Abstract: Long non-coding RNAs (lncRNAs) are related to a variety of human diseases. However, little is known about the role of lncRNA in intervertebral disc degeneration (IDD). LncRNA expression profile of human IDD were downloaded from Gene Expression Omnibus (GEO) database. Potential biomarkers and therapeutic drugs for IDD were analyzed by weighted gene co-expression network analysis (WGCNA), R software package Limma, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We identified 1455 differentially expressed genes and 423 differentially expressed lncRNAs. Twenty-six co-expression modules were obtained, among them, the tan, brown, and turquoise modules were most closely related to IDD. The turquoise module contained a large number of differential expressed lncRNAs and genes, these genes were mainly enriched in the MAPK signaling pathway, TGF-beta signaling pathway. Furthermore, we obtained 11,857 LmiRM-Degenerated, these lncRNAs and genes showed higher differential expression multiples and higher expression correlation. After constructing a disease-gene interaction network, 25 disease-specific genes and 9 disease-specific lncRNAs were identified. Combined with the drug-target gene interaction network, three drugs, namely, Calcium citrate, Calcium Phosphate, and Calcium phosphate dihydrate, which may have curative effects on IDD, were determined. Finally, a genetic diagnosis model and lncRNA diagnosis model with 100% diagnostic performance in both the training data set and the validation data set were established based on these genes and lncRNA. This study provided new diagnostic features for IDD and could help design personalized treatment of IDD.

Abstract: Background: Drug repurposing provides an effective method for high-speed, low-risk drug development. Clinical phenotype-based screening exceeded target-based approaches in discovering first-in-class small-molecule drugs. However, most of these approaches predict only binary phenotypic associations between drugs and diseases; the types of drug and diseases have not been well exploited. Principally, the clinical phenotypes of a known drug can be divided into indications (Is), side effects (SEs), and contraindications (CIs). Incorporating these different clinical phenotypes of drug-disease associations (DDAs) can improve the prediction accuracy of the DDAs. Methods: We develop Drug Disease Interaction Type (DDIT), a user-friendly online predictor that supports drug repositioning by submitting known Is, SEs, and CIs for a target drug of interest. The dataset for Is, SEs, and CIs was extracted from PREDICT, SIDER, and MED-RT, respectively. To unify the names of the drugs and diseases, we mapped their names to the Unified Medical Language System (UMLS) ontology using Rest API. We then integrated multiple clinical phenotypes into a conditional restricted Boltzmann machine (RBM) enabling the identification of different phenotypes of drug-disease associations, including the prediction of as yet unknown DDAs in the input. Results: By 10-fold cross-validation, we demonstrate that DDIT can effectively capture the latent features of the drug-disease association network and represents over 0.217 and over 0.072 improvement in AUC and AUPR, respectively, for predicting the clinical phenotypes of DDAs compared with the classic K-nearest neighbors method (KNN, including drug-based KNN and disease-based KNN), Random Forest, and XGBoost. By conducting leave-one-drug-class-out cross-validation, the AUC and AUPR of DDIT demonstrated an improvement of 0.135 in AUC and 0.075 in AUPR compared to any of the other four methods. Within the top 10 predicted indications, side effects, and contraindications, 7/10, 9/10, and 9/10 hit known drug-disease associations. Overall, DDIT is a useful tool for predicting multiple clinical phenotypic types of drug-disease associations.

Abstract: Salvianolic acid C (SAC) is a major bioactive component of Salvia miltiorrhiza Bunge (Danshen), a Chinese herb for treating ischemic stroke (IS). However, the mechanism by which SAC affects the IS has not yet been evaluated, thus a network pharmacology integrated molecular docking strategy was performed to systematically evaluate its pharmacological mechanisms, which were further validated in rats with cerebral ischemia. A total of 361 potential SAC-related targets were predicted by SwissTargetPrediction and PharmMapper, and a total of 443 IS-related targets were obtained from DisGeNET, DrugBank, OMIM, and Therapeutic Target database (TTD) databases. SAC-related targets were hit by the 60 targets associated with IS. By Gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment combined with the protein-protein interaction (PPI) network and cytoHubba plug-ins, nine related signaling pathways (proteoglycans in cancer, pathways in cancer, PI3K-Akt signaling pathway, Focal adhesion, etc.), and 20 hub genes were identified. Consequently, molecular docking indicated that SAC may interact with the nine targets (F2, MMP7, KDR, IGF1, REN, PPARG, PLG, ACE and MMP1). Four of the target proteins (VEGFR2, MMP1, PPARgamma and IGF1) were verified using western blot. This study comprehensively analyzed pathways and targets related to the treatment of IS by SAC. The results of western blot also confirmed that the SAC against IS is mainly related to anti-inflammatory and angiogenesis, which provides a reference for us to find and explore the effective anti-IS drugs.

Abstract: Drug-drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug-drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.

Abstract: Persistence is a transient state that poses an important health concern in cancer therapy. The mechanisms associated with persister phenotypes are highly diverse and complex, and many aspects of persister cell physiology remain to be explored. We applied a melanoma cell line and panel of chemotherapeutic agents to show that melanoma persister cells are not necessarily preexisting dormant cells; in fact, they may be induced by cancer chemotherapeutics. Our metabolomics analysis and phenotype microarray assays further demonstrated a transient upregulation in Krebs cycle metabolism in persister cells. We also verified that targeting electron transport chain activity can significantly reduce melanoma persister levels. The reported metabolic remodeling feature seems to be a conserved characteristic of melanoma persistence, as it has been observed in various melanoma persister subpopulations derived from a diverse range of chemotherapeutics. Elucidating a global metabolic mechanism that contributes to persister survival and reversible switching will ultimately foster the development of novel cancer therapeutic strategies.

Abstract: Streptococcus pneumoniae is a notorious pathogen that affects approximately 450 million people worldwide and causes up to four million deaths per annum. Despite availability of antibiotics (i.e., penicillin, doxycycline, or clarithromycin) and conjugate vaccines (e.g., PCVs), it is still challenging to treat because of its drug resistance ability. The rise of antibiotic resistance in S. pneumoniae is a major source of concern across the world. Computational subtractive genomics is one of the most applied techniques in which the whole proteome of the bacterial pathogen is gradually reduced to a limited number of potential therapeutic targets. Whole-genome sequencing has greatly reduced the time required and provides more opportunities for drug target identification. The goal of this work is to evaluate and analyze metabolic pathways in serotype 14 of S. pneumonia to identify potential drug targets. In the present study, 47 potent drug targets were identified against S. pneumonia by employing the computational subtractive genomics approach. Among these, two proteins are prioritized (i.e., 4-oxalocrotonate tautomerase and Sensor histidine kinase uniquely present in S. pneumonia) as novel drug targets and selected for further structure-based studies. The identified proteins may provide a platform for the discovery of a lead drug candidate that may be capable of inhibiting these proteins and, therefore, could be helpful in minimizing the associated risk related to the drug-resistant S. pneumoniae. Finally, these enzymatic proteins could be of prime interest against S. pneumoniae to design rational targeted therapy.

Abstract: There is a critical requirement for alternative strategies to provide the better treatment in colorectal cancer (CRC). Hence, our goal was to propose novel biomarkers as well as drug candidates for its treatment through differential interactome based drug repositioning. Differentially interacting proteins and their modules were identified, and their prognostic power were estimated through survival analyses. Drug repositioning was carried out for significant target proteins, and candidate drugs were analyzed via in silico molecular docking prior to in vitro cell viability assays in CRC cell lines. Six modules (mAPEX1, mCCT7, mHSD17B10, mMYC, mPSMB5, mRAN) were highlighted considering their prognostic performance. Drug repositioning resulted in eight drugs (abacavir, ribociclib, exemestane, voriconazole, nortriptyline hydrochloride, theophylline, bromocriptine mesylate, and tolcapone). Moreover, significant in vitro inhibition profiles were obtained in abacavir, nortriptyline hydrochloride, exemestane, tolcapone, and theophylline (positive control). Our findings may provide new and complementary strategies for the treatment of CRC.

Abstract: Background: Wutou Decoction (WTD), as a classic prescription, has been generally used to treat rheumatoid arthritis (RA) for two thousand years in China. However, the potential protective effects of WTD on rheumatoid arthritis and its possible mechanism have rarely been reported. Purpose: The aim of this study was to explore the possible mechanism of WTD against RA and a promising alternative candidate for RA therapy. Methods: A model of collagen-induced arthritis (CIA) was constructed in rats to assess the therapeutic effects of WTD. Histopathological staining, immunofluorescence, and western blotting of synovial sections were conducted to detect the antiangiogenic effects of WTD. Then, cell viability assays, flow cytometry, scratch healing assays, and invasion assays were conducted to explore the effects of WTD on MH7A human fibroblast-like synoviocyte (FLS) cell proliferation, apoptosis, migration, and invasion in vitro. The ability of WTD to induce blood vessel formation after MH7A cell and human umbilical vein endothelial cell line (HUVEC) coculture with WTD intervention was detected by a tube formation assay. The mechanisms of WTD were screened by network pharmacology and confirmed by in vivo and in vitro experiments. Results: WTD ameliorated the symptoms and synovial pannus hyperplasia of CIA rats. Treatment with WTD inhibited MH7A cell proliferation, migration, and invasion and promoted MH7A apoptosis. WTD could inhibit MH7A cell expression of proangiogenic factors, including VEGF and ANGI, to induce HUVEC tube formation. Furthermore, the PI3K-AKT-mTOR-HIF-1alpha pathway was enriched as a potential target of WTD for the treatment of RA through network pharmacology enrichment analysis. Finally, it was confirmed in vitro and in vivo that WTD inhibits angiogenesis in RA by interrupting the PI3K-AKT-mTOR-HIF-1alpha pathway. Conclusion: WTD can inhibit synovial hyperplasia and angiogenesis, presumably by inhibiting the migration and invasion of MH7A cells and blocking the production of proangiogenic effectors in MH7A cells. The possible underlying mechanism by which WTD ameliorates angiogenesis in RA is the PI3K-AKT-mTOR-HIF-1alpha pathway.

Abstract: Biomedical and life science literature is an essential way to publish experimental results. With the rapid growth of the number of new publications, the amount of scientific knowledge represented in free text is increasing remarkably. There has been much interest in developing techniques that can extract this knowledge and make it accessible to aid scientists in discovering new relationships between biological entities and answering biological questions. Making use of the word2vec approach, we generated word vector representations based on a corpus consisting of over 16 million PubMed abstracts. We developed a text mining pipeline to produce word2vec embeddings with different properties and performed validation experiments to assess their utility for biomedical analysis. An important pre-processing step consisted in the substitution of synonymous terms by their preferred terms in biomedical databases. Furthermore, we extracted gene-gene networks from two embedding versions and used them as prior knowledge to train Graph-Convolutional Neural Networks (CNNs) on large breast cancer gene expression data and on other cancer datasets. Performances of resulting models were compared to Graph-CNNs trained with protein-protein interaction (PPI) networks or with networks derived using other word embedding algorithms. We also assessed the effect of corpus size on the variability of word representations. Finally, we created a web service with a graphical and a RESTful interface to extract and explore relations between biomedical terms using annotated embeddings. Comparisons to biological databases showed that relations between entities such as known PPIs, signaling pathways and cellular functions, or narrower disease ontology groups correlated with higher cosine similarity. Graph-CNNs trained with word2vec-embedding-derived networks performed sufficiently good for the metastatic event prediction tasks compared to other networks. Such performance was good enough to validate the utility of our generated word embeddings in constructing biological networks. Word representations as produced by text mining algorithms like word2vec, therefore are able to capture biologically meaningful relations between entities. Our generated embeddings are publicly available at https://github.com/genexplain/Word2vec-based-Networks/blob/main/README.md.

Abstract: Purpose: Atopic dermatitis (AD) is a common chronic inflammatory skin disorder associated with immune dysregulation and barrier dysfunction. In this study, we investigated immunological biomarkers for AD diagnosis and treatment using CIBERSORT to identify immune cell infiltration characteristics. Patients and Methods: Common differentially expressed genes (DEGs) of lesioned (LS) vs non-lesioned (NL) groups were obtained from public datasets (GSE140684 and GSE99802). We performed functional enrichment analysis and selected hub genes from the protein-protein interaction (PPI) network. The hub genes were then subjected to transcription factor (TF), microRNA (miRNA), long non-coding RNA (lncRNA), drug interaction, and protein subcellular localization analyses. We also performed correlation analysis on differentially expressed immune cells, TFs, and hub genes. Receiver operating characteristic (ROC) curve analysis and binomial least absolute shrinkage and selection operator (LASSO) regression analysis were employed to assess the expression of hub genes in the GSE99802, GSE140684, GSE58558, GSE120721, and GSE36842 datasets. Results: We identified 238 common DEGs and 25 hub genes. Additionally, we predicted TFs, miRNAs, lncRNA, drugs, and protein subcellular localizations. The proportions of activated dendritic cells (DCs) and CD4+ memory T cells were relatively high in the LS skin. Expression levels of the TF FOXC1 were negatively correlated with target genes and the abundance of two immune cell types. The LASSO model showed that GZMB, CXCL1, and CD274 are candidate diagnostic biomarkers. Conclusion: Our study suggests that downregulated expression of FOXC1 expression may enhance the levels of chemokines, chemokine receptors, T cell receptor signaling molecules, activating CD4+ memory T cells and DCs in AD.