Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems.

In the drug discovery process, time and costs are the most typical problems resulting from the experimental screening of drug-target interactions (DTIs). To address these limitations, many computational methods have been developed to achieve more accurate predictions. However, identifying DTIs mostly rely on separate learning tasks with drug and target features that neglect interaction representation between drugs and target. In addition, the lack of these relationships may lead to a greatly impaired performance on the prediction of DTIs. Aiming at capturing comprehensive drug-target representations and simplifying the network structure, we propose an integrative approach with a convolution broad learning system for the DTI prediction (ConvBLS-DTI) to reduce the impact of the data sparsity and incompleteness. First, given the lack of known interactions for the drug and target, the weighted K-nearest known neighbors (WKNKN) method was used as a preprocessing strategy for unknown drug-target pairs. Second, a neighborhood regularized logistic matrix factorization (NRLMF) was applied to extract features of updated drug-target interaction information, which focused more on the known interaction pair parties. Then, a broad learning network incorporating a convolutional neural network was established to predict DTIs, which can make classification more effective using a different perspective. Finally, based on the four benchmark datasets in three scenarios, the ConvBLS-DTI's overall performance out-performed some mainstream methods. The test results demonstrate that our model achieves improved prediction effect on the area under the receiver operating characteristic curve and the precision-recall curve.

Alternative polyadenylation quantitative trait methylation mapping in human cancers provides clues into the molecular mechanisms of APA.

Genetic variants are involved in the orchestration of alternative polyadenylation (APA) events, while the role of DNA methylation in regulating APA remains unclear. We generated a comprehensive atlas of APA quantitative trait methylation sites (apaQTMs) across 21 different types of cancer (1,612 to 60,219 acting in cis and 4,448 to 142,349 in trans). Potential causal apaQTMs in non-cancer samples were also identified. Mechanistically, we observed a strong enrichment of cis-apaQTMs near polyadenylation sites (PASs) and both cis- and trans-apaQTMs in proximity to transcription factor (TF) binding regions. Through the integration of ChIP-signals and RNA-seq data from cell lines, we have identified several regulators of APA events, acting either directly or indirectly, implicating novel functions of some important genes, such as TCF7L2, which is known for its involvement in type 2 diabetes and cancers. Furthermore, we have identified a vast number of QTMs that share the same putative causal CpG sites with five different cancer types, underscoring the roles of QTMs, including apaQTMs, in the process of tumorigenesis. DNA methylation is extensively involved in the regulation of APA events in human cancers. In an attempt to elucidate the potential underlying molecular mechanisms of APA by DNA methylation, our study paves the way for subsequent experimental validations into the intricate biological functions of DNA methylation in APA regulation and the pathogenesis of human cancers. To present a comprehensive catalog of apaQTM patterns, we introduce the Pancan-apaQTM database, available at https://pancan-apaqtm-zju.shinyapps.io/pancanaQTM/.

Edge-Type Hyperintense Intracranial Artery Plaque: A Potential MRI Biomarker of Stroke Recurrence.

BACKGROUND: Identifying patients at high risk of stroke recurrence is important for stroke prevention and treatment. PURPOSE: To explore the characteristics of T1 hyperintense plaques (HIP) and their relationship with stroke recurrence in patients with symptomatic intracranial atherosclerotic stenosis (sICAS). STUDY TYPE: Retrospective. POPULATION: One hundred fifty-seven patients with moderate-to-severe (≥50%) nonocclusive sICAS and MRI studies (42 females and 115 males, mean age 58.69 ± 10.68 years). FIELD STRENGTH/SEQUENCE: 3D higher-resolution black-blood T1-weighted fast-spin-echo sequence at 3.0 T. ASSESSMENT: HIP (signal intensity [SI] of plaque-to-adjacent gray matter >1.0 on non-contrast T1-weighted images) and non-HIP plaques were identified. HIP plaques were categorized as edge type (high SI adjacent to lumen) and non-edge type (high SI within plaque). Clinical and imaging features of different plaque types were compared. Stroke recurrence was assessed through telephone or medical records at 3 and 6 months, and then once a year post-MRI. The relationship between edge type and non-edge types HIP with stroke recurrence was analyzed. STATISTICAL TESTS: Student's t test, Mann-Whitney U-test, chi square test and Fisher's exact test to compare features between plaque types. Kaplan-Meier curves (with log-rank tests) and Cox proportional hazards regression to assess relationship between stroke recurrence and different plaque types. A two-tailed P-value of <0.05 was considered statistically significant. RESULTS: Of 157 culprit lesions, 87 (55%) were HIPs (43 edge type, 44 non-edge type) and 70 (45%) were non-HIPs. Plaque thickness, area, and volume were significantly higher for HIPs than for non-HIPs. Among patients with HIPs, edge type was significantly more likely in the posterior circulation (53.5% vs. 27.3%), and had significantly higher plaque thickness, length, area, volume, plaque burden, and remodeling index than non-edge type. Edge-type HIP was significantly more common than non-edge HIP in patients with diabetes mellitus (51.2% vs. 29.5%) and dyslipidemia (79.1% vs. 54.5%). During median follow-up of 27 months, 33 patients experienced stroke recurrence. Recurrence was associated with edge-type HIP (adjusted hazard ratio = 2.83; 95% confidence interval: 1.40-5.69), both in the overall cohort (34.9% vs. 15.8%) and in patients with HIP (34.9% vs. 9.0%). Age ≥60 years and edge-type HIP had a significant interaction. DATA CONCLUSIONS: Hyperintense plaque may be categorized as edge type or non-edge type. Edge-type HIP may be a potential MRI biomarker of stroke recurrence. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Distinguishing Intracranial Diabetes-Related Atherosclerotic Plaques: A High-Resolution Magnetic Resonance Imaging-Based Radiomics Study.

INTRODUCTION: Diabetes markedly affects the formation and development of intracranial atherosclerosis. The study was aimed at evaluating whether radiomics features can help distinguish plaques primarily associated with diabetes. MATERIALS AND METHODS: We retrospectively analyzed patients who were admitted to our center because of acute ischemic stroke due to intracranial atherosclerosis between 2016 and 2022. Clinical data, blood biomarkers, conventional plaque features, and plaque radiomics features were collected for all patients. Odds ratios (ORs) with 95% confidence intervals (CIs) were determined from logistic regression models. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to describe diagnostic performance. The DeLong test was used to compare differences between models. RESULTS: Overall, 157 patients (115 men; mean age, 58.7 ± 10.7 years) were enrolled. Multivariate logistic regression analysis showed that plaque length (OR: 1.17; 95% CI: 1.07-1.28) and area (OR: 1.13; 95% CI: 1.02-1.24) were independently associated with diabetes. On combining plaque length and area as a conventional model, the AUCs of the training and validation cohorts for identifying diabetes patients were 0.789 and 0.720, respectively. On combining radiomics features on T1WI and contrast-enhanced T1WI sequences, a better diagnostic value was obtained in the training and validation cohorts (AUC: 0.889 and 0.861). The DeLong test showed the model combining radiomics and conventional plaque features performed better than the conventional model in both cohorts (p < 0.05). CONCLUSIONS: The use of radiomics features of intracranial plaques on high-resolution magnetic resonance imaging can effectively distinguish culprit plaques with diabetes as the primary pathological cause, which will provide new avenues of research into plaque formation and precise treatment.

ConSpaS: a contrastive learning framework for identifying spatial domains by integrating local and global similarities.

Spatial transcriptomics is a rapidly growing field that aims to comprehensively characterize tissue organization and architecture at single-cell or sub-cellular resolution using spatial information. Such techniques provide a solid foundation for the mechanistic understanding of many biological processes in both health and disease that cannot be obtained using traditional technologies. Several methods have been proposed to decipher the spatial context of spots in tissue using spatial information. However, when spatial information and gene expression profiles are integrated, most methods only consider the local similarity of spatial information. As they do not consider the global semantic structure, spatial domain identification methods encounter poor or over-smoothed clusters. We developed ConSpaS, a novel node representation learning framework that precisely deciphers spatial domains by integrating local and global similarities based on graph autoencoder (GAE) and contrastive learning (CL). The GAE effectively integrates spatial information using local similarity and gene expression profiles, thereby ensuring that cluster assignment is spatially continuous. To improve the characterization of the global similarity of gene expression data, we adopt CL to consider the global semantic information. We propose an augmentation-free mechanism to construct global positive samples and use a semi-easy sampling strategy to define negative samples. We validated ConSpaS on multiple tissue types and technology platforms by comparing it with existing typical methods. The experimental results confirmed that ConSpaS effectively improved the identification accuracy of spatial domains with biologically meaningful spatial patterns, and denoised gene expression data while maintaining the spatial expression pattern. Furthermore, our proposed method better depicted the spatial trajectory by integrating local and global similarities.

scHOIS: Determining Cell Heterogeneity Through Hierarchical Clustering Based on Optimal Imputation Strategy.

Advances in single-cell RNA sequencing (scRNA-seq) technology provide an unbiased and high-throughput analysis of each cell at single-cell resolution, and further facilitate the development of cellular heterogeneity analysis. Despite the promise of scRNA-seq, the data generated by this method are sparse and noisy because of the presence of dropout events, which can greatly impact downstream analyses such as differential gene expression, cell type annotation, and linage trajectory reconstruction. The development of effective and robust computational methods to address both dropout and clustering are thus urgently needed. In this study, we propose a flexible, accurate two-stage algorithm for single cell heterogeneity analysis via hierarchical clustering based on an optimal imputation strategy, called scHOIS. At the first stage, masked non-negative matrix factorization is applied to approximate the original observed scRNA-seq data, with optimal rank determined by variance analysis. At the second stage, hierarchical clustering is applied to group the imputed cells using Pearson correlation to measure similarity, with the optimal number of clusters determined by integrating three classical indexes. We performed extensive experiments on real-world datasets, which showed that scHOIS effectively and robustly distinguished cellular differences and that the clustering performance of this algorithm was superior to that of other state-of-the-art methods.

Mechanotransductive receptor Piezo1 as a promising target in the treatment of fibrosis diseases.

Fibrosis could happen in every organ, leading to organic malfunction and even organ failure, which poses a serious threat to global health. Early treatment of fibrosis has been reported to be the turning point, therefore, exploring potential correlates in the pathogenesis of fibrosis and how to reverse fibrosis has become a pressing issue. As a mechanism-sensitive cationic calcium channel, Piezo1 turns on in response to changes in the lipid bilayer of the plasma membrane. Piezo1 exerts multiple biological roles, including inhibition of inflammation, cytoskeletal stabilization, epithelial-mesenchymal transition, stromal stiffness, and immune cell mechanotransduction, interestingly enough. These processes are closely associated with the development of fibrotic diseases. Recent studies have shown that deletion or knockdown of Piezo1 attenuates the onset of fibrosis. Therefore, in this paper we comprehensively describe the biology of this gene, focusing on its potential relevance in pulmonary fibrosis, renal fibrosis, pancreatic fibrosis, and cardiac fibrosis diseases, except for the role of drugs (agonists), increased intracellular calcium and mechanical stress using this gene in alleviating fibrosis.

NG-SEM: an effective non-Gaussian structural equation modeling framework for gene regulatory network inference from single-cell RNA-seq data.

Inference of gene regulatory network (GRN) from gene expression profiles has been a central problem in systems biology and bioinformatics in the past decades. The tremendous emergency of single-cell RNA sequencing (scRNA-seq) data brings new opportunities and challenges for GRN inference: the extensive dropouts and complicated noise structure may also degrade the performance of contemporary gene regulatory models. Thus, there is an urgent need to develop more accurate methods for gene regulatory network inference in single-cell data while considering the noise structure at the same time. In this paper, we extend the traditional structural equation modeling (SEM) framework by considering a flexible noise modeling strategy, namely we use the Gaussian mixtures to approximate the complex stochastic nature of a biological system, since the Gaussian mixture framework can be arguably served as a universal approximation for any continuous distributions. The proposed non-Gaussian SEM framework is called NG-SEM, which can be optimized by iteratively performing Expectation-Maximization algorithm and weighted least-squares method. Moreover, the Akaike Information Criteria is adopted to select the number of components of the Gaussian mixture. To probe the accuracy and stability of our proposed method, we design a comprehensive variate of control experiments to systematically investigate the performance of NG-SEM under various conditions, including simulations and real biological data sets. Results on synthetic data demonstrate that this strategy can improve the performance of traditional Gaussian SEM model and results on real biological data sets verify that NG-SEM outperforms other five state-of-the-art methods.

Analysis of the spatial-temporal distribution characteristics of hepatitis E in Jiangsu province from 2005 to 2020.

Objective: This study attempts to analyze the spatial clustering and spatial-temporal distribution characteristics of hepatitis E (HE) at the county (city and district) level in Jiangsu province to provide a scientific basis for the prevention and control of HE. Method: The information on HE cases reported in the Chinese Center for Disease Control and Prevention Information System from 2005 to 2020 was collected for spatial autocorrelation analysis and spatial-temporal clustering analysis. Result: From 2005 to 2020, 48,456 HE cases were reported in Jiangsu province, with an average annual incidence rate of 3.87/100,000. Male cases outnumbered female cases (2.46:1), and the incidence was highest in the 30-70 years of age group (80.50%). Farmers accounted for more than half of all cases (59.86%), and in terms of the average annual incidence, the top three cities were all in Zhenjiang city. Spatial autocorrelation analysis showed that Global Moran's I of HE incidence varied from 0.232 to 0.513 for the years. From 2005 to 2020, 31 counties (cities and districts) had high and statistically significant HE incidence, and two clustering areas were detected by spatial-temporal scanning. Conclusion: HE incidence in Jiangsu province from 2005 to 2020 was stable, with age and gender differences, regional clustering, and spatial-temporal clustering. Further investigation of HE clustering areas is necessary to formulate corresponding targeted prevention and control measures.

Eco-friendly simultaneous extraction of pectins and phenolics from passion fruit (Passiflora edulis Sims) peel: Process optimization, physicochemical properties, and antioxidant activity.

The objective of this study was to simultaneously extract passion fruit (Passiflora edulis) peel pectins and phenolics using deep eutectic solvents, to evaluate their physicochemical properties and antioxidant activity. By taking L-proline: citric acid (Pro-CA) as the optimal solvent, the effect of extraction parameters on the yields of extracted passion fruit peel pectins (PFPP) and total phenolic content (TPC) was explored by response surfaces methodology (RSM). A maximum pectin yield (22.63%) and the highest TPC (9.68 mg GAE/g DW) were attained under 90 °C, extraction solvent pH = 2, extraction time of 120 min and L/S ratio of 20 mL/g. In addition, Pro-CA-extracted pectins (Pro-CA-PFPP) and HCl-extracted pectins (HCl-PFPP) were subjected to high performance gel permeation chromatography (HPGPC), Fourier transform infrared spectroscopy (FT-IR), thermogram analysis (TG/DTG) and rheological measurements. Results verified that the Mw and thermal stability of Pro-CA-PFPP were higher than those of HCl-PFPP. The PFPP solutions featured a non-Newtonian behavior, and compared with commercially pectin solution, PFPP solution exhibited a stronger antioxidant activity. Additionally, passion fruit peel extract (PFPE) exhibited stronger antioxidant effects than PFPP. The results of ultra-performance liquid chromatography hybrid triple quadrupole-linear ion trap mass spectrometry (UPLC-Qtrap-MS) and high performance liquid chromatography (HPLC) analysis showed that (-)-epigallocatechin, gallic acid, epicatechin, kaempferol-3-O-rutin and myricetin were the main phenolic compounds in PFPE and PFPP. Our results suggest that Pro-CA can be considered as an eco-friendly solvent for high-efficient extraction of high-value compounds from agricultural by-products.

Breast Cancer Mutations HER2V777L and PIK3CAH1047R Activate the p21-CDK4/6-Cyclin D1 Axis to Drive Tumorigenesis and Drug Resistance.

In metastatic breast cancer, HER2-activating mutations frequently co-occur with mutations in PIK3CA, TP53, or CDH1. Of these co-occurring mutations, HER2 and PIK3CA are the most commonly comutated gene pair, with approximately 40% of HER2-mutated breast cancers also having activating mutations in PIK3CA. To study the effects of co-occurring HER2 and PIK3CA mutations, we generated genetically engineered mice with the HER2V777L; PIK3CAH1047R transgenes (HP mice) and studied the resulting breast cancers both in vivo as well as ex vivo using cancer organoids. HP breast cancers showed accelerated tumor formation in vivo and increased invasion and migration in in vitro assays. HP breast cancer cells were resistant to the pan-HER tyrosine kinase inhibitor, neratinib, but were effectively treated with neratinib plus the HER2-targeted antibody-drug conjugate trastuzumab deruxtecan. Proteomic and RNA-seq analysis of HP breast cancers identified increased gene expression of cyclin D1 and p21WAF1/Cip1 and changes in cell-cycle markers. Combining neratinib with CDK4/6 inhibitors was another effective strategy for treating HP breast cancers, with neratinib plus palbociclib showing a statistically significant reduction in development of mouse HP tumors as compared to either drug alone. The efficacy of both the neratinib plus trastuzumab deruxtecan and neratinib plus palbociclib combinations was validated using a human breast cancer patient-derived xenograft with very similar HER2 and PIK3CA mutations to the HP mice. Further, these two drug combinations effectively treated spontaneous lung metastasis in syngeneic mice transplanted with HP breast cancer organoids. This study provides valuable preclinical data to support the ongoing phase 1 clinical trials of these drug combinations in breast cancer. SIGNIFICANCE: In HER2-mutated breast cancer, PIK3CA mutation activates p21-CDK4/6-cyclin D1 signaling to drive resistance to HER2-targeted therapies, which can be overcome using CDK4/6 inhibitors.

Paeoniflorin, ferulic acid, and atractylenolide III improved LPS-induced neuroinflammation of BV2 microglia cells by enhancing autophagy.

Microglia hyperactivation is an important cause of neuroinflammation in Alzheimer's disease (AD). Paeoniflorin (PF), ferulic acid (FA), and atractylenolide III (ATL) are potent in anti-inflammation and neuroprotection. Multiple components can act on different targets simultaneously to exert synergistic therapeutic effects and exploring the synergistic potential between compounds is an important area of research. We investigated the effects of PF, FA, and ATL, alone or in combination, on LPS-induced neuroinflammation and autophagy in BV2 microglia cells. We found that PF, FA, and ATL, alone or in combination, significantly reduced the production of inflammatory factors such as IL-6, IL-1ß, and TNF-α, especially in the PF + FA + ATL group, which performed the best. In addition, the combination of PF, FA, and ATL significantly increased the expression of autophagy-related proteins p-AMPK, p-ULK1, Beclin1, LC3, and TFEB and decreased the expression of p62. Moreover, the restoration of autophagic flux by the combination of PF, FA, and ATL was abrogated by the addition of the autophagy inhibitor Wortmannin. In conclusion, PF, FA, and ATL have a synergistic effect in reducing LPS-induced inflammatory factor release from BV2 microglia cells, and its protective effect may be through activation of the AMPK/ULK1/TFEB autophagic signaling pathway.

Incremental value of enhanced plaque length for identifying intracranial atherosclerotic culprit plaques: a high-resolution magnetic resonance imaging study.

OBJECTIVES: Besides plaque enhancement grade, the incremental value of enhancement-related high-resolution MRI features in defining culprit plaques needs further evaluation. This study was focused on assessing whether plaque enhancement features contribute to culprit plaque identification and further risk stratification. METHODS: We retrospectively studied patients who experienced an acute ischaemic stroke and transient ischaemic attack due to intracranial atherosclerosis from 2016 to 2022. The enhancement features included enhancement grade, enhanced length, and enhancement quadrant. Associations between plaque enhancement features and culprit plaques, as well as diagnostic value, were investigated using logistic regression and receiver operating characteristic analyses. RESULTS: Overall, 287 plaques were identified, of which 231 (80.5%) and 56 (19.5%) were classified as culprit and non-culprit plaques, respectively. Comparison of the pre- and post-enhancement images revealed enhanced length longer than the plaque length in 46.32% of the culprit plaques. Multivariate logistic regression showed that enhanced length longer than plaque length (OR 6.77; 95% CI 2.47-18.51) and grade II enhancement (OR 7.00; 95% CI 1.69-28.93) were independently associated with culprit plaques. The area under the curve value for the combination of stenosis and plaque enhancement grade for the diagnosis of culprit plaques was 0.787, which increased significantly to 0.825 on the addition of enhanced length longer than the plaque length (p = 0.026 for DeLong's test). CONCLUSIONS: Enhanced length longer than the plaque length and grade II enhancement were independently associated with culprit plaques. The combination of the enhanced plaque features resulted in better culprit plaque identification.

Protective Effect of Ferulic Acid on Lipopolysaccharide-Induced BV2 Microglia Inflammation via AMPK/mTOR Signaling Pathway.

In neurodegenerative diseases, microglial activation and neuroinflammation are essential for the control and progression of neurodegenerative diseases. Mitigating microglium-induced inflammation is one strategy for hindering the progression of neurodegenerative diseases. Ferulic acid (FA) is an effective anti-inflammatory agent, but its potential role and regulation mechanism in neuroinflammatory reactions have not been fully studied. In this study, the neuroinflammation model was established by lipopolysaccharide (LPS), and the inhibitory effect of FA on neuroinflammation of BV2 microglia was studied. The results showed that FA significantly reduced the production and expression of reactive oxygen species (ROS), tumor necrosis factor-α (TNF-α), leukocyte-6 (IL-6) and interleukin-1ß (IL-1ß). We further studied the mechanism of FA's regulation of LPS-induced BV2 neuroinflammation and found that FA can significantly reduce the expression of mTOR in BV2 microglia induced by LPS, and significantly increase the expression of AMPK, indicating that FA may have an anti-inflammatory effect by activating the AMPK/mTOR signaling pathway to regulate the release of inflammatory mediators (such as NLRP3, caspase-1 p20 and IL-1ß). We further added an autophagy inhibitor (3-MA) and an AMPK inhibitor (compound C, CC) for reverse verification. The results showed that FA's inhibitory effects on TNF-α, IL-6 and IL-1ß and its regulatory effect on AMPK/mTOR were destroyed by 3-MA and CC, which further indicated that FA's inhibitory effect on neuroinflammation is related to its activation of the AMPK/mTOR autophagy signaling pathway. In a word, our experimental results show that FA can inhibit LPS-induced neuroinflammation of BV2 microglia by activating the AMPK/mTOR signaling pathway, and FA may be a potential drug for treating neuroinflammatory diseases.

Loss of GLTSCR1 causes congenital heart defects by regulating NPPA transcription.

Precise and specific spatiotemporal domains of gene expression regulation are critical for embryonic development. Recent studies have identified GLTSCR1 as a gene transcriptional elongation regulator in cancer research. However, the function of GLTSCR1, especially in embryonic development, remains poorly understood. Here, we found that GLTSCR1 was essential for cardiac development because Gltscr1 knockout (Gltscr1-/-) led to embryonic lethality in mice with severe congenital heart defects (CHDs). Ventricular septal defect and double outflow right ventricular were also observed in neural crest cells with conditional deletion of Gltscr1, which were associated with neonatal lethality in mice. Mechanistically, GLTSCR1 deletion promoted NPPA expression by coordinating the CHD risk G allele of rs56153133 in the NPPA enhancer and releasing the transcription factor ZNF740-binding site on the NPPA promoter. These findings demonstrated that GLTSCR1 acts as a candidate CHD-related gene.

Epithelial Gab1 calibrates RIPK3-dependent necroptosis to prevent intestinal inflammation.

As a hallmark of inflammatory bowel disease (IBD), elevated intestinal epithelial cell (IEC) death compromises the gut barrier, activating the inflammatory response and triggering more IEC death. However, the precise intracellular machinery that prevents IEC death and breaks this vicious feedback cycle remains largely unknown. Here, we report that Grb2-associated binder 1 (Gab1) expression is decreased in patients with IBD and inversely correlated with IBD severity. Gab1 deficiency in IECs accounted for the exacerbated colitis induced by dextran sodium sulfate owing to sensitizing IECs to receptor-interaction protein kinase 3-mediated (RIPK3-mediated) necroptosis, which irreversibly disrupted the homeostasis of the epithelial barrier and promoted intestinal inflammation. Mechanistically, Gab1 negatively regulated necroptosis signaling through inhibiting the formation of RIPK1/RIPK3 complex in response to TNF-α. Importantly, administration of RIPK3 inhibitor revealed a curative effect in epithelial Gab1-deficient mice. Further analysis indicated mice with Gab1 deletion were prone to inflammation-associated colorectal tumorigenesis. Collectively, our study defines a protective role for Gab1 in colitis and colitis-driven colorectal cancer by negatively regulating RIPK3-dependent necroptosis, which may serve as an important target to address necroptosis and intestinal inflammation-related disease.

The developmental regulator HAND1 inhibits gastric carcinogenesis through enhancing ER stress apoptosis via targeting CHOP and BAK which is augmented by cisplatin.

Epigenetic disruption of tumor suppressor genes, particularly aberrant CpG methylation, plays a crucial role in gastric cancer (GC) pathogenesis. Through CpG methylome and expression profiling, a developmental transcription factor - Hand-And-Neural-crest-Derivative-expressed 1 (HAND1), was identified methylated and downregulated in GC. However, its role and underlying mechanisms in GC progression are poorly understood. Here, we show that HAND1 was frequently downregulated in GC by promoter methylation, and significantly correlated with tumor progression and poor prognosis of GC patients. High expression of HAND1 in GC patients was associated with significantly higher 5-year overall survival rates. Ectopic expression of HAND1 inhibited GC cell growth and migration in vitro and in vivo. HAND1 expression increased ROS levels and cytosolic Ca2+ concentration, enhanced cisplatin-induced apoptosis through endoplasmic reticulum (ER) stress/mitochondria-mediated apoptosis. Knockdown of CHOP and BAK attenuated HAND1-induced cell apoptosis. Overexpression of CHOP increased BAK expression. HAND1 interacts with CHOP, also directly binds to CHOP and BAK promoters and positively regulates BAK transcription. Thus, the present study demonstrates that HAND1 is a tumor suppressor gene methylated in GC, induces ER stress and apoptosis via CHOP and BAK, which is augmented by cisplatin. Low HAND1 expression is an independent poor prognostic factor for GC. The tumor-specific methylation of HAND1 promoter could be a candidate biomarker for GC.

Dynamic characteristics of a COVID-19 outbreak in Nanjing, Jiangsu province, China.

Objectives: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage B.1.617.2 (also named the Delta variant) was declared as a variant of concern by the World Health Organization (WHO). This study aimed to describe the outbreak that occurred in Nanjing city triggered by the Delta variant through the epidemiological parameters and to understand the evolving epidemiology of the Delta variant. Methods: We collected the data of all COVID-19 cases during the outbreak from 20 July 2021 to 24 August 2021 and estimated the distribution of serial interval, basic and time-dependent reproduction numbers (R0 and Rt), and household secondary attack rate (SAR). We also analyzed the cycle threshold (Ct) values of infections. Results: A total of 235 cases have been confirmed. The mean value of serial interval was estimated to be 4.79 days with the Weibull distribution. The R0 was 3.73 [95% confidence interval (CI), 2.66-5.15] as estimated by the exponential growth (EG) method. The Rt decreased from 4.36 on 20 July 2021 to below 1 on 1 August 2021 as estimated by the Bayesian approach. We estimated the household SAR as 27.35% (95% CI, 22.04-33.39%), and the median Ct value of open reading frame 1ab (ORF1ab) genes and nucleocapsid protein (N) genes as 25.25 [interquartile range (IQR), 20.53-29.50] and 23.85 (IQR, 18.70-28.70), respectively. Conclusions: The Delta variant is more aggressive and transmissible than the original virus types, so continuous non-pharmaceutical interventions are still needed.

Predicting incidence of hepatitis E for thirteen cities in Jiangsu Province, China.

Hepatitis E has placed a heavy burden on China, especially in Jiangsu Province, so accurately predicting the incidence of hepatitis E benefits to alleviate the medical burden. In this paper, we propose a new attentive bidirectional long short-term memory network (denoted as BiLSTM-Attention) to predict the incidence of hepatitis E for all 13 cities in Jiangsu Province, China. Besides, we also explore the performance of adding meteorological factors and the Baidu (the most widely used Chinese search engine) index as additional training data for the prediction of our BiLSTM-Attention model. SARIMAX, GBDT, LSTM, BiLSTM, and BiLSTM-Attention models are tested in this study, based on the monthly incidence rates of hepatitis E, meteorological factors, and the Baidu index collected from 2011 to 2019 for the 13 cities in Jiangsu province, China. From January 2011 to December 2019, a total of 29,339 cases of hepatitis E were detected in all cities in Jiangsu Province, and the average monthly incidence rate for each city is 0.359 per 100,000 persons. Root mean square error (RMSE) and mean absolute error (MAE) are used for model selection and performance evaluation. The BiLSTM-Attention model considering meteorological factors and the Baidu index has the best performance for hepatitis E prediction in all cities, and it gets at least 10% improvement in RMSE and MAE for all 13 cities in Jiangsu province, which means the model has significantly improved the learning ability, generalizability, and prediction accuracy when comparing with others.

A novel methylated cation channel TRPM4 inhibited colorectal cancer metastasis through Ca2+/Calpain-mediated proteolysis of FAK and suppression of PI3K/Akt/mTOR signaling pathway.

Colorectal cancer (CRC) is an aggressive malignancy with poor prognosis. It is imperative to elucidate the potential molecular mechanisms that regulate CRC cell aggressiveness. In present study, the transient receptor potential melastatin 4 (TRPM4), a calcium-activated nonselective cation channel, is downregulated in CRC as a novel methylated tumor suppressor gene (TSG). The reduced mRNA level of TRPM4 is due to the epigenetic methylation of its promoter CpG island (CGI). Moreover, ectopic expression of TRPM4 inhibited tumor growth and metastasis both in vitro and in vivo. Our experiments also demonstrate that TRPM4 restructures the CRC cytoskeleton and activates the Ca2+-mediated calpain pathway through enhancing calcium influx. The western blot analysis shows that the expression of focal adhesion kinase (FAK), a calpain-mediated proteolytic substrate, is markedly suppressed after ectopic overexpression of TRPM4, besides, Akt (also known as protein kinase B, PKB), phosphatidylinositol 3-kinase (PI3K) as well as its central target mTOR have significantly decreased expression accompanied by elevated E-cadherin and restrained matrix metalloproteinases (MMP2/MMP9) expression. The inhibition of protease calpain effectively relieves the retard of FAK/Akt signals and reverses the migration suppression of TRPM4. Taken together, TRPM4, identified as a novel methylated TSG, employs intracellular Ca2+ signals to activate calpain-mediated cleavage of FAK and impede CRC migration and invasion through modulating the PI3K/Akt/mTOR signaling cascade, providing the first evidence that TRPM4 is likely to be a significant biomarker and potential target for CRC therapy.