VSTH: a user-friendly web server for structure-based virtual screening on Tianhe-2.

SUMMARY: VSTH is a user-friendly web server with the complete workflow for virtual screening. By self-customized visualization software, users can interactively prepare protein files, set docking sites as well as view binding conformers in a target protein in a few clicks. We provide serval purchasable ligand libraries for selection. And, we integrate six open-source docking programs as computing engine, or as conformational sampling tools for DLIGAND2. Users can select various docking methods simultaneously and personalize computing parameters. After docking processing, user can filter docking conformations by ranked scores, or cluster-based molecular similarity to find highly populated clusters of low-energy conformations. AVAILABILITY AND IMPLEMENTATION: The VSTH web server is free and open to all users at https://matgen.nscc-gz.cn/VirtualScreening.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

[18F]AlF-CBP imaging of type I collagen for non-invasive monitoring of pulmonary fibrosis in preclinical models.

PURPOSE: Pulmonary fibrosis is an irreversible scar-forming condition for which there is a lack of non-invasive and specific methods for monitoring its progression and therapy efficacy. However, the disease is known to be accompanied by collagen accumulation. Here, we developed a novel positron emission tomography (PET) probe targeting type I collagen to evaluate its utility for the non-invasive assessment of pulmonary fibrosis. METHODS: We designed a 18F-labeled PET probe ([18F]AlF-CBP) to target type I collagen and evaluated its binding affinity, specificity and stability in vitro. PET with [18F]AlF-CBP, CT, histopathology, immunofluorescence, and biochemical indice were performed to assess and quantify type I collagen levels and pulmonary fibrosis progression and treatment in murine models. Dynamic PET/CT studies of [18F]AlF-CBP were conducted to assess lung fibrosis in non-human primate models. RESULTS: [18F]AlF-CBP was successfully prepared, and in vitro and in vivo tests showed high stability (> 95%) and type I collagen specificity (IC50 = 0.36 µM). The lungs of the fibrotic murine model showed more elevated probe uptake and retention compared to the control group, and there was a positive correlation between the radioactivity uptake signals and the degree of fibrosis (CT: R2 = 0.89, P < 0.0001; hydroxyproline levels: R2 = 0.89, P < 0.0001). PET signals also correlated well with mean lung density in non-human primate models of pulmonary fibrosis (R2 = 0.84, P < 0.0001). CONCLUSION: [18F]AlF-CBP PET imaging is a promising non-invasive method for specific monitoring of lung fibrosis progression and therapy efficacy.

Genomic characterization of a newly established esophageal squamous cell carcinoma cell line from China and published esophageal squamous cell carcinoma cell lines.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent malignancies and a major cause of cancer related death worldwide, especially in China. Cell lines are widely used disease models for basic medical research, however, well characterized ESCC cell models from China were seldom reported. Misidentifying and cross-contaminations of cell lines also hamper the way of producing solid and reproductive data. METHODS: CSEC216 was originated from a 45-year-old male ESCC patient from Chaoshan littoral, China. Specimens were minced into fragments and seeded in T-25 flask for primary culture. Immunoflourescence staining was performed for identifying the origination and proliferation activity. In vitro migration and invasion abilities was tested by transwell assay. DNA Short Tandem Repeats profiling was implemented for cell authorization. Karyotype was investigated by spectrum karyotyping. Whole genome sequencing was utilized to investigate genomic alterations. Background information and genomic mutation data of published ESCC cell lines were obtained from online databases. RESULTS: CSEC216 was an uncontaminated cell line, exhibited epithelial cell features with polygonal morphology and adherent growth as monolayer. Immuno staining demonstrated its epithelial origination and high proliferation rate. The Population Doubling time was 29.7 h. The karyotype demonstrated tumor cell patterns with aneuploidy and complex chromosomal aberrations. Mutation signatures, genes with SNA or CNA of CSEC216 and published ESCC cell lines were similar with the mutation spectrum of original ESCC tumors. CONCLUSIONS: ESCC cell line CSEC216 from high incidence region in China was established with no cross-contamination. Biological features were studied. Genomic mutation features of CSEC216 and 28 ESCC cell lines were characterized which provided thorough cytogenetic background that facilitated future usage.

Genetic Alterations in Esophageal Tissues From Squamous Dysplasia to Carcinoma.

BACKGROUND & AIMS: Esophageal squamous cell carcinoma (ESCC) is the most common subtype of esophageal cancer. Little is known about the genetic changes that occur in esophageal cells during the development of ESCC. We performed next-generation sequence analyses of esophageal nontumor, intraepithelial neoplasia (IEN), and ESCC tissues from the same patients to track genetic changes during tumor development. METHODS: We performed whole-genome, whole-exome, or targeted sequence analyses of 227 esophageal tissue samples from 70 patients with ESCC undergoing resection at Shantou University Medical College in China from 2012 through 2015 (no patients had received chemotherapy or radiation therapy); we analyzed normal tissues, tissues with simple hyperplasia, dysplastic tissues (IEN), and ESCC tissues collected from different regions of the esophagus at the same time. We also obtained 1191 nontumor esophageal biopsy specimens from the Chaoshan region (a high-risk region for ESCC) of China (a high-risk region for ESCC) and performed immunohistochemical and histologic analyses to detect inflammation. RESULTS: IEN and ESCC tissues had similar mutations and copy number alterations, at similar frequencies; these differed from mutations detected in tissues with simple hyperplasia. IEN tissues had mutations associated with apolipoprotein B messenger RNA editing enzyme, catalytic polypeptide-like-mediated mutagenesis (a DNA damage mutational signature). Genetic analyses indicated that most ESCCs were formed from early stage IEN clones. Trunk mutations (mutations shared by >10% of paired IEN and ESCC tissues) were in genes that regulate DNA repair and cell apoptosis, proliferation and adhesion. Mutations in TP53 and CDKN2A and copy number alterations in 11q (contains CCND1), 3q (contains SOX2), 2q (contains NFE2L2), and 9p (contains CDKN2A) were considered to be trunk variants; these were dominant mutations detected at high frequencies in clones of paired IEN and ESCC samples. In the esophageal biopsy samples from high-risk individuals (residing in the Chaoshan region), 68.9% had an evidence of chronic inflammation; the level of inflammation was correlated with atypical cell structures and markers of DNA damage. CONCLUSIONS: We analyzed mutations and gene copy number changes in nontumor, IEN, and ESCC samples, collected from 70 patients. IEN and ESCCs each had similar mutations and markers of genomic instability, including apolipoprotein B messenger RNA editing enzyme, catalytic polypeptide-like. Genomic changes observed in precancerous lesions might be used to identify patients at risk for ESCC.

[Spatial Prediction and Influencing Factors Analysis of Soil Salinization in Coastal Area Based on MGWR].

Quantitative analysis of the spatial non-stationary characteristics of soil salinization influencing factors and the prediction of its spatial distribution are of great significance for the rational use of coastal saline soil resources and the formulation of local prevention and control measures. In this study, the Hekou District of Dongying City, Shandong Province, was used as the study area, and the descriptive statistics of soil salinization status were conducted using classical statistical methods. Spatial autocorrelation theory was used to explore the characteristics of global and local spatial structure of soil salinization in the study area. Influential factors related to soil salinity were selected, and multivariate linear regression （MLR）, geographically weighted regression （GWR）, and multi-scale geographically weighted regression （MGWR） methods were used to model and predict the spatial distribution of soil salinity in the study area and to analyze the spatial heterogeneity of the effects of different influencing factors on soil salinity. The results showed that： ① The mean value of soil salinity in the study area was 5.84 g·kg-1, indicating severe salinization, with a global Moran's I index of 0.19 （P<0.00） and obvious spatial aggregation characteristics. ② Among the three models, the MGWR model had the highest modeling accuracy. Compared with that of the MLR model, the Radj2 of GWR and MGWR improved by 0.05 and 0.07, respectively, and the RSS decreased by 210.13 and 179.95, respectively. ③ The results of MGWR regression showed that the spatial distribution of soil salinity appeared to be mainly affected by the middle soil salinity, soil clay content, and vegetation cover from the mean values of standardized regression coefficients of different influencing factors. Different influencing factors had significant spatial non-stationary characteristics on soil salinization. ④ The results of the spatial distribution prediction of soil salinity in MGWR showed that the areas of high soil salinity （≥6 g·kg-1） were mainly distributed in the northern part of the study area, with an overall spatial trend of decreasing from the coast to the interior. The results of the study can be used as a reference for the analysis and predictive mapping of factors affecting soil salinization in the county and on a larger scale using MGWR.

[Spatiotemporal Variation Characteristics and Driving Factors of Cultivated Land NPP in the Shandong Area Around the Bohai Sea].

The Bohai Rim Plain is an important grain-producing area in China. The cultivated land resources have great potential for production, but there are many influencing factors. Understanding the spatiotemporal change characteristics and driving factors of the net primary productivity （NPP） of cultivated land vegetation in this region is of great significance to improve the regional cultivated land production conditions, excavate and enhance the production capacity of cultivated land, and ensure national food security. In this study, Theil-Sen Median trend analysis, Mann-Kendall significance test, Hurst index, and other methods were used to explore the spatiotemporal change characteristics, stability, and sustainability of regional cultivated land NPP. The influence of driving factors on the spatial heterogeneity of cultivated land NPP was analyzed by using optical parameters-based geographical detectors. The results showed that： ① From 2001 to 2019, due to the expansion of construction land during industrialization and urbanization, the cumulative decrease in the area of cultivated land in the Shandong area around the Bohai Sea was 2 004.51 km2. ② During the selected research period, the interannual variation of average cultivated land NPP showed a fluctuating and increasing trend. In terms of spatial distribution, the NPP of cultivated land was bounded by the Dongying District, with the spatial heterogeneity in the north being significantly lower than that in the south. ③ The area with increasing NPP of cultivated land accounted for 88.06% of the total area of cultivated land, mainly with low and medium fluctuations. The NPP of cultivated land will maintain an overall sustained trend of increase across the region in the future. ④ The average annual relative humidity study area had the strongest explanatory power for the spatial variability of NPP in cropland, with a q-value of 0.26, followed by surface soil salinity and subsoil salinity, with q-values greater than 0.2. The interactions between the different drivers all showed either nonlinear enhancement or bifactorial enhancement. The results of this study will help to reveal the characteristics of the changes in cultivated land production capacity and its driving forces in the Bohai Sea region and also provide a theoretical basis for the ecological protection and sustainable development of the region.

MDSC-targeting gold nanoparticles enhance PD-1 tumor immunotherapy by inhibiting NLRP3 inflammasomes.

Myeloid-derived suppressor cells (MDSCs) play a crucial role in the immune escape mechanisms that limit the efficacy of immunotherapeutic strategies. In the tumor microenvironment, NLRP3 inflammasome-driven Interleukin-1ß (IL-1ß) production serves to dampen antitumor immune responses, promoting tumor growth, progression, and immunosuppression. In this study, we revealed that gold nanoparticles (Au NPs) with a size of 30 nm disrupted NLRP3 inflammasome, but not other inflammasomes, in bone marrow-derived macrophages through abrogating NLRP3-NEK7 interactions mediated by reactive oxygen species (ROS). Density functional theory (DFT) calculations provided insights into the mechanism underlying the exceptional ROS scavenging capabilities of Au NPs. Additionally, when coupled with H6, a small peptide targeting MDSCs, Au NPs demonstrated the capacity to effectively reduce IL-1ß levels and diminish the MDSCs population in tumor microenvironment, leading to enhanced T cell activation and increased immunotherapeutic efficacy in mouse tumor models that are sensitive and resistant to PD-1 inhibition. Our findings unraveled a novel approach wherein peptide-modified Au NPs relieved the suppressive impact of the tumor microenvironment by inhibiting MDSCs-mediated IL-1ß release, which is the first time reported the employing a nanostrategy at modulating MDSCs to reverse the immunosuppressive microenvironment and may hold promise as a potential therapeutic agent for cancer immunotherapy.

Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients.

Deep learning models have been developed for various predictions in glioma; yet, they were constrained by manual segmentation, task-specific design, or a lack of biological interpretation. Herein, we aimed to develop an end-to-end multi-task deep learning (MDL) pipeline that can simultaneously predict molecular alterations and histological grade (auxiliary tasks), as well as prognosis (primary task) in gliomas. Further, we aimed to provide the biological mechanisms underlying the model's predictions. We collected multiscale data including baseline MRI images from 2776 glioma patients across two private (FAHZU and HPPH, n = 1931) and three public datasets (TCGA, n = 213; UCSF, n = 410; and EGD, n = 222). We trained and internally validated the MDL model using our private datasets, and externally validated it using the three public datasets. We used the model-predicted deep prognosis score (DPS) to stratify patients into low-DPS and high-DPS subtypes. Additionally, a radio-multiomics analysis was conducted to elucidate the biological basis of the DPS. In the external validation cohorts, the MDL model achieved average areas under the curve of 0.892-0.903, 0.710-0.894, and 0.850-0.879 for predicting IDH mutation status, 1p/19q co-deletion status, and tumor grade, respectively. Moreover, the MDL model yielded a C-index of 0.723 in the TCGA and 0.671 in the UCSF for the prediction of overall survival. The DPS exhibits significant correlations with activated oncogenic pathways, immune infiltration patterns, specific protein expression, DNA methylation, tumor mutation burden, and tumor-stroma ratio. Accordingly, our work presents an accurate and biologically meaningful tool for predicting molecular subtypes, tumor grade, and survival outcomes in gliomas, which provides personalized clinical decision-making in a global and non-invasive manner.

3DStructGen: an interactive web-based 3D structure generation for non-periodic molecule and crystal.

BACKGROUND: The increasing number of organic and inorganic structures promotes the development of the "Big Data" in chemistry and material science, and raises the need for cross-platform and web-based methods to search, view and edit structures. Many web-based three-dimensional (3D) structure tools have been developed for displaying existing models, building new models, and preparing initial input files for external calculations. But few of these tools can deal with crystal structures. RESULTS: We developed a user-friendly and versatile program based on standard web techniques, such as Hyper Text Markup Language 5 (HTML5), Cascade Style Sheet (CSS) and JavaScript. Both non-periodic organic molecule and crystal structure can be visualized, built and edited interactively. The atom, bond, angle and dihedral in a molecule can be viewed and modified using sample mouse operations. A wide range of cheminformatics algorithms for crystal structure are provided, including cleaving surfaces, establishing vacuum layers, and building supercells. Four displayed styles, namely "Primitive cell", "Original", "In-cell" and "Packing" can be used to visualize a unit cell. Additionally, the initial input files for Vienna Ab-initio Simulation Package (VASP) and Gaussian can be obtained by interacting with dialog boxes in 3DStructGen. CONCLUSIONS: 3DStructGen is a highly platform-independent program. It can provide web service independently or can be integrated into other web platforms. Other than local desktop software, it does not require any additional effort to install the system but a web browser supporting HTML5. 3DStructGen may play a valuable role in online chemistry education and pre-processing of quantum calculations. The program has been released under MIT open-source license and is available on: https://matgen.nscc-gz.cn/Tools.html.

Chronic inflammation-associated genomic instability paves the way for human esophageal carcinogenesis.

Chronic inflammation is associated with increased risk of cancer development, whereas the link between chronic inflammation and esophageal carcinogenesis is still obscure heretofore. This study aimed to investigate the relationship between chronic inflammation and DNA damage, as well as the possible role of DNA damage in esophageal carcinogenic process. Endoscopic esophageal biopsies from 109 individuals from Chaoshan littoral, a high-risk region for esophageal squamous cell carcinoma (ESCC), were examined to evaluate the association between chronic inflammation and histological severity, while additional 204 esophageal non-tumor samples from patients with ESCC were collected. Immunohistochemistry was performed to detect the oxidative DNA damage and DNA double-strand breaks (DSBs). Significantly positive correlation was observed between degree of chronic inflammation and esophageal precursor lesions (rs = 0.37, P < 0.01). Immunohistochemical analysis showed that oxidative DNA damage level was positively correlated with the degree of chronic inflammation (rs = 0.21, P < 0.05). Moreover, the level of oxidative DNA damage positively correlated with histological severity (rs = 0.49, P < 0.01). We found that the extent of DSBs was progressively increased with inflammation degree (P < 0.01) and the progression of precancerous lesions (P < 0.001). Collectively, these findings provide evidence linking chronic inflammation-associated genomic instability with esophageal carcinogenesis and suggest possibilities for early detection and intervention of esophageal carcinogenesis.