RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding.

MOTIVATION: Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC-peptide binding is vital for the development of cancer therapeutic vaccines. While recent deep learning-based methods have achieved significant performance in predicting MHC-peptide binding affinity, most of them separately encode MHC molecules and peptides as inputs, potentially overlooking critical interaction information between the two. RESULTS: In this work, we propose RPEMHC, a new deep learning approach based on residue-residue pair encoding to predict the binding affinity between peptides and MHC, which encode an MHC molecule and a peptide as a residue-residue pair map. We evaluate the performance of RPEMHC on various MHC-II-related datasets for MHC-peptide binding prediction, demonstrating that RPEMHC achieves better or comparable performance against other state-of-the-art baselines. Moreover, we further construct experiments on MHC-I-related datasets, and experimental results demonstrate that our method can work on both two MHC classes. These extensive validations have manifested that RPEMHC is an effective tool for studying MHC-peptide interactions and can potentially facilitate the vaccine development. AVAILABILITY: The source code of the method along with trained models is freely available at https://github.com/lennylv/RPEMHC.

CAPLA: improved prediction of protein-ligand binding affinity by a deep learning approach based on a cross-attention mechanism.

MOTIVATION: Accurate and rapid prediction of protein-ligand binding affinity is a great challenge currently encountered in drug discovery. Recent advances have manifested a promising alternative in applying deep learning-based computational approaches for accurately quantifying binding affinity. The structure complementarity between protein-binding pocket and ligand has a great effect on the binding strength between a protein and a ligand, but most of existing deep learning approaches usually extracted the features of pocket and ligand by these two detached modules. RESULTS: In this work, a new deep learning approach based on the cross-attention mechanism named CAPLA was developed for improved prediction of protein-ligand binding affinity by learning features from sequence-level information of both protein and ligand. Specifically, CAPLA employs the cross-attention mechanism to capture the mutual effect of protein-binding pocket and ligand. We evaluated the performance of our proposed CAPLA on comprehensive benchmarking experiments on binding affinity prediction, demonstrating the superior performance of CAPLA over state-of-the-art baseline approaches. Moreover, we provided the interpretability for CAPLA to uncover critical functional residues that contribute most to the binding affinity through the analysis of the attention scores generated by the cross-attention mechanism. Consequently, these results indicate that CAPLA is an effective approach for binding affinity prediction and may contribute to useful help for further consequent applications. AVAILABILITY AND IMPLEMENTATION: The source code of the method along with trained models is freely available at https://github.com/lennylv/CAPLA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Intermittent short-duration reoxygenation relieves high-altitude pulmonary hypertension via NOX4/H2O2/PPAR-γ axis.

High-altitude pulmonary hypertension (HAPH) is a severe and progressive disease that can lead to right heart failure. Intermittent short-duration reoxygenation at high altitude is effective in alleviating HAPH; however, the underlying mechanisms are unclear. In the present study, a simulated 5,000-m hypoxia rat model and hypoxic cultured pulmonary artery smooth muscle cells (PASMCs) were used to evaluate the effect and mechanisms of intermittent short-duration reoxygenation. The results showed that intermittent 3-h/per day reoxygenation (I3) effectively attenuated chronic hypoxia-induced pulmonary hypertension and reduced the content of H2O2 and the expression of NADPH oxidase 4 (NOX4) in lung tissues. In combination with I3, while the NOX inhibitor apocynin did not further alleviate HAPH, the mitochondrial antioxidant MitoQ did. Furthermore, in PASMCs, I3 attenuated hypoxia-induced PASMCs proliferation and reversed the activated HIF-1α/NOX4/PPAR-γ axis under hypoxia. Targeting this axis offset the protective effect of I3 on hypoxia-induced PASMCs proliferation. The present study is novel in revealing a new mechanism for preventing HAPH and provides insights into the optimization of intermittent short-duration reoxygenation.

Synthesis of Crystalline Phosphine-Graphdiyne with Self-Adaptive p-π Conjugation.

"Dynamic" behavior materials with high surface activity and the ability of chemical bond conversion are the frontier materials in the field of renewable energy. The outstanding feature of these materials is that they have adaptive electronic properties that external stimuli can adjust. An original discovery in a new crystalline two-dimensional phosphine-graphdiyne (P-GDY) material is described here. Although the p-π conjugation of most trivalent phosphorus π-systems is insignificant because of the pyramidal configuration, the lone pair electrons of phosphorus atoms participate strongly in the delocalization under the influence of the interlayer van der Waals forces in P-GDY. Due to the dynamically reversible nature of noncovalent interactions (p-π conjugation), P-GDY exhibits a specific adaptive behavior and realizes the responsive reversible transport of a lithium ion by regulating p-π interactions. Our findings would provide the potential to develop a new family of responsive materials with tunable structures.

MCnebula: Critical Chemical Classes for the Classification and Boost Identification by Visualization for Untargeted LC-MS/MS Data Analysis.

Untargeted mass spectrometry is a robust tool for biology, but it usually requires a large amount of time on data analysis, especially for system biology. A framework called Multiple-Chemical nebula (MCnebula) was developed herein to facilitate the LC-MS data analysis process by focusing on critical chemical classes and visualization in multiple dimensions. This framework consists of three vital steps as follows: (1) abundance-based classes (ABC) selection algorithm, (2) critical chemical classes to classify "features" (corresponding to compounds), and (3) visualization as multiple Child-Nebulae (network graph) with annotation, chemical classification, and structure. Notably, MCnebula can be used to explore the classification and structural characteristic of unknown compounds beyond the limit of the spectral library. Moreover, it is intuitive and convenient for pathway analysis and biomarker discovery because of its function of ABC selection and visualization. MCnebula was implemented in the R language. A series of tools in R packages were provided to facilitate downstream analysis in an MCnebula-featured way, including feature selection, homology tracing of top features, pathway enrichment analysis, heat map clustering analysis, spectral visualization analysis, chemical information query, and output analysis reports. The broad utility of MCnebula was illustrated by a human-derived serum data set for metabolomics analysis. The results indicated that "Acyl carnitines" were screened out by tracing structural classes of biomarkers, which was consistent with the reference. A plant-derived data set was investigated to achieve a rapid annotation and discovery of compounds in E. ulmoides.

TransPPMP: predicting pathogenicity of frameshift and non-sense mutations by a Transformer based on protein features.

MOTIVATION: Protein structure can be severely disrupted by frameshift and non-sense mutations at specific positions in the protein sequence. Frameshift and non-sense mutation cases can also be found in healthy individuals. A method to distinguish neutral and potentially disease-associated frameshift and non-sense mutations is of practical and fundamental importance. It would allow researchers to rapidly screen out the potentially pathogenic sites from a large number of mutated genes and then use these sites as drug targets to speed up diagnosis and improve access to treatment. The problem of how to distinguish between neutral and potentially disease-associated frameshift and non-sense mutations remains under-researched. RESULTS: We built a Transformer-based neural network model to predict the pathogenicity of frameshift and non-sense mutations on protein features and named it TransPPMP. The feature matrix of contextual sequences computed by the ESM pre-training model, type of mutation residue and the auxiliary features, including structure and function information, are combined as input features, and the focal loss function is designed to solve the sample imbalance problem during the training. In 10-fold cross-validation and independent blind test set, TransPPMP showed good robust performance and absolute advantages in all evaluation metrics compared with four other advanced methods, namely, ENTPRISE-X, VEST-indel, DDIG-in and CADD. In addition, we demonstrate the usefulness of the multi-head attention mechanism in Transformer to predict the pathogenicity of mutations-not only can multiple self-attention heads learn local and global interactions but also functional sites with a large influence on the mutated residue can be captured by attention focus. These could offer useful clues to study the pathogenicity mechanism of human complex diseases for which traditional machine learning methods fall short. AVAILABILITY AND IMPLEMENTATION: TransPPMP is available at https://github.com/lennylv/TransPPMP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying modifications on DNA-bound histones with joint deep learning of multiple binding sites in DNA sequence.

MOTIVATION: Histone modifications are epigenetic markers that impact gene expression by altering the chromatin structure or recruiting histone modifiers. Their accurate identification is key to unraveling the mechanisms by which they regulate gene expression. However, the solutions for this task can be improved by exploiting multiple relationships from dataset and exploring designs of learning models, for example jointly learning technology. RESULTS: This article proposes a deep learning-based multi-objective computational approach, iHMnBS, to identify which of the seven typical histone modifications a DNA sequence may choose to bind, and which parts of the DNA sequence bind to them. iHMnBS employs a customized dataset that allows the marking of modifications contained in histones that may bind to any position in the DNA sequence. iHMnBS tries to mine the information implicit in this richer data by means of deep neural networks. In comprehensive comparisons, iHMnBS outperforms a baseline method, and the probability of binding to modified histones assigned to a representative nucleotide of a DNA sequence can serve as a reference for biological experiments. Since the interaction between transcription factors and histone modifications has an important role in gene expression, we extracted a number of sequence patterns that may bind to transcription factors, and explored their possible impact on disease. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/lennylv/iHMnBS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Safety of sugammadex for reversal of neuromuscular block: A postmarketing study based on the World Health Organization pharmacovigilance database.

AIM: Residual neuromuscular blockade is a common complication after general anaesthesia. Sugammadex can reverse the action of aminosteroid neuromuscular blockers. This study aimed to explore sugammadex safety issues in the real world and determine the spectrum of adverse reactions. METHODS: All sugammadex-related adverse events reported in VigiBase between 2010 and 2019 were classified by group queries according to the Medical Dictionary for Regulatory Activities. A disproportionality analysis of data was performed using the information component (IC); positive IC values were deemed significant. RESULTS: Overall, 16 219 410 adverse events were reported and 2032 were associated with sugammadex. The frequent reactions were recurrence of neuromuscular blockade (n = 54, IC 6.74, IC025 6.33), laryngospasm (n = 53, IC 6.05, IC025 5.64), bronchospasm (n = 119, IC 5.63, IC025 5.36) and bradycardia (n = 169, IC 5.13, IC025 4.90). Fatal cases were more likely among patients with cardiac disorders, especially those over 65 years. In addition, the common adverse drug reactions (ADRs) differed between different age groups (P < .01). ADRs were higher in the 0-17 years age group than in other age groups. The onset time of common ADRs was typically within 1 day and 68.9% occurred within half an hour after sugammadex administration. CONCLUSIONS: Anaesthesiologists should carefully monitor the anaesthesia recovery period to correct the ADRs caused by sugammadex and recommend monitoring neuromuscular function throughout the anaesthesia process. Sugammadex should be used carefully in patients with cardiovascular diseases, and electrocardiography and hemodynamic changes should be monitored after medication.

Simultaneous Prediction of Interaction Sites on the Protein and Peptide Sides of Complexes through Multilayer Graph Convolutional Networks.

Identifying the binding residues of protein-peptide complexes is essential for understanding protein function mechanisms and exploring drug discovery. Recently, many computational methods have been developed to predict the interaction sites of either protein or peptide. However, to our knowledge, no prediction method can simultaneously identify the interaction sites on both the protein and peptide sides. Here, we propose a deep graph convolutional network (GCN)-based method called GraphPPepIS to predict the interaction sites of protein-peptide complexes using protein and peptide structural information. We also propose a companion method, SeqPPepIS, for assisting with the lack of structural information and the flexibility of peptides. SepPPepIS replaces the peptide structural features in GraphPPepIS by learning features from peptide sequences. We performed a comprehensive evaluation of the benchmark data sets, and the results show that our two methods outperform state-of-the-art methods on the accurate interaction sites of both protein and peptide sides. We show that our methods can help improve protein-peptide docking. For docking data sets, our methods maintain robust performance in identifying binding sites, thereby enhancing the prediction of peptide binding poses. Finally, we visualized the analysis of protein and peptide graph embedding to demonstrate the learning ability of graph convolution in predicting interaction sites, which was mainly obtained through the shared parameters of a protein graph and peptide graph.

DeepMPSF: A Deep Learning Network for Predicting General Protein Phosphorylation Sites Based on Multiple Protein Sequence Features.

Phosphorylation, as one of the most important post-translational modifications, plays a key role in various cellular physiological processes and disease occurrences. In recent years, computer technology has been gradually applied to the prediction of protein phosphorylation sites. However, most existing methods rely on simple protein sequence features that provide limited contextual information. To overcome this limitation, we propose DeepMPSF, a phosphorylation site prediction model based on multiple protein sequence features. There are two types of features: sequence semantic features, which comprise protein residue type information and relative position information within protein sequence, and protein background biophysical features, which include global semantic information containing more comprehensive protein background information obtained from pretrained models. To extract these features, DeepMPSF employs two separate subnetworks: the S71SFE module and the BBFE module, which automatically extract high-level semantic features. Our model incorporates a learning strategy for handling imbalanced datasets through ensemble learning during training and prediction. DeepMPSF is trained and evaluated on a well-established dataset of human proteins. Comparing the analysis with other benchmark methods reveals that DeepMPSF outperforms in predicting both S/T residues and Y residues. In particular, DeepMPSF showed excellent generalization performance in cross-species blind test performance, with an average improvement of 5.63%/5.72%, 22.28%/25.94%, 20.11%/17.49%, and 26.40%/28.33% for Mus musculus/Rattus norvegicus test sets in area under curves (AUCs) of ROC curve, AUC of the PR curve, F1-score, and MCC metrics, respectively. Furthermore, it also shows excellent performance in the latest updated case of natural proteins with functional phosphorylation sites. Through an ablation study and visual analysis, we uncover that the design of different feature modules significantly contributes to the accurate classification of DeepMPSF, which provides valuable insights for predicting phosphorylation sites and offers effective support for future downstream research.

The Role of eIF5A1 in LPS-Induced Neuronal Remodeling of the Nucleus Accumbens in the Depression.

BACKGROUND: The pathogenesis of depression is complex, with the brain's reward system likely to play an important role. The nucleus accumbens (NAc) is a key region in the brain that integrates reward signals. Lipopolysaccharides (LPS) can induce depressive-like behaviors and enhance neuroplasticity in NAc, but the underlying mechanism is still unknown. We previously found that eukaryotic translation initiation factor A1 (eIF5A1) acts as a ribosome-binding protein to regulate protein translation and to promote neuroplasticity. METHODS: In the present study, LPS was administered intraperitoneally to rats and the expression and cellular location of eIF5A1 was then investigated by RT-PCR, Western blotting and immunofluorescence. Subsequently, a neuron-specific lentivirus was used to regulate eIF5A1 expression in vivo and in vitro. Neuroplasticity was then examined by Golgi staining and by measurement of neuronal processes. Finally, proteomic analysis was used to identify proteins regulated by eIF5A1. RESULTS: The results showed that eIF5A1 expression was significantly increased in the NAc neurons of LPS rats. Following the knockdown of eIF5A1 in NAc neurons, the LPS-induced increases in neuronal arbors and spine density were significantly attenuated. Depression-like behaviors were also reduced. Neurite outgrowth of NAc neurons in vitro also increased or decreased in parallel with the increase or decrease in eIF5A1 expression, respectively. The proteomic results showed that eIF5A1 regulates the expression of many neuroplasticity-related proteins in neurons. CONCLUSIONS: These results confirm that eIF5A1 is involved in LPS-induced depression-like behavior by increasing neuroplasticity in the NAc. Our study also suggests the brain's reward system may play an important role in the pathogenesis of depression.

Hypoxia Inhibits Cell Cycle Progression and Cell Proliferation in Brain Microvascular Endothelial Cells via the miR-212-3p/MCM2 Axis.

Hypoxia impairs blood-brain barrier (BBB) structure and function, causing pathophysiological changes in the context of stroke and high-altitude brain edema. Brain microvascular endothelial cells (BMECs) are major structural and functional elements of the BBB, and their exact role in hypoxia remains unknown. Here, we first deciphered the molecular events that occur in BMECs under 24 h hypoxia by whole-transcriptome sequencing assay. We found that hypoxia inhibited BMEC cell cycle progression and proliferation and downregulated minichromosome maintenance complex component 2 (Mcm2) expression. Mcm2 overexpression attenuated the inhibition of cell cycle progression and proliferation caused by hypoxia. Then, we predicted the upstream miRNAs of MCM2 through TargetScan and miRanDa and selected miR-212-3p, whose expression was significantly increased under hypoxia. Moreover, the miR-212-3p inhibitor attenuated the inhibition of cell cycle progression and cell proliferation caused by hypoxia by regulating MCM2. Taken together, these results suggest that the miR-212-3p/MCM2 axis plays an important role in BMECs under hypoxia and provide a potential target for the treatment of BBB disorder-related cerebrovascular disease.

Developmental validation of the Microreader Group Y Direct ID System: A novel six-dye typing system with 54 Y-chromosomal loci for forensic application.

Y-chromosome, as a gender-determined biological marker, is inherited only between fathers and sons. The Y-chromosome short tandem repeats (Y-STRs) play an essential role in paternity lineage tracing as well as sexual assault cases. The Microreader Group Y Direct ID System as a six-dye multiplex amplification kit, including 53 Y-STR and one Y-Indel locus, would improve performance and aid in obtaining more information through a greater number of loci with high polymorphism. In the present study, to verify the accuracy and efficiency of the kit, developmental validation was conducted by investigating sensitivity, species specificity, PCR inhibition, male-male and male-female mixtures, and reproducibility. The kit was tested using 311 male samples from Han and Qiang populations in Sichuan Province. The results showed that this kit had fairly high power for forensic discrimination (Han: haplotype diversity [HD] = 1, Qiang: HD = 0.999944). Additionally, 44 confirmed father-son pairs were also genotyped, among which 69 distinct haplotypes could be obtained. These father-son pairs cannot be distinguished by commonly used Y-STR panels, indicating that adding these extra Y-STRs to a single panel can achieve better discrimination performance. Collectively, the Microreader Group Y Direct ID System is robust and informative for forensic applications.

Intermittent short-duration reoxygenation protects against simulated high altitude-induced pulmonary hypertension in rats.

High-altitude pulmonary hypertension (HAPH) is a severe and progressive disease caused by chronic hypoxia and subsequent pulmonary vascular remodeling. No cure is currently available owing to an incomplete understanding about vascular remodeling. It is believed that hypoxia-induced diseases can be prevented by treating hypoxia. Thus, this study aimed to determine whether daily short-duration reoxygenation at sea level attenuates pulmonary hypertension under high-altitude hypoxia. To this end, a simulated 5000-m hypoxia rat model and hypoxic cultured human pulmonary artery smooth muscle cells were used to evaluate the effect of short-duration reoxygenation. Results show that intermittent, not continuous, short-duration reoxygenation effectively attenuates hypoxia-induced pulmonary hypertension. The mechanisms underlining the protective effects involved that intermittent, short-duration reoxygenation prevented functional and structural remodeling of pulmonary arteries and proliferation, migration, and phenotypic conversion of pulmonary artery smooth muscle cells under hypoxia. The specific genes or potential molecular pathways responsible for mediating the protective effects were also characterised by RNA sequencing. Further, the frequency and the total time of intermittent reoxygenation affected its preventive effect of HAPH, which was likely attributable to augmented oxidative stress. Hence, daily intermittent, not continuous, short-duration reoxygenation partially prevented pulmonary hypertension induced by 5000-m hypoxia in rats. This study is novel in revealing a new potential method in preventing HAPH. It gives insights into the selection and optimisation of oxygen supply schemes in high-altitude areas.

A deep clustering-based mass spectral data visualization strategy for anti-renal fibrotic lead compound identification from natural products.

Natural products have been a key source of drug lead discovery. However, their identification has long been a challenge even with the state-of-the-art analysis technologies like high-resolution mass spectrometry (MS) due to their complexity. Emerging in silico chemical structure prediction tools have provided time-saving and highly efficient approaches for identification of these complex samples. Nevertheless, the interpretation of these MS annotations into key supporting evidence towards specific questions is still a bottleneck in medicinal and biological fields. Here we present a deep clustering-based MS data visualization strategy (MCnebula), integrated with the influential open-source automatic MS annotation platform SIRIUS and in vivo and in vitro methods, to screen and validate potential lead compounds from natural products. MCnebula could provide multi-layer clustering profiles with chemical ontologies and comparative analysis of differential treatments. Plantaginis Semen (PS) is commonly used for treating kidney disease and usually stir-fried with salt water to enhance its anti-renal fibrosis effect, but the reason behind this remains unclear. Taking PS as an example, we comprehensively identified and compared the raw and processed PS extracts with SIRIUS-MCnebula, and screened potential anti-renal fibrotic lead compounds using weighted fold change analysis. Eighty-nine components were identified in PS with isoacteoside, calceolarioside B, 2'-acetylacteoside, and plantainoside D being screened and validated to treat renal fibrosis. The novel developed mass spectral data visualization strategy combined with biological function investigation and validation workflow could not only accelerate the discovery of lead compounds from medicinal natural products, but also shed new light on the traditional processing theory.

Multisource Attention-Mechanism-Based Encoder-Decoder Model for Predicting Drug-Drug Interaction Events.

Many computational methods have been proposed to predict drug-drug interactions (DDIs), which can occur when combining drugs to treat various diseases, but most mainly utilize single-source features of drugs, which is inadequate for drug representation. To fill this gap, we propose two attention-mechanism-based encoder-decoder models that incorporate multisource information: one is MAEDDI, which can predict DDIs, and the other is MAEDDIE, which can make further DDI-associated event predictions for drug pairs with DDIs. To better express the drug feature, we used three encoding methods to encode the drugs, integrating the self-attention mechanism, cross-attention mechanism, and graph attention network to construct a multisource feature fusion network. Experiments showed that both MAEDDI and MAEDDIE performed better than some state-of-the-art methods in various validation attempts at different experimental tasks. The visualization analysis showed that the semantic features of drug pairs learned from our models had a good drug representation. In practice, MAEDDIE successfully screened 43 DDI events on favipiravir, an influenza antiviral drug, with a success rate of nearly 50%. Our model achieved competitive results, mainly owing to the design of sequence-based, structural, biochemical, and statistical multisource features. Moreover, different encoders constructed based on different features learn the interrelationship information between drug pairs, and the different representations of these drug pairs are incorporated to predict the target problem. All of these encoders were designed to better characterize the complex DDI relationships, allowing us to achieve high generalization in DDI and DDI-associated event predations.

Control one-dimensional length of rectangular pore on graphene membrane for better desalination performance.

At present, there is a general contradiction between permeability and selectivity of reverse osmosis (RO) membranes for desalination; a membrane with higher water permeability will give a lower salt rejection or selectivity, and vice versa. In this work, single-layer nanoporous graphene is used as RO membrane to investigate the effects of pore shape to reduce this contradiction by molecular dynamics simulations. Two kinds of pores (round and rectangular pores) with different sizes are simulated. For round pore, although the water permeability increases with the increase of the pore size, the salt rejection rate drops rapidly. For rectangular pore, reasonable designed pore structure can achieve improved water permeability and high salt rejection of graphene membrane by keeping one-dimensional length (i.e. the width) of the pore less than the size of the hydrated ions and increasing the other dimensional length. The restriction of one dimension can prevent the passage of hydrated ions through the pore effectively. This 'one-dimensional restriction' provides a simple strategy for designing RO membrane with variable pore structures to obtain a better desalination performance.

Superhydrophobic Carbon Nanotube Network Membranes for Membrane Distillation: High-Throughput Performance and Transport Mechanism.

Despite increasing sustainable water purification, current desalination membranes still suffer from insufficient permeability and treatment efficiency, greatly hindering extensive practical applications. In this work, we provide a new membrane design protocol and molecule-level mechanistic understanding of vapor transport for the treatment of hypersaline waters via a membrane distillation process by rationally fabricating more robust metal-based carbon nanotube (CNT) network membranes, featuring a superhydrophobic superporous surface (80.0 ± 2.3% surface porosity). With highly permeable ductile metal hollow fibers as substrates, the construction of a superhydrophobic (water contact angle ∼170°) CNT network layer endows the membranes with not only almost perfect salt rejection (over 99.9%) but a promising water flux (43.6 L·m-2·h-1), which outperforms most existing inorganic distillation membranes. Both experimental and molecular dynamics simulation results indicate that such an enhanced water flux can be ascribed to an ultra-low liquid-solid contact interface (∼3.23%), allowing water vapor to rapidly transport across the membrane structure via a combined mechanism of Knudsen diffusion (more dominant) and viscous flow while efficiently repelling high-salinity feed via forming a Cassie-Baxter state. A more hydrophobic surface is more in favor of not only water desorption from the CNT outer surface but superfast and frictionless water vapor transport. By constructing a new superhydrophobic triple-phase interface, the conceptional design strategy proposed in this work can be expected to be extended to other membrane material systems as well as more water treatment applications.

The OPG/RANKL/RANK system modulates calcification of common carotid artery in simulated microgravity rats by regulating NF-κB pathway.

Functional and structural adaptation of common carotid artery could be one of the important causes of postflight orthostatic intolerance after microgravity exposure, the mechanisms of which remain unclear. Recent evidence indicates that long-term spaceflight increases carotid artery stiffness, which might present a high risk to astronaut health and postflight working ability. Studies have suggested that vascular calcification is a common pathological change in cardiovascular diseases that is mainly manifested as an increase in vascular stiffness. Therefore, this study investigated whether simulated microgravity induces calcification of common carotid artery and to elucidate the underlying mechanisms. Four-week-old hindlimb-unweighted (HU) rats were used to simulate the deconditioning effects of microgravity on cardiovascular system. We found that simulated microgravity induced vascular smooth muscle cell (VSMC) osteogenic differentiation and medial calcification, increased receptor activator of nuclear factor κB (NF-κB) ligand (RANKL) and RANK expression, and enhanced NF-κB activation in rat common carotid artery. In vitro activation of the RANK pathway with exogenous RANKL, a RANK ligand, increased RANK and osteoprotegerin (OPG) expression in HU rats. Moreover, the expression of osteogenic markers and activation of NF-κB in HU rats were further enhanced by exogenous RANKL but suppressed by the RANK inhibitor osteoprotegerin fusion protein (OPG-Fc). These results indicated that the OPG/RANKL/RANK system modulates VSMC osteogenic differentiation and medial calcification of common carotid artery in simulated microgravity rats by regulating the NF-kB pathway.

Stress conditions induced circRNAs profile of extracellular vesicles in brain microvascular endothelial cells.

Cerebral ischemia causes hypoxic injury and inflammation, and brain microvascular endothelial cells (BMVECs) dysfunction is an initial stage of blood-brain barrier disruption. Endothelial cells secrete extracellular vesicles (EVs) that are involved in intercellular signal transduction. EVs contain a variety of RNAs, proteins, and metabolites. Circular RNA (circRNA) is a member of the non-coding RNA. The expression profile and potential function of circRNAs in BMVECs are unknown. Here, human BMVECs have undergone hypoxia or TNF-α induction, and the changes in circRNAs were measured by RNA sequencing. A total of 70 circRNAs showed differential expression, including 43 previously unrecorded circRNAs and 27 recorded circRNAs. Since astrocyte end-feet encircle endothelial cells, they are considered the main targets of the EVs from BMVEC. The miRNA sequence data and bioinformatics were used to predict the circRNA-miRNA-mRNA networks in astrocytes. The gene ontology (GO) analysis showed the main downstream targets of circRNAs are DNA transcription regulation and protein kinase-related signaling pathways. These results suggest that altering circRNAs may be a potential therapeutic target for cerebral ischemia induced hypoxic injury and inflammation.