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Background: DNA-binding hot spots are dominant and fundamental residues that contribute most of the binding free energy yet accounting for a small portion of protein–DNA interfaces. As experimental methods for identifying hot spots are time-consuming and costly, high-efficiency computational approaches are emerging as alternative pathways to experimental methods. Results: Herein, we present a new computational method, termed inpPDH, for hot spot prediction. To improve the prediction performance, we extract hybrid features which incorporate traditional features and new interfacial neighbor properties. To remove redundant and irrelevant features, feature selection is employed using a two-step feature selection strategy. Finally, a subset of 7 optimal features are chosen to construct the predictor using support vector machine. The results on the benchmark dataset show that this proposed method yields significantly better prediction accuracy than those previously published methods in the literature. Moreover, a user-friendly web server for inpPDH is well established and is freely available at http://bioinfo.ahu.edu.cn/inpPDH. Conclusions: We have developed an accurate improved prediction model, inpPDH, for hot spot residues in protein–DNA binding interfaces by given the structure of a protein–DNA complex. Moreover, we identify a comprehensive and useful feature subset including the proposed interfacial neighbor features that has an important strength for identifying hot spot residues. Our results indicate that these features are more effective than the conventional features considered previously, and that the combination of interfacial neighbor features and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spot residues in protein–DNA complexes. [ABSTRACT FROM AUTHOR]

Background: Identification of hot spots in protein-DNA interfaces provides crucial information for the research on protein-DNA interaction and drug design. As experimental methods for determining hot spots are time-consuming, labor-intensive and expensive, there is a need for developing reliable computational method to predict hot spots on a large scale. Results: Here, we proposed a new method named sxPDH based on supervised isometric feature mapping (S-ISOMAP) and extreme gradient boosting (XGBoost) to predict hot spots in protein-DNA complexes. We obtained 114 features from a combination of the protein sequence, structure, network and solvent accessible information, and systematically assessed various feature selection methods and feature dimensionality reduction methods based on manifold learning. The results show that the S-ISOMAP method is superior to other feature selection or manifold learning methods. XGBoost was then used to develop hot spots prediction model sxPDH based on the three dimensionality-reduced features obtained from S-ISOMAP. Conclusion: Our method sxPDH boosts prediction performance using S-ISOMAP and XGBoost. The AUC of the model is 0.773, and the F1 score is 0.713. Experimental results on benchmark dataset indicate that sxPDH can achieve generally better performance in predicting hot spots compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background: Digital histopathology provides valuable information for clinical decision-making. We hypothesized that a deep risk network (DeepRisk) based on digital pathology signature (DPS) derived from whole-slide images could improve the prognostic value of the tumor, node, and metastasis (TNM) staging system and offer chemotherapeutic benefits for gastric cancer (GC). Methods: DeepRisk is a multi-scale, attention-based learning model developed on 1120 GCs in the Zhongshan dataset and validated with two external datasets. Then, we assessed its association with prognosis and treatment response. The multi-omics analysis and multiplex Immunohistochemistry were conducted to evaluate the potential pathogenesis and spatial immune contexture underlying DPS. Results: Multivariate analysis indicated that the DPS was an independent prognosticator with a better C-index (0.84 for overall survival and 0.71 for disease-free survival). Patients with low-DPS after neoadjuvant chemotherapy responded favorably to treatment. Spatial analysis indicated that exhausted immune clusters and increased infiltration of CD11b+CD11c+ immune cells were present at the invasive margin of high-DPS group. Multi-omics data from the Cancer Genome Atlas-Stomach adenocarcinoma (TCGA-STAD) hint at the relevance of DPS to myeloid derived suppressor cells infiltration and immune suppression. Conclusion: DeepRisk network is a reliable tool that enhances prognostic value of TNM staging and aid in precise treatment, providing insights into the underlying pathogenic mechanisms. [ABSTRACT FROM AUTHOR]

RNA silencing plays an important role in plant antiviral responses, which trigger the production of virus-derived small interfering RNAs (vsiRNAs). The competing endogenous RNA (ceRNA) hypothesis revealed a unique mechanism in which circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs) can interact with small RNAs to regulate the expression of corresponding target mRNAs. Sugarcane mosaic virus (SCMV) infection causes severe economic losses in maize (Zea mays L.) production worldwide. This study compared and analyzed characteristics of vsiRNAs derived from SCMV and their target genes in resistant (Chang7-2) and susceptible (Mo17) maize inbred lines through whole-transcriptome RNA sequencing and degradome sequencing. The results showed that 706 transcripts were targeted by 204 vsiRNAs, including 784 vsiRNA-target gene pairs. Furthermore, ceRNA networks of circRNA/lncRNA-vsiRNA-mRNA in response of maize to SCMV infection were obtained, including 3 differentially expressed (DE) circRNAs, 36 DElncRNAs, 105 vsiRNAs, and 342 DEmRNAs in Mo17 plants, and 3 DEcircRNAs, 35 DElncRNAs, 23 vsiRNAs, and 87 DEmRNAs in Chang7-2 plants. Our results also showed that the transcripts of ZmDCLs, ZmAGOs, and ZmRDRs were differentially accumulated in resistant and susceptible maize inbred lines after SCMV infection. These findings provide valuable insights into the relationship between SCMV-derived vsiRNAs and potential ceRNAs fine-tuning the SCMV-maize interaction and offer novel clues to reveal the mechanism underlying the pathogenesis of SCMV. [ABSTRACT FROM AUTHOR]

The Drug Response Gene Expression Associated Map, also referred as "DREAM" (http://bio-big-data.cn:8080/DREAM), is a manually curated database of experimentally supported protein-coding RNAs and drugs associations in human cancers. The current version of the DREAM documents 3048 entries about scientific literatures supported drug sensitivity or drug intervention related protein-coding RNAs from PubMed database and 195 high-throughput microarray data about drug sensitivity or drug intervention related protein-coding RNAs data from GEO database. Each entry in DREAM database contains detailed information on protein-coding RNA, drug, cancer, and other information including title, PubMed ID, journal, publish time. The DREAM database also provides some data visualization and online analysis services such as volcano plot, GO/KEGG enrichment function analysis, and novel drug discovery analysis. We hope the DREAM database should serve as a valuable resource for clinical practice and basic research, which could help researchers better understand the effects of protein-coding RNAs on drug response in human cancers. [ABSTRACT FROM AUTHOR]

Background: Ulcerative colitis (UC) is a chronic and recurrent disease without satisfactory treatment strategies. Dental pulp stem cell (DPSC) transplantation has been proposed as a potential therapy for UC. This study aimed to investigate the therapeutic effects of the rat hepatocyte growth factor (HGF) gene transduced into DPSCs for UC. Methods: The therapeutic effects of HGF-DPSCs transplanted intravenously into a rat model of UC induced by 5% dextran sulphate sodium (DSS) were compared with the other treatment groups (LV-HGF group, DPSCs group and GFP-DPSCs group). Immunofluorescence and immunohistochemistry were used to observe the localization and proliferation of HGF-DPSCs at the site of colon injury. The expression levels of inflammatory factors were detected by real-time quantitative PCR (RT-PCR) and western blotting. The oxidative stress markers were detected by ELISA. DAI scores and body weight changes were used to macroscopically evaluate the treatment of rats in each group. Results: Immunofluorescence and immunohistochemistry assays showed that HGF-DPSCs homed to colon injury sites and colocalized with intestinal stem cell (ISC) markers (Bmi1, Musashi1 and Sox9) and significantly promoted protein expression (Bmi1, Musashi1, Sox9 and PCNA). Anti-inflammatory cytokine (TGF-β and IL-10) expression was the highest in the HGF-DPSCs group compared with the other treatment groups, while the expression of pro-inflammatory cytokines (TNF-α and INF-γ) was the lowest. Additionally, the oxidative stress response results showed that malondialdehyde (MDA) and myeloperoxidase (MPO) expression decreased while superoxide dismutase (SOD) expression increased, especially in the HGF-DPSCs group. The DAI scores showed a downward trend with time in the five treatment groups, whereas body weight increased, and the changes were most prominent in the HGF-DPSCs group. Conclusions: The study indicated that HGF-DPSCs can alleviate injuries to the intestinal mucosa by transdifferentiating into ISC-like cells, promoting ISC-like cell proliferation, suppressing inflammatory responses and reducing oxidative stress damage, which provides new ideas for the clinical treatment of UC. [ABSTRACT FROM AUTHOR]

Background: An imbalance between circulating neuroprotective and neurotoxic T cell subsets leads to poor prognosis in acute ischaemic stroke (AIS). Preclinical studies have indicated that the soluble form of the interleukin-2 receptor α (sIL-2Rα)-IL-2 complex regulates T cell differentiation. However, the association between sIL-2Rα levels and AIS remains unclear.Methods: A total of 201 first-ever AIS patients within 24 h after stroke onset and 76 control subjects were recruited. The National Institutes of Health Stroke Scale (NIHSS) score and 3-month functional outcome (modified Rankin Scale [mRS] score) at admission were assessed. Plasma sIL-2Rα and IL-2 levels at admission were measured. Prognostic significance was identified by using univariate and multivariate logistic regression analyses.Results: Patients with poor functional outcomes at 3 months had significantly higher levels of sIL-2Rα and lower levels of IL-2 than patients with good outcomes. Moreover, sIL-2Rα levels showed a strong positive correlation with NIHSS and mRS scores (p < 0.0001), whereas IL-2 levels were negatively correlated with mRS scores (p < 0.01). Univariate analyses showed that higher sIL-2Rα and IL-2 levels were associated with an increased and reduced risk of unfavourable outcomes, respectively. After adjusting for confounding variables, the sIL-2Rα level remained independently associated with an increased risk of an unfavourable outcome, and adding sIL-2Rα levels to the conventional risk factor model significantly improved risk reclassification (net reclassification improvement 17.56%, p = 0.003; integrated discrimination improvement 5.78%, p = 0.0003).Conclusions: sIL-2Rα levels represent a novel, independent prognostic marker that can improve the currently used risk stratification of AIS patients. Our findings also highlight that elevated plasma sIL-2Rα and IL-2 levels manifested opposite correlations with functional outcome, underlining the importance of IL-2/IL-2R autocrine loops in AIS. [ABSTRACT FROM AUTHOR]

Background: Transmissible gastroenteritis virus (TGEV), a member of the family Coronaviridae, causes lethal watery diarrhea in piglets. Previous studies have revealed that the coronaviruses develop various strategies to evade the host innate immunity through the inhibition of nuclear factor kappa B (NF-κB) signaling pathway. However, the ability of TGEV to inhibit the host innate immune response by modulating the NF-κB signaling pathway is not clear. Methods: In this study, a dual luciferase reporter assay was used to confirm the inhibition of NF-κB by TGEV infection and to identify the major viral proteins involved in the inhibition of NF-κB signaling. Real-time quantitative PCR was used to quantify the mRNA expression of inflammatory factors. The deubiquitination of Nsp3 domains and its effect on IκBα and p65 were analyzed by western blotting. The ubiquitination level of IκBα was analyzed by immunoprecipitation. Results: In ST and IPEC-J2 cells, TGEV exhibited a dose-dependent inhibition of NF-κB activity. Individual TGEV protein screening revealed the high potential of non-structural protein 3 (Nsp3) to inhibit NF-κB signaling, and leading to the downregulation of the NF-κB-induced cytokine production. We demonstrated that the inhibitory effect of Nsp3 was mainly mediated through the suppression of IκBα degradation as well as the inhibition of p65 phosphorylation and nuclear translocation. Furthermore, the amino acid residues at positions 590–1,215 in Nsp3 were demonstrated to inhibit the degradation of IκBα by inhibiting the IκBα ubiquitination. Conclusion: TGEV infection can inhibit the activation of the NF-κB signaling pathway, which is mainly mediated by Nsp3 through the canonical pathway. The amino acid residues at positions 590–1,215 in Nsp3 compose the critical domain that mediates NF-κB inhibition. We speculate that this inhibitory effect is likely to be related to the structure of PLP2 with deubiquitinating enzyme activity of the amino acid residues at positions 590–1,215 in Nsp3. Our study provides a better understanding of the TGEV-mediated innate immune modulation and lays the basis for studies on the pathogenesis of coronavirus. [ABSTRACT FROM AUTHOR]