No abstract

In silico prediction of genomic long non-coding RNAs (lncRNAs) is prerequisite to the construction and elucidation of non-coding regulatory network. Chromatin modifications marked by chromatin regulators are important epigenetic features, which can be captured by prevailing high-throughput approaches such as ChIP sequencing. We demonstrate that the accuracy of lncRNA predictions can be greatly improved when incorporating high-throughput chromatin modifications over mouse embryonic stem differentiation toward adult Cerebellum by logistic regression with LASSO regularization. The discriminating features include H3K9me3, H3K27ac, H3K4me1, open reading frames and several repeat elements. Importantly, chromatin information is suggested to be complementary to genomic sequence information, highlighting the importance of an integrated model. Applying integrated model, we obtain a list of putative lncRNAs based on uncharacterized fragments from transcriptome assembly. We demonstrate that the putative lncRNAs have regulatory roles in vicinity of known gene loci by expression and Gene Ontology enrichment analysis. We also show that the lncRNA expression specificity can be efficiently modeled by the chromatin data with same developmental stage. The study not only supports the biological hypothesis that chromatin can regulate expression of tissue-specific or developmental stage-specific lncRNAs but also reveals the discriminating features between lncRNA and coding genes, which would guide further lncRNA identifications and characterizations.

Long non-coding RNAs (lncRNAs) as a key group of non-coding RNAs have gained widely attention. Though lncRNAs have been functionally annotated and systematic explored in higher mammals, few are under systematical identification and annotation. Owing to the expression specificity, known lncRNAs expressed in embryonic brain tissues remain still limited. Considering a large number of lncRNAs are only transcribed in brain tissues, studies of lncRNAs in developmental brain are therefore of special interest. Here, publicly available RNA-sequencing (RNA-seq) data in embryonic brain are integrated to identify thousands of embryonic brain lncRNAs by a customized pipeline. A significant proportion of novel transcripts have not been annotated by available genomic resources. The putative embryonic brain lncRNAs are shorter in length, less spliced and show less conservation than known genes. The expression of putative lncRNAs is in one tenth on average of known coding genes, while comparable with known lncRNAs. From chromatin data, putative embryonic brain lncRNAs are associated with active chromatin marks, comparable with known lncRNAs. Embryonic brain expressed lncRNAs are also indicated to have expression though not evident in adult brain. Gene Ontology analysis of putative embryonic brain lncRNAs suggests that they are associated with brain development. The putative lncRNAs are shown to be related to possible cis-regulatory roles in imprinting even themselves are deemed to be imprinted lncRNAs. Re-analysis of one knockdown data suggests that four regulators are associated with lncRNAs. Taken together, the identification and systematic analysis of putative lncRNAs would provide novel insights into uncharacterized mouse non-coding regions and the relationships with mammalian embryonic brain development.

There is increasing evidence suggesting that dysregulation of some microRNAs (miRNAs) may contribute to tumor progression and metastasis and have been proposed to be key regulators of diverse biological processes such as transcriptional regulation, cell growth and tumorigenesis. Previous studies have shown that miR-137 is dysregulated in some malignancies, but its role in bladder cancer is still unknown. In our study, we find that miR-137 is up-regulated in human bladder cancer tissues and cell lines. Moreover, the higher level of miR-137 was associated with pM or pTNM stage in clinical bladder cancer patients. Enforced expression of miR-137 in bladder cancer cells significantly enhanced their proliferation, migration and invasion. Bioinformatics analysis identified the tumor suppressor gene PAQR3 as a potential miR-137 target gene. Further studies indicated that miR-137 suppressed the expression of PAQR3 by binding to its 3′-untranslated region. Silencing of PAQR3 by small interfering RNAs phenocopied the effects of miR-137 overexpression, whereas restoration of PAQR3 in bladder cancer cells bladder cancer cells overexpressing miR-137, partially reversed the suppressive effects of miR-137. These findings indicate that miR-137 could be a potential oncogene in bladder cancer.