Motivation Alzheimer’s disease (AD) is a serious degenerative brain disease and the most common cause of dementia. The current available drugs for AD provide symptomatic benefit, but there is no effective drug to cure the disease. The emergence of large-scale genomic, pharmacological data provides new opportunities for drug discovery and drug repositioning as a promising strategy in searching novel drug for AD. Results In this study, we took advantage of our increasing understanding based on systems biology approaches on the pathway and network levels and perturbation data sets from the Library of Integrated Network-Based Cellular Signatures (LINCS) to introduce a systematic computational process to discover new drugs implicated in AD. Firstly, we collected 561 genes that have reported to be risk genes of AD, and applied functional enrichment analysis on these genes. Then, by quantifying proximity between 5595 molecule drugs and AD based on human interactome, we filtered out 1092 drugs that were proximal to the disease. We further performed an Inverted Gene Set Enrichment analysis on these drug candidates, which allowed us to estimate effect of perturbations on gene expression and identify 24 potential drug candidates for AD treatment. Results from this study also provided insights for understanding the molecular mechanisms underlying AD. As a useful systematic method, our approach can also be used to identify efficacious therapies for other complex diseases. Supplementary information Supplementary data are available at Bioinformatics online.

Breast cancer is a common malignant tumor among women whose prognosis is largely determined by the period and accuracy of diagnosis. We here propose to identify a robust DNA methylation-based breast cancer-specific diagnostic signature. Genome-wide DNA methylation and gene expression profiles of breast cancer patients along with their adjacent normal tissues from the Cancer Genome Atlas (TCGA) were obtained as the training set. CpGs that with significantly elevated methylation level in breast cancer than not only their adjacent normal tissues and the other ten common cancers from TCGA but also the healthy breast tissues from the Gene Expression Omnibus (GEO) were finally remained for logistic regression analysis. Another independent breast cancer DNA methylation dataset from GEO was used as the testing set. Lots of CpGs were hyper-methylated in breast cancer samples compared with adjacent normal tissues, which tend to be negatively correlated with gene expressions. Eight CpGs located at RIIAD1, ENPP2, ESPN, and ETS1, were finally retained. The diagnostic model was reliable in separating BRCA from normal samples. Besides, chromatin accessibility status of RIIAD1, ENPP2, ESPN and ETS1 showed great differences between MCF-7 and MDA-MB-231 cell lines. In conclusion, the present study should be helpful for breast cancer early and accurate diagnosis.

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Major depressive disorder (MDD) is a serious mental disease with negative effects on both mental and physical health of the patient. Currently, antidepressants are among the major ways to ease or treat MDD. However, the existing antidepressants have limited efficacy in treating MDD, with a large fraction of patients either responding inadequately or differently to antidepressants during the treatment. Pharmacogenetics studies have found that the genetic features of some genes are associated with the antidepressant efficacy. In order to obtain a better understanding on the relationship between the genetic factors and antidepressant treatment response, we compiled a list of 233 single-nucleotide polymorphisms (SNPs) significantly associated with the antidepressant efficacy in treating MDD. Of the 13 non-synonymous SNPs in the list, three (rs1065852, rs3810651, and rs117986340) may influence the structures and function of the corresponding proteins. Besides, the influence of rs1065852 on the structure of CYP2D6 was further investigated via molecular dynamics simulations. Our results showed that compared to the native CYP2D6 the flexibility of the F-G loop was reduced in the mutant. As a portion of the substrate access channel, the lower flexibility of F-G loop may reduce the ability of the substrates to enter the channel, which may be the reason for the lower enzyme activity of mutant. This study may help us to understand the impact of genetic variation on antidepressant efficacy and provide clues for developing new antidepressants.

Lung cancer is one of the leading causes of cancer-associated death in the world. It is of great importance to explore new therapeutic targets. Traditional Chinese medicine formula Feiyanning has been clinically administered in China for more than a decade and raised attention due to its anticancer effect in lung cancer. However, the underlying molecular mechanisms remain to be elucidated. In the present study, we carried out cellular and molecular assays to examine the antitumor activities and understand the mechanism of the Feiyanning formula in lung cancer cells. The cellular viability of Feiyanning-treated lung cancer cells was evaluated by Cell Counting Kit-8. The effect of the Feiyanning formula on cellular migration and invasion of lung cancer cells was examined by wound healing and transwell assays. Transcriptome and chromatin accessibility analysis by RNA-seq and ATAC-seq was performed to investigate the underlying molecular mechanisms. Our results revealed that the Feiyanning formula inhibited the cellular activities of proliferation, migration, and invasion in non-small cell lung cancer cell lines A549, H1975, and 95D. Furthermore, we observed that the transcriptional activity of the migration-associated genes was downregulated upon Feiyanning formula treatment in non-small cell lung cancer cells. The chromatin accessibility of the Feiyanning-treated lung cancer genome tended to decrease, and the regulation of the cellular component movement biological process and PI3K-AKT pathway were enriched among these altered genomic regions. Taken together, the present study suggested that Feiyanning formula exerted the antitumor effects by modulating the expression and chromatin accessibility levels of migration-associated genes.