Project description:Blood consists of different cell populations with distinct functions and correspondingly, distinct gene expression profiles. In this study, global miRNA expression profiling was performed across a panel of nine human immune cell subsets (neutrophils, eosinophils, monocytes, B cells, NK cells, CD4 T cells, CD8 T cells, mDCs and pDCs) to identify cell-type specific miRNAs. mRNA expression profiling was performed on the same samples to determine if miRNAs specific to certain cell types down-regulated expression levels of their target genes. Six cell-type specific miRNAs (miR-143; neutrophil specific, miR-125; T cells and neutrophil specific, miR-500; monocyte and pDC specific, miR-150; lymphoid cell specific, miR-652 and miR-223; both myeloid cell specific) were negatively correlated with expression of their predicted target genes. These results were further validated using an independent cohort where similar immune cell subsets were isolated and profiled for both miRNA and mRNA expression. miRNAs which negatively correlated with target gene expression in both cohorts were identified as candidates for miRNA/mRNA regulatory pairs and were used to construct a cell-type specific regulatory network. miRNA/mRNA pairs formed two distinct clusters in the network corresponding to myeloid (nine miRNAs) and lymphoid lineages (two miRNAs). Several myeloid specific miRNAs targeted common genes including ABL2, EIF4A2, EPC1 and INO80D; these common targets were enriched for genes involved in the regulation of gene expression (p<9.0E-7). Those miRNA might therefore have significant further effect on gene expression by repressing the expression of genes involved in transcriptional regulation. The miRNA and mRNA expression profiles reported in this study form a comprehensive transcriptome database of various human blood cells and serve as a valuable resource for elucidating the role of miRNA mediated regulation in the establishment of immune cell identity.
Project description:MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally. MiRNAs were shown to play an important role in development and disease, and accurately determining the networks regulated by these miRNAs in a specific condition is of great interest. Early work on miRNA target prediction has focused on using static sequence information. More recently, researchers have combined sequence and expression data to identify such targets in various conditions.We developed the Protein Interaction-based MicroRNA Modules (PIMiM), a regression-based probabilistic method that integrates sequence, expression and interaction data to identify modules of mRNAs controlled by small sets of miRNAs. We formulate an optimization problem and develop a learning framework to determine the module regulation and membership. Applying PIMiM to cancer data, we show that by adding protein interaction data and modeling cooperative regulation of mRNAs by a small number of miRNAs, PIMiM can accurately identify both miRNA and their targets improving on previous methods. We next used PIMiM to jointly analyze a number of different types of cancers and identified both common and cancer-type-specific miRNA regulators.Supplementary data are available at Bioinformatics online.
Project description:BACKGROUND: Many classifiers which are constructed with chosen gene markers have been proposed to forecast the prognosis of patients who suffer from breast cancer. However, few of them has been applied in clinical practice because of the bad generalization, which results from the situation that markers selected by one method are very different from those obtained by another method, and thus such markers always lack discriminative capability in the other data sets. METHODS: In this work, a new ensemble classifier, on the basis of context specific miRNA regulation modules, has been proposed to forecast the metastasis risk of cancer sufferers. First, we defined all of the miRNAs which regulate the same context as a module that contains miRNAs and their regulating context, and applied the CoMi (Context-specific miRNA activity) score in order to illustrate a miRNA's effect which happened in a particular background; then the miRNA regulation modules with distinguishing abilities were detected and each of them was responsible for building a weak classifier separately; at last, by using majority voting strategy, we integrated all weak classifiers to establish an ensembled one that was applied to forecast the prognosis of patients who suffer from cancer. RESULTS: After comparing, the results on the cohorts containing over 1,000 samples showed that the proposed ensemble classifier is superior to other three classifiers based on miRNA expression profiles, mRNA expression profiles and CoMi activity patterns respectively. Significantly, our method outperforms the representative works. Moreover, the detected modules from different data sets show great stability (with p-value of 6.40e-08). For investigating the biological significance of those selected modules, case studies have been done by us and the results suggested that our method do help to reveal latent mechanism in metastasis of breast cancer. CONCLUSIONS: One context specific miRNA regulation module can uncover one critical biological process and its involved miRNAs that are related to the cancer outcome, and several modules together can help to study the biological mechanism in cancer metastasis, thus the classifer based on ensembling multiple classifers which were built with different context specific miRNA regulation modules has showed promising performances in terms with both prediction accuracy and generalization.
Project description:MicroRNAs (miRNAs) play crucial roles in complex cellular networks by binding to the messenger RNAs (mRNAs) of protein coding genes. It has been found that miRNA regulation is often condition-specific. A number of computational approaches have been developed to identify miRNA activity specific to a condition of interest using gene expression data. However, most of the methods only use the data in a single condition, and thus, the activity discovered may not be unique to the condition of interest. Additionally, these methods are based on statistical associations between the gene expression levels of miRNAs and mRNAs, so they may not be able to reveal real gene regulatory relationships, which are causal relationships.We propose a novel method to infer condition-specific miRNA activity by considering (i) the difference between the regulatory behavior that an miRNA has in the condition of interest and its behavior in the other conditions; (ii) the causal semantics of miRNA-mRNA relationships. The method is applied to the epithelial-mesenchymal transition (EMT) and multi-class cancer (MCC) datasets. The validation by the results of transfection experiments shows that our approach is effective in discovering significant miRNA-mRNA interactions. Functional and pathway analysis and literature validation indicate that the identified active miRNAs are closely associated with the specific biological processes, diseases and pathways. More detailed analysis of the activity of the active miRNAs implies that some active miRNAs show different regulation types in different conditions, but some have the same regulation types and their activity only differs in different conditions in the strengths of regulation.The R and Matlab scripts are in the Supplementary materials.
Project description:MicroRNA (miRNA) plays a critical role in a wide variety of biological processes. Profiling miRNA expression during differentiation of embryonic stem cells will help to understand the regulation pathway of differentiation, which in turn may elucidate disease mechanisms. The identified miRNAs could then serve as a new group of possible therapeutic targets. In the present paper, miRNA expression profiles were determined during cardiomyocyte-specific differentiation and maturation of murine embryonic stem (ES) cells. For this purpose a homogeneous cardiomyocyte population was generated from a transgenic murine ES cell line. Two high throughput array platforms (Affymetrix and Febit) were used for miRNA profiling in order to compare the effect of the platforms on miRNA profiling as well as to increase the validity of target miRNA identification. Four time points (i.e. day 0, day 12, day 19 and day 26) were chosen for the miRNA profiling study, which corresponded to different stages during cardiomyocyte-specific differentiation and maturation. Fifty platform and pre-processing method-independent miRNAs were identified as being regulated during the differentiation and maturation processes. The identification of these miRNAs is an important step for characterizing and understanding the events involved in cardiomyocyte-specific differentiation of ES cells and may also highlight candidate target molecules for therapeutic purposes.
Project description:MicroRNAs (miRNAs) are a set of small, non-protein-coding RNAs that regulate gene expression at the post-transcriptional level. Maturation of miRNAs comprises several regulated steps resulting in approximately 22-nucleotide single-stranded mature miRNAs. Regulation of miRNA expression can occur both at the transcriptional level and at the post-transcriptional level during miRNA processing. Recent studies have elucidated specific aspects of the well-regulated nature of miRNA processing involving various regulatory proteins, editing of miRNA transcripts, and cellular location. In addition, single nucleotide polymorphisms in miRNA genes can also affect the processing efficiency of primary miRNA transcripts. In this review we present an overview of the currently known regulatory pathways of miRNA processing and provide a basis to understand how aberrant miRNA processing may arise and may be involved in pathophysiological conditions such as cancer.
Project description:Epigenetic mechanisms are important regulators of cell type-specific genes, including miRNAs. In order to identify cell type-specific miRNAs regulated by epigenetic mechanisms, we undertook a global analysis of miRNA expression and epigenetic states in three isogenic pairs of human mammary epithelial cells (HMEC) and human mammary fibroblasts (HMF), which represent two differentiated cell types typically present within a given organ, each with a distinct phenotype and a distinct epigenotype. While miRNA expression and epigenetic states showed strong interindividual concordance within a given cell type, almost 10% of the expressed miRNA showed a cell type-specific pattern of expression that was linked to the epigenetic state of their promoter. The tissue-specific miRNA genes were epigenetically repressed in nonexpressing cells by DNA methylation (38%) and H3K27me3 (58%), with only a small set of miRNAs (21%) showing a dual epigenetic repression where both DNA methylation and H3K27me3 were present at their promoters, such as MIR10A and MIR10B. Individual miRNA clusters of closely related miRNA gene families can each display cell type-specific repression by the same or complementary epigenetic mechanisms, such as the MIR200 family, and MIR205, where fibroblasts repress MIR200C/141 by DNA methylation, MIR200A/200B/429 by H3K27me3, and MIR205 by both DNA methylation and H3K27me3. Since deregulation of many of the epigenetically regulated miRNAs that we identified have been linked to disease processes such as cancer, it is predicted that compromise of the epigenetic control mechanisms is important for this process. Overall, these results highlight the importance of epigenetic regulation in the control of normal cell type-specific miRNA expression.
Project description:BACKGROUND: Current microRNA (miRNA) research in progress has engendered rapid accumulation of expression data evolving from microarray experiments. Such experiments are generally performed over different tissues belonging to a specific species of metazoan. For disease diagnosis, microarray probes are also prepared with tissues taken from similar organs of different candidates of an organism. Expression data of miRNAs are frequently mapped to co-expression networks to study the functions of miRNAs, their regulation on genes and to explore the complex regulatory network that might exist between Transcription Factors (TFs), genes and miRNAs. These directions of research relating miRNAs are still not fully explored, and therefore, construction of reliable and compatible methods for mining miRNA co-expression networks has become an emerging area. This paper introduces a novel method for mining the miRNA co-expression networks in order to obtain co-expressed miRNAs under the hypothesis that these might be regulated by common TFs. RESULTS: Three co-expression networks, configured from one patient-specific, one tissue-specific and a stem cell-based miRNA expression data, are studied for analyzing the proposed methodology. A novel compactness measure is introduced. The results establish the statistical significance of the sets of miRNAs evolved and the efficacy of the self-pruning phase employed by the proposed method. All these datasets yield similar network patterns and produce coherent groups of miRNAs. The existence of common TFs, regulating these groups of miRNAs, is empirically tested. The results found are very promising. A novel visual validation method is also proposed that reflects the homogeneity as well as statistical properties of the grouped miRNAs. This visual validation method provides a promising and statistically significant graphical tool for expression analysis. CONCLUSION: A heuristic mining methodology that resembles a clustering motivation is proposed in this paper. However, there remains a basic difference between the mining method and a clustering approach. The heuristic approach can produce priority modules (PM) from an miRNA co-expression network, by employing a self-pruning phase, which are analyzed for statistical and biological significance. The mining algorithm minimizes the space/time complexity of the analysis, and also handles noise in the data. In addition, the mining method reveals promising results in the unsupervised analysis of TF-miRNA regulation.
Project description:MicroRNAs (miRNAs) are small, noncoding RNAs that inhibit translation and promote mRNA decay. The levels of mature miRNAs are the result of different rates of transcription, processing, and turnover. The noncanonical polymerase Gld2 has been implicated in the stabilization of miR-122, possibly through catalyzing 3' monoadenylation; however, there is little evidence that this relationship is one of cause and effect. Here, we biochemically characterize Gld2's involvement in miRNA monoadenylation and its effect on miRNA stability. We find that Gld2 directly monoadenylates and stabilizes specific miRNA populations in human fibroblasts and that sensitivity to monoadenylation-induced stability depends on nucleotides in the miRNA 3' end. These results establish a mechanism of miRNA stability and resulting posttranscriptional gene regulation.
Project description:Although microRNAs are being extensively studied for their involvement in cancer and development, little is known about their roles in Alzheimer's disease (AD). In this study, we used microarrays for the first joint profiling and analysis of miRNAs and mRNAs expression in brain cortex from AD and age-matched control subjects. These data provided the unique opportunity to study the relationship between miRNA and mRNA expression in normal and AD brains. Using a non-parametric analysis, we showed that the levels of many miRNAs can be either positively or negatively correlated with those of their target mRNAs. Comparative analysis with independent cancer datasets showed that such miRNA-mRNA expression correlations are not static, but rather context-dependent. Subsequently, we identified a large set of miRNA-mRNA associations that are changed in AD versus control, highlighting AD-specific changes in the miRNA regulatory system. Our results demonstrate a robust relationship between the levels of miRNAs and those of their targets in the brain. This has implications in the study of the molecular pathology of AD, as well as miRNA biology in general.