Prediction of protein-ligand binding affinity via deep learning models.

Accurately predicting the binding affinity between proteins and ligands is crucial in drug screening and optimization, but it is still a challenge in computer-aided drug design. The recent success of AlphaFold2 in predicting protein structures has brought new hope for deep learning (DL) models to accurately predict protein-ligand binding affinity. However, the current DL models still face limitations due to the low-quality database, inaccurate input representation and inappropriate model architecture. In this work, we review the computational methods, specifically DL-based models, used to predict protein-ligand binding affinity. We start with a brief introduction to protein-ligand binding affinity and the traditional computational methods used to calculate them. We then introduce the basic principles of DL models for predicting protein-ligand binding affinity. Next, we review the commonly used databases, input representations and DL models in this field. Finally, we discuss the potential challenges and future work in accurately predicting protein-ligand binding affinity via DL models.

Prediction of allosteric druggable pockets of cyclin-dependent kinases.

Cyclin-dependent kinase (Cdk) proteins play crucial roles in the cell cycle progression and are thus attractive drug targets for therapy against such aberrant cell cycle processes as cancer. Since most of the available Cdk inhibitors target the highly conserved catalytic ATP pocket and their lack of specificity often lead to side effects, it is imperative to identify and characterize less conserved non-catalytic pockets capable of interfering with the kinase activity allosterically. However, a systematic analysis of these allosteric druggable pockets is still in its infancy. Here, we summarize the existing Cdk pockets and their selectivity. Then, we outline a network-based pocket prediction approach (NetPocket) and illustrate its utility for systematically identifying the allosteric druggable pockets with case studies. Finally, we discuss potential future directions and their challenges.

The inhibition effect of caffeic acid on NOX/ROS-dependent macrophages M1-like polarization contributes to relieve the LPS-induced mice mastitis.

The mammary gland is an adipose tissue containing not only adipocytes but also epithelial, endothelial, and immune cells. Epithelial cells and macrophages, as the integral components of the immune system, are on the front line of defense against infection. Our preliminary work proved that caffeic acid (CA) can effectively inhibit the inflammatory cascade of bovine mammary epithelial cells (BMEC) induced by lipopolysaccharide (LPS) and maintain cellular integrity and viability. Here, we investigated the therapeutic effect of CA on LPS-induced mice mastitis and explored its regulatory mechanism on macrophage inflammatory response induced by LPS in vitro. Firstly, the mice mastitis model was established by intramammary injection with 10 µg LPS, after which different CA doses (5, 10, 15 mg/kg) were administered. Then, the pathological section, myeloperoxidase (MPO) activity, proinflammatory factors and chemokines releasement, and redox state of mammary tissues were assessed, confirming CA's effectiveness on mice mastitis. In vitro, we validated the therapeutic relevance of CA in relieving LPS-induced RAW264.7 inflammatory and oxidative stress responses. Moreover, we further provided evidence that CA significantly reduced LPS-induced reactive oxygen species (ROS) generation via NADPH oxidase (NOX), which improved the imbalance relationship between nuclear factor kappa-B (NF-κB) and NF-E2 p45-related factor 2 (Nrf2) and led to a marked weakening of M1 polarization. The NOX-ROS signal inhibited by CA weakened the oxidative burst and neutrophil chemotaxis of macrophages, thus alleviating the immune cascade in mammary gland tissue and reducing the LPS-induced inflammatory damage. Collectively, CA would be a potential candidate or antibacterial synergist for curbing mastitis.

ANXA1 is identified as a key gene associated with high risk and T cell infiltration in primary sclerosing cholangitis.

BACKGROUND: Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease, with unclear pathogenesis. Although immune disorders, especially T cell infiltration, are thought to play a vital role in PSC, the specific pathogenesis mechanisms remain incompletely understood. This study evaluated the potential key gene associated with the PSC pathogenesis and analyzed the associations of the key gene with prognosis and immune cell infiltration by combining bioinformatics analysis and experimental verification. METHODS: Transcriptome data of PSC and normal human liver tissues (GSE159676) were obtained from the gene expression omnibus database. Differentially expressed genes (DEGs) were identified, and differences in biological states were analyzed. A protein-protein interaction (PPI) network was constructed. Hub genes were identified, and their expression was verified using transcriptome data of mice fed 0.1% 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) and Mdr2-/- mice (GSE179993, GSE80776), as well as by immunohistochemistry staining on clinical samples. The correlations between the key gene and other factors were evaluated by Pearson's correlation coefficient. Immune cell infiltration into human liver (GSE159676) was analyzed by xCell and verified by immunofluorescence staining on PSC liver samples. RESULTS: Of the 185 DEGs identified, 113 were upregulated and 72 were downregulated genes in PSC. Genes associated with immune cell infiltration and fibrosis were significantly enriched in PSC. PPI network showed close interactions among DEGs. A module strongly associated with immune infiltration was identified, with annexin A1 (ANXA1) being the core gene. High expression of ANXA1 in PSC was confirmed in two public datasets and by immunohistochemistry staining on clinical samples. High ANXA1 expression was strongly associated with high-risk score for PSC. Also, ANXA1 expression was positively associated with chemokines and chemokine receptors and with the infiltration of immune cells, especially T cells, into liver with PSC. Immune infiltration, fibrosis, and cancer-related processes were markedly enriched in PSC with high expression of ANXA1. CONCLUSION: ANXA1 is a key gene associated with high risk and infiltration of immune cells, especially T cells, in PSC. These findings provide new insight into the key biomarker of PSC and suggest that targeting ANXA1 may be a valuable strategy for the treatment of PSC.

DLSSAffinity: protein-ligand binding affinity prediction via a deep learning model.

Evaluating the protein-ligand binding affinity is a substantial part of the computer-aided drug discovery process. Most of the proposed computational methods predict protein-ligand binding affinity using either limited full-length protein 3D structures or simple full-length protein sequences as the input features. Thus, protein-ligand binding affinity prediction remains a fundamental challenge in drug discovery. In this study, we proposed a novel deep learning-based approach, DLSSAffinity, to accurately predict the protein-ligand binding affinity. Unlike the existing methods, DLSSAffinity uses the pocket-ligand structural pairs as the local information to predict short-range direct interactions. Besides, DLSSAffinity also uses the full-length protein sequence and ligand SMILES as the global information to predict long-range indirect interactions. We tested DLSSAffinity on the PDBbind benchmark. The results showed that DLSSAffinity achieves Pearson's R = 0.79, RMSE = 1.40, and SD = 1.35 on the test set. Comparing DLSSAffinity with the existing state-of-the-art deep learning-based binding affinity prediction methods, the DLSSAffinity model outperforms other models. These results demonstrate that combining global sequence and local structure information as the input features of a deep learning model can improve the accuracy of protein-ligand binding affinity prediction.

RPocket: an intuitive database of RNA pocket topology information with RNA-ligand data resources.

BACKGROUND: RNA regulates a variety of biological functions by interacting with other molecules. The ligand often binds in the RNA pocket to trigger structural changes or functions. Thus, it is essential to explore and visualize the RNA pocket to elucidate the structural and recognition mechanism for the RNA-ligand complex formation. RESULTS: In this work, we developed one user-friendly bioinformatics tool, RPocket. This database provides geometrical size, centroid, shape, secondary structure element for RNA pocket, RNA-ligand interaction information, and functional sites. We extracted 240 RNA pockets from 94 non-redundant RNA-ligand complex structures. We developed RPDescriptor to calculate the pocket geometrical property quantitatively. The geometrical information was then subjected to RNA-ligand binding analysis by incorporating the sequence, secondary structure, and geometrical combinations. This new approach takes advantage of both the atom-level precision of the structure and the nucleotide-level tertiary interactions. The results show that the higher-level topological pattern indeed improves the tertiary structure prediction. We also proposed a potential mechanism for RNA-ligand complex formation. The electrostatic interactions are responsible for long-range recognition, while the Van der Waals and hydrophobic contacts for short-range binding and optimization. These interaction pairs can be considered as distance constraints to guide complex structural modeling and drug design. CONCLUSION: RPocket database would facilitate RNA-ligand engineering to regulate the complex formation for biological or medical applications. RPocket is available at http://zhaoserver.com.cn/RPocket/RPocket.html .

Hepatic macrophages: Key players in the development and progression of liver fibrosis.

Hepatic fibrosis is a common pathological process involving persistent liver injury with various etiologies and subsequent inflammatory responses that occur in chronic liver diseases. If left untreated, liver fibrosis can progress to liver cirrhosis, hepatocellular carcinoma and eventually, liver failure. Unfortunately, to date, there is no effective treatment for liver fibrosis, with the exception of liver transplantation. Although the pathophysiology of liver fibrosis is multifactorial and includes the activation of hepatic stellate cells, which are known to drive liver fibrogenesis, hepatic macrophages have emerged as central players in the development of liver fibrosis and regression. Hepatic macrophages, which consist of resident macrophages (Kupffer cells) and monocyte-derived macrophages, have been shown to play an intricate role in the initiation of inflammatory responses to liver injury, progression of fibrosis, and promotion of fibrosis resolution. These features have made hepatic macrophages uniquely attractive therapeutic targets in the fight against hepatic fibrosis. In this review, we synthesised the literature to highlight the functions and regulation of heterogeneity in hepatic macrophages. Furthermore, using the existing findings, we attempt to offer insights into the molecular mechanisms underlying the phenotypic switch from fibrogenic macrophages to restorative macrophages, the regulation of heterogeneity, and modes of action for hepatic macrophages. A better understanding of these mechanisms may guide the development of novel anti-fibrotic therapies (eg macrophage subset-targeted treatments) to combat liver fibrosis in the future.

Lignin as a MALDI matrix for small molecules: a proof of concept.

Driven by the interest in metabolomic studies and the progress of imaging techniques, small molecule analysis is booming, while it remains challenging to be realized by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Herein, lignin, the second most abundant biomass in nature, was applied as a dual-ion-mode MALDI matrix for the first time to analyze small molecules. The low ionization efficiency and strong optical absorption properties make lignin a potential MALDI matrix in small molecule analysis. A total of 30 different small molecules were identified qualitatively and six kinds of representative molecules were detected quantitatively with a good linear response (R2 > 0.995). To verify the accuracy of our quantitative method in MALDI, myricitrin, a major bioactive component in Chinese bayberry, was analyzed in different cultivars and tissues. The myricitrin content in real samples detected by MALDI was highly consistent (R2 > 0.999) with that detected by high-performance liquid chromatography, thus indicating the applicability of the lignin matrix. Further characterization by ultraviolet and nuclear magnetic resonance spectroscopy was carried out to explain the possible mechanism of lignin as a matrix and provide more theories for a rational matrix design.

Parallel On-Target Derivatization for Mass Calibration and Rapid Profiling of N-Glycans by MALDI-TOF MS.

Glycosylation is an important post-translational modification of proteins, and abnormal glycosylation is involved in a variety of diseases. Accurate and rapid profiling of N-glycans by matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is still technically challenging and hampered mainly by mass drift of instrument, manual identification of spectrum peaks, and poor cocrystallization with traditional matrices besides low ionization efficiency of analytes. In the present study, a parallel on-target derivatization strategy (POTDS), on the basis of two rationally combined matrices, i.e., 3-hydrazinobenzoic acid plus DHB (DHB/3HBA) and quinoline-3-carbohydrazide plus DHB (DHB/Q3CH), was proposed for mass calibration and rapid detection of reducing N-glycans. Both DHB/3HBA and DHB/Q3CH show high derivatization efficiency and can improve the ionization efficiency of reducing N-glycans significantly. For mass calibration, in combination with dextrans, DHB/3HBA and DHB/Q3CH prove to be highly sensitive matrices facilitating both MS and MS2 calibration for N-glycans in dual polarities. For rapid identification, the regular mass difference observed for each N-glycan labeled with Q3CH and 3HBA respectively can eliminate the occurrence of false positives and promote automated identification of N-glycans in complex samples. For relative quantitation, the acid-base pair of DHB/Q3CH generates a concentrated cocrystallization of glycan-matrix mixtures at the edge of the droplet uniformly, exhibiting good linearity (R2 > 0.998) and accuracy (RSD ≤ 10%). Furthermore, the established POTDS was successfully utilized to assess N-glycans of serum from HCC patients, revealing potential for biomarker discovery in clinical practice.

Rapid quality control of medicine and food dual purpose plant polysaccharides by matrix assisted laser desorption/ionization mass spectrometry.

With their multiple biological activities and health benefit effects, polysaccharides from medicine and food dual purpose plants (MFDPPPs) have been extensively applied in many fields, including in medical treatments, stock farming, and cosmetics. However, to date, quality issues of MFDPPPs and technologies for the analysis of polysaccharides have posed challenges to chemists. Reported herein is a rapid and high-throughput quality control method for analyzing MFDPPPs, based on matrix assisted laser desorption/ionization mass spectrometry (MALDI-MS). For the analysis of illegally added and doped substances, ferroferric oxide nanoparticles were employed as the MALDI matrix to avoid small molecule interference. Qualitatively, high sensitivity was obtained for both illegal drugs and glucose. Quantitatively, the best linear response (R2 > 0.99) was attained in the concentration range from 0.005 to 1 mg mL-1 for glucose. For the analysis of polysaccharides, 2,5-dihydroxybenzoic acid/N-methylaniline was employed as the MALDI matrix to increase the detection sensitivity and mass range coverage. Furthermore, the established method was successfully applied to the analysis of supplements from Astragalus polysaccharides and Lentinan real samples, showing its potential in quality control for MFDPPPs.

A computational study of Tat-CDK9-Cyclin binding dynamics and its implication in transcription-dependent HIV latency.

HIV is a virus that attacks the T cells. HIV may either actively replicate or become latent within host cells for years. Since HIV uses its own protein Tat to hijack the host CDK9-Cyclin complex for transcription, Tat is implicated in transcription-dependent HIV latency. To quantify the impact of Tat binding, we propose a computational framework to probe the dynamics of the CDK9-Cyclin interface and the ATP pocket reorganization upon binding by different Tat mutants. Specifically, we focus on mutations at three Tat residues P10, W11, and N12 that are known to interact directly with CDK9 based on the crystal structure of the Tat-CDK9-Cyclin complex. Our molecular dynamics simulations show that the CDK9-Cyclin interface becomes slightly weaker for P10S and W11R mutants but tighter for the K12N mutant. Furthermore, the side chain orientation of residue K48 in the ATP pocket of CDK9 is similar to the inactive state in P10S and W11R simulations, but similar to the active state in K12N simulations. These are consistent with some existing but puzzling observations of latency for these mutants. This framework may hence help gain a better understanding of the role of Tat in the transcription-dependent HIV latency establishment.

Trading Imbalance in Chinese Stock Market-A High-Frequency View.

Although an imbalance of buying and selling profoundly affects the formation of market trends, a fine-granularity investigation of this perplexity of trading behavior is still missing. Instead of using existing entropy measures, this paper proposed a new indicator based on transaction dataset that enables us to inspect both the direction and the magnitude of this imbalance at high frequency, which we call "polarity". The polarity aims to measure the unevenness of the very essence trading desire based on the most micro decision making units. We investigate the relationship between the polarity and the return at both market-level and stock-level and find that the autocorrelated polarities cause a positive relation between lagged polarities and returns, while the current polarity is the opposite. It is also revealed that these associations shift according to the market conditions. In fact, when aggregating the one-minute polarities into daily signals, we find not only significant correlations disclosed by the market polarity and market emotion, but also the reliability of these signals in terms of reflecting the transitions of market-level behavior. These results imply that our presented polarity can reflect the market sentiment and condition in real time. Indeed, the trading polarity provides a new indicator from a high-frequency perspective to understand and foresee the market's behavior in a data-driven manner.

HKPocket: human kinase pocket database for drug design.

BACKGROUND: The kinase pocket structural information is important for drug discovery targeting cancer or other diseases. Although some kinase sequence, structure or drug databases have been developed, the databases cannot be directly used in the kinase drug study. Therefore, a comprehensive database of human kinase protein pockets is urgently needed to be developed. RESULTS: Here, we have developed HKPocket, a comprehensive Human Kinase Pocket database. This database provides sequence, structure, hydrophilic-hydrophobic, critical interactions, and druggability information including 1717 pockets from 255 kinases. We further divided these pockets into 91 pocket clusters using structural and position features in each kinase group. The pocket structural information would be useful for preliminary drug screening. Then, the potential drugs can be further selected and optimized by analyzing the sequence conservation, critical interactions, and hydrophobicity of identified drug pockets. HKPocket also provides online visualization and pse files of all identified pockets. CONCLUSION: The HKPocket database would be helpful for drug screening and optimization. Besides, drugs targeting the non-catalytic pockets would cause fewer side effects. HKPocket is available at http://zhaoserver.com.cn/HKPocket/HKPocket.html.

DIRECT: RNA contact predictions by integrating structural patterns.

BACKGROUND: It is widely believed that tertiary nucleotide-nucleotide interactions are essential in determining RNA structure and function. Currently, direct coupling analysis (DCA) infers nucleotide contacts in a sequence from its homologous sequence alignment across different species. DCA and similar approaches that use sequence information alone typically yield a low accuracy, especially when the available homologous sequences are limited. Therefore, new methods for RNA structural contact inference are desirable because even a single correctly predicted tertiary contact can potentially make the difference between a correct and incorrectly predicted structure. Here we present a new method DIRECT (Direct Information REweighted by Contact Templates) that incorporates a Restricted Boltzmann Machine (RBM) to augment the information on sequence co-variations with structural features in contact inference. RESULTS: Benchmark tests demonstrate that DIRECT achieves better overall performance than DCA approaches. Compared to mfDCA and plmDCA, DIRECT produces a substantial increase of 41 and 18%, respectively, in accuracy on average for contact prediction. DIRECT improves predictions for long-range contacts and captures more tertiary structural features. CONCLUSIONS: We developed a hybrid approach that incorporates a Restricted Boltzmann Machine (RBM) to augment the information on sequence co-variations with structural templates in contact inference. Our results demonstrate that DIRECT is able to improve the RNA contact prediction.

Combination Strategy of Reactive and Catalytic Matrices for Qualitative and Quantitative Profiling of N-Glycans in MALDI-MS.

Qualitative and quantitative analysis of N-glycans by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is hampered mainly by the low ionization efficiency of analytes and their poor cocrystallization with traditional organic acid matrices. In the present study, a combination strategy of reactive and catalytic matrices (CSRCM) was proposed for the on-target derivatization and detection of reducing N-glycans: a novel reactive matrix, i.e., 2,5-dihydroxybenzohydrazide (DHBH), having a skeleton structure similar to that of DHB, was designed and synthesized, and this reactive matrix was mixed with catalytic matrix DHB to form a rationally combinatorial matrix (DHB/DHBH). Qualitatively, DHB/DHBH could improve the ionization efficiency of reducing carbohydrates significantly. Quantitatively, the acid-base chemistry of DHB/DHBH leads to a uniform cocrystallization of analytes-matrix mixtures. Consequently, CSRCM provides accurate quantitation for N-glycans with high derivatization efficiency and good linearity (R2 > 0.99) within 2 orders of magnitude on the basis of an internal standard method. Furthermore, the CSRCM was successfully applied to evaluating N-glycan in serum samples of colorectal cancer patients, thus showing potential in clinical applications for biomarker discovery.

RBind: computational network method to predict RNA binding sites.

Motivation: Non-coding RNA molecules play essential roles by interacting with other molecules to perform various biological functions. However, it is difficult to determine RNA structures due to their flexibility. At present, the number of experimentally solved RNA-ligand and RNA-protein structures is still insufficient. Therefore, binding sites prediction of non-coding RNA is required to understand their functions. Results: Current RNA binding site prediction algorithms produce many false positive nucleotides that are distance away from the binding sites. Here, we present a network approach, RBind, to predict the RNA binding sites. We benchmarked RBind in RNA-ligand and RNA-protein datasets. The average accuracy of 0.82 in RNA-ligand and 0.63 in RNA-protein testing showed that this network strategy has a reliable accuracy for binding sites prediction. Availability and implementation: The codes and datasets are available at https://zhaolab.com.cn/RBind. Supplementary information: Supplementary data are available at Bioinformatics online.

Enterobactin-Specific Antibodies Induced by a Novel Enterobactin Conjugate Vaccine.

Enterobactin (Ent)-mediated high-affinity iron acquisition is critical for Gram-negative bacteria to survive in the host. Given the bacteriostatic effect of lipocalin resulting from its potent Ent-binding ability, immune intervention directly targeting Ent is promising for iron-dependent pathogen control. Recently, an Ent conjugate vaccine was reported, but it still has several significant weaknesses. In this study, we sought to develop an innovative Ent conjugate vaccine that can induce a high level of antibodies directed against Ent and to provide solid evidence demonstrating siderophore-binding capacity of Ent-specific antibodies. Using a simple method, we successfully conjugated purified Ent to different carriers, including keyhole limpet hemocyanin (KLH), bovine serum albumin, and CmeC, a vaccine candidate for Campylobacter control. Subcutaneous immunization of rabbits with the KLH-Ent conjugate triggered a strong systemic IgG immune response with an up to 16,384-fold increase in IgG titer directed against whole conjugate and an up to 4,096-fold increase in the level of specific anti-Ent IgG. To evaluate the ability of Ent-specific IgG to bind to the Ent derivatives present in vivo, various Ent derivatives were chemically synthesized and a unique enzyme-linked immunosorbent assay method was developed. The Ent-specific IgG also displayed exceptional reactivity to ferric Ent, a linear trimer of Ent, and different salmochelins. Growth assays further demonstrated that the Ent-specific antibodies significantly inhibited Ent-dependent growth of Campylobacter spp. and Escherichia coli Collectively, this study reports an efficient method to prepare a new type of Ent conjugate vaccines for inducing a high level of Ent-specific antibodies, which can bind to various Ent derivatives and display lipocalin-like bacteriostatic features.IMPORTANCE Ent-mediated high-affinity iron acquisition is a universal and critical contributor for Gram-negative pathogens to survive and infect hosts. Published information has supported an innovative immune intervention strategy that directly targets Ent to starve pathogens by limiting the availability of iron to be utilized. Compared to a recently published Ent conjugate, there are three advantages of the vaccine described in this study: ease of preparation, induction of high titer of anti-Ent IgG, and the ability of Ent-specific antibodies to bind various Ent derivatives, including the salmochelins that help enteric pathogens evade sequestration of siderophores by host lipocalins. In addition, the Ent-specific antibodies were demonstrated to function similarly to lipocalin to interfere with the Ent-dependent growth of Campylobacter and E. coli under iron-restricted conditions. This study has significant potential for broader applications to prevent and control various Gram-negative infections in humans and animals.

The role of ERp44 in glucose and lipid metabolism.

Endoplasmic Reticulum Protein 44 (ERp44) is a member of the PDI family, named for a molecular weight of 44 kD. White adipose tissue has metabolic and endocrine functions that are important to metabolism. The role of ERp44 in glucose and lipid metabolism is not known yet. The current study was undertaken to investigate the implication of ERp44 in glucose and lipid metabolism. In this study, we generated and characterized ERp44-/- mice. We used type 2 diabetes models and ERp44 knockout mice to show the implication of ERp44 in glucose and lipid metabolism. Knockout newborns had lower blood glucose compared to wild-type. Adult knockouts had abnormal intraperitoneal, glucose, insulin and pyruvic acid tolerance. Lipocytes were smaller and fewer in knockout mice compared to wild-type. Knockouts resisted to high-fat diet-induced obesity. ERp44 expression in white adipose tissue decreased significantly in type 2 diabetes models. Results suggest that ERp44 is closely associated with glucose and lipid metabolism.

Integration of hepatitis B virus S gene impacts on hepatitis B surface antigen levels in patients with antiviral therapy.

BACKGROUND AND AIM: The aim of this study is to investigate the impact of hepatitis B virus (HBV) S gene integration on serum hepatitis B surface antigen (HBsAg) levels in chronic hepatitis B with long-term nucleos(t)ide analogue (NUC) therapy. METHODS: Chronic hepatitis B patients who performed liver biopsy at baseline and treated with long-term NUC therapy were recruited. The integration of HBV S gene in baseline liver biopsy specimen was detected by Alu polymerase chain reaction method. Serum HBsAg levels were measured at baseline and the second year and the fourth year after NUC therapy by Roche reagent, respectively. Serum HBsAg levels between HBV S gene integrated group and nonintegrated group were compared and analyzed. RESULTS: Seventy patients were eligible for this study. Among them, 11 (15.7%) were found to have HBV S gene integration in their baseline liver biopsy specimens. Similar significant decrease of HBsAg levels was found in both integrated and nonintegrated groups (2.63 vs 2.65 log IU/mL, P = 0.478) after the first 2 years of NUC therapy. Thereafter, the decrease of HBsAg level from 2 to 4 years after therapy was largely unchanged in integrated group as compared with that of nonintegrated group (0.1 vs 2.53 log IU/mL, P = 0.002), with statistical difference. CONCLUSIONS: Serum HBsAg could be originated from the expression of the integrated HBV S gene in patients with S gene integration, which implicated the limitations when regarding HBsAg as a surrogate biomarker of covalently closed circular DNA activity and as an indicator of safe NUC discontinuation.

RNA-binding protein CUGBP1 regulates insulin secretion via activation of phosphodiesterase 3B in mice.

AIMS/HYPOTHESIS: CUG-binding protein 1 (CUGBP1) is a multifunctional RNA-binding protein that regulates RNA processing at several stages including translation, deadenylation and alternative splicing, as well as RNA stability. Recent studies indicate that CUGBP1 may play a role in metabolic disorders. Our objective was to examine its role in endocrine pancreas function through gain- and loss-of-function experiments and to further decipher the underlying molecular mechanisms. METHODS: A mouse model in which type 2 diabetes was induced by a high-fat diet (HFD; 60% energy from fat) and mice on a standard chow diet (10% energy from fat) were compared. Pancreas-specific CUGBP1 overexpression and knockdown mice were generated. Different lengths of the phosphodiesterase subtype 3B (PDE3B) 3' untranslated region (UTR) were cloned for luciferase reporter analysis. Purified CUGBP1 protein was used for gel shift experiments. RESULTS: CUGBP1 is present in rodent islets and in beta cell lines; it is overexpressed in the islets of diabetic mice. Compared with control mice, the plasma insulin level after a glucose load was significantly lower and glucose clearance was greatly delayed in mice with pancreas-specific CUGBP1 overexpression; the opposite results were obtained upon pancreas-specific CUGBP1 knockdown. Glucose- and glucagon-like peptide1 (GLP-1)-stimulated insulin secretion was significantly attenuated in mouse islets upon CUGBP1 overexpression. This was associated with a strong decrease in intracellular cAMP levels, pointing to a potential role for cAMP PDEs. CUGBP1 overexpression had no effect on the mRNA levels of PDE1A, 1C, 2A, 3A, 4A, 4B, 4D, 7A and 8B subtypes, but resulted in increased PDE3B expression. CUGBP1 was found to directly bind to a specific ATTTGTT sequence residing in the 3' UTR of PDE3B and stabilised PDE3B mRNA. In the presence of the PDE3 inhibitor cilostamide, glucose- and GLP-1-stimulated insulin secretion was no longer reduced by CUGBP1 overexpression. Similar to CUGBP1, PDE3B was overexpressed in the islets of diabetic mice. CONCLUSIONS/INTERPRETATION: We conclude that CUGBP1 is a critical regulator of insulin secretion via activating PDE3B. Repressing this protein might provide a potential strategy for treating type 2 diabetes.