ER-anchored CRTH2 antagonizes collagen biosynthesis and organ fibrosis via binding LARP6.

Excessive deposition of extracellular matrix, mainly collagen protein, is the hallmark of organ fibrosis. The molecular mechanisms regulating fibrotic protein biosynthesis are unclear. Here, we find that chemoattractant receptor homologous molecule expressed on TH2 cells (CRTH2), a plasma membrane receptor for prostaglandin D2, is trafficked to the endoplasmic reticulum (ER) membrane in fibroblasts in a caveolin-1-dependent manner. ER-anchored CRTH2 binds the collagen mRNA recognition motif of La ribonucleoprotein domain family member 6 (LARP6) and promotes the degradation of collagen mRNA in these cells. In line, CRTH2 deficiency increases collagen biosynthesis in fibroblasts and exacerbates injury-induced organ fibrosis in mice, which can be rescued by LARP6 depletion. Administration of CRTH2 N-terminal peptide reduces collagen production by binding to LARP6. Similar to CRTH2, bumetanide binds the LARP6 mRNA recognition motif, suppresses collagen biosynthesis, and alleviates bleomycin-triggered pulmonary fibrosis in vivo. These findings reveal a novel anti-fibrotic function of CRTH2 in the ER membrane via the interaction with LARP6, which may represent a therapeutic target for fibrotic diseases.

Dynamic category-sensitive hypergraph inferring and homo-heterogeneous neighbor feature learning for drug-related microbe prediction.

MOTIVATION: The microbes in human body play a crucial role in influencing the functions of drugs, as they can regulate the activities and toxicities of drugs. Most recent methods for predicting drug-microbe associations are based on graph learning. However, the relationships among multiple drugs and microbes are complex, diverse, and heterogeneous. Existing methods often fail to fully model the relationships. In addition, the attributes of drug-microbe pairs exhibit long-distance spatial correlations, which previous methods have not integrated effectively. RESULTS: We propose a new prediction method named DHDMP which is designed to encode the relationships among multiple drugs and microbes and integrate the attributes of various neighbor nodes along with the pairwise long-distance correlations. First, we construct a hypergraph with dynamic topology, where each hyperedge represents a specific relationship among multiple drug nodes and microbe nodes. Considering the heterogeneity of node attributes across different categories, we developed a node category-sensitive hypergraph convolution network to encode these diverse relationships. Second, we construct homogeneous graphs for drugs and microbes respectively, as well as drug-microbe heterogeneous graph, facilitating the integration of features from both homogeneous and heterogeneous neighbors of each target node. Third, we introduce a graph convolutional network with cross-graph feature propagation ability to transfer node features from homogeneous to heterogeneous graphs for enhanced neighbor feature representation learning. The propagation strategy aids in the deep fusion of features from both types of neighbors. Finally, we design spatial cross-attention to encode the attributes of drug-microbe pairs, revealing long-distance correlations among multiple pairwise attribute patches. The comprehensive comparison experiments showed our method outperformed state-of-the-art methods for drug-microbe association prediction. The ablation studies demonstrated the effectiveness of node category-sensitive hypergraph convolution network, graph convolutional network with cross-graph feature propagation, and spatial cross-attention. Case studies on three drugs further showed DHDMP's potential application in discovering the reliable candidate microbes for the interested drugs. AVAILABILITY AND IMPLEMENTATION: Source codes and supplementary materials are available at https://github.com/pingxuan-hlju/DHDMP.

Multi-scale topology and position feature learning and relationship-aware graph reasoning for prediction of drug-related microbes.

MOTIVATION: The human microbiome may impact the effectiveness of drugs by modulating their activities and toxicities. Predicting candidate microbes for drugs can facilitate the exploration of the therapeutic effects of drugs. Most recent methods concentrate on constructing of the prediction models based on graph reasoning. They fail to sufficiently exploit the topology and position information, the heterogeneity of multiple types of nodes and connections, and the long-distance correlations among nodes in microbe-drug heterogeneous graph. RESULTS: We propose a new microbe-drug association prediction model, NGMDA, to encode the position and topological features of microbe (drug) nodes, and fuse the different types of features from neighbors and the whole heterogeneous graph. First, we formulate the position and topology features of microbe (drug) nodes by t-step random walks, and the features reveal the topological neighborhoods at multiple scales and the position of each node. Second, as the features of nodes are high-dimensional and sparse, we designed an embedding enhancement strategy based on supervised fully connected autoencoders to form the embeddings with representative features and the more discriminative node distributions. Third, we propose an adaptive neighbor feature fusion module, which fuses features of neighbors by the constructed position- and topology-sensitive heterogeneous graph neural networks. A novel self-attention mechanism is developed to estimate the importance of the position and topology of each neighbor to a target node. Finally, a heterogeneous graph feature fusion module is constructed to learn the long-distance correlations among the nodes in the whole heterogeneous graph by a relationship-aware graph transformer. Relationship-aware graph transformer contains the strategy for encoding the connection relationship types among the nodes, which is helpful for integrating the diverse semantics of these connections. The extensive comparison experimental results demonstrate NGMDA's superior performance over five state-of-the-art prediction methods. The ablation experiment shows the contributions of the multi-scale topology and position feature learning, the embedding enhancement strategy, the neighbor feature fusion, and the heterogeneous graph feature fusion. Case studies over three drugs further indicate that NGMDA has ability in discovering the potential drug-related microbes. AVAILABILITY AND IMPLEMENTATION: Source codes and Supplementary Material are available at https://github.com/pingxuan-hlju/NGMDA.

Prediction of drug-disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets.

MOTIVATION: The development process of a new drug is time-consuming and costly. Thus, identifying new uses for approved drugs, named drug repositioning, is helpful for speeding up the drug development process and reducing development costs. Existing drug-related disease prediction methods mainly focus on single or multiple drug-disease heterogeneous networks. However, heterogeneous networks, and drug subnets and disease subnet contained in heterogeneous networks cover the common topology information between drug and disease nodes, the specific information between drug nodes and the specific information between disease nodes, respectively. RESULTS: We design a novel model, CTST, to extract and integrate common and specific topologies in multiple heterogeneous networks and subnets. Multiple heterogeneous networks composed of drug and disease nodes are established to integrate multiple kinds of similarities and associations among drug and disease nodes. These heterogeneous networks contain multiple drug subnets and a disease subnet. For multiple heterogeneous networks and subnets, we then define the common and specific representations of drug and disease nodes. The common representations of drug and disease nodes are encoded by a graph convolutional autoencoder with sharing parameters and they integrate the topological relationships of all nodes in heterogeneous networks. The specific representations of nodes are learned by specific graph convolutional autoencoders, respectively, and they fuse the topology and attributes of the nodes in each subnet. We then propose attention mechanisms at common representation level and specific representation level to learn more informative common and specific representations, respectively. Finally, an integration module with representation feature level attention is built to adaptively integrate these two representations for final association prediction. Extensive experimental results confirm the effectiveness of CTST. Comparison with six latest methods and case studies on five drugs further verify CTST has the ability to discover potential candidate diseases.

ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug-protein interaction prediction.

MOTIVATION: Effective computational methods to predict drug-protein interactions (DPIs) are vital for drug discovery in reducing the time and cost of drug development. Recent DPI prediction methods mainly exploit graph data composed of multiple kinds of connections among drugs and proteins. Each node in the graph usually has topological structures with multiple scales formed by its first-order neighbors and multi-order neighbors. However, most of the previous methods do not consider the topological structures of multi-order neighbors. In addition, deep integration of the multi-modality similarities of drugs and proteins is also a challenging task. RESULTS: We propose a model called ALDPI to adaptively learn the multi-scale topologies and multi-modality similarities with various significance levels. We first construct a drug-protein heterogeneous graph, which is composed of the interactions and the similarities with multiple modalities among drugs and proteins. An adaptive graph learning module is then designed to learn important kinds of connections in heterogeneous graph and generate new topology graphs. A module based on graph convolutional autoencoders is established to learn multiple representations, which imply the node attributes and multiple-scale topologies composed of one-order and multi-order neighbors, respectively. We also design an attention mechanism at neighbor topology level to distinguish the importance of these representations. Finally, since each similarity modality has its specific features, we construct a multi-layer convolutional neural network-based module to learn and fuse multi-modality features to obtain the attribute representation of each drug-protein node pair. Comprehensive experimental results show ALDPI's superior performance over six state-of-the-art methods. The results of recall rates of top-ranked candidates and case studies on five drugs further demonstrate the ability of ALDPI to discover potential drug-related protein candidates. CONTACT: zhang@hlju.edu.cn.

Learning global dependencies and multi-semantics within heterogeneous graph for predicting disease-related lncRNAs.

MOTIVATION: Long noncoding RNAs (lncRNAs) play an important role in the occurrence and development of diseases. Predicting disease-related lncRNAs can help to understand the pathogenesis of diseases deeply. The existing methods mainly rely on multi-source data related to lncRNAs and diseases when predicting the associations between lncRNAs and diseases. There are interdependencies among node attributes in a heterogeneous graph composed of all lncRNAs, diseases and micro RNAs. The meta-paths composed of various connections between them also contain rich semantic information. However, the existing methods neglect to integrate attribute information of intermediate nodes in meta-paths. RESULTS: We propose a novel association prediction model, GSMV, to learn and deeply integrate the global dependencies, semantic information of meta-paths and node-pair multi-view features related to lncRNAs and diseases. We firstly formulate the global representations of the lncRNA and disease nodes by establishing a self-attention mechanism to capture and learn the global dependencies among node attributes. Second, starting from the lncRNA and disease nodes, respectively, multiple meta-pathways are established to reveal different semantic information. Considering that each meta-path contains specific semantics and has multiple meta-path instances which have different contributions to revealing meta-path semantics, we design a graph neural network based module which consists of a meta-path instance encoding strategy and two novel attention mechanisms. The proposed meta-path instance encoding strategy is used to learn the contextual connections between nodes within a meta-path instance. One of the two new attention mechanisms is at the meta-path instance level, which learns rich and informative meta-path instances. The other attention mechanism integrates various semantic information from multiple meta-paths to learn the semantic representation of lncRNA and disease nodes. Finally, a dilated convolution-based learning module with adjustable receptive fields is proposed to learn multi-view features of lncRNA-disease node pairs. The experimental results prove that our method outperforms seven state-of-the-art comparing methods for lncRNA-disease association prediction. Ablation experiments demonstrate the contributions of the proposed global representation learning, semantic information learning, pairwise multi-view feature learning and the meta-path instance encoding strategy. Case studies on three cancers further demonstrate our method's ability to discover potential disease-related lncRNA candidates. CONTACT: zhang@hlju.edu.cn or peiliangwu@ysu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Briefings in Bioinformatics online.

Fully connected autoencoder and convolutional neural network with attention-based method for inferring disease-related lncRNAs.

Since abnormal expression of long noncoding RNAs (lncRNAs) is often closely related to various human diseases, identification of disease-associated lncRNAs is helpful for exploring the complex pathogenesis. Most of recent methods concentrate on exploiting multiple kinds of data related to lncRNAs and diseases for predicting candidate disease-related lncRNAs. These methods, however, failed to deeply integrate the topology information from the meta-paths that are composed of lncRNA, disease and microRNA (miRNA) nodes. We proposed a new method based on fully connected autoencoders and convolutional neural networks, called ACLDA, for inferring potential disease-related lncRNA candidates. A heterogeneous graph that consists of lncRNA, disease and miRNA nodes were firstly constructed to integrate similarities, associations and interactions among them. Fully connected autoencoder-based module was established to extract the low-dimensional features of lncRNA, disease and miRNA nodes in the heterogeneous graph. We designed the attention mechanisms at the node feature level and at the meta-path level to learn more informative features and meta-paths. A module based on convolutional neural networks was constructed to encode the local topologies of lncRNA and disease nodes from multiple meta-path perspectives. The comprehensive experimental results demonstrated ACLDA achieves superior performance than several state-of-the-art prediction methods. Case studies on breast, lung and colon cancers demonstrated that ACLDA is able to discover the potential disease-related lncRNAs.

GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug-protein interaction prediction.

MOTIVATION: Identifying proteins that interact with drugs plays an important role in the initial period of developing drugs, which helps to reduce the development cost and time. Recent methods for predicting drug-protein interactions mainly focus on exploiting various data about drugs and proteins. These methods failed to completely learn and integrate the attribute information of a pair of drug and protein nodes and their attribute distribution. RESULTS: We present a new prediction method, GVDTI, to encode multiple pairwise representations, including attention-enhanced topological representation, attribute representation and attribute distribution. First, a framework based on graph convolutional autoencoder is constructed to learn attention-enhanced topological embedding that integrates the topology structure of a drug-protein network for each drug and protein nodes. The topological embeddings of each drug and each protein are then combined and fused by multi-layer convolution neural networks to obtain the pairwise topological representation, which reveals the hidden topological relationships between drug and protein nodes. The proposed attribute-wise attention mechanism learns and adjusts the importance of individual attribute in each topological embedding of drug and protein nodes. Secondly, a tri-layer heterogeneous network composed of drug, protein and disease nodes is created to associate the similarities, interactions and associations across the heterogeneous nodes. The attribute distribution of the drug-protein node pair is encoded by a variational autoencoder. The pairwise attribute representation is learned via a multi-layer convolutional neural network to deeply integrate the attributes of drug and protein nodes. Finally, the three pairwise representations are fused by convolutional and fully connected neural networks for drug-protein interaction prediction. The experimental results show that GVDTI outperformed other seven state-of-the-art methods in comparison. The improved recall rates indicate that GVDTI retrieved more actual drug-protein interactions in the top ranked candidates than conventional methods. Case studies on five drugs further confirm GVDTI's ability in discovering the potential candidate drug-related proteins. CONTACT: zhang@hlju.edu.cn Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA-disease association prediction.

Identifying disease-related microRNAs (miRNAs) assists the understanding of disease pathogenesis. Existing research methods integrate multiple kinds of data related to miRNAs and diseases to infer candidate disease-related miRNAs. The attributes of miRNA nodes including their family and cluster belonging information, however, have not been deeply integrated. Besides, the learning of neighbor topology representation of a pair of miRNA and disease is a challenging issue. We present a disease-related miRNA prediction method by encoding and integrating multiple representations of miRNA and disease nodes learnt from the generative and adversarial perspective. We firstly construct a bilayer heterogeneous network of miRNA and disease nodes, and it contains multiple types of connections among these nodes, which reflect neighbor topology of miRNA-disease pairs, and the attributes of miRNA nodes, especially miRNA-related families and clusters. To learn enhanced pairwise neighbor topology, we propose a generative and adversarial model with a convolutional autoencoder-based generator to encode the low-dimensional topological representation of the miRNA-disease pair and multi-layer convolutional neural network-based discriminator to discriminate between the true and false neighbor topology embeddings. Besides, we design a novel feature category-level attention mechanism to learn the various importance of different features for final adaptive fusion and prediction. Comparison results with five miRNA-disease association methods demonstrated the superior performance of our model and technical contributions in terms of area under the receiver operating characteristic curve and area under the precision-recall curve. The results of recall rates confirmed that our model can find more actual miRNA-disease associations among top-ranked candidates. Case studies on three cancers further proved the ability to detect potential candidate miRNAs.

Learning multi-scale heterogenous network topologies and various pairwise attributes for drug-disease association prediction.

MOTIVATION: Identifying new therapeutic effects for the approved drugs is beneficial for effectively reducing the drug development cost and time. Most of the recent computational methods concentrate on exploiting multiple kinds of information about drugs and disease to predict the candidate associations between drugs and diseases. However, the drug and disease nodes have neighboring topologies with multiple scales, and the previous methods did not fully exploit and deeply integrate these topologies. RESULTS: We present a prediction method, multi-scale topology learning for drug-disease (MTRD), to integrate and learn multi-scale neighboring topologies and the attributes of a pair of drug and disease nodes. First, for multiple kinds of drug similarities, multiple drug-disease heterogenous networks are constructed respectively to integrate the similarities and associations related to drugs and diseases. Moreover, each heterogenous network has its specific topology structure, which is helpful for learning the corresponding specific topology representation. We formulate the topology embeddings for each drug node and disease node by random walking on each heterogeneous network, and the embeddings cover the neighboring topologies with different scopes. Because the multi-scale topology embeddings have context relationships, we construct Bi-directional long short-term memory-based module to encode these embeddings and their relationships and learn the neighboring topology representation. We also design the attention mechanisms at feature level and at scale level to obtain the more informative pairwise features and topology embeddings. A module based on multi-layer convolutional networks is constructed to learn the representative attributes of the drug-disease node pair according to their related similarity and association information. Comprehensive experimental results indicate that MTRD achieves the superior performance than several state-of-the-art methods for predicting drug-disease associations. MTRD also retrieves more actual drug-disease associations in the top-ranked candidates of the prediction result. Case studies on five drugs further demonstrate MTRD's ability in discovering the potential candidate diseases for the interested drugs.

Magnetic Anisotropy and Relaxation in Four-Coordinate Cobalt(II) Single-Ion Magnets with a [CoIIO4] Core.

A mononuclear four-coordinate Co(II) complex with a [CoIIO4] core, namely, PPN[Li(MeOH)4][Co(L)2] (1) (PPN = bis(phosphoranediyl)iminium; H2L = perfluoropinacol), has been studied by X-ray crystallography, magnetic characterization, and theoretical calculations. This complex presents a severely distorted coordination geometry. The O-Co-O bite angle is 83.42°/83.65°, and the dihedral twist angle between the O-Co-O chelate planes is 55.6°. The structural distortion results in a large easy-axis magnetic anisotropy with D = -104(1) cm-1 and a transverse component with |E| = +4(2) cm-1. Alternating current (ac) susceptibility measurements demonstrate that 1 exhibits slow relaxation of magnetization at zero static field. However, the frequency-dependent out-of-phase (χ"M) susceptibilities of 1 at 0 Oe do not show a characteristic maximum. Upon the application of a dc field or the dilution with a diamagnetic Zn matrix, the quantum tunneling of magnetization (QTM) process can be successfully suppressed. Notably, after dilution with the Zn matrix, the obtained sample exhibits a structure different from that of the pristine complex. In this altered sample, the asymmetric unit does not contain the Li(MeOH)4+ cation, resulting in an O-Co-O bite angle of 86.05° and a dihedral twist angle of 75.84°, thereby leading to an approximate D2d symmetry. Although such differences are not desirable for magnetic studies, this study still gives some insights. Theoretical calculations reveal that the D parameter is governed by the O-Co-O bite angle, in line with our previous report for other tetrahedral Co(II) complex with a [CoIIN4] core. On the other hand, the rhombic component is found to increase as the dihedral angle deviates from 90°. These findings provide valuable guidelines for fine-tuning the magnetic properties of Co(II) complexes.

Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs.

As the long non-coding RNAs (lncRNAs) play important roles during the incurrence and development of various human diseases, identifying disease-related lncRNAs can contribute to clarifying the pathogenesis of diseases. Most of the recent lncRNA-disease association prediction methods utilized the multi-source data about the lncRNAs and diseases. A single lncRNA may participate in multiple disease processes, and multiple lncRNAs usually are involved in the same disease process synergistically. However, the previous methods did not completely exploit the biological characteristics to construct the informative prediction models. We construct a prediction model based on adaptive hypergraph and gated convolution for lncRNA-disease association prediction (AGLDA), to embed and encode the biological characteristics about lncRNA-disease associations, the topological features from the entire heterogeneous graph perspective, and the gated enhanced pairwise features. First, the strategy for constructing hyperedges is designed to reflect the biological characteristic that multiple lncRNAs are involved in multiple disease processes. Furthermore, each hyperedge has its own biological perspective, and multiple hyperedges are beneficial for revealing the diverse relationships among multiple lncRNAs and diseases. Second, we encode the biological features of each lncRNA (disease) node using a strategy based on dynamic hypergraph convolutional networks. The strategy may adaptively learn the features of the hyperedges and formulate the dynamically evolved hypergraph topological structure. Third, a group convolutional network is established to integrate the entire heterogeneous topological structure and multiple types of node attributes within an lncRNA-disease-miRNA graph. Finally, a gated convolutional strategy is proposed to enhance the informative features of the lncRNA-disease node pairs. The comparison experiments indicate that AGLDA outperforms seven advanced prediction methods. The ablation studies confirm the effectiveness of major innovations, and the case studies validate AGLDA's ability in application for discovering potential disease-related lncRNA candidates.

Mask-Guided Target Node Feature Learning and Dynamic Detailed Feature Enhancement for lncRNA-Disease Association Prediction.

Identifying new relevant long noncoding RNAs (lncRNAs) for various human diseases can facilitate the exploration of the causes and progression of these diseases. Recently, several graph inference methods have been proposed to predict disease-related lncRNAs by exploiting the topological structure and node attributes within graphs. However, these methods did not prioritize the target lncRNA and disease nodes over auxiliary nodes like miRNA nodes, potentially limiting their ability to fully utilize the features of the target nodes. We propose a new method, mask-guided target node feature learning and dynamic detailed feature enhancement for lncRNA-disease association prediction (MDLD), to enhance node feature learning for improved lncRNA-disease association prediction. First, we designed a heterogeneous graph masked transformer autoencoder to guide feature learning, focusing more on the features of target lncRNA (disease) nodes. The target nodes were increasingly masked as training progressed, which helps develop a more robust prediction model. Second, we developed a graph convolutional network with dynamic residuals (GCNDR) to learn and integrate the heterogeneous topology and features of all lncRNA, disease, and miRNA nodes. GCNDR employs an interlayer residual strategy and a residual evolution strategy to mitigate oversmoothing caused by multilayer graph convolution. The interlayer residual strategy estimates the importance of node features learned in the previous GCN encoding layer for nodes in the current encoding layer. Additionally, since there are dependencies in the importance of features of individual lncRNA (disease, miRNA) nodes across multiple encoding layers, a gated recurrent unit-based strategy is proposed to encode these dependencies. Finally, we designed a perspective-level attention mechanism to obtain more informative features of lncRNA and disease node pairs from the perspectives of mask-enhanced and dynamic-enhanced node features. Cross-validation experimental results demonstrated that MDLD outperformed 10 other state-of-the-art prediction methods. Ablation experiments and case studies on candidate lncRNAs for three diseases further proved the technical contributions of MDLD and its capability to discover disease-related lncRNAs.

Biological Interactions Mediate Soil Functions by Altering Rare Microbial Communities.

Soil microbes, the main driving force of terrestrial biogeochemical cycles, facilitate soil organic matter turnover. However, the influence of the soil fauna on microbial communities remains poorly understood. We investigated soil microbiota dynamics by introducing competition and predation among fauna into two soil ecosystems with different fertilization histories. The interactions significantly affected rare microbial communities including bacteria and fungi. Predation enhanced the abundance of C/N cycle-related genes. Rare microbial communities are important drivers of soil functional gene enrichment. Key rare microbial taxa, including SM1A02, Gammaproteobacteria, and HSB_OF53-F07, were identified. Metabolomics analysis suggested that increased functional gene abundance may be due to specific microbial metabolic activity mediated by soil fauna interactions. Predation had a stronger effect on rare microbes, functional genes, and microbial metabolism compared to competition. Long-term organic fertilizer application increased the soil resistance to animal interactions. These findings provide a comprehensive understanding of microbial community dynamics under soil biological interactions, emphasizing the roles of competition and predation among soil fauna in terrestrial ecosystems.

HPPO-Derived Meroterpenoids from the Marine-Derived Fungus Penicillium sp. SCSIO 41691.

Seven new 4-hydroxy-6-phenyl-2H-pyran-2-one (HPPO) derived meroterpenoids, 1-methyl-12a,12b-epoxyarisugacin M (1), 1-methyl-4a,12b-epoxyarisugacin M (2), 2,3-dihydroxy-3,4a-epoxy-12a-dehydroxyisoterreulactone A (3), 2-hydroxy-12a-dehydroxyisoterreulactone A (4), 3'-demethoxyterritrems B' (5), 4a-hydroxyarisugacin P (6), and 1-epi-arisugacin H (7), together with two known analogues (8 and 9), were isolated from the marine-derived fungal strain Penicillium sp. SCSIO 41691. Their structures were elucidated by spectroscopic methods, and the absolute configurations of compounds 1 and 3 were determined by single-crystal X-ray diffraction. Among them, 1 and 2 had a unique methyl migration in the basic meroterpenoid skeleton with a 12a,12b-epoxy or 4a,12b-epoxy group, and 3 was a highly oxygenated HPPO-derived meroterpenoid featuring a rare 6/5/6/6/6/6 hexacyclic system with a 3,4a-epoxy group. Biologically, 5 exhibited inhibitory activity against lipopolysaccharide-induced nitric oxide production in RAW 264.7 cells with an IC50 value of 21 µM, more potent than the positive control indomethacin.

Facile constructed meroterpenoids with novel hexadecahydroacephenanthrylene carbon skeleton using the biotransformation and chemical synthesis method.

Bioaspermeroterpenoid A (1), the first meroterpenoid with an unprecedented hexadecahydroacephenanthrylene carbon skeleton, together with two analogues bioaspermeroterpenoids B and C (2 and 3) were co-isolated from the biotransformation extract of aspermeroterpene C by the fungus Penicillium herquei GZU-31-6. On the other hand, bioaspermeroterpenoid Aa (1a) featuring the same hexadecahydroacephenanthrylene carbon skeleton was synthesized from the precursor aspermeroterpene C by the nucleophilic addition reaction in the presence of CH3ONa. Furthermore, bioaspermeroterpenoids A and C showed good inhibitory activities against lipopolysaccharide (LPS)-induced nitric oxide (NO) production in RAW 264.7 cells with IC50 values of 26.08 and 7.50 µM, respectively, compared to the positive control (Indomethacin, IC50 24.1 µM). Especially, bioaspermeroterpenoids A and C also significantly suppressed the protein expression of iNOS and COX-2 at the concentration of 12.5 µM.

Prevalence and risk factors of low vitamin D levels in children and adolescents with familial hypokalemic periodic paralysis.

Patients with familial hypokalemic periodic paralysis (HOKPP) experience episodes of reversible immobility and are at an increased risk of limited sunlight exposure, potentially leading to vitamin D deficiency. However, there is a lack of data on vitamin D levels in this population. We investigated serum vitamin D levels and their associated factors in children with HOKPP. This study included 170 genetically-confirmed children with HOKPP, aged 3-18 years, and 170 age-, sex-, and body mass index (BMI)-matched healthy controls from the Korean Channelopathy Study, a prospective controlled investigation. Anthropometric and clinical characteristics were recorded, and serum levels of calcium, ionized calcium, phosphorus, alkaline phosphatase, 25-hydroxyvitamin D, and intact parathyroid hormone (PTH) were analyzed. Vitamin D deficiency (< 20 ng/mL) was observed in 87.0% of the patients compared to 45.5% of the controls (P < 0.05) during the summer-fall season. During the winter-spring season, 91.7% of the patients and 73.4% of the controls were deficient (P < 0.05). A strong positive correlation was found between onset age of the first paralytic attack and vitamin D levels (r = 0.78, P < 0.01). Conversely, the frequency and duration of paralytic attacks were negatively correlated with vitamin D levels (r = -0.82 and r = -0.65, P < 0.01, respectively). Age, BMI, age at onset, frequency and duration of attacks, and PTH levels were independently associated with vitamin D levels (ß = -0.10, -0.12, 0.19, -0.27, -0.21, and -0.13, P < 0.05, respectively). CONCLUSIONS: Vitamin D deficiency was highly prevalent in children with HOKPP, and vitamin D levels correlated with various disease characteristics. We recommend routine screening for vitamin D levels in these patients to address this prevalent deficiency. Considering the high prevalence of vitamin D deficiency observed, further research on other diseases characterized by reversible immobility is warranted. WHAT IS KNOWN: • A correlation between immobility and low serum vitamin D levels has been established. However, the vitamin D status of patients with familial hypokalemic periodic paralysis (HOKPP) who experience periods of reversible immobility remains unknown. WHAT IS NEW: • Vitamin D deficiency was highly prevalent in children with HOKPP, and vitamin D levels correlated with various disease characteristics.

Biotransformation of Glaucic Acid Isolated from the Root of Lindera glauca by the Endophytic Fungi Penicillium Chrysogenum SHJ-07.

Glaucic acid isolated from the root of Lindera glauca, was investigated by the biotransformation methods via the endophytic fungi, resulting in the production of five new glausesquiterpenes A-E (1-5), along with a known analogue 6. Their structures were elucidated based on spectroscopic methods and electronic circular dichroism (ECD) calculations. In the bioassays, glausesquiterpene A (1) showed good inhibitory activity of NO production in LPS-activated RAW 264.7 macrophages with an IC50 value of 20.1 µM than positive control (Indomethacin, IC50 24.1 µM). Further in vitro studies demonstrated that glausesquiterpene A significantly suppressed the protein expression of iNOS and COX-2 at the concentration of 25.0 µM.

Exosomes Derived from Hypertrophic Scar Fibroblasts Suppress Melanogenesis in Normal Human Epidermal Melanocytes.

Post-burn hypertrophic scars often exhibit abnormal pigmentation. Exosomes play important roles in maintaining normal physiological homeostasis and in the pathological development of diseases. This study investigated the effects of the exosomes derived from hypertrophic scar fibroblasts (HTSFs) on melanocytes, which are pigment-producing cells. Normal fibroblasts (NFs) and HTSFs were isolated and cultured from normal skin and hypertrophic scar (HTS) tissue. Both the NF- and HTSF-exosomes were isolated from a cell culture medium and purified using a column-based technique. The normal human epidermal melanocytes were treated with both exosomes at a concentration of 100 µg/mL at different times. The cell proliferation, melanin content in the medium, apoptotic factors, transcription factors, melanin synthesis enzymes, signaling, signal transduction pathways, and activators of transcription factors (STAT) 1, 3, 5, and 6 were investigated. Compared with the Dulbecco's phosphate-buffered saline (DPBS)-treated controls and NF-exosomes, the HTSF-exosomes decreased the melanocyte proliferation and melanin secretion. The molecular patterns of apoptosis, proliferation, melanin synthesis, Smad and non-Smad signaling, and STATs were altered by the treatment with the HTSF-exosomes. No significant differences were observed between the DPBS-treated control and NF-exosome-treated cells. HTSF-derived exosomes may play a role in the pathological epidermal hypopigmentation observed in patients with HTS.

The BTB/TAZ domain-containing protein CmBT1-mediated CmANR1 ubiquitination negatively regulates root development in chrysanthemum.

Roots are fundamental for plants to adapt to variable environmental conditions. The development of a robust root system is orchestrated by numerous genetic determinants and, among them, the MADS-box gene ANR1 has garnered substantial attention. Prior research has demonstrated that, in chrysanthemum, CmANR1 positively regulates root system development. Nevertheless, the upstream regulators involved in the CmANR1-mediated regulation of root development remain unidentified. In this study, we successfully identified bric-a-brac, tramtrack and broad (BTB) and transcription adapter putative zinc finger (TAZ) domain protein CmBT1 as the interacting partner of CmANR1 through a yeast-two-hybrid (Y2H) screening library. Furthermore, we validated this physical interaction through bimolecular fluorescence complementation and pull-down assays. Functional assays revealed that CmBT1 exerted a negative influence on root development in chrysanthemum. In both in vitro and in vivo assays, it was evident that CmBT1 mediated the ubiquitination of CmANR1 through the ubiquitin/26S proteasome pathway. This ubiquitination subsequently led to the degradation of the CmANR1 protein and a reduction in the transcription of CmANR1-targeted gene CmPIN2, which was crucial for root development in chrysanthemum. Genetic analysis suggested that CmBT1 modulated root development, at least in part, by regulating the level of CmANR1 protein. Collectively, these findings shed new light on the regulatory role of CmBT1 in degrading CmANR1 through ubiquitination, thereby repressing the expression of its targeted gene and inhibiting root development in chrysanthemum.