A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks.

MOTIVATION: A large number of studies have shown that circular RNA (circRNA) affects biological processes by competitively binding miRNA, providing a new perspective for the diagnosis, and treatment of human diseases. Therefore, exploring the potential circRNA-miRNA interactions (CMIs) is an important and urgent task at present. Although some computational methods have been tried, their performance is limited by the incompleteness of feature extraction in sparse networks and the low computational efficiency of lengthy data. RESULTS: In this paper, we proposed JSNDCMI, which combines the multi-structure feature extraction framework and Denoising Autoencoder (DAE) to meet the challenge of CMI prediction in sparse networks. In detail, JSNDCMI integrates functional similarity and local topological structure similarity in the CMI network through the multi-structure feature extraction framework, then forces the neural network to learn the robust representation of features through DAE and finally uses the Gradient Boosting Decision Tree classifier to predict the potential CMIs. JSNDCMI produces the best performance in the 5-fold cross-validation of all data sets. In the case study, seven of the top 10 CMIs with the highest score were verified in PubMed. AVAILABILITY: The data and source code can be found at https://github.com/1axin/JSNDCMI.

Chondroitin polymerizing factor promotes development and progression of colorectal cancer via facilitating transcription of VEGFB.

Colorectal cancer (CRC) is a highly prevalent malignancy affecting the digestive system on a global scale. This study aimed to explore the previously unexplored role of CHPF in the progression of CRC. Our results revealed a significant upregulation of CHPF expression in CRC tumour tissues compared to normal tissues, with its levels correlating with tumour malignancy. In vitro experiments using CRC cell lines demonstrated that inhibiting CHPF expression suppressed cell proliferation, colony formation and cell migration, while promoting apoptosis. Conversely, overexpressing CHPF had the opposite effect. Additionally, our xenograft models in mice confirmed the inhibitory impact of CHPF knockdown on CRC progression using various cell models. Mechanistic investigations unveiled that CHPF may enhance VEGFB expression through E2F1-mediated transcription. Functionally, suppressing VEGFB expression successfully mitigated the oncogenic effects induced by CHPF overexpression. Collectively, these findings suggest that CHPF may act as a tumour promoter in CRC, operating in a VEGFB-dependent manner and could be a potential target for therapeutic interventions in CRC treatment.

Rare variant analyses validate known ALS genes in a multi-ethnic population and identifies ANTXR2 as a candidate in PLS.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting over 300,000 people worldwide. It is characterized by the progressive decline of the nervous system that leads to the weakening of muscles which impacts physical function. Approximately, 15% of individuals diagnosed with ALS have a known genetic variant that contributes to their disease. As therapies that slow or prevent symptoms continue to develop, such as antisense oligonucleotides, it is important to discover novel genes that could be targets for treatment. Additionally, as cohorts continue to grow, performing analyses in ALS subtypes, such as primary lateral sclerosis (PLS), becomes possible due to an increase in power. These analyses could highlight novel pathways in disease manifestation. METHODS: Building on our previous discoveries using rare variant association analyses, we conducted rare variant burden testing on a substantially larger multi-ethnic cohort of 6,970 ALS patients, 166 PLS patients, and 22,524 controls. We used intolerant domain percentiles based on sub-region Residual Variation Intolerance Score (subRVIS) that have been described previously in conjunction with gene based collapsing approaches to conduct burden testing to identify genes that associate with ALS and PLS. RESULTS: A gene based collapsing model showed significant associations with SOD1, TARDBP, and TBK1 (OR = 19.18, p = 3.67 × 10-39; OR = 4.73, p = 2 × 10-10; OR = 2.3, p = 7.49 × 10-9, respectively). These genes have been previously associated with ALS. Additionally, a significant novel control enriched gene, ALKBH3 (p = 4.88 × 10-7), was protective for ALS in this model. An intolerant domain-based collapsing model showed a significant improvement in identifying regions in TARDBP that associated with ALS (OR = 10.08, p = 3.62 × 10-16). Our PLS protein truncating variant collapsing analysis demonstrated significant case enrichment in ANTXR2 (p = 8.38 × 10-6). CONCLUSIONS: In a large multi-ethnic cohort of 6,970 ALS patients, collapsing analyses validated known ALS genes and identified a novel potentially protective gene, ALKBH3. A first-ever analysis in 166 patients with PLS found a candidate association with loss-of-function mutations in ANTXR2.

Circular RNA: A new expectation for cardiovascular diseases.

Circular RNA (circRNA) is a class of RNA with the 5' and 3' ends connected covalently to form a closed loop structure and characterized by high stability, conserved sequences and tissue specificity, which is caused by special reverse splicing methods. Currently, it has become a hot spot for research. With the discovery of its powerful regulatory functions and roles, the molecular mechanisms and future value of circRNA in participating in and regulating biological and pathological processes are becoming increasingly apparent. Among them is the increasing prevalence of cardiovascular diseases (CVDs). Many studies have elucidated that circRNA plays a crucial role in the development and progression of CVDs. Therefore, circRNA shows its advantages and brilliant expectations in the field of CVDs. In this review, we describe the biogenesis, bioinformatics detection and function of circRNA and discuss the role of circRNA and its effects on CVDs, including atherosclerosis, myocardial infarction, cardiac hypertrophy and heart failure, myocardial fibrosis, cardiac senescence, pulmonary hypertension, and diabetic cardiomyopathy by different mechanisms. That shows circRNA advantages and brilliant expectations in the field of CVDs.

Transrectal Drainage Tube Use for Preventing Postendoscopic Submucosal Dissection Coagulation Syndrome in Patients With Colorectal Lesions: A Multicenter Randomized Controlled Clinical Trial.

INTRODUCTION: Postendoscopic submucosal dissection (ESD) coagulation syndrome (PECS) prevention is one of the common postoperative complications of colorectal ESD. Considering the increasing incidence of PECS, it is critical to investigate various prevention methods. The objective of this study was to evaluate the efficacy of transrectal drainage tubes (TDTs) in PECS prevention in patients following colorectal ESD. METHODS: From July 2022 to July 2023, a multicenter, randomized controlled clinical trial was conducted in 3 hospitals in China. Patients with superficial colorectal lesions ≥20 mm who had undergone ESD for a single lesion were enrolled. Initially, 229 patients were included in the study and 5 were excluded. Two hundred twenty-four were randomly assigned to the TDT and non-TDT group in the end. This open-label study utilized a parallel design with a 1:1 allocation ratio, and endoscopists and patients were not blind to the randomization, and a 24 Fr drainage tube was inserted approximately 10-15 cm above the anus after the ESD under the endoscopy and tightly attached to a drainage bag. The TDTs were removed in 1-3 days following the ESD. RESULTS: A total of 229 eligible patients were enrolled in this study, and 5 patients were excluded. Ultimately, 224 patients were assigned to the TDT group (n = 112) and non-TDT group (n = 112). The median age for the patients was 63.45 years (IQR 57-71; 59 men [52.68%]) in the TDT group and 60.95 years (IQR 54-68; 60 men [53.57%]) in the non-TDT group. Intention-to-treat analysis showed patients in the TDT group had a lower incidence of PECS than patients in the non-TDT group (7 [6.25%] vs 20 [17.86%]; relative risk, 0.350; 95% confidence interval [CI], 0.154-0.795; P = 0.008). In the subgroup analysis, TDTs were found to prevent PECS in patients of the female gender (odd ratio, 0.097; 95% CI, 0.021-0.449; P = 0.001), tumor size <4 cm (odd ratio, 0.203; 95% CI, 0.056-0.728; P = 0.011), tumor located in the left-sided colorectum (odd ratio, 0. 339 95% CI, 0.120-0.957; P = 0.035), and shorter procedure time (<45 minutes) (odd ratio, 0.316; 95% CI, 0.113-0.879; P = 0.023). The tube fell off in 1 case (0.89%) accidentally ahead of time. No TDT-related complication was observed. DISCUSSION: The results from this randomized clinical study indicate that the application of TDTs effectively reduced the incidence of PECS in patients after colorectal ESD ( chictr.org.cn Identifier: ChiCTR2200062164).

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.

A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

Validation and update of a clinical score model to predict technical difficulty of colorectal endoscopic submucosal dissection: a multicenter prospective cohort study.

BACKGROUND AND AIMS: The Zhongshan colorectal endoscopic submucosal dissection (CR-ESD) score model was proposed to grade the technical difficulty of CR-ESD. The objective of this study was to prospectively validate and update the score model. METHODS: A multicenter prospective cohort analysis of CR-ESD was conducted. Individual data on patients, lesions, and outcomes of CR-ESD were used to validate the original model and further refine the difficulty of the prediction model. Data were randomly divided into discovery and internal validation cohorts. A multivariate Cox regression analysis was conducted on the discovery cohort to develop an updated risk-scoring system, which was then validated. RESULTS: Five hundred forty-eight patients with 565 colorectal lesions treated by ESD from 4 hospitals were included. In the prospective validation cohort, the area under the receiver-operating characteristic (ROC) curve for the original model was .707. Six risk factors were identified and assigned point values: tumor size (2 points for 30-50 mm, 3 points for ≥50 mm), at least two-thirds circumference of the lesion (3 points), tumor location in the cecum (2 points) or flexure (2 points), laterally spreading tumor-nongranular lesions (1 point), preceding biopsy sampling (1 point), and NBI International Colorectal Endoscopic type 3 (3 points). The updated model had an area under the ROC curve of .738 in the discovery cohort and of .782 in the validation cohort. Cases were categorized into easy (score = 0-1), intermediate (score = 2-3), difficult (score = 4-6), and very difficult (score ≥7) groups. Satisfactory discrimination and calibration were observed. CONCLUSIONS: The original model achieved an acceptable level of prediction in the prospective cohort. The updated model exhibited superior performance and can be used in place of the previous version. (Clinical trial registration number: ChiCTR2100047087.).

Long-term outcomes of endoscopic resection for well-differentiated nonampullary duodenal neuroendocrine tumors.

BACKGROUND AND AIMS: Nonampullary duodenal neuroendocrine tumors (NAD-NETs) are rare, with limited evidence regarding endoscopic treatment. This study investigated the efficacy and safety of endoscopic resection of well-differentiated NAD-NETs and evaluated long-term outcomes, including local recurrence and metastasis. METHODS: Seventy-eight patients with NAD-NETs who underwent endoscopic resection between January 2011 and August 2022 were included. Clinicopathologic characteristics and treatment outcomes were collected and analyzed. RESULTS: En-bloc resection was achieved for 74 tumors (94.9%) and R0 resection for 68 tumors (87.2%). Univariate analysis identified tumors in the second part of the duodenum, tumor size ≥10 mm, and muscularis propria invasion as risk factors for noncurative resection. Two patients with R1 resection (vertical margin involvement) and 2 patients with lymphovascular invasion underwent additional surgery. Four patients experienced adverse events (5.1%), including 2 cases of delayed bleeding and 2 cases of perforation, all successfully managed conservatively. During a median follow-up period of 62.6 months, recurrence and lymph node metastasis were only detected in 1 patient with R1 resection 3 months after the original procedure. CONCLUSIONS: Endoscopic resection is safe and effective and provides a favorable long-term outcome for patients with well-differentiated NAD-NETs without regional lymph node or distant metastasis.

Hydrogen sulfide donors across time: From origins to cutting-edge applications.

This review aims to analyze the developmental trajectory of hydrogen sulfide (H2S) donors over the past three decades and explore the historical background, research hotspots, and emerging trends in related fields from a temporal perspective. A total of 5092 literature articles on H2S donors were retrieved from the Web of Science Core Collection (WoSCC), encompassing 1303 journals, 20638 authors, 10992 institutions, and 459 countries and regions. Utilizing CiteSpace as a bibliometric tool, historical features, evolving active topics, and emerging trends in the field of H2S donors were identified. Over the past 30 years, the field of H2S donors has remained in a prominent stage. This article discusses both inorganic and organic types of H2S donors, including NaHS and Na2S, GYY4137, AP39, and AP123, as well as briefly outlines research and applications of H2S donors in nanotechnology, advanced materials, composite materials, nanostructures, and optical properties. Mechanistically, the review outlines how H2S donors regulate cellular signal transduction, anti-inflammatory responses, neuroprotection, and other pathways within the organism by modulating protein S-sulfhydration, antioxidant effects, and interactions with metal proteins. In terms of applications, the review summarizes the extensive use of H2S donors in biomedical research, encompassing cardiovascular, neurological, anti-inflammatory, and anti-cancer characteristics, as well as their potential applications in the treatment of metabolic diseases. Finally, challenges and limitations faced by H2S donor research are discussed, and potential future research directions are proposed.

Hydrogen sulfide attenuates atherosclerosis induced by low shear stress by sulfhydrylating endothelium NFIL3 to restrain MEST mediated endothelial mesenchymal transformation.

BACKGROUND: Endothelial-mesenchymal transition (EndMT) induced by low shear stress plays an important role in the development of atherosclerosis. However, little is known about the correlation between hydrogen sulfide (H2S), a protective gaseous mediator in atherosclerosis and the process of EndMT. METHODS: We constructed a stable low-shear-stress-induced(2 dyn/cm2) EndMT model, acombined with the pretreatment method of hydrogen sulfide slow release agent(GYY4137). The level of MEST was detected in the common carotid artery of ApoE-/- mice with local carotid artery ligation. The effect of MEST on atherosclerosis development in vivo was verified using ApoE-/- mice were given tail-vein injection of endothelial-specific overexpressed and knock-down MEST adeno-associated virus (AAV). RESULTS: These findings confirmed that MEST is up-regulated in low-shear-stress-induced EndMT and atherosclerosis. In vivo experiments showed that MEST gene overexpression significantly promoted EndMT and aggravated the development of atherosclerotic plaques and MEST gene knockdown significantly inhibited EndMT and delayed the process of atherosclerosis. In vitro, H2S inhibits the expression of MEST and EndMT induced by low shear stress and inhibits EndMT induced by MEST overexpression. Knockdown of NFIL3 inhibit the up regulation of MEST and EndMT induced by low shear stress in HUVECs. CHIP-qPCR assay and Luciferase Reporter assay confirmed that NFIL3 binds to MEST DNA, increases its transcription and H2S inhibits the binding of NFIL3 and MEST DNA, weakening NFIL3's transcriptional promotion of MEST. Mechanistically, H2S increased the sulfhydrylation level of NFIL3, an important upstream transcription factors of MEST. In part, transcription factor NFIL3 restrain its binding to MEST DNA by sulfhydration. CONCLUSIONS: H2S negatively regulate the expression of MEST by sulfhydrylation of NFIL3, thereby inhibiting low-shear-stress-induced EndMT and atherosclerosis.

Ang-1 and VEGF: central regulators of angiogenesis.

Angiopoietin-1 (Ang-1) and Vascular Endothelial Growth Factor (VEGF) are central regulators of angiogenesis and are often inactivated in various cardiovascular diseases. VEGF forms complexes with ETS transcription factor family and exerts its action by downregulating multiple genes. Among the target genes of the VEGF-ETS complex, there are a significant number encoding key angiogenic regulators. Phosphorylation of the VEGF-ETS complex releases transcriptional repression on these angiogenic regulators, thereby promoting their expression. Ang-1 interacts with TEK, and this phosphorylation release can be modulated by the Ang-1-TEK signaling pathway. The Ang-1-TEK pathway participates in the transcriptional activation of VEGF genes. In summary, these elements constitute the Ang-1-TEK-VEGF signaling pathway. Additionally, Ang-1 is activated under hypoxic and inflammatory conditions, leading to an upregulation in the expression of TEK. Elevated TEK levels result in the formation of the VEGF-ETS complex, which, in turn, downregulates the expression of numerous angiogenic genes. Hence, the Ang-1-dependent transcriptional repression is indirect. Reduced expression of many target genes can lead to aberrant angiogenesis. A significant overlap exists between the target genes regulated by Ang-1-TEK-VEGF and those under the control of the Ang-1-TEK-TSP-1 signaling pathway. Mechanistically, this can be explained by the replacement of the VEGF-ETS complex with the TSP-1 transcriptional repression complex at the ETS sites on target gene promoters. Furthermore, VEGF possesses non-classical functions unrelated to ETS and DNA binding. Its supportive role in TSP-1 formation may be exerted through the VEGF-CRL5-VHL-HIF-1α-VH032-TGF-ß-TSP-1 axis. This review assesses the regulatory mechanisms of the Ang-1-TEK-VEGF signaling pathway and explores its significant overlap with the Ang-1-TEK-TSP-1 signaling pathway.

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution.

Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.

RBNE-CMI: An Efficient Method for Predicting circRNA-miRNA Interactions via Multiattribute Incomplete Heterogeneous Network Embedding.

Circular RNA (circRNA)-microRNA (miRNA) interaction (CMI) plays crucial roles in cellular regulation, offering promising perspectives for disease diagnosis and therapy. Therefore, it is necessary to employ computational methods for the rapid and cost-effective prediction of potential circRNA-miRNA interactions. However, the existing methods are limited by incomplete data; therefore, it is difficult to model molecules with different attributes on a large scale, which greatly hinders the efficiency and performance of prediction. In this study, we propose an effective method for predicting circRNA-miRNA interactions, called RBNE-CMI, and introduce a framework that can embed incomplete multiattribute CMI heterogeneous networks. By combining the proposed method, we integrate different data sets in the CMI prediction field into one incomplete network for modeling, achieving superior performance in 5-fold cross-validation. Moreover, in the prediction task based on complete data, the proposed method still achieves better performance than the known model. In addition, in the case study, we successfully predicted 18 of the 20 potential cancer biomarkers. The data and source code can be found at https://github.com/1axin/RBNE-CMI.

Salinity-mediated enhancement of quaternary ammonium compounds resistance and removal in endophytic bacteria LSE01.

The widespread usage of quaternary ammonium compounds (QACs) as disinfectants during the COVID-19 pandemic poses significant environmental risks, such as toxicity to organisms and the emergence of superbugs. In this study, different inorganic salts (NaCl, KCl, CaCl2, MgCl2) were used to induce endophytes LSE01 isolated from hyperaccumulating plants. After five generations of cultivation under 80 g/L NaCl, the minimum inhibitory concentration (MIC) of LSE01 to QACs increased by about 3-fold, while its degradation extent increased from 8% to 84% for C12BDMA-Cl and 5%-89% for C14BDMA-Cl. Transmission electron microscopy (TEM) and three-dimensional fluorescence spectra indicated that the cells induced by high concentration of salt caused plasmolysis and secreted more bound extracellular polymeric substances (B-EPS); these changes are likely to be an important reason for the observed increased resistance and enhanced degradation extent of LSE01 to QACs. Our findings suggest that salt-induction could be an effective way to enhance the resistance and removal of toxic organic pollutants by functional microorganisms.

Clinical outcomes of endoscopic resection for the treatment of intermediate- or high-risk gastric small gastrointestinal stromal tumors: a multicenter retrospective study.

BACKGROUND AND AIMS: Many studies of gastric gastrointestinal stromal tumors (g-GISTs) following endoscopic resection (ER) have typically focused on tumor size, with most tumors at low risk of aggressiveness after risk stratification. There have been few systematic studies on the oncologic outcomes of intermediate- or high-risk g-GISTs after ER. METHODS: From January 2014 to January 2020, we retrospectively collected patients considered at intermediate- or high-risk of g-GISTs according to the modified NIH consensus classification system. The primary outcome was overall survival (OS). RESULTS: Six hundred and seventy nine (679) consecutive patients were diagnosed with g-GISTs and treated by ER between January 2014 and January 2020 in three hospitals in Shanghai, China. 43 patients (20 males and 23 females) were confirmed at intermediate-or high-risk. The mean size of tumors was 2.23 ± 1.01 cm. The median follow-up period was 62.02 ± 15.34 months, with a range of 28 to 105 months. There were no recurrences or metastases, even among patients having R1 resections. The 5-year OS rate was 97.4% (42/43). CONCLUSION: ER for intermediate- or high-risk gastric small GISTs is a feasible and safe method, which allows for a wait-and-see approach before determining the necessity for imatinib adjuvant or surgical treatment. This approach to g-GISTs does require that patients undergo close follow-up.

Crystal structure of a far-red-sensing cyanobacteriochrome reveals an atypical bilin conformation and spectral tuning mechanism.

Cyanobacteriochromes (CBCRs) are small, linear tetrapyrrole (bilin)-binding photoreceptors in the phytochrome superfamily that regulate diverse light-mediated adaptive processes in cyanobacteria. More spectrally diverse than canonical red/far-red-sensing phytochromes, CBCRs were thought to be restricted to sensing visible and near UV light until recently when several subfamilies with far-red-sensing representatives (frCBCRs) were discovered. Two of these frCBCRs subfamilies have been shown to incorporate bilin precursors with larger pi-conjugated chromophores, while the third frCBCR subfamily uses the same phycocyanobilin precursor found in the bulk of the known CBCRs. To elucidate the molecular basis of far-red light perception by this third frCBCR subfamily, we determined the crystal structure of the far-red-absorbing dark state of one such frCBCR Anacy_2551g3 from Anabaena cylindrica PCC 7122 which exhibits a reversible far-red/orange photocycle. Determined by room temperature serial crystallography and cryocrystallography, the refined 2.7-Å structure reveals an unusual all-Z,syn configuration of the phycocyanobilin (PCB) chromophore that is considerably less extended than those of previously characterized red-light sensors in the phytochrome superfamily. Based on structural and spectroscopic comparisons with other bilin-binding proteins together with site-directed mutagenesis data, our studies reveal protein-chromophore interactions that are critical for the atypical bathochromic shift. Based on these analyses, we propose that far-red absorption in Anacy_2551g3 is the result of the additive effect of two distinct red-shift mechanisms involving cationic bilin lactim tautomers stabilized by a constrained all-Z,syn conformation and specific interactions with a highly conserved anionic residue.

Noncovalent Peptide Assembly Enables Crystalline, Permutable, and Reactive Thiol Frameworks.

Though thiols are exceptionally versatile, their high reactivity has also hindered the synthesis and characterization of well-defined thiol-containing porous materials. Leveraging the mild conditions of the noncovalent peptide assembly, we readily synthesized and characterized a number of frameworks with thiols displayed at many unique positions and in several permutations. Importantly, nearly all assemblies were structurally determined using single-crystal X-ray diffraction to reveal their rich sequence-structure landscape and the cooperative noncovalent interactions underlying their assembly. These observations and supporting molecular dynamics calculations enabled rational engineering by the positive and negative design of noncovalent interactions. Furthermore, the thiol-containing frameworks undergo diverse single-crystal-to-single-crystal reactions, including toxic metal ion coordination (e.g., Cd2+, Pb2+, and Hg2+), selective uptake of Hg2+ ions, and redox transformations. Notably, we find a framework that supports thiol-nitrosothiol interconversion, which is applicable for biocompatible nitric oxide delivery. The modularity, ease of synthesis, functionality, and well-defined nature of these peptide-based thiol frameworks are expected to accelerate the design of complex materials with reactive active sites.

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis.

BACKGROUND: Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy. METHODS: We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning. RESULTS: To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF. CONCLUSIONS: All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

EndMT-derived mesenchymal stem cells: a new therapeutic target to atherosclerosis treatment.

Cardiovascular diseases, such as coronary artery disease and stroke, are the main threats to human health worldwide. Atherosclerosis, a chronic inflammatory disorder, plays a role as an initiator of all of the above-mentioned diseases. Cell therapy for diseases has attracted widespread attention. Mesenchymal stem cells (MSCs) are a type of stem cell that still exist in adults and have the characteristics of self-renewal ability, pluripotent differentiation potential, immunomodulation, tissue regeneration, anti-inflammation and low immunogenicity. In light of the properties of MSCs, some researchers have begun to target MSCs to create a possible way to alleviate atherosclerosis. Most of these studies are focused on MSC transplantation, injecting MSCs to modulate macrophages, the key inflammatory cell in atherosclerosis plaque. According to recent studies, researchers found that endothelial-to-mesenchymal transition (EndMT) has something to do with atherosclerosis development. A new cell type MSC might also appear during the EndMT process. In this article, we summarize the characteristics of MSCs, the latest progress of MSC research and its application prospects, and in view of the process of EndMT occurring in atherosclerosis, we propose some new ideas for the treatment of atherosclerosis by targeting MSCs.