibenzofuran-centered trinuclear and hexanuclear gold clusters were purposefully synthesized to correlate their HA-involved structures with the chiroptical response. The triaurated benzofuranylium complexes exhibit strong circular dichroism (CD) response in solution but CPL silence even in solid film. In contrast, the hexa-aurated homologues display strong CD and intense CPL signals in both aggregated state and solid film (luminescence anisotropy factor glum up to 10-3). Their amplified chiroptical response is finally ascribed to the dominant intermolecular exciton couplings of large assemblies formed through the HA-tailored aggregation of hexanuclear compounds.

Microbiota-derived tryptophan metabolites indole-3-lactic acid is associated with intestinal ischemia/reperfusion injury via positive regulation of YAP and Nrf2.

BACKGROUND: Lactobacillus has been demonstrated to serve a protective role in intestinal injury. However, the relationship between Lactobacillus murinus (L. murinus)-derived tryptophan metabolites and intestinal ischemia/reperfusion (I/R) injury yet to be investigated. This study aimed to evaluate the role of L. murinus-derived tryptophan metabolites in intestinal I/R injury and the underlying molecular mechanism. METHODS: Liquid chromatograph mass spectrometry analysis was used to measure the fecal content of tryptophan metabolites in mice undergoing intestinal I/R injury and in patients undergoing cardiopulmonary bypass (CPB) surgery. Immunofluorescence, quantitative RT-PCR, Western blot, and ELISA were performed to explore the inflammation protective mechanism of tryptophan metabolites in WT and Nrf2-deficient mice undergoing intestinal I/R, hypoxia-reoxygenation (H/R) induced intestinal organoids. RESULTS: By comparing the fecal contents of three L. murinus-derived tryptophan metabolites in mice undergoing intestinal I/R injury and in patients undergoing cardiopulmonary bypass (CPB) surgery. We found that the high abundance of indole-3-lactic acid (ILA) in the preoperative feces was associated with better postoperative intestinal function, as evidenced by the correlation of fecal metabolites with postoperative gastrointestinal function, serum I-FABP and D-Lactate levels. Furthermore, ILA administration improved epithelial cell damage, accelerated the proliferation of intestinal stem cells, and alleviated the oxidative stress of epithelial cells. Mechanistically, ILA improved the expression of Yes Associated Protein (YAP) and Nuclear Factor erythroid 2-Related Factor 2 (Nrf2) after intestinal I/R. The YAP inhibitor verteporfin (VP) reversed the anti-inflammatory effect of ILA, both in vivo and in vitro. Additionally, we found that ILA failed to protect epithelial cells from oxidative stress in Nrf2 knockout mice under I/R injury. CONCLUSIONS: The content of tryptophan metabolite ILA in the preoperative feces of patients is negatively correlated with intestinal function damage under CPB surgery. Administration of ILA alleviates intestinal I/R injury via the regulation of YAP and Nrf2. This study revealed a novel therapeutic metabolite and promising candidate targets for intestinal I/R injury treatment.