Metabolomic landscape of macrophage discloses an anabolic signature of dengue virus infection and antibody-dependent enhancement of viral infection.

Dengue virus (DENV) infection causes dengue fever, the most prevalent arthropod-transmitted viral disease worldwide. Viruses are acellular parasites and obligately rely on host cell machinery for reproduction. Previous studies have indicated metabolomic changes in endothelial cell models and sera of animal models and patients with dengue fever. To probe the immunometabolic mechanism of DENV infection, here, we report the metabolomic landscape of a human macrophage cell model of DENV infection and its antibody-dependent enhancement. DENV infection of THP-1-derived macrophages caused 202 metabolic variants, of which amino acids occupied 23.7%, fatty acids 21.78%, carbohydrates 10.4%, organic acids 13.37%, and carnitines 10.4%. These metabolomic changes indicated an overall anabolic signature, which was characterized by the global exhaustion of amino acids, increases of cellular fatty acids, carbohydrates and pentoses, but decreases of acylcarnitine. Significant activation of metabolic pathways of glycolysis, pentose phosphate, amino acid metabolism, and tricarboxylic acid cycle collectively support the overall anabolism to meet metabolic demands of DENV replication and immune activation by viral infection. Totally 88 of 202 metabolic variants were significantly changed by DENV infection, 36 of which met the statistical standard (P<0.05, VIP>1.5) of differentially expressed metabolites, which were the predominantly decreased variants of acylcarnitine and the increased variants of fatty acids and carbohydrates. Remarkably, 11 differentially expressed metabolites were significantly distinct between DENV only infection and antibody-dependent enhancement of viral infection. Our data suggested that the anabolic activation by DENV infection integrates the viral replication and anti-viral immune activation.

Developmental adcyap1b loss leads to hemorrhage, disrupted hemostasis, and a blood coagulation cascade in zebrafish.

BACKGROUND: Pituitary adenylate cyclase-activating polypeptide is a neuropeptide with diverse roles in biological processes. Its involvement in the blood coagulation cascade is unclear. OBJECTIVES: This study unraveled adcyap1b's role in blood coagulation using clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 in zebrafish. Effects were validated via adcyap1b knockdown. Gene expression changes in adcyap1b mutants were explored, linking them to clotting disorders. An analysis of proca gene splicing illuminated its role in adcyap1b-related anticoagulation deficiencies. METHODS: Zebrafish were genetically modified using clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 to induce adcyap1b knockout. Morpholino-mediated gene knockdown was employed for validation. Expression levels of coagulation factors, anticoagulant proteins, and fibrinolytic system genes were assessed in adcyap1b mutant zebrafish. Alternative splicing of proca gene was analyzed. RESULTS: Adcyap1b mutant zebrafish exhibited severe hemorrhage, clotting disorders, and disrupted blood coagulation. Morpholino-mediated knockdown replicated observed phenotypes. Downregulation in transcripts related to coagulation factors V and IX, anticoagulation protein C, and plasminogen was observed. Abnormal alternative splicing of the proca gene was identified, providing a mechanistic explanation for anticoagulation system deficiencies. CONCLUSION: Adcyap1b plays a crucial role in maintaining zebrafish blood coagulation and hemostasis. Its influence extends to the regulation of procoagulant and anticoagulant pathways, with abnormal alternative splicing contributing to observed deficiencies. These findings unveil a novel aspect of adcyap1b function, offering potential insights into similar processes in mammalian systems.

ABLNCPP: Attention Mechanism-Based Bidirectional Long Short-Term Memory for Noncoding RNA Coding Potential Prediction.

With the continuous development of ribosome profiling, sequencing technology, and proteomics, evidence is mounting that noncoding RNA (ncRNA) may be a novel source of peptides or proteins. These peptides and proteins play crucial roles in inhibiting tumor progression and interfering with cancer metabolism and other essential physiological processes. Therefore, identifying ncRNAs with coding potential is vital to ncRNA functional research. However, existing studies perform well in classifying ncRNAs and mRNAs, and no research has been explicitly raised to distinguish whether ncRNA transcripts have coding potential. For this reason, we propose an attention mechanism-based bidirectional LSTM network called ABLNCPP to assess the coding possibility of ncRNA sequences. Considering the sequential information loss in previous methods, we introduce a novel nonoverlapping trinucleotide embedding (NOLTE) method for ncRNAs to obtain embeddings containing sequential features. The extensive evaluations show that ABLNCPP outperforms other state-of-the-art models. In general, ABLNCPP overcomes the bottleneck of ncRNA coding potential prediction and is expected to provide valuable contributions to cancer discovery and treatment in the future. The source code and data sets are freely available at https://github.com/YinggggJ/ABLNCPP.

PDIA2 Bridges Endoplasmic Reticulum Stress and Metabolic Reprogramming During Malignant Transformation of Chronic Colitis.

Background: Chronic inflammation contributes to approximately 20% of cancers; the underlying mechanisms are still elusive. Here, using an animal model of colitis to colon-cancerous transformation, we demonstrated that endoplasmic reticulum (ER) stress couples with metabolic reprogramming to promote a malignant transformation of chronic inflammation. Methods: The animal model for chronic colitis to colon-cancerous transformation was established in C57BL/6N mice by azoxymethane (AOM) and dextran sodium sulfate (DSS) treatments. The differential proteins in control and AOM/DSS-treated colon mucosa were determined using proteomic analysis; the kinetics of metabolic modifications were monitored by mitochondrial oxygen flux, extracellular acidification, and targeted metabolomics; the molecule linker between ER stress and metabolic modifications were identified by coimmunoprecipitation, KEGG pathway analysis, and the subcutaneous tumor model using gene-specific knockdown colon cancer cells. Tissue array analysis were used to evaluate the differential protein in cancer and cancer-adjacent tissues. Results: AOM/DSS treatment induced 38 tumors in 10 mice at the 14th week with the mean tumor size 9.35 ± 3.87 mm2, which was significantly decreased to 5.85 ± 0.95 mm2 by the ER stress inhibitor 4-phenylbutyric acid (4PBA). Seven differential proteins were determined from control (1,067 ± 48) and AOM/DSS-treated mucosa (1,077 ± 59); the level of ER protein PDIA2 (protein disulfide isomerase-associated 2) was increased over 7-fold in response to AOM/DSS treatment. PDIA2 interacted with 420 proteins that were involved in 8 signaling pathways, in particular with 53 proteins in metabolic pathways. PDIA2 translocated from ER to mitochondria and interacted with the components of complexes I and II to inhibit oxophosphorylation but increase glycolysis. Knockdown PDIA2 in colon cancer cells restored the metabolic imbalance and significantly repressed tumor growth in the xenograft animal model. 4PBA therapy inhibited the AOM/DSS-mediated overexpression of PDIA2 and metabolic modifications and suppressed colon cancer growth. In clinic, PDIA2 was overexpressed in colon cancer tissues rather than cancer-adjacent tissues and was related with the late stages and lymph node metastasis of colon cancer. Conclusions: Persistent ER stress reprograms the metabolism to promote the malignant transformation of chronic colitis; PDIA2 serves as a molecule linker between ER stress and metabolic reprogramming. The inhibition of ER stress restores metabolic homeostasis and attenuates the cancerous transformation of chronic inflammation.

Predicting circRNA-drug sensitivity associations via graph attention auto-encoder.

BACKGROUND: Circular RNAs (circRNAs) play essential roles in cancer development and therapy resistance. Many studies have shown that circRNA is closely related to human health. The expression of circRNAs also affects the sensitivity of cells to drugs, thereby significantly affecting the efficacy of drugs. However, traditional biological experiments are time-consuming and expensive to validate drug-related circRNAs. Therefore, it is an important and urgent task to develop an effective computational method for predicting unknown circRNA-drug associations. RESULTS: In this work, we propose a computational framework (GATECDA) based on graph attention auto-encoder to predict circRNA-drug sensitivity associations. In GATECDA, we leverage multiple databases, containing the sequences of host genes of circRNAs, the structure of drugs, and circRNA-drug sensitivity associations. Based on the data, GATECDA employs Graph attention auto-encoder (GATE) to extract the low-dimensional representation of circRNA/drug, effectively retaining critical information in sparse high-dimensional features and realizing the effective fusion of nodes' neighborhood information. Experimental results indicate that GATECDA achieves an average AUC of 89.18% under 10-fold cross-validation. Case studies further show the excellent performance of GATECDA. CONCLUSIONS: Many experimental results and case studies show that our proposed GATECDA method can effectively predict the circRNA-drug sensitivity associations.

In silico drug repositioning based on integrated drug targets and canonical correlation analysis.

BACKGROUND: Besides binding to proteins, the most recent advances in pharmacogenomics indicate drugs can regulate the expression of non-coding RNAs (ncRNAs). The polypharmacological feature in drugs enables us to find new uses for existing drugs (namely drug repositioning). However, current computational methods for drug repositioning mainly consider proteins as drug targets. Meanwhile, these methods identify only statistical relationships between drugs and diseases. They provide little information about how drug-disease associations are formed at the molecular target level. METHODS: Herein, we first comprehensively collect proteins and two categories of ncRNAs as drug targets from public databases to construct drug-target interactions. Experimentally confirmed drug-disease associations are downloaded from an established database. A canonical correlation analysis (CCA) based method is then applied to the two datasets to extract correlated sets of targets and diseases. The correlated sets are regarded as canonical components, and they are used to investigate drug's mechanism of actions. We finally develop a strategy to predict novel drug-disease associations for drug repositioning by combining all the extracted correlated sets. RESULTS: We receive 400 canonical components which correlate targets with diseases in our study. We select 4 components for analysis and find some top-ranking diseases in an extracted set might be treated by drugs interfacing with the top-ranking targets in the same set. Experimental results from 10-fold cross-validations show integrating different categories of target information results in better prediction performance than only using proteins or ncRNAs as targets. When compared with 3 state-of-the-art approaches, our method receives the highest AUC value 0.8576. We use our method to predict new indications for 789 drugs and confirm 24 predictions in the top 1 predictions. CONCLUSIONS: To the best of our knowledge, this is the first computational effort which combines both proteins and ncRNAs as drug targets for drug repositioning. Our study provides a biologically relevant interpretation regarding the forming of drug-disease associations, which is useful for guiding future biomedical tests.

Isarubrolone C Promotes Autophagic Degradation of Virus Proteins via Activating ATG10S in HepG2 Cells.

Isarubrolone C is a bioactive polycyclic tropoloalkaloid from Streptomyces. Our previous study showed that isarubrolone C could trigger autophagy. Here, we report isarubrolone C potential in broad-spectrum antiviral effect and its antiviral mechanism in vitro. Our results show that isarubrolone C activated autophagy and reduced levels of viral proteins in the cells harboring HCV-CORE/NS5B, HBx, ZIKV-NS5, and HIV-RT, respectively. The role of isarubrolone C in suppression of the viral proteins was via an autophagic degradation pathway rather than a proteasome pathway. Co-immunoprecipitation assays revealed that isarubrolone C promoted both autophagy flux opening and the viral proteins being enwrapped in autolysosomes. PCR assays showed that isarubrolone C elevated the transcription levels of ATG10/ATG10S and IL28A. Further, ATG10S high expression could efficiently enhance IL28A expression and the ability of isarubrolone C to degrade the viral proteins by promoting the colocalization of viral proteins with autolysosomes. Additionally, knockdown of endogenous IL28A caused both losses of the isarubrolone C antiviral effect and autolysosome formation. These results indicate that the role of isarubrolone C antiviruses is achieved by triggering the autophagic mechanism, which is mediated by endogenous ATG10S and IL28A activation. This is the first report about isarubrolone C potential of in vitro broad-spectrum antiviruses.

LDAH2V: Exploring Meta-Paths Across Multiple Networks for lncRNA-Disease Association Prediction.

Accumulating evidence has demonstrated dysfunctions of long non-coding RNAs (lncRNAs) are involved in various complex human diseases. However, even today, the relationships between lncRNAs and diseases remain unknown in most cases. Developing effective computational approaches to identify potential lncRNA-disease associations has become a hot topic. Existing network-based approaches are usually focused on the intrinsic features of lncRNAs and diseases but ignore the heterogeneous information of biological networks. Considering the limitations in previous methods, we propose LDAH2V, an efficient computational framework for predicting potential lncRNA-disease associations. LDAH2V uses the HIN2Vec to calculate the meta-path and feature vector for each lncRNA-disease pair in the heterogeneous information network (HIN), which consists of lncRNA similarity network, disease similarity network, miRNA similarity network, and the associations between them. Then, a Gradient Boosting Tree (GBT) classifier to predict lncRNA-disease associations is built with the feature vectors. The results show that LDAH2V performs significantly better than the four existing state-of-the-art methods and gains an AUC of 0.97 in the 10-fold cross-validation test. Furthermore, case studies of colon cancer and ovarian cancer-related lncRNAs have been confirmed in related databases and medical literature.

DeepciRGO: functional prediction of circular RNAs through hierarchical deep neural networks using heterogeneous network features.

BACKGROUND: Circular RNAs (circRNAs) are special noncoding RNA molecules with closed loop structures. Compared with the traditional linear RNA, circRNA is more stable and not easily degraded. Many studies have shown that circRNAs are involved in the regulation of various diseases and cancers. Determining the functions of circRNAs in mammalian cells is of great significance for revealing their mechanism of action in physiological and pathological processes, diagnosis and treatment of diseases. However, determining the functions of circRNAs on a large scale is a challenging task because of the high experimental costs. RESULTS: In this paper, we present a hierarchical deep learning model, DeepciRGO, which can effectively predict gene ontology functions of circRNAs. We build a heterogeneous network containing circRNA co-expressions, protein-protein interactions and protein-circRNA interactions. The topology features of proteins and circRNAs are calculated using a novel representation learning approach HIN2Vec across the heterogeneous network. Then, a deep multi-label hierarchical classification model is trained with the topology features to predict the biological process function in the gene ontology for each circRNA. In particular, we manually curated a benchmark dataset containing 185 GO annotations for 62 circRNAs, namely, circRNA2GO-62. The DeepciRGO achieves promising performance on the circRNA2GO-62 dataset with a maximum F-measure of 0.412, a recall score of 0.400, and an accuracy of 0.425, which are significantly better than other state-of-the-art RNA function prediction methods. In addition, we demonstrate the considerable potential of integrating multiple interactions and association networks. CONCLUSIONS: DeepciRGO will be a useful tool for accurately annotating circRNAs. The experimental results show that integrating multi-source data can help to improve the predictive performance of DeepciRGO. Moreover, The model also can combine RNA structure and sequence information to further optimize predictive performance.

Liver toxicity of macrolide antibiotics in zebrafish.

Macrolide antibiotics (macrolides) are among the most commonly prescribed antibiotics worldwide and are used for a wide range of infections, but macrolides also expose people to the risk of adverse events include hepatotoxicity. Here, we report the liver toxicity of macrolides with different structures in zebrafish. The absorption, distribution, metabolism, excretion and toxicology (ADMET) parameters of macrolide compounds were predicted and contrasted by utilizing in silico analysis. Fluorescence imaging and Oil Red O stain assays showed all the tested macrolide drugs induced liver degeneration, changed liver size and liver steatosis in larval zebrafish. Through RNA-seq analysis, we found seven co-regulated differentially expressed genes (co-DEGs) associated with metabolism, apoptosis and immune system biological processes, and two co-regulated significant pathways including amino sugar and nucleotide sugar metabolism and apoptosis signaling pathway. We found that only fosab of seven co-DEGs was in the two co-regulated significant pathways. fosab encoded proto-oncogene c-Fos, which was closely associated with liver diseases. The whole-mount in situ hybridization showed high transcription of c-Fos induced by macrolide compounds mainly in the liver region of zebrafish larvae. Cell Counting Kit-8 (CCK-8) and lactate dehydrogenase (LDH) leakage assays revealed that macrolides exerts significant cytotoxic effects on L02 cells. qRT-PCR and western blot analysis demonstrated macrolides also promoted human c-Fos expression in L02 cells. The c-Fos overexpression significantly reduced cell viability by using CCK-8 assay. These data indicate that hepatotoxicity induced by macrolides may be correlated with c-Fos expression activated by these compounds. This study may provide a biomarker for the further investigations on the mechanism of hepatotoxicity induced by macrolide drugs with different structures, and extend our understanding for improving rational clinical application of macrolides.

IL28A protein homotetramer structure is required for autolysosomal degradation of HCV-NS5A in vitro.

Interferon lambda-2 (IL28A) has a wide antiviral effect with fewer side-effects. Autophagy is a host mechanism to maintain intracellular homeostasis and defends invasion of pathogenic microorganisms. HCV NS5A can disable host defense systems to support HCV replication. Thus, molecular mechanism of interaction among interferon lambda, autophagy, and HCV was concerned and explored in this study. We report that HCV NS5A activated an incomplete autophagy by promoting the autophagic ubiquitylation-like enzymes ATG3, ATG5, ATG7, ATG10, and autophagosome maker LC3B, but blocked autophagy flux; IL28A bound to NS5A at NS5A-ISDR region, and degraded HCV-NS5A by promoting autolysosome formations in HepG2 cells. A software prediction of IL28A protein conformation indicated a potential structure of IL28A homotetramer; the first α-helix of IL28A locates in the interfaces among the four IL28A chains to maintain IL28A homotetrameric conformation. Co-IP and cell immunofluorescence experiments with sequential deletion mutants demonstrate that IL28A preferred a homotetramer conformation to a monomer in the cells; the IL28A homotetramer is positively correlated with autolysosomal degradation of HCV NS5A and the other HCV proteins. Summarily, the first α-helix of IL28A protein is the key domain for maintaining IL28A homotetramer which is required for promoting formation of autolysosomes and degradation of HCV proteins in vitro.

A new transcription factor ATG10S activates IFNL2 transcription by binding at an IRF1 site in HepG2 cells.

IFNL2 is a potent antiviral interferon, but the regulation of its gene expression is not fully clear. Here, we report the regulation of ATG10S for IFNL2 transcription. Through sequential deletion of the IFNL2 promoter sequence, we found LP1-1, a fragment of the promoter responding to ATG10S activity. Subcellular localization and DNA immunoprecipitation assays showed ATG10S translocating into the nucleus and binding to LP1-1. Online prediction for transcription factor binding sites showed an IRF1 targeting locus in LP1-1. Luciferase assays, RT-PCR, and western blot analysis revealed a core motif (CAAGAC) existing in LP1-1, which determined ATG10S and IRF1 activity; individual nucleotide substitution showed that the functional nucleotides of ATG10S targeting were C1, A3, and C6, and the ones associated with IRF1 were A3 and G4 within the core motif. Co-immunoprecipitation assays revealed ATG10S combination with KPNA1/importin α, KPNB1/importin ß, and IRF1. The knockdown of endogenous IRF1 increased ATG10S activity on IFNL2 transcription. These results indicate that ATG10S as a transcription factor competes with IRF1 for the same binding site to promote IFNL2 gene transcription. Abbreviations: ATG10: autophagy related 10; ATG10S: the shorter isoform of autophagy related 10; BD: binding domain; CM: core motif; co-IP: co-immunoprecipitation; GFP: green fluorescent protein; HCV: hepatitis C virus; IF: immunofluorescence; IFN: interferon; IRF: interferon regulatory factor; LP: lambda promoter; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; RLU: relative light unit; SQSTM1: sequestosome 1.

DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model.

MicroRNAs (miRNAs) are a highly abundant collection of functional non-coding RNAs involved in cellular regulation and various complex human diseases. Although a large number of miRNAs have been identified, most of their physiological functions remain unknown. Computational methods play a vital role in exploring the potential functions of miRNAs. Here, we present DeepMiR2GO, a tool for integrating miRNAs, proteins and diseases, to predict the gene ontology (GO) functions based on multiple deep neuro-symbolic models. DeepMiR2GO starts by integrating the miRNA co-expression network, protein-protein interaction (PPI) network, disease phenotype similarity network, and interactions or associations among them into a global heterogeneous network. Then, it employs an efficient graph embedding strategy to learn potential network representations of the global heterogeneous network as the topological features. Finally, a deep multi-label classification network based on multiple neuro-symbolic models is built and used to annotate the GO terms of miRNAs. The predicted results demonstrate that DeepMiR2GO performs significantly better than other state-of-the-art approaches in terms of precision, recall, and maximum F-measure.

Hepatomas are exquisitely sensitive to pharmacologic ascorbate (P-AscH-).

Rationale: Ascorbate is an essential micronutrient known for redox functions at normal physiologic concentrations. In recent decades, pharmacological ascorbate has been found to selectively kill tumour cells. However, the dosing frequency of pharmacologic ascorbate in humans has not yet been defined. Methods: We determined that among five hepatic cell lines, Huh-7 cells were the most sensitive to ascorbate. The effects of high-dose ascorbate on hepatoma were therefore assessed using Huh-7 cells and xenograft tumour mouse model. Results: In Huh-7 cells, ascorbate induced a significant increase in the percentage of cells in the G0/G1 phase, apoptosis and intracellular levels of ROS. High doses of ascorbate (4.0 pmol cell-1), but not low doses of ascorbate (1.0 pmol cell-1), also served as a pro-drug that killed hepatoma cells by altering mitochondrial respiration. Furthermore, in a Huh-7 cell xenograft tumour mouse model, intraperitoneal injection of ascorbate (4.0 g/kg/3 days) but not a lower dose of ascorbate (2.0 g/kg/3 days) significantly inhibited tumour growth. Gene array analysis of HCC tumour tissue from xenograft mice given IP ascorbate (4.0 g/kg/3 days) identified changes in the transcript levels of 192 genes/ncRNAs involved in insulin receptor signalling, metabolism and mitochondrial respiration. Consistent with the array data, gene expression levels of AGER, DGKK, ASB2, TCP10L2, Lnc-ALCAM-3, and Lnc-TGFBR2-1 were increased 2.05-11.35 fold in HCC tumour tissue samples from mice treated with high-dose ascorbate, and IHC staining analysis also verified that AGER/RAGE and DGKK proteins were up-regulated, which implied that AGER/RAGE and DGKK activation might be related to oxidative stress, leading to hepatoma cell death. Conclusions: Our studies identified multiple mechanisms are responsible for the anti-tumour activity of ascorbate and suggest high doses of ascorbate with less frequency will act as a novel therapeutic agent for liver cancer in vivo.

Switch of NAD Salvage to de novo Biosynthesis Sustains SIRT1-RelB-Dependent Inflammatory Tolerance.

A typical inflammatory response sequentially progresses from pro-inflammatory, immune suppressive to inflammatory repairing phases. Although the physiological inflammatory response resolves in time, severe acute inflammation usually sustains immune tolerance and leads to high mortality, yet the underlying mechanism is not completely understood. Here, using the leukemia-derived THP-1 human monocytes, healthy and septic human peripheral blood mononuclear cells (PBMC), we report that endotoxin dose-dependent switch of nicotinamide adenine dinucleotide (NAD) biosynthesis pathways sustain immune tolerant status. Low dose endotoxin triggered nicotinamide phosphoribosyltransferase (NAMPT)-dependent NAD salvage activity to adapt pro-inflammation. In contrast, high dose endotoxin drove a shift of NAD synthesis pathway from early NAMPT-dependent NAD salvage to late indoleamine 2,3-dioxygenase-1 (IDO1)-dependent NAD de novo biosynthesis, leading to persistent immune suppression. This is resulted from the IDO1-dependent expansion of nuclear NAD pool and nuclear NAD-dependent prolongation of sirtuin1 (SIRT1)-directed epigenetics of immune tolerance. Inhibition of IDO1 activity predominantly decreased nuclear NAD level, which promoted sequential dissociations of immunosuppressive SIRT1 and RelB from the promoter of pro-inflammatory TNF-α gene and broke endotoxin tolerance. Thus, NAMPT-NAD-SIRT1 axis adapts pro-inflammation, but IDO1-NAD-SIRT1-RelB axis sustains endotoxin tolerance during acute inflammatory response. Remarkably, in contrast to the prevention of sepsis death of animal model by IDO1 inhibition before sepsis initiation, we demonstrated that the combination therapy of IDO1 inhibition by 1-methyl-D-tryptophan (1-MT) and tryptophan supplementation rather than 1-MT administration alone after sepsis onset rescued sepsis animals, highlighting the translational significance of tryptophan restoration in IDO1 targeting therapy of severe inflammatory diseases like sepsis.

Isarubrolones Containing a Pyridooxazinium Unit from Streptomyces as Autophagy Activators.

Isarubrolones are bioactive polycyclic tropoloalkaloids from Streptomyces. Three new isarubrolones (2-4), together with the known isarubrolone C (1) and isatropolones A (5) and C (6, 3( R)-hydroxyisatropolone A), were identified from Streptomyces sp. CPCC 204095. The structures of these compounds were determined using a combination of mass spectrometry, 1D and 2D NMR spectroscopy, and ECD. Compounds 3 and 4 feature a pyridooxazinium unit, which is rarely seen in natural products. Compound 6 could conjugate with amino acids or amines to expand the structural diversity of isarubrolones with a pentacyclic or hexacyclic core. Importantly, 1 and 3-6 were found to induce complete autophagy.

Multiplex detection of miRNAs based on aggregation-induced emission luminogen encoded microspheres.

Herein, we report a multiplex detection platform based on a suspension array with aggregation-induced emission luminogen (AIEgen) barcodes for simultaneous quantitative measurement of let-7b-5p, miR-16-5p and miR-19b-3p, which are associated with gastric cancer. A detection strategy by using a flow cytometer is proposed, which utilizes AIEgen-encoded microspheres to quantify the target miRNAs, and phycoerythrin as a fluorescence reporter on the detection probes to provide quantitative signals. This multiplex assay shows good specificity for recognizing single base mismatch, and possesses excellent sensitivity with limits of detection (LODs) ranging from 0.43 to 0.76 nM for the three miRNAs. The approach could be extended to the simultaneous detection of more target miRNAs by designing specific detection probes and increasing the number of fluorescence barcodes. We could foresee it holding great potential in future laboratory research and clinical applications due to its flexibility, strong multiplexed ability and good detection performance.

KATZLGO: Large-Scale Prediction of LncRNA Functions by Using the KATZ Measure Based on Multiple Networks.

Aggregating evidences have shown that long non-coding RNAs (lncRNAs) generally play key roles in cellular biological processes such as epigenetic regulation, gene expression regulation at transcriptional and post-transcriptional levels, cell differentiation, and others. However, most lncRNAs have not been functionally characterized. There is an urgent need to develop computational approaches for function annotation of increasing available lncRNAs. In this article, we propose a global network-based method, KATZLGO, to predict the functions of human lncRNAs at large scale. A global network is constructed by integrating three heterogeneous networks: lncRNA-lncRNA similarity network, lncRNA-protein association network, and protein-protein interaction network. The KATZ measure is then employed to calculate similarities between lncRNAs and proteins in the global network. We annotate lncRNAs with Gene Ontology (GO) terms of their neighboring protein-coding genes based on the KATZ similarity scores. The performance of KATZLGO is evaluated on a manually annotated lncRNA benchmark and a protein-coding gene benchmark with known function annotations. KATZLGO significantly outperforms state-of-the-art computational method both in maximum F-measure and coverage. Furthermore, we apply KATZLGO to predict functions of human lncRNAs and successfully map 12,318 human lncRNA genes to GO terms.

Integrating Multiple Heterogeneous Networks for Novel LncRNA-Disease Association Inference.

Accumulating experimental evidence has indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes implicated in many human diseases. However, only relatively few experimentally supported lncRNA-disease associations have been reported. Developing effective computational methods to infer lncRNA-disease associations is becoming increasingly important. Current network-based algorithms typically use a network representation to identify novel associations between lncRNAs and diseases. But these methods are concentrated on specific entities of interest (lncRNAs and diseases) and they do not allow to consider networks with more than two types of entities. Considering the limitations in previous computational methods, we develop a new global network-based framework, LncRDNetFlow, to prioritize disease-related lncRNAs. LncRDNetFlow utilizes a flow propagation algorithm to integrate multiple networks based on a variety of biological information including lncRNA similarity, protein-protein interactions, disease similarity, and the associations between them to infer lncRNA-disease associations. We show that LncRDNetFlow performs significantly better than the existing state-of-the-art approaches in cross-validation. To further validate the reproducibility of the performance, we use the proposed method to identify the related lncRNAs for ovarian cancer, glioma, and cervical cancer. The results are encouraging. Many predicted lncRNAs in the top list have been verified by the biological studies.