GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions.

MOTIVATION: Interaction between transcription factor (TF) and its target genes establishes the knowledge foundation for biological researches in transcriptional regulation, the number of which is, however, still limited by biological techniques. Existing computational methods relevant to the prediction of TF-target interactions are mostly proposed for predicting binding sites, rather than directly predicting the interactions. To this end, we propose here a graph attention-based autoencoder model to predict TF-target gene interactions using the information of the known TF-target gene interaction network combined with two sequential and chemical gene characters, considering that the unobserved interactions between transcription factors and target genes can be predicted by learning the pattern of the known ones. To the best of our knowledge, the proposed model is the first attempt to solve this problem by learning patterns from the known TF-target gene interaction network. RESULTS: In this paper, we formulate the prediction task of TF-target gene interactions as a link prediction problem on a complex knowledge graph and propose a deep learning model called GraphTGI, which is composed of a graph attention-based encoder and a bilinear decoder. We evaluated the prediction performance of the proposed method on a real dataset, and the experimental results show that the proposed model yields outstanding performance with an average AUC value of 0.8864 +/- 0.0057 in the 5-fold cross-validation. It is anticipated that the GraphTGI model can effectively and efficiently predict TF-target gene interactions on a large scale. AVAILABILITY: Python code and the datasets used in our studies are made available at https://github.com/YanghanWu/GraphTGI.

Prokaryotic virus host prediction with graph contrastive augmentaion.

Prokaryotic viruses, also known as bacteriophages, play crucial roles in regulating microbial communities and have the potential for phage therapy applications. Accurate prediction of phage-host interactions is essential for understanding the dynamics of these viruses and their impacts on bacterial populations. Numerous computational methods have been developed to tackle this challenging task. However, most existing prediction models can be constrained due to the substantial number of unknown interactions in comparison to the constrained diversity of available training data. To solve the problem, we introduce a model for prokaryotic virus host prediction with graph contrastive augmentation (PHPGCA). Specifically, we construct a comprehensive heterogeneous graph by integrating virus-virus protein similarity and virus-host DNA sequence similarity information. As the backbone encoder for learning node representations in the virus-prokaryote graph, we employ LGCN, a state-of-the-art graph embedding technique. Additionally, we apply graph contrastive learning to augment the node representations without the need for additional labels. We further conducted two case studies aimed at predicting the host range of multi-species phages, helping to understand the phage ecology and evolution.

Knowledge graph embedding for profiling the interaction between transcription factors and their target genes.

Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learning manner on a knowledge graph that we specially constructed for this problem. The KGE-TGI model relies on topology information rather than being driven by gene expression data. In this paper, we formulate the task of predicting interaction types of transcript factor and target genes as a multi-label classification problem for link types on a heterogeneous graph, coupled with solving another link prediction problem that is inherently related. We constructed a ground truth dataset as benchmark and evaluated the proposed method on it. As a result of the 5-fold cross experiments, the proposed method achieved average AUC values of 0.9654 and 0.9339 in the tasks of link prediction and link type classification, respectively. In addition, the results of a series of comparison experiments also prove that the introduction of knowledge information significantly benefits to the prediction and that our methodology achieve state-of-the-art performance in this problem.

Heterogeneous graph embedding model for predicting interactions between TF and target gene.

MOTIVATION: Identifying the target genes of transcription factors (TFs) is of great significance for biomedical researches. However, using biological experiments to identify TF-target gene interactions is still time consuming, expensive and limited to small scale. Existing computational methods for predicting underlying genes for TF to target is mainly proposed for their binding sites rather than the direct interaction. To bridge this gap, we in this work proposed a deep learning prediction model, named HGETGI, to identify the new TF-target gene interaction. Specifically, the proposed HGETGI model learns the patterns of the known interaction between TF and target gene complemented with their involvement in different human disease mechanisms. It performs prediction based on random walk for meta-path sampling and node embedding in a skip-gram manner. RESULTS: We evaluated the prediction performance of the proposed method on a real dataset and the experimental results show that it can achieve the average area under the curve of 0.8519 ± 0.0731 in fivefold cross validation. Besides, we conducted case studies on the prediction of two important kinds of TF, NFKB1 and TP53. As a result, 33 and 32 in the top-40 ranking lists of NFKB1 and TP53 were successfully confirmed by looking up another public database (hTftarget). It is envisioned that the proposed HGETGI method is feasible and effective for predicting TF-target gene interactions on a large scale. AVAILABILITY AND IMPLEMENTATION: The source code and dataset are available at https://github.com/PGTSING/HGETGI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of parasternal intercostal block on surgical site wound infection and pain in patients undergoing cardiac surgery: A meta-analysis.

This study aimed to assess the effect of parasternal intercostal block on postoperative wound infection, pain, and length of hospital stay in patients undergoing cardiac surgery. PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, VIP, and Wanfang databases were extensively queried using a computer, and randomised controlled studies (RCTs) from the inception of each database to July 2023 were sought using keywords in English and Chinese language. Literature quality was assessed using Cochrane-recommended tools, and the included data were collated and analysed using Stata 17.0 software for meta-analysis. Ultimately, eight RCTs were included. Meta-analysis revealed that utilising parasternal intercostal block during cardiac surgery significantly reduced postoperative wound pain (standardised mean difference [SMD] = -1.01, 95% confidence intervals [CI]: -1.70 to -0.31, p = 0.005) and significantly shortened hospital stay (SMD = -0.40, 95% CI: -0.77 to -0.04, p = 0.029), though it may increase the risk of wound infection (OR = 5.03, 95% CI:0.58-44.02, p = 0.144); however, the difference was not statistically significant. The application of parasternal intercostal block during cardiac surgery can significantly reduce postoperative pain and shorten hospital stay. This approach is worth considering for clinical implementation. Decisions regarding its adoption should be made in conjunction with the relevant clinical indices and surgeon's experience.

GBDR: a Bayesian model for precise prediction of pathogenic microorganisms using 16S rRNA gene sequences.

BACKGROUND: Recent evidences have suggested that human microorganisms participate in important biological activities in the human body. The dysfunction of host-microbiota interactions could lead to complex human disorders. The knowledge on host-microbiota interactions can provide valuable insights into understanding the pathological mechanism of diseases. However, it is time-consuming and costly to identify the disorder-specific microbes from the biological "haystack" merely by routine wet-lab experiments. With the developments in next-generation sequencing and omics-based trials, it is imperative to develop computational prediction models for predicting microbe-disease associations on a large scale. RESULTS: Based on the known microbe-disease associations derived from the Human Microbe-Disease Association Database (HMDAD), the proposed model shows reliable performance with high values of the area under ROC curve (AUC) of 0.9456 and 0.8866 in leave-one-out cross validations and five-fold cross validations, respectively. In case studies of colorectal carcinoma, 80% out of the top-20 predicted microbes have been experimentally confirmed via published literatures. CONCLUSION: Based on the assumption that functionally similar microbes tend to share the similar interaction patterns with human diseases, we here propose a group based computational model of Bayesian disease-oriented ranking to prioritize the most potential microbes associating with various human diseases. Based on the sequence information of genes, two computational approaches (BLAST+ and MEGA 7) are leveraged to measure the microbe-microbe similarity from different perspectives. The disease-disease similarity is calculated by capturing the hierarchy information from the Medical Subject Headings (MeSH) data. The experimental results illustrate the accuracy and effectiveness of the proposed model. This work is expected to facilitate the characterization and identification of promising microbial biomarkers.

MicroRNA-small molecule association identification: from experimental results to computational models.

Small molecule is a kind of low molecular weight organic compound with variety of biological functions. Studies have indicated that small molecules can inhibit a specific function of a multifunctional protein or disrupt protein-protein interactions and may have beneficial or detrimental effect against diseases. MicroRNAs (miRNAs) play crucial roles in cellular biology, which makes it possible to develop miRNA as diagnostics and therapeutic targets. Several drug-like compound libraries were screened successfully against different miRNAs in cellular assays further demonstrating the possibility of targeting miRNAs with small molecules. In this review, we summarized the concept and functions of small molecule and miRNAs. Especially, five aspects of miRNA functions were exhibited in detail with individual examples. In addition, four disease states that have been linked to miRNA alterations were summed up. Then, small molecules related to four important miRNAs miR-21, 122, 4644 and 27 were selected for introduction. Some important publicly accessible databases and web servers of the experimentally validated or potential small molecule-miRNA associations were discussed. Identifying small molecule targeting miRNAs has become an important goal of biomedical research. Thus, several experimental and computational models have been developed and implemented to identify novel small molecule-miRNA associations. Here, we reviewed four experimental techniques used in the past few years to search for small-molecule inhibitors of miRNAs, as well as three types of models of predicting small molecule-miRNA associations from different perspectives. Finally, we summarized the limitations of existing methods and discussed the future directions for further development of computational models.

Profiles of Cytokinins Metabolic Genes and Endogenous Cytokinins Dynamics during Shoot Multiplication In Vitro of Phalaenopsis.

Shoot multiplication induced by exogenous cytokinins (CKs) has been commonly used in Phalaenopsis micropropagation for commercial production. Despite this, mechanisms of CKs action on shoot multiplication remain unclear in Phalaenopsis. In this study, we first identified key CKs metabolic genes, including six isopentenyltransferase (PaIPTs), six cytokinin riboside 5' monophosphate phosphoribohydrolase (PaLOGs), and six cytokinin dehydrogenase (PaCKXs), from the Phalaenopsis genome. Then, we investigated expression profiles of these CKs metabolic genes and endogenous CKs dynamics in shoot proliferation by thidiazuron (TDZ) treatments (an artificial plant growth regulator with strong cytokinin-like activity). Our data showed that these CKs metabolic genes have organ-specific expression patterns. The shoot proliferation in vitro was effectively promoted with increased TDZ concentrations. Following TDZ treatments, the highly expressed CKs metabolic genes in micropropagated shoots were PaIPT1, PaLOG2, and PaCKX4. By 30 days of culture, TDZ treatments significantly induced CK-ribosides levels in micropropagated shoots, such as tZR and iPR (2000-fold and 200-fold, respectively) as compared to the controls, whereas cZR showed only a 10-fold increase. Overexpression of PaIPT1 and PaLOG2 by agroinfiltration assays resulted in increased CK-ribosides levels in tobacco leaves, while overexpression of PaCKX4 resulted in decreased CK-ribosides levels. These findings suggest de novo biosynthesis of CKs induced by TDZ, primarily in elevation of tZR and iPR levels. Our results provide a better understanding of CKs metabolism in Phalaenopsis micropropagation.

RNA methylation and diseases: experimental results, databases, Web servers and computational models.

Ribonucleic acid (RNA) methylation is a type of posttranscriptional modifications occurring in all kingdoms of life. It is strongly related to important biological process, thus making it linked to a number of human diseases. Owing to the development of high-throughput sequencing technology, plenty of achievement had been obtained in RNA methylation research recently. Meanwhile, various computational models have been developed to analyze and mining increasing RNA methylation data. In this review, we first made a brief introduction about eight types of most popular RNA methylation, the biological functions of RNA methylation, the relationship between RNA methylation and disease and five important RNA methylation-related diseases. The research of RNA methylation is based on sequencing data processing, and effective bioinformatics techniques can benefit better understanding of RNA methylation. We further introduced seven publicly available RNA methylation-related databases, and some important publicly available RNA-methylation-related Web servers and software for RNA methylation site identification, differential analysis and so on. Furthermore, we provided detailed analysis of the state-of-the-art computational models used in these Web servers and software. We also analyzed the limitations of these models and discussed the future directions of developing computational models for RNA methylation research.

iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation.

Found in recent research, tumor cell invasion, proliferation, or other biological processes are controlled by circular RNA. Understanding the association between circRNAs and diseases is an important way to explore the pathogenesis of complex diseases and promote disease-targeted therapy. Most methods, such as k-mer and PSSM, based on the analysis of high-throughput expression data have the tendency to think functionally similar nucleic acid lack direct linear homology regardless of positional information and only quantify nonlinear sequence relationships. However, in many complex diseases, the sequence nonlinear relationship between the pathogenic nucleic acid and ordinary nucleic acid is not much different. Therefore, the analysis of positional information expression can help to predict the complex associations between circRNA and disease. To fill up this gap, we propose a new method, named iCDA-CGR, to predict the circRNA-disease associations. In particular, we introduce circRNA sequence information and quantifies the sequence nonlinear relationship of circRNA by Chaos Game Representation (CGR) technology based on the biological sequence position information for the first time in the circRNA-disease prediction model. In the cross-validation experiment, our method achieved 0.8533 AUC, which was significantly higher than other existing methods. In the validation of independent data sets including circ2Disease, circRNADisease and CRDD, the prediction accuracy of iCDA-CGR reached 95.18%, 90.64% and 95.89%. Moreover, in the case studies, 19 of the top 30 circRNA-disease associations predicted by iCDA-CGR on circRDisease dataset were confirmed by newly published literature. These results demonstrated that iCDA-CGR has outstanding robustness and stability, and can provide highly credible candidates for biological experiments.

In silico prediction of potential miRNA-disease association using an integrative bioinformatics approach based on kernel fusion.

Accumulating experimental evidence has demonstrated that microRNAs (miRNAs) have a huge impact on numerous critical biological processes and they are associated with different complex human diseases. Nevertheless, the task to predict potential miRNAs related to diseases remains difficult. In this paper, we developed a Kernel Fusion-based Regularized Least Squares for MiRNA-Disease Association prediction model (KFRLSMDA), which applied kernel fusion technique to fuse similarity matrices and then utilized regularized least squares to predict potential miRNA-disease associations. To prove the effectiveness of KFRLSMDA, we adopted leave-one-out cross-validation (LOOCV) and 5-fold cross-validation and then compared KFRLSMDA with 10 previous computational models (MaxFlow, MiRAI, MIDP, RKNNMDA, MCMDA, HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA). Outperforming other models, KFRLSMDA achieved AUCs of 0.9246 in global LOOCV, 0.8243 in local LOOCV and average AUC of 0.9175 ± 0.0008 in 5-fold cross-validation. In addition, respectively, 96%, 100% and 90% of the top 50 potential miRNAs for breast neoplasms, colon neoplasms and oesophageal neoplasms were confirmed by experimental discoveries. We also predicted potential miRNAs related to hepatocellular cancer by removing all known related miRNAs of this cancer and 98% of the top 50 potential miRNAs were verified. Furthermore, we predicted potential miRNAs related to lymphoma using the data set in the old version of the HMDD database and 80% of the top 50 potential miRNAs were confirmed. Therefore, it can be concluded that KFRLSMDA has reliable prediction performance.

Predicting miRNA-disease association based on inductive matrix completion.

Motivation: It has been shown that microRNAs (miRNAs) play key roles in variety of biological processes associated with human diseases. In Consideration of the cost and complexity of biological experiments, computational methods for predicting potential associations between miRNAs and diseases would be an effective complement. Results: This paper presents a novel model of Inductive Matrix Completion for MiRNA-Disease Association prediction (IMCMDA). The integrated miRNA similarity and disease similarity are calculated based on miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. The main idea is to complete the missing miRNA-disease association based on the known associations and the integrated miRNA similarity and disease similarity. IMCMDA achieves AUC of 0.8034 based on leave-one-out-cross-validation and improved previous models. In addition, IMCMDA was applied to five common human diseases in three types of case studies. In the first type, respectively, 42, 44, 45 out of top 50 predicted miRNAs of Colon Neoplasms, Kidney Neoplasms, Lymphoma were confirmed by experimental reports. In the second type of case study for new diseases without any known miRNAs, we chose Breast Neoplasms as the test example by hiding the association information between the miRNAs and Breast Neoplasms. As a result, 50 out of top 50 predicted Breast Neoplasms-related miRNAs are verified. In the third type of case study, IMCMDA was tested on HMDD V1.0 to assess the robustness of IMCMDA, 49 out of top 50 predicted Esophageal Neoplasms-related miRNAs are verified. Availability and implementation: The code and dataset of IMCMDA are freely available at https://github.com/IMCMDAsourcecode/IMCMDA. Supplementary information: Supplementary data are available at Bioinformatics online.

Fermentative production of the unnatural amino acid L-2-aminobutyric acid based on metabolic engineering.

BACKGROUND: L-2-aminobutyric acid (L-ABA) is an unnatural amino acid that is a key intermediate for the synthesis of several important pharmaceuticals. To make the biosynthesis of L-ABA environmental friendly and more suitable for the industrial-scale production. We expand the nature metabolic network of Escherichia coli using metabolic engineering approach for the production of L-ABA. RESULTS: In this study, Escherichia coli THR strain with a modified pathway for threonine-hyperproduction was engineered via deletion of the rhtA gene from the chromosome. To redirect carbon flux from 2-ketobutyrate (2-KB) to L-ABA, the ilvIH gene was deleted to block the L-isoleucine pathway. Furthermore, the ilvA gene from Escherichia coli W3110 and the leuDH gene from Thermoactinomyces intermedius were amplified and co-overexpressed. The promoter was altered to regulate the expression strength of ilvA* and leuDH. The final engineered strain E. coli THR ΔrhtAΔilvIH/Gap-ilvA*-Pbs-leuDH was able to produce 9.33 g/L of L-ABA with a yield of 0.19 g/L/h by fed-batch fermentation in a 5 L bioreactor. CONCLUSIONS: This novel metabolically tailored strain offers a promising approach to fulfill industrial requirements for production of L-ABA.

Ultralow-Content Palladium Dispersed in Covalent Organic Framework for Highly Efficient and Selective Semihydrogenation of Alkynes.

Developing noble-metal-based catalysts with ultralow loading to achieve excellent performance for selective hydrogenation of alkynes under mild reaction conditions is highly desirable but still faces huge challenges. To this end, a SO3H-anchored covalent organic framework (COF-SO3H) as the support was deliberately designed, and then ultralow-content Pd (0.38 wt %) was loaded by a wet-chemistry immersion dispersion method. The resulting Pd0.38/COF-SO3H composite exhibits outstanding performance for the selective hydrogenation of phenylacetylene with 97.06% conversion and 93.15% selectivity to styrene under mild reaction conditions (1 bar of H2, 25 °C). Noticeably, the turnover frequency value reaches as high as 3888 h-1, which outperforms most of reported catalysts for such use. Moreover, such a catalyst also exhibits excellent activity for a series of other alkynes and high stability without obvious loss of catalytic performance after five consecutive cycles.

RFSMMA: A New Computational Model to Identify and Prioritize Potential Small Molecule-MiRNA Associations.

More and more studies found that many complex human diseases occur accompanied by aberrant expression of microRNAs (miRNAs). Small molecule (SM) drugs have been utilized to treat complex human diseases by affecting the expression of miRNAs. Several computational methods were proposed to infer underlying associations between SMs and miRNAs. In our study, we proposed a new calculation model of random forest based small molecule-miRNA association prediction (RFSMMA) which was based on the known SM-miRNA associations in the SM2miR database. RFSMMA utilized the similarity of SMs and miRNAs as features to represent SM-miRNA pairs and further implemented the machine learning algorithm of random forest to train training samples and obtain a prediction model. In RFSMMA, integrating multiple kinds of similarity can avoid the bias of single similarity and choosing more reliable features from original features can represent SM-miRNA pairs more accurately. We carried out cross validations to assess predictive accuracy of RFSMMA. As a result, RFSMMA acquired AUCs of 0.9854, 0.9839, 0.7052, and 0.9917 ± 0.0008 under global leave-one-out cross validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV, and 5-fold cross validation, respectively, under data set 1. Based on data set 2, RFSMMA obtained AUCs of 0.8456, 0.8463, 0.6653, and 0.8389 ± 0.0033 under four cross validations according to the order mentioned above. In addition, we implemented a case study on three common SMs, namely, 5-fluorouracil, 17ß-estradiol, and 5-aza-2'-deoxycytidine. Among the top 50 associated miRNAs of these three SMs predicted by RFSMMA, 31, 32, and 28 miRNAs were verified, respectively. Therefore, RFSMMA is shown to be an effective and reliable tool for identifying underlying SM-miRNA associations.

GIMDA: Graphlet interaction-based MiRNA-disease association prediction.

MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA-Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA-disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave-one-out cross-validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five-fold cross-validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA-disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures.

HNMDA: heterogeneous network-based miRNA-disease association prediction.

Recently, accumulating evidences have shown that microRNAs (miRNAs) could play key roles in the development and progression of multiple important human diseases. Nonetheless, due to the shortcoming of being expensive and time-consuming existing in experimental approaches, computational methods are needed for the prediction of potential miRNA-disease associations. In our study, we proposed a computational model named Heterogeneous Network-based MiRNA-Disease Association prediction (HNMDA) for the latent miRNA-disease association prediction by integrating known miRNA-disease associations, miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. The Gaussian interaction profile kernel similarity can make up for the shortages of the traditional similarity calculation methods. Furthermore, we applied a heterogeneous network-based method, in which we first implemented a network diffusion algorithm of random walk with restart, and then we applied a method to find the optimal projection from miRNA space to disease space, which enabled the prediction of new miRNA-disease associations that are not experimentally confirmed so far. In the cross-validation, HNMDA obtained the AUC of 0.8394, which achieved improvement compared with previous methods. In the case studies of breast neoplasms, esophageal neoplasms and kidney neoplasms based on known miRNA-disease associations in the HMDD V2.0 database, there were 82, 76 and 84% of top 50 predicted related miRNAs that were confirmed to have associations with these three diseases, respectively. In the further case studies for new diseases without any known related miRNAs and the case using HMDD V1.0 database as known associations, there were also high ratio of the predicted miRNAs confirmed by experimental reports.

Upregulation of microRNA-335-5p reduces inflammatory responses by inhibiting FASN through the activation of AMPK/ULK1 signaling pathway in a septic mouse model.

Sepsis, as a systemic inflammatory response syndrome (SIRS) subtype, is generally characterized by infection. Emerging evidence has highlighted dysregulated microRNAs (miRNAs) are involved in the progression of sepsis. The aim of the study was to investigate the effects of miR-335-5p on inflammatory responses in a septic mouse model. The hypothesis was subsequently asserted that the FASN gene and AMPK/ULK1 signaling pathway may participate in the regulation of miR-335-5p. A septic mouse model was established in order to validate the effect of miR-335-5p on the inflammatory response by means of suppressing the endogenous expression of FASN by siRNA against FASN in endothelial cells. A target prediction program and luciferase activity was employed to ascertain as to whether miR--335-5p targets FASN. The levels of inflammatory factors including IL-6 and IL-1ß were determined by means of ELISA assay. RT-qPCR and western blot analysis were used to determine the AMPK/ULK1 signaling pathway-, apoptosis- and autophagy-related genes. Flow cytometry was employed in order to evaluate sepsis-induced cell apoptosis in response to miR-335-5p and FASN alternations. FASN was identified as a target gene of miR--335-5p. Gain- and loss-of-function studies revealed that miR-335-5p acted to enhance autophagy, reduce cell apoptosis, promote cell cycle entry in endothelial cells, and reduce inflammatory response through the modulation of pro- and anti-apoptotic factors in endothelial cells. The effect of miR-335-5p on endothelial cells was increased when FASN was suppressed by siRNA as well as when the AMPK/ULK1 signaling pathway was activated, suggesting that miR-335-5p influences sepsis by targeting and inhibiting FASN, and activating the AMPK/ULK1 signaling pathway. Our study provides evidence indicating that overexpressed miR-335-5p enhances autophagy by targeting FASN through activation of the AMPK/ULK1 signaling pathway working to alleviate the inflammatory response in septic mouse models, emphasizing the value of the functional upregulation of miR-335-5p as therapeutic strategy for sepsis.

Hybrid Catalyst of a Metal-Organic Framework, Metal Nanoparticles, and Oxide That Enables Strong Steric Constraint and Metal-Support Interaction for the Highly Effective and Selective Hydrogenation of Cinnamaldehyde.

In this work, we designed a hybrid catalyst composed of a metal-organic framework (MOF), Pt nanoparticles (NPs), and ferric oxide, namely, Co-MOF-74@(Pt@Fe2O3), which enables not only high turnover frequencies of up to 245.7 h-1 but also ultrahigh 100% selectivity toward cinnamyl alcohol in the hydrogenation of cinnamaldehyde under mild conditions. This excellent performance is attributed to the fact that such a hybrid catalyst enables not only strong steric constraint to provide the favored C═O adsorption of cinnamaldehyde but also strong metal-support interaction to lower the electron density of Pt NPs.

Functionalizing a Metal-Organic Framework by a Photoassisted Multicomponent Postsynthetic Modification Approach Showing Highly Effective Hg(II) Removal.

We present here the first use of a photoassisted multicomponent postsynthetic modification method to anchor a ZIF-90 scaffold with a pyrimidinethione fragment. The resultant materials, namely, ZIF-90-THP and ZIF-90-THF, show ultrahigh Hg(II) adsorption capacity values of up to 596 and 403 mg/g, respectively, relative to the pristine ZIF-90, which just affords a corresponding value of 47 mg/g, suggesting a 12.7- and 8.6-fold enhancement in the Hg(II) adsorption capacity.