No abstract

Recently, microRNAs (miRNA), small noncoding RNAs, have taken center stage in the field of human molecular oncology. However, their roles in tumor biology remain largely unknown. According to the assumption that miRNAs implicated in a specific tumor phenotype will show aberrant regulation of their target genes, we introduce an approach based on the miRNA target-dysregulated network (MTDN) to prioritize novel disease miRNAs. Target genes have predicted binding sites for any miRNA. The MTDN is constructed by combining computational target prediction with miRNA and mRNA expression profiles in tumor and nontumor tissues. Application of the proposed method to prostate cancer reveals that known prostate cancer miRNAs are characterized by a greater number of dysregulations and coregulators and the tendency to coregulate with each other and that they share a higher proportion of targets with other prostate cancer miRNAs. Support vector machine classifier, based on these features and changes in miRNA expression, is constructed and gives an average overall prediction accuracy of 0.8872 in cross-validation tests. The classifier is then applied to miRNAs in the MTDN. Functions enriched by dysregulated targets of novel predicted miRNAs are closely associated with oncogenesis. In addition, predicted cancer miRNAs within families or from different families show combinatorial dysregulation of target genes, as revealed by analysis of the MTDN modular organization. Finally, 3 miRNA target regulations are verified to hold in prostate cancer cells by transfection assays. These results show that the network-centric method could prioritize novel disease miRNAs and model how oncogenic lesions are mediated by miRNAs, providing important insights into tumorigenesis.

Three-dimensional macromolecular structures shed critical light on biological mechanism and facilitate development of small molecule inhibitors. Clinical success of raltegravir, a potent inhibitor of HIV-1 integrase, demonstrated the utility of this viral DNA recombinase as an antiviral target. A variety of partial integrase structures reported in the past 16 years have been instrumental and very informative to the field. Nonetheless, because integrase protein fragments are unable to functionally engage the viral DNA substrate critical for strand transfer inhibitor binding, the early structures did little to materially impact drug development efforts. However, recent results based on prototype foamy virus integrase have fully reversed this trend, as a number of X-ray crystal structures of active integrase-DNA complexes revealed key mechanistic details and moreover established the foundation of HIV-1 IN strand transfer inhibitor action. In this review we discuss the landmarks in the progress of IN structural biology during the past 17 years.

Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP450) inhibition is an important consideration in drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP450 isoform. In this study, we developed a multitask model for concurrent inhibition prediction of five major CYP450 isoforms, namely, 1A2, 2C9, 2C19, 2D6, and 3A4. The model was built by training a multitask autoencoder deep neural network (DNN) on a large dataset containing more than 13 000 compounds, extracted from the PubChem BioAssay Database. We demonstrate that the multitask model gave better prediction results than that of single-task models, previous reported classifiers, and traditional machine learning methods on an average of five prediction tasks. Our multitask DNN model gave average prediction accuracies of 86.4% for the 10-fold cross-validation and 88.7% for the external test datasets. In addition, we built linear regression models to quantify how the other tasks contributed to the prediction difference of a given task between single-task and multitask models, and we explained under what conditions the multitask model will outperform the single-task model, which suggested how to use multitask DNN models more effectively. We applied sensitivity analysis to extract useful knowledge about CYP450 inhibition, which may shed light on the structural features of these isoforms and give hints about how to avoid side effects during drug development. Our models are freely available at http://repharma.pku.edu.cn/deepcyp/home.php or http://www.pkumdl.cn/deepcyp/home.php .

Endometriosis is a complex and enigmatic disease that arises from the interplay among multiple genetic and environmental factors. The defining feature of endometriosis is the deposition and growth of endometrial tissues at sites outside of the uterine cavity. Studies to date have established that endometriosis is heritable but have not addressed the causal genetic variants for this disease. Here, we conducted whole-exome sequencing to comprehensively search for somatic mutations in both eutopic and ectopic endometrium from 16 endometriosis patients and five normal control patients using laser capture microdissection. We compared the mutational landscape of ectopic endometrium with the corresponding eutopic sample from endometriosis patients compared with endometrium from normal women and identified previously unreported mutated genes and pathway alternations. Statistical analysis of exome data identified that most genes were specifically mutated in both eutopic and ectopic endometrium cells. In particular, genes that are involved in biological adhesion, cell-cell junctions, and chromatin-remodeling complex(es) were identified, which partially supports the retrograde menstruation theory that proposes that endometrial cells are refluxed through the fallopian tubes during menstruation and implanted onto the peritoneum or pelvic organs. Conspicuously, when we compared exomic mutation data for paired eutopic and ectopic endometrium, we identified a mutational signature in both endometrial types for which no overlap in somatic single nucleotide variants were observed. These mutations occurred in a mutually exclusive manner, likely because of the discrepancy in endometriosis pathology and physiology, as eutopic endometrium rapidly regrows, and ectopic endometrial growth is inert. Our findings provide, to our knowledge, an unbiased view of the landscape of genetic alterations in endometriosis and vital information for indicating that genetic alterations in cytoskeletal and chromatin-remodeling proteins could be involved in the pathogenesis of endometriosis, thus implicating a novel therapeutic possibility for endometriosis.

Clustered, Regularly Interspaced Short Palindromic Repeats and their associated Cas proteins (CRISPR-Cas) provide prokaryotes with a mechanism for defense against mobile genetic elements (MGEs). A CRISPR locus is a molecular memory of MGE encounters. It contains an array of short sequences, called spacers, that generally have sequence identity to MGEs. Three different CRISPR loci have been identified among strains of the opportunistic pathogen Enterococcus faecalis. CRISPR1 and CRISPR3 are associated with the cas genes necessary for blocking MGEs, but these loci are present in only a subset of E. faecalis strains. The orphan CRISPR2 lacks cas genes and is ubiquitous in E. faecalis, although its spacer content varies from strain to strain. Because CRISPR2 is a variable locus occurring in all E. faecalis, comparative analysis of CRISPR2 sequences may provide information about the clonality of E. faecalis strains. We examined CRISPR2 sequences from 228 E. faecalis genomes in relationship to subspecies phylogenetic lineages (sequence types; STs) determined by multilocus sequence typing (MLST), and to a genome phylogeny generated for a representative 71 genomes. We found that specific CRISPR2 sequences are associated with specific STs and with specific branches on the genome tree. To explore possible applications of CRISPR2 analysis, we evaluated 14 E. faecalis bloodstream isolates using CRISPR2 analysis and MLST. CRISPR2 analysis identified two groups of clonal strains among the 14 isolates, an assessment that was confirmed by MLST. CRISPR2 analysis was also used to accurately predict the ST of a subset of isolates. We conclude that CRISPR2 analysis, while not a replacement for MLST, is an inexpensive method to assess clonality among E. faecalis isolates, and can be used in conjunction with MLST to identify recombination events occurring between STs.

No abstract

Continued vulnerability to relapse during abstinence is characteristic of cocaine addiction and suggests that drug-induced neuroadaptations persist during abstinence. However, the precise cellular and molecular attributes of these adaptations remain equivocal. One possibility is that cocaine self-administration leads to enduring changes in DNA methylation. To address this possibility, we isolated neurons from medial prefrontal cortex and performed high throughput DNA sequencing to examine changes in DNA methylation following cocaine self-administration. Twenty-nine genomic regions became persistently differentially methylated during cocaine self-administration, and an additional 28 regions became selectively differentially methylated during abstinence. Altered DNA methylation was associated with isoform-specific changes in the expression of co-localizing genes. These results provide the first neuron-specific, genome-wide profile of changes in DNA methylation induced by cocaine self-administration and protracted abstinence. Moreover, our findings suggest that altered DNA methylation facilitates long-term behavioral adaptation in a manner that extends beyond the perpetuation of altered transcriptional states.