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Excessive inflammation leads to periodontitis, which inhibits the osteogenic differentiation of human dental pulp stem cells (hDPSCs), irreversibly injured and difficultly repaired for the important dental pulp. Hence, it is necessary to study the functional gene to enhance the osteogenic differentiation of hDPSCs. Previous found that SNHG7 expression was increased in the osteogenic differentiation of hDPSCs. However, the regulatory functions of SNHG7 on osteogenic differentiation of hDPSCs in the inflammatory microenvironment still remains unknown. In this study, hDPSCs treatment with 50 ng/mL TNF-α to mimic the inflammatory microenvironment, then cultured in osteoblast differentiation medium for 14 days. SNHG7, miR-6512–3p, BSP, DSPP, DMP-1, RUNX2 and OPN in hDPSCs were detect by RT-qPCR. We found that SNHG7 expression was reduced during the osteogenic differentiation of hDPSCs after different concentrations TNF-α treatment. SNHG7 overexpression improved the TNF-α-induced suppression of calcium deposition, ALP activity, and the expression of BSP, DSPP, DMP-1, RUNX2 and OPN. Furthermore, SNHG7 can sponge with miR-6512–3p. miR-6512–3p expression was increased during the osteogenic differentiation of hDPSCs after different concentrations TNF-α treatment while inhibited after SNHG7 overexpression. knockdown of miR-6512–3p improved the TNF-α-induced suppression of calcium deposition, ALP activity, and the expression of BSP, DSPP, DMP-1, RUNX2 and OPN. Finally, miR-6512–3p overexpression reversed the effect of SNHG7 on the osteo/dentinogenic differentiation of TNF-α-treated hDPSCs. In conclusions, SNHG7 improves the osteogenic differentiation of hDPSCs by inhibiting miR-6512–3p expression under 50 ng/mL TNF-α-induced inflammatory environment, which provided potential targets for the treatment of periodontitis. • ng/mL TNF-α treatment inhibited the hDPSCs osteo/dentinogenic differentiation. • SNHG7 expression was reduced in hDPSCs after 50 ng/mL TNF-α treatment. • SNHG7 improved the osteo/dentinogenic differentiation of TNF-α-induced hDPSCs. • SNHG7 inhibited miR-6512–3p expression in TNF-α-induced hDPSCs. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC's full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future. • A new MDD discrimination method MFMC is proposed. • MFMC is tested on REST-meta-MDD including over 2400 subjects. • MFMC outperforms existing methods with significant improvements. • The good generalizability of MFMC is verified. • MFMC identifies 13 features related with 9 brain regions important for MDD. [ABSTRACT FROM AUTHOR]

Abstract Non-small cell lung cancer (NSCLC) patients harboring EGFR mutation who initially respond to EGFR-TKI will gradually develop acquired resistance. There is still a challenge to treat EGFR-TKI resistant NSCLC patients. Polyphyllin I (PP I), a steroidal saponin isolated from Paris polyphylla., has been exhibited antitumor activities against various carcinomas. However, its mechanism in treating EGFR-TKI resistant NSCLC has not been well elucidated. In this study, we found that PP I suppressed the cell viability and induced apoptosis of gefitinib-resistant NSCLC cells and xenograft models. These therapeutic efficacies were associated with down-regulated level of MALAT1, leading to inactivation of STAT3 signaling pathway. The cell viability inhibition and apoptosis inducing in gefitinib-resistant NSCLC triggered by PP I were abolished by MALAT1 overexpression, while the cell viability inhibition and apoptosis inducing triggered by PP I were potentiated by MALAT1 knockdown. These findings suggest that, in vitro and in vivo, PP I inhibits the viability and induces apoptosis of gefitinib-resistant NSCLC by down-regulating MALAT1 and inactivating STAT3 signaling pathway. Thus, PPI could serve a promising therapeutic agent for the treatment of gefitinib-resistant NSCLC. Highlights • There is still a challenge to treat EGFR-TKI resistant NSCLC. • PP I suppressed the cell viability and induced apoptosis of drug-resistant NSCLC. • PP I might down-regulate MALAT1 expression and inactivate STAT3 signaling pathway. • PP I could serve a promising therapeutic agent for gefitinib-resistant NSCLC. [ABSTRACT FROM AUTHOR]

• Silencing TRIM66 hindered cell malignant phenotype of non-small cell lung cancer; • TRIM66 interacted with MMP9 in non-small cell lung cancer; • MMP9 activated TGF-β/SMAD pathway; • TRIM66/MMP9/TGF-β/SMAD axis promoted non-small cell lung cancer progression. To investigate regulatory function and underlying mechanism of TRIM66 in non-small cell lung cancer (NSCLC). TRIM66 and MMP9 expression in NSCLC cells and tissues was assayed via qRT-PCR and western blot. CCK-8, colony formation, Transwell and flow cytometry assays were conducted to measure cell functional alternations in NSCLC. Western blot was employed to measure expression as well as phosphorylation levels of epithelial-mesenchymal transition-(EMT) and TGF-β/SMAD pathways-related proteins. Co-immunoprecipitation (Co-IP) assay was done to probe interaction between TRIM66 and MMP9. Xenograft in vivo experiment and tumor metastasis model in nude mice were utilized to investigate effects of TRIM66 on tumor growth of NSCLC. TRIM66 and MMP9 were conspicuously highly expressed in NSCLC cells and tissues. High TRIM66 level was markedly correlated with metastasis. Silencing TRIM66 prominently repressed the proliferation, migration and invasion of transfected cells, while inducing cell apoptosis. Whereas forced expression of TRIM66 exerted the opposite effect. The aberrant expression of TRIM66 modulated EMT pathway. TRIM66 also regulated MMP9 expression, and the interaction between them was validated by Co-IP assay. Overexpression of MMP9 could activate TGF-β/SMAD pathway. Rescue experiments manifested that si-MMP9 or SB431542 could partially reverse phenotypes induced by TRIM66. In vivo experiments revealed that silencing TRIM66 could hamper NSCLC tumor growth and metastasis. TRIM66 and MMP9 were up-regulated in NSCLC. TRIM66 facilitated the malignant progression of NSCLC through modulating MMP9-mediated TGF-β/SMAD pathway. [ABSTRACT FROM AUTHOR]

Drug-target interactions (DTIs) play a key role in drug development and discovery processes. Wet lab prediction of DTIs is time-consuming, expensive, and tedious. Fortunately, computational approaches can identify new interactions (drug-target pairs) and accelerate the process of drug repurposing. However, a vast number of interactions remain undiscovered; therefore, we proposed a deep learning-based method (deepACTION) for predicting potential or unknown DTIs. Here, each drug chemical structure and protein sequence are transformed according to structural and sequence information using different descriptors to represent their features correctly. There have been some challenges, such as the high dimensionality and class imbalance of data during the prediction process. To address these problems, we developed the MMIB technique to balance the majority and minority instances in the dataset and utilized a LASSO model to handle the high dimensionality of the data. In addition, we trained the convolutional neural network algorithm with balanced and reduced features for accurate prediction of DTIs. In this study, the AUC is considered a primary evaluation metric for comparing the performance of the deep ACTION model with that of existing methods by a 5-fold cross-validation test. Our experiential dataset obtained from the DrugBank database and our deepACTION model achieved an AUC of 0.9836 for this dataset. The experimental results ensured that the model can predict significant numbers of new DTIs and provide complete information to motivate scientists to develop drugs. Image 1 • Computational model deepACTION is proposed for predicting Drug-Target Interactions (DTIs). • Different types of drug-protein features are generated by using Rcpi (package) and PROFEAT server. • MMIB technique is developed for resolving class imbalance, and LASSO is utilized to manage the high dimensionality. • Convolutional Neural Networks (CNNs) algorithm is used as a classifier. • Achieves the best prediction performance and can effectively predict the potential DTIs. [ABSTRACT FROM AUTHOR]

Accurate identification of drug-target interaction (DTI) is a crucial and challenging task in the drug discovery process, having enormous benefit to the patients and pharmaceutical company. The traditional wet-lab experiments of DTI is expensive, time-consuming, and labor-intensive. Therefore, many computational techniques have been established for this purpose; although a huge number of interactions are still undiscovered. Here, we present pdti-EssB, a new computational model for identification of DTI using protein sequence and drug molecular structure. More specifically, each drug molecule is transformed as the molecular substructure fingerprint. For a protein sequence, different descriptors are utilized to represent its evolutionary, sequence, and structural information. Besides, our proposed method uses data balancing techniques to handle the imbalance problem and applies a novel feature eliminator to extract the best optimal features for accurate prediction. In this paper, four classes of DTI benchmark datasets are used to construct a predictive model with XGBoost. Here, the auROC is utilized as an evaluation metric to compare the performance of pdti-EssB method with recent methods, applying five-fold cross-validation. Finally, the experimental results indicate that our proposed method is able to outperform other approaches in predicting DTI, and introduces new drug-target interaction samples based on prediction probability scores. pdti-EssB webserver is available online at http://pdtiessb-uestc.com/ Image 1 • Computational model pdti-EssB is proposed for predicting Drug-Target Interactions (DTIs). • MSF, PSSM-Bigram, PseAAC and SPIDER2 are used for extracting Drug-target features. • CUS and EnsRFS techniques are proposed for resolving class imbalance problem and high dimensionality of data. • Boosting algorithm is utilized as a classifier. • Achieves the best prediction performance and can effectively predict the potential DTIs. [ABSTRACT FROM AUTHOR]

The tumorgenesis process of lung cancer involves the regulatory dysfunctions of multiple pathways. Although many signaling pathways have been identified to be associated with lung cancer, there are little quantitative models of how inactions between genes change during the process from normal to cancer. These changes belong to different dynamic co-expressions patterns. We quantitatively analyzed differential co-expression of gene pairs in four datasets. Each dataset included a large number of lung cancer and normal samples. By overlapping their results, we got 14 highly confident gene pairs with consistent co-expression change patterns. Some of they, such as ARHGAP30 and GIMAP4, had been recorded in STRING network database while some of them were novel discoveries, such as C9orf135 and MORN5, TEKT1 and TSPAN1 were positively correlated in both normal and cancer but more correlated in normal than cancer. These gene pairs revealed the underlying mechanisms of lung cancer occurrence. • We analyzed differential co-expression of gene pairs in four datasets. • We got 14 highly confident gene pairs with consistent co-expression change patterns. • We uesd an R package DiffCorr. [ABSTRACT FROM AUTHOR]