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Abstract Target-aware drug discovery has greatly accelerated the drug discovery process to design small-molecule ligands with high binding affinity to disease-related protein targets. Conditioned on targeted proteins, previous works utilize various kinds of deep generative models and have shown great potential in generating molecules with strong protein-ligand binding interactions. However, beyond binding affinity, effective drug molecules must manifest other essential properties such as high drug-likeness, which are not explicitly addressed by current target-aware generative methods. In this article, aiming to bridge the gap of multi-objective target-aware molecule generation in the field of deep learning-based drug discovery, we propose ParetoDrug, a Pareto Monte Carlo Tree Search (MCTS) generation algorithm. ParetoDrug searches molecules on the Pareto Front in chemical space using MCTS to enable synchronous optimization of multiple properties. Specifically, ParetoDrug utilizes pretrained atom-by-atom autoregressive generative models for the exploration guidance to desired molecules during MCTS searching. Besides, when selecting the next atom symbol, a scheme named ParetoPUCT is proposed to balance exploration and exploitation. Benchmark experiments and case studies demonstrate that ParetoDrug is highly effective in traversing the large and complex chemical space to discover novel compounds with satisfactory binding affinities and drug-like properties for various multi-objective target-aware drug discovery tasks.

Abstract The human digital twin (HDT) emerges as a promising human-centric technology in Industry 5.0, but challenges remain in human modeling and simulation. Digital human modeling (DHM) provides solutions for modeling and simulating human physical and cognitive aspects to support ergonomic analysis. However, it has limitations in real-time data usage, personalized services, and timely interaction. The emerging HDT concept offers new possibilities by integrating multi-source data and artificial intelligence for continuous monitoring and assessment. Hence, this paper reviews the evolution from DHM to HDT and proposes a unified HDT framework from a human factors perspective. The framework comprises the physical twin, the virtual twin, and the linkage between these two. The virtual twin integrates human modeling and AI engines to enable model-data-hybrid-enabled simulation. HDT can potentially upgrade traditional ergonomic methods to intelligent services through real-time analysis, timely feedback, and bidirectional interactions. Finally, the future perspectives of HDT for industrial applications as well as technical and social challenges are discussed. In general, this study outlines a human factors perspective on HDT for the first time, which is useful for cross-disciplinary research and human factors innovation to enhance the development of HDT in industry.

This paper selects development indicators of primary industry, secondary industry, and tertiary industry, as well as indicators of public lifestyle, to measure the impact of economic development and general public lifestyle on the environment. A grey relational degree model is used to analyse Tianjin’s evolving characteristics from 2000 to 2020 on the balanced development of the economy and environment. The results suggest that: Firstly, Tianjin’s overall coupling degree between economic growth and environmental change varies from 0.56 to 0.75, which is at a moderate coordination level, and the degree of coordination between tertiary industry and environment is high. Secondly, taking time as the axis, the relational degree is significant between economic growth and environmental change. From 2015 to 2020, the synergy between the development of the secondary industry and the environment declined, indicating that the development of the secondary sector has caused some environmental destruction. On these grounds, Tianjin should give full play to its traditional advantages and comparative advantages, lay emphasis on the optimisation and upgrading of its industrial structure in the process of continuously deepening the integration of Beijing-Tianjin-Hebei, promote the deep integration of manufacturing and service industry, and boost the quality its economic development.

Background/Objectives: Cyberbullying is an increasingly serious issue that negatively impacts the mental and physical health of adolescents. This study aims to report the prevalence rates of adolescent cyberbullying–victimization and its associated related factors, providing a scientific basis for targeted efforts to protect the mental and physical well-being of adolescents; Methods: From March to May 2019, there were 13 high schools and 33 middle schools in Yixing, with a student ratio of 2:1 between middle and high school. Using a random cluster sampling method, we selected four high schools and three middle schools based on this ratio, resulting in a total of 13,258 students. We conducted a survey using a self-designed questionnaire to investigate the experiences of adolescents with cyberbullying and victimization, comparing the differences in cyberbullying–victimization based on various demographic characteristics. Additionally, we employed a multifactorial logistic regression model to analyze the associated factors; Results: The rate of adolescents who declared themselves as cyberbully-victims is 2.9%. The results of the logistic regression analysis indicate that being male, having both parents working outside the home, experiencing occasional or large conflicts among family members, being subjected to punishment-and-abuse child discipline, always or often using social software (websites), enjoying playing single or multiplayer games, self-smoking, and self-drinking were associated with a higher likelihood of being a cyberbully-victim (p < 0.05); Conclusions: Adolescent cyberbullying–victimization is affected by personal, family, and social factors. Therefore, comprehensive strategies and measures are needed to intervene in this problem.

Abstract Machine learning-based scoring functions (MLSFs) have shown potential for improving virtual screening capabilities over classical scoring functions (SFs). Due to the high computational cost in the process of feature generation, the numbers of descriptors used in MLSFs and the characterization of protein–ligand interactions are always limited, which may affect the overall accuracy and efficiency. Here, we propose a new SF called TB-IECS (theory-based interaction energy component score), which combines energy terms from Smina and NNScore version 2, and utilizes the eXtreme Gradient Boosting (XGBoost) algorithm for model training. In this study, the energy terms decomposed from 15 traditional SFs were firstly categorized based on their formulas and physicochemical principles, and 324 feature combinations were generated accordingly. Five best feature combinations were selected for further evaluation of the model performance in regard to the selection of feature vectors with various length, interaction types and ML algorithms. The virtual screening power of TB-IECS was assessed on the datasets of DUD-E and LIT-PCBA, as well as seven target-specific datasets from the ChemDiv database. The results showed that TB-IECS outperformed classical SFs including Glide SP and Dock, and effectively balanced the efficiency and accuracy for practical virtual screening.

Protein loop modeling is a challenging yet highly nontrivial task in protein structure prediction. Despite recent progress, existing methods including knowledge-based, ab initio, hybrid, and deep learning (DL) methods fall substantially short of either atomic accuracy or computational efficiency. To overcome these limitations, we present KarmaLoop, a novel paradigm that distinguishes itself as the first DL method centered on full-atom (encompassing both backbone and side-chain heavy atoms) protein loop modeling. Our results demonstrate that KarmaLoop considerably outperforms conventional and DL-based methods of loop modeling in terms of both accuracy and efficiency, with the average RMSDs of 1.77 and 1.95 Å for the CASP13+14 and CASP15 benchmark datasets, respectively, and manifests at least 2 orders of magnitude speedup in general compared with other methods. Consequently, our comprehensive evaluations indicate that KarmaLoop provides a state-of-the-art DL solution for protein loop modeling, with the potential to hasten the advancement of protein engineering, antibody–antigen recognition, and drug design.

A maximum power point tracking (MPPT) method based on improved mayfly algorithm (IMA) with shading detection is proposed to realize the global MPPT with multiple-peak P-V characteristic curve under partial shading condition (PSC) and rapid MPPT with single-peak P-V characteristic curve under uniform irradiance condition (UIC) for photovoltaic array in this paper. Firstly, the characteristic of current-voltage curve in the ideal current source region for photovoltaic array is analyzed, and a shading detection strategy is proposed to monitor the shading condition of photovoltaic array to identify the multi-peak and single-peak on P-V curve. Secondly, the IMA with the elimination strategy is proposed to realize multi-peak MPPT. Meanwhile, the IMA with the bisection searching strategy is utilized to realize quick single-peak MPPT. Finally, simulation and experiment are conducted to verify the effectiveness of the proposed IMA MPPT method, and the results show that the IMA MPPT method can not only identify multi-peak and single-peak effectively, but also improve the tracking efficiency and accuracy in single-peak and multi-peak MPPT scenarios.

Abstract As an important member of ion channels family, the voltage-gated sodium channel (VGSC/Nav) is associated with a variety of diseases, including epilepsy, migraine, ataxia, etc., and has always been a hot target for drug design and discovery. Many subtype-selective modulators targeting VGSCs have been reported, and some of them have been approved for clinical applications. However, the drug design resources related to VGSCs are insufficient, especially the lack of accurate and extensive compound data toward VGSCs. To fulfill this demand, we develop the Voltage-gated Sodium Channels Database (VGSC-DB). VGSC-DB is the first open-source database for VGSCs, which provides open access to 6055 data records, including 3396 compounds from 173 references toward nine subtypes of Navs (Nav1.1 ~ Nav1.9). A total of 28 items of information is included in each data record, including the chemical structure, biological activity (IC50/EC50), target, binding site, organism, chemical and physical properties, etc. VGSC-DB collects the data from small-molecule compounds, toxins and various derivatives. Users can search the information of compounds by text or structure, and the advanced search function is also supported to realize batch query. VGSC-DB is freely accessible at http://cadd.zju.edu.cn/vgsc/ , and all the data can be downloaded in XLSX/SDF file formats. Graphical Abstract

Abstract Exosomes mediate intercellular communication by transmitting active molecules. The function of long noncoding RNA (lncRNA) H19 in autoimmune liver injury is unclear. Concanavalin A (ConA)‐induced liver injury is well‐characterized immune‐mediated hepatitis. Here, we showed that lncRNA H19 expression was increased in the liver after ConA treatment, accompanied by increased exosome secretion. Moreover, injection of AAV‐H19 aggravated ConA‐induced hepatitis, with an increase in hepatocyte apoptosis. However, GW4869, an exosome inhibitor, alleviated ConA‐induced liver injury and inhibited the upregulation of lncRNA H19. Intriguingly, lncRNA H19 expression in the liver was significantly downregulated, after macrophage depletion. Importantly, the lncRNA H19 was primarily expressed in type I macrophage (M1) and encapsulated in M1‐derived exosomes. Furthermore, H19 was transported from M1 to hepatocytes via exosomes, and exosomal H19 dramatically induced hepatocytes apoptosis both in vitro and vivo. Mechanistically, H19 upregulated the transcription of hypoxia‐inducible factor‐1 alpha (HIF‐1α), which accumulated in the cytoplasm and mediated hepatocyte apoptosis by upregulating p53. M1‐derived exosomal lncRNA H19 plays a pivotal role in ConA‐induced hepatitis through the HIF‐1α–p53 signaling pathway. These findings identify M1 macrophage‐derived exosomal H19 as a novel target for the treatment of autoimmune liver diseases.

In this paper, we propose a sensorless control strategy for permanent magnet synchronous motors (PMSMs) based on an adaptive-gain sliding mode observer (ASMO) with precise compensation. Firstly, the observer adopts a saturation function with a continuous boundary layer as the switching function to avoid the delay effect of the low-pass filter. In addition, the adaptive-gain method is designed based on a conventional sliding mode observer (SMO) with constant-gain and a saturation function. The adaptive-gain mathematical model is simplified by establishing a nonlinear feedback channel of an SMO. Secondly, in order to improve the practicability and facilitate debugging, the explicit stability condition of an ASMO is deduced according to Lyapunov’s second method. According to the proposed adaptive method, the chattering caused by the mismatch of the sliding-mode gain is suppressed, and the observable speed range is also improved. Thirdly, the position estimation delay problem of an SMO with a continuous switching function is analyzed in detail from the perspective of frequency characteristics. Then, a precise compensation method is proposed for the delay problem, which greatly improves the position estimation accuracy and the control performance. Finally, the correctness of the theory and the feasibility of the ASMO with precise compensation for the sensorless control of PMSMs are verified by experiments.

Abstract Breast cancer (BRCA) is the primary cause of mortality among females globally. The combination of advanced genomic analysis with proteomics characterization to construct a protein prognostic model will help to screen effective biomarkers and find new therapeutic directions. This study obtained proteomics data from The Cancer Proteome Atlas (TCPA) dataset and clinical data from The Cancer Genome Atlas (TCGA) dataset. Kaplan–Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G). Based on risk curves, survival curves, receiver operating characteristic curves, and independent prognostic analysis, the protein prognostic model could be viewed as an independent factor to accurately predict the survival time of BRCA patients. We further validated that this prognostic model had good predictive performance in the GSE88770 dataset. The expression of 6 proteins was significantly associated with the overall survival of BRCA patients. The 6 proteins and encoding genes were differentially expressed in normal and primary tumor tissues and in different BRCA stages. In addition, we verified the expression of 3 differential proteins by immunohistochemistry and found that CDH3 and EIF4G1 were significantly higher in breast cancer tissues. Functional enrichment analysis indicated that the 6 genes were mainly related to the HIF-1 signaling pathway and the PI3K-AKT signaling pathway. This study suggested that the prognosis-related proteins might serve as new biomarkers for BRCA diagnosis, and that the risk model could be used to predict the prognosis of BRCA patients.

Abstract Background Cetuximab is used for colorectal cancer (CRC) treatment. However, the early biomarker of treatment efficacy of cetuximab has not been identified. Methods After 1 year of cetuximab treatment, patients were divided into an effective group and an ineffective group. The interleukin‐33 (IL‐33) level and the distribution of lymphatic cells in patients were investigated by analyzing the peripheral blood mononuclear cells via flow cytometry analysis and ELISA. The correlation between IL‐33 immunomodulatory effect and cetuximab treatment efficacy was determined through experiments in vivo and in vitro. Results The IL‐33 level in the peripheral blood was increased at 4 weeks after cetuximab administration of effective group, meanwhile, the osteopontin (OPN) was reduced. Whereas neither IL‐33 level nor OPN level of ineffective patients changed. In the effective group, the number of natural killer (NK) and CD8+ T cells were increased. Moreover, CD137 and CD107a expression on NK cells were higher in the effective group compared to the ineffective group. In vitro cetuximab treatment also increased the number of NK and CD8+ T cells as well as CD137 and CD107a expression upon IL‐33 stimulation. Moreover, the secretion of OPN was inhibited by IL‐33 administration in cetuximab‐treated PBMCs from the effective group patients. IL‐33 upregulated the cytotoxicity of NK cells and inhibited tumor cells growth in the effective cetuximab treatment mice. Conclusion Effective cetuximab treatment induced a change of IL‐33 and OPN at the early stage and triggered the NK cells antitumor activity. Consequently, significantly increased IL‐33 level and decreased OPN level in the peripheral blood at the early treatment are proposed as potential predictors of cetuximab treatment efficacy.

ABSTRACT The gut microbiome is involved in metabolic disorders. Osteopontin (OPN), as a key cytokine, contributes to various inflammation-related diseases. The underlying role of OPN in the microbiome remains poorly understood. Here, we investigated whether OPN could modulate metabolic disorders by affecting gut microbiota. In our present study, we found that the expression of OPN was elevated in individuals with obesity compared to that observed in healthy controls. There was a positive correlation between plasma OPN levels and body mass index (BMI) in humans. Moreover, OPN significantly exacerbated lipid accumulation and metabolic disorders in high-fat diet (HFD)-fed mice. Importantly, OPN significantly aggravated HFD-induced gut dysbiosis with a key signature profile. Fecal microbiota transplantation also supported the role of OPN in HFD-induced metabolic disorders in a microbiota-dependent manner. Moreover, the microbiome shift of OPN-deficient mice would be compensated to resemble those of wild-type mice by feeding with either OPN-containing milk or recombinant OPN protein in vivo. Furthermore, metagenomic analysis showed that OPN induced a higher abundance of Dorea and a lower abundance of Lactobacillus, which were positively and negatively correlated with body weight, respectively. Indeed, the abundance of Dorea was significantly decreased after Lactobacillus administration, suggesting that OPN may regulate the intestinal abundance of Dorea by reducing the colonization of Lactobacillus. We further confirmed that OPN decreased the adhesion of Lactobacillus to intestinal epithelial cells through the Notch signaling pathway. This study suggested that OPN could exacerbate HFD-induced metabolic dysfunctions through the OPN-induced alteration of the gut microbiome. Therefore, OPN could be a potential therapeutic target for metabolic syndrome. IMPORTANCE Gut microbiota are involved in metabolic disorders. However, microbiome-based therapeutic interventions are not always effective, which might be due to interference of the host factors. Here, we identified a strong positive correlation between OPN levels and BMI in humans. Next, we confirmed that OPN could aggravate high-fat diet-induced metabolic disorders in mice. Importantly, we found that fecal microbiota transplantation from OPN-deficient mice significantly alleviated metabolic disorders in WT mice. OPN directly induces the remodeling of the gut microbiota both in vitro and in vivo. These findings indicate that OPN could contribute to metabolic disorders by inducing an alteration of gut microbiota. OPN regulated the relative abundance of Lactobacillus by decreasing the adhesion of Lactobacillus to intestinal epithelial cells through the Notch signaling pathway. These data identify OPN as a potential pharmaceutical target for weight control and for the treatment of metabolic disorders.

Gastrointestinal dysfunction is a common symptom of acute mountain sickness (AMS). The gut microbiota and γδ T cells play critical roles in intestinal disease. However, the mechanistic link between the microbiota and γδ T cells in hypoxia-induced intestinal injury remains unclear. Here, we show that hypoxia-induced intestinal damage was significantly alleviated after microbiota depletion with antibiotics. Hypoxia modulated gut microbiota composition by promoting antimicrobial peptides angiogenin-4 secretions. The abundance of Clostridium in the gut of mice after hypoxia significantly decreased, while the abundance of Desulfovibrio significantly increased. Furthermore, Desulfovibrio-derived phosphatidylethanolamine and phosphatidylcholine promoted γδ T cell activation. In CD1d-deficient mice, the levels of intraepithelial IL-17A and γδ T cells and intestinal damage were significantly decreased compared with those in wild-type mice under hypoxia. Mechanistically, phospholipid metabolites from Desulfovibrio are presented by intestinal epithelial CD1d to induce the proliferation of IL-17A-producing γδ T cells, which aggravates intestinal injury. Gut microbiota-derived metabolites promote hypoxia-induced intestinal injury via CD1d-dependent γδ T cells, suggesting that phospholipid metabolites and γδ T cells can be targets for AMS therapy.

Abstract Structure-based drug design depends on the detailed knowledge of the three-dimensional (3D) structures of protein–ligand binding complexes, but accurate prediction of ligand-binding poses is still a major challenge for molecular docking due to deficiency of scoring functions (SFs) and ignorance of protein flexibility upon ligand binding. In this study, based on a cross-docking dataset dedicatedly constructed from the PDBbind database, we developed several XGBoost-trained classifiers to discriminate the near-native binding poses from decoys, and systematically assessed their performance with/without the involvement of the cross-docked poses in the training/test sets. The calculation results illustrate that using Extended Connectivity Interaction Features (ECIF), Vina energy terms and docking pose ranks as the features can achieve the best performance, according to the validation through the random splitting or refined-core splitting and the testing on the re-docked or cross-docked poses. Besides, it is found that, despite the significant decrease of the performance for the threefold clustered cross-validation, the inclusion of the Vina energy terms can effectively ensure the lower limit of the performance of the models and thus improve their generalization capability. Furthermore, our calculation results also highlight the importance of the incorporation of the cross-docked poses into the training of the SFs with wide application domain and high robustness for binding pose prediction. The source code and the newly-developed cross-docking datasets can be freely available at https://github.com/sc8668/ml_pose_prediction and https://zenodo.org/record/5525936 , respectively, under an open-source license. We believe that our study may provide valuable guidance for the development and assessment of new machine learning-based SFs (MLSFs) for the predictions of protein–ligand binding poses.

To address the geological hazard posed by unstable loess slopes prone to collapse and landslides, a high-strength geopolymer cementing material was developed utilizing green steel slag–fly ash as its primary constituent and activated through the application of sodium silicate alkalinity. The mechanical properties and microstructure changes of loess under varying dosages of steel slag–fly ash geopolymers and curing age were investigated through a series of tests, including unconfined compressive strength, direct shear, disintegration, electron microscope scanning, and X-ray diffraction. The findings indicate that the incorporation of geopolymers can significantly enhance the internal friction angle, cohesion, and unconfined compressive strength of loess, while mitigating the disintegration quantity and rate of stabilized soil. When 20% geopolymer is mixed into the solidified soil and cured for 28 days, the resulting solidified soil exhibits an internal friction angle of 31.12°, a cohesion of 81.09 kPa, and an unconfined compressive strength of 570.86 kPa. These values are 1.62 times, 1.76 times, and 3.36 times higher than those of loess, respectively. Moreover, the solidified soil shows minimal disintegration within 1800 s, with only 1.97% disintegration. The curing age of solidified soil has a significant impact on its curing effect. Enhancing the curing time can considerably enhance the mechanical properties of solidified soil. When the geopolymer content is 20% and the curing time is extended to 28 days, the internal friction angle, cohesion, and unconfined compressive strength increase by approximately 0.23 times, 0.48 times, and 1.61 times, respectively, compared to a curing time of 7 days. By analyzing SEM and XRD, it was found that the hydration of steel slag–fly ash geopolymer produces C-S-H and C-A-S-H cementing materials, which effectively fill the gaps between soil particles and enhance the mechanical properties of solidified soil. The research findings can serve as a theoretical foundation for the consolidation of loess subgrade utilizing steel slag–fly ash geopolymer.

Abstract Severe coronavirus disease 2019 (COVID-19) is characterized by symptoms of lymphopenia and multiorgan damage, but the underlying mechanisms remain unclear. To explore the function of N6-methyladenosine (m6A) modifications in COVID-19, we performed microarray analyses to comprehensively characterize the m6A epitranscriptome. The results revealed distinct global m6A profiles in severe and mild COVID-19 patients. Programmed cell death and inflammatory response were the major biological processes modulated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Further, RBM15, a major m6A methyltransferase, was significantly elevated and positively correlated with disease severity. Silencing RBM15 drastically reduced lymphocyte death in vitro. Knockdown of RBM15 remarkably suppressed the expression levels of multitarget genes related to programmed cell death and inflammatory response. This study shows that SARS-CoV-2 infection alters the m6A epitranscriptome of lymphocytes, particularly in the case of severe patients. RBM15 regulated host immune response to SARS-CoV-2 by elevating m6A modifications of multitarget genes. These findings indicate that RBM15 can serve as a target for the treatment of COVID-19.

Abstract Virtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: (1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein–ligand interactions; (2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; (3) the online prediction module that provides some well-constructed target-specific SFs for VS and an additional generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.973 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS.

Abstract Background Hepatitis B virus (HBV) infection is a crucial risk factor for hepatocellular carcinoma (HCC). However, its underlying mechanism remains understudied. Methods Microarray analysis was conducted to compare the genes and miRNAs in liver tissue from HBV-positive and HBV-negative HCC patients. Biological functions of these biomarkers in HBV-related HCC were validated via in vitro and in vivo experiments. Furthermore, we investigated the effect of HBV on the proliferation and migration of tumor cells in HBV-positive HCC tissue. Bioinformatics analysis was then performed to validate the clinical value of the biomarkers in a large HCC cohort. Results We found that a gene, MINPP1 from the glycolytic bypass metabolic pathway, has an important biological function in the development of HBV-positive HCC. MINPP1 is down-regulated in HBV-positive HCC and could inhibit the proliferation and migration of the tumor cells. Meanwhile, miRNA-30b-5p was found to be a stimulator for the proliferation of tumor cell through glycolytic bypass in HBV-positive HCC. More importantly, miRNA-30b-5p could significantly downregulate MINPP1 expression. Metabolic experiments showed that the miRNA-30b-5p/MINPP1 axis is able to accelerate the conversion of glucose to lactate and 2,3-bisphosphoglycerate (2,3-BPG). In the HBV-negative HCC cells, miRNA-30b-5p/MINPP1 could not regulate the glycolytic bypass to promote the tumorigenesis. However, once HBV was introduced into these cells, miRNA-30b-5p/MINPP1 significantly enhanced the proliferation, migration of tumor cells, and promoted the glycolytic bypass. We further revealed that HBV infection promoted the expression of miRNA-30b-5p through the interaction of HBV protein P (HBp) with FOXO3. Bioinformatics analysis on a large cohort dataset showed that high expression of MINPP1 was associated with favorable survival of HBV-positive HCC patients, which could lead to a slower progress of this disease. Conclusion Our study found that the HBp/FOXO3/miRNA-30b-5p/MINPP1 axis contributes to the development of HBV-positive HCC cells through the glycolytic bypass. We also presented miRNA-30b-5p/MINPP1 as a novel biomarker for HBV-positive HCC early diagnosis and a potential pharmaceutical target for antitumor therapy.

The Hippo signaling pathway, which is historically considered as a dominator of organ development and homeostasis has recently been implicated as an immune regulator. However, its role in host defense against influenza A virus (IAV) has not been widely investigated. Here, we found that IAV could activate the Hippo effectors Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) through physical binding of the IAV non-structural protein 1 (NS1) with C-terminal domain of YAP/TAZ, facilitating their nuclear location. Meanwhile, YAP/TAZ downregulated the expression of pro-inflammatory and anti-viral cytokines against IAV infection, therefore benefiting virus replication and host cell apoptosis. A mouse model of IAV infection further demonstrated Yap deficiency protected mice against IAV infection, relieving lung injury. Mechanistically, YAP/TAZ blocked anti-viral innate immune signaling via downregulation of Toll-like receptor 3 (TLR3) expression. YAP directly bound to the putative TEADs binding site on the promoter region of TLR3. The elimination of acetylated histone H3 occupancy in the TLR3 promoter resulted in its transcriptional silence. Moreover, treatment of Trichostatin A, a histone deacetylases (HDACs) inhibitor or disruption of HDAC4/6 reversed the inhibition of TLR3 expression by YAP/TAZ, suggesting HDAC4/6 mediated the suppression function of YAP/TAZ. Taken together, we uncovered a novel immunomodulatory mechanism employed by IAV, where YAP/TAZ antagonize TLR3-mediated innate immunity.

Covalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limited knowledge of covalent binding sites has hindered the discovery of novel ligands. Therefore, developing in silico methods to identify covalent binding sites is highly desirable. Here, we propose DeepCoSI, the first structure-based deep graph learning model to identify ligandable covalent sites in the protein. By integrating the characterization of the binding pocket and the interactions between each cysteine and the surrounding environment, DeepCoSI achieves state-of-the-art predictive performances. The validation on two external test sets which mimic the real application scenarios shows that DeepCoSI has strong ability to distinguish ligandable sites from the others. Finally, we profiled the entire set of protein structures in the RCSB Protein Data Bank (PDB) with DeepCoSI to evaluate the ligandability of each cysteine for covalent ligand design, and made the predicted data publicly available on website.

BackgroundChronic liver fibrosis is an inevitable stage for the development of patients with chronic hepatitis B (CHB). However, anti-fibrotic therapies have been unsuccessful so far. The biological functions and molecular mechanisms of long non-coding RNAs (lncRNAs) in the host immune system during chronic hepatitis B virus (HBV) infection, especially in fibrosis, are still largely unknown.MethodThe total RNA of peripheral blood mononuclear cells (PBMCs) from asymptomatic carriers (ASCs) or CHB receiving at least 8 years of anti-viral treatments was analyzed using Arraystar microarray and validated via quantitative real-time PCR (qRT-PCR). Correlation analysis was conducted based on correlation coefficients, Clusterprofile, and RNA Interactome Database (RAID). The functions of lncRNA in monocytes were determined via loss-of-function RNAi or gain-of-function lentivirus assays. The expression levels of mRNAs or proteins were evaluated using qRT-PCR, western blotting assay, or enzyme linked immunosorbent assays (ELISA).ResultsA total of 1,042 mRNA transcripts (630 up-regulated and 412 down-regulated) were identified being differentially expressed between ASC and CHB patients. Through enrichment analysis we focused on the transforming growth factor beta (TGF-β) signaling pathway and validated their expression in a larger cohort. Moreover, we found that lncRNA ENST00000519726 (lncRNA-HEIM) was highly expressed in monocytes and further up-regulated upon HBV infection. LncRNA-HEIM played an important role in CHB patients with long-term antiviral treatments, and its elevated expression was remarkably correlated with the TGF-β signaling pathway, especially with the two members namely TGF-β and SMAD4. Furthermore, altering the endogenous lncRNA-HEIM level in monocytes significantly affected the production of TGF-β, as well as the fibrosis of hepatic stellate cells by affecting the expression of collagen I and α-smooth muscle actin (α-SMA).ConclusionThese findings not only added knowledge to the understanding of the roles of which lncRNA-HEIM played in the activation of HSCs in CHB patients with long-term medication, but also provided a promising therapeutic target in the future treatment for liver fibrosis.

Abstract Background Renal cell carcinoma (RCC) is the most common kidney cancer and includes several molecular and histological subtypes with different clinical characteristics. The combination of DNA methylation and gene expression data can improve the classification of tumor heterogeneity, by incorporating differences at the epigenetic level and clinical features. Methods In this study, we identified the prognostic methylation and constructed specific prognosis-subgroups based on the DNA methylation spectrum of RCC from the TCGA database. Results Significant differences in DNA methylation profiles among the seven subgroups were revealed by consistent clustering using 3389 CpGs that indicated that were significant differences in prognosis. The specific DNA methylation patterns reflected differentially in the clinical index, including TNM classification, pathological grade, clinical stage, and age. In addition, 437 CpGs corresponding to 477 genes of 151 samples were identified as specific hyper/hypomethylation sites for each specific subgroup. A total of 277 and 212 genes corresponding to DNA methylation at promoter sites were enriched in transcription factor of GKLF and RREB-1, respectively. Finally, Bayesian network classifier with specific methylation sites was constructed and was used to verify the test set of prognoses into DNA methylation subgroups, which was found to be consistent with the classification results of the train set. DNA methylation-based classification can be used to identify the distinct subtypes of renal cell carcinoma. Conclusions This study shows that DNA methylation-based classification is highly relevant for future diagnosis and treatment of renal cell carcinoma as it identifies the prognostic value of each epigenetic subtype.

Coronavirus disease-2019 (COVID-19) is a novel respiratory disease induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It remains poorly understood how the host immune system responds to the infection during disease progression. We applied microarray analysis of the whole genome transcriptome to peripheral blood mononuclear cells (PBMCs) taken from severe and mild COVID-19 patients as well as healthy controls. Functional enrichment analysis of genes associated with COVID-19 severity indicated that disease progression is featured by overactivation of myeloid cells and deficient T cell function. The upregulation of TLR6 and MMP9, which promote the neutrophils-mediated inflammatory response, and the downregulation of SKAP1 and LAG3, which regulate T cells function, were associated with disease severity. Importantly, the regulation of these four genes was absent in patients with influenza A (H1N1). And compared with stimulation with hemagglutinin (HA) of H1N1 virus, the regulation pattern of these genes was unique in PBMCs response to Spike protein of SARS-CoV-2 ex vivo. Our data also suggested that severe SARS-CoV-2 infection largely silenced the response of type I interferons (IFNs) and altered the proportion of immune cells, providing a potential mechanism for the hypercytokinemia. This study indicates that SARS-CoV-2 infection impairs inflammatory and immune signatures in patients, especially those at severe stage. The potential mechanisms underpinning severe COVID-19 progression include overactive myeloid cells, impaired function of T cells, and inadequate induction of type I IFNs signaling.

Background: The tumor immune microenvironment (TIME) is an external immune system that regulates tumorigenesis. However, cellular interactions involving the TIME in hepatocellular carcinoma (HCC) are poorly characterized.Methods: In this study, we used multidimensional bioinformatic methods to comprehensively analyze cellular TIME characteristics in 735 HCC patients. Additionally, we explored associations involving TIME molecular subtypes and gene types and clinicopathological features to construct a prognostic signature.Results: Based on their characteristics, we classified TIME and gene signatures into three phenotypes (TIME T1–3) and two gene clusters (Gene G1–2), respectively. Further analysis revealed that Gene G1 was associated with immune activation and surveillance and included CD8+ T cells, natural killer cell activation, and activated CD4+ memory T cells. In contrast, Gene G2 was characterized by increased M0 macrophage and regulatory T cell levels. After calculation of principal component algorithms, a TIME score (TS) model, including 78 differentially expressed genes, was constructed based on TIME phenotypes and gene clusters. Furthermore, we observed that the Gene G2 cluster was characterized by high TS, and Gene G1 was characterized by low TS, which correlated with poor and favorable prognosis of HCC, respectively. Correlation analysis showed that TS had a positive association with several clinicopathologic signatures [such as grade, stage, tumor (T), and node (N)] and known somatic gene mutations (such as TP53 and CTNNB1). The prognostic value of the TS model was verified using external data sets.Conclusion: We constructed a TS model based on differentially expressed genes and involving immune phenotypes and demonstrated that the TS model is an effective prognostic biomarker and predictor for HCC patients.

Hepatocellular carcinoma (HCC) is an immune-related tumor, that the type and number of tumor-infiltrated immune cells can serve as biomarkers for the clinical application. In this study, we constructed the immune model for diagnostic and prognostic prediction of HCC based on the systematic bioinformatics analyses on the component of immune cells from large samples transcriptome. CIBERSORT analysis found that the component of immune cells between 513 HCC and 473 adjacent normal tissues was different. M0 macrophages and regulatory T cells were mainly enriched in tumor tissues, whereas the CD8+ T cell and activated CD4+ memory T cells were the most in normal tissues. Using random forest and LASSO analyses, eleven immune cell types were mined out to construct the immune diagnostic model (IDG), which showed high efficiency in distinguishing cancer from normal tissues both in testing and validation groups. In addition, the immune prognostic model (IPG) consisting of five types of immune cells was constructed using the LASSO-Cox algorithm. It showed that HCC patients of the high-risk group had a significantly shorter survival time than those of low-risk group in testing, validation, and entire cohorts. Besides, Nomogram plots and decision curve analyses revealed that the IPG was positively associated with the HCC clinical classification of the Barcelona Clinic Liver Cancer (BCLC) stage, and showing more accuracy of prediction than independent BCLC stage. Related analyses found that IDG positively correlated with epithelial-mesenchymal transition (EMT) and cytotoxic factor-related genes and negatively correlated with immune checkpoint regulators related genes. From the GSEA analysis of the biological function of genes related to IPG, it was found that the genes of the high-risk group were enriched in some tumorigenesis related pathways, such as DNA replication, cell cycle, and PPARA. Therefore, this study identified IDG and IPG as efficient biomarkers for the diagnosis and prognosis of HCC.

The adaptive immune system uses several strategies to generate a repertoire of T cell receptors (TCR) with sufficient diversity to recognize the universe of potential pathogens. However, it remains unclear how differences in the T cell receptor (TCR) contribute to heterogeneity in T cell state. In this study, we used polychromatic flow cytometry to isolate highly pure CD4+/CD8+ naive and memory T cells, and applied deep sequencing to characterize corresponding TCR β-chain (TCRβ) complementary-determining region 3 (CDR3) repertoires. We find that shorter TCRβ CDR3s with fewer insertions were highly enriched during thymic selection. Antigen-experienced T cells (memory T cells) harbor shorter CDR3s vs. naive T cells. Moreover, the public TCRβ CDR3 clonotypes within cell subsets or interindividual tend to have shorter CDR3 length and a significantly larger size compared with “private” clonotypes. Taken together, shorter CDR3s highly enriched during thymic selection and antigen-driven selection, and further enriched in public T-cell responses. These results indicated that it may be evolutionary pressures drive short CDR3s to recognize most of antigen in nature.

Background: This study is to report characteristics of people killed in road traffic crashes and to describe major patterns of traffic crashes in China.Methods: Descriptive and inferential statistical analyses were conducted. Road traffic death national data, population denominator data and motor vehicles data of 2009 were obtained from the Bureau of Traffic Management at Ministry of Public Security and National Bureau of Statistics. The association between the fatalities from road traffic crashes and selected demographic factors, the time distribution, crash patterns, crash causes, and road user category were assessed in χ2 analyses.Results: Road traffic crashes in China disproportionably affected the following populations: males, persons 21-65 yr of age and adults aged more than 65 yr, persons living in rural areas, pedestrians, passengers, motorcyclists and bicyclists. Approximately 50% of fatalities of road traffic crash occurred in Eastern regions. The number of road traffic deaths was higher in daytime than in nighttime. Road traffic deaths in frontal crashes, side-to-side crash and crashes with an object or a person were more common than in rear-end crashes. In about 92% of road traffic deaths, auto drivers were believed to be responsible for the fatal crash. Major crash causing factors were speeding, careless driving, driving without a license, driving in the wrong lane, and driving after drinking alcohol.Conclusions: Road traffic deaths accounted for about 70,000 premature deaths in China which should be taken into account.