Discovery of microRNA-mRNA modules via population-based probabilistic learning

MOTIVATION: MicroRNAs (miRNAs) and mRNAs constitute an important part of gene regulatory networks, influencing diverse biological phenomena. Elucidating closely related miRNAs and mRNAs can be an essential first step towards the discovery of their combinatorial effects on different cellular states. Here, we propose a probabilistic learning method to identify synergistic miRNAs involving regulation of their condition-specific target genes (mRNAs) from multiple information sources, i.e. computationally predicted target genes of miRNAs and their respective expression profiles. RESULTS: We used data sets consisting of miRNA-target gene binding information and expression profiles of miRNAs and mRNAs on human cancer samples. Our method allowed us to detect functionally correlated miRNA-mRNA modules involved in specific biological processes from multiple data sources by using a balanced fitness function and efficient searching over multiple populations. The proposed algorithm found two miRNA-mRNA modules, highly correlated with respect to their expression and biological function. Moreover, the mRNAs included in the same module showed much higher correlations when the related miRNAs were highly expressed, demonstrating our method's ability for finding coherent miRNA-mRNA modules. Most members of these modules have been reported to be closely related with cancer. Consequently, our method can provide a primary source of miRNA and target sets presumed to constitute closely related parts of gene regulatory pathways.     

Identifying functional miRNA-mRNA regulatory modules with correspondence latent dirichlet allocation

MOTIVATION: MicroRNAs (miRNAs) are small non-coding RNAs that cause mRNA degradation and translational inhibition. They are important regulators of development and cellular homeostasis through their control of diverse processes. Recently, great efforts have been made to elucidate their regulatory mechanism, but the functions of most miRNAs and their precise regulatory mechanisms remain elusive. With more and more matched expression profiles of miRNAs and mRNAs having been made available, it is of great interest to utilize both expression profiles to discover the functional regulatory networks of miRNAs and their target mRNAs for potential biological processes that they may participate in. RESULTS: We present a probabilistic graphical model to discover functional miRNA regulatory modules at potential biological levels by integrating heterogeneous datasets, including expression profiles of miRNAs and mRNAs, with or without the prior target binding information. We applied this model to a mouse mammary dataset. It effectively captured several biological process specific modules involving miRNAs and their target mRNAs. Furthermore, without using prior target binding information, the identified miRNAs and mRNAs in each module show a large proportion of overlap with predicted miRNA target relationships, suggesting that expression profiles are crucial for both target identification and discovery of regulatory modules.     

星形细胞瘤生存预后相关miRNA-mRNA调控关系对筛选

利用GEO数据库中的芯片数据,筛选与星形细胞瘤生存预后相关的miRNA-mRNA调控关系对,为后续研究提供理论支持.下载芯片数据利用R语言进行差异表达分析,得到星形细胞瘤较正常组织表达显著改变的miRNA与mRNA;通过miRNA靶基因预测,将靶基因与差异表达mRNA取交集,明确mRNA与miRNA之间的关系;利用GE...     

Integrated analysis of microRNA-target interactions with clinical outcomes for cancers

Background

Clinical statement alone is not enough to predict the progression of disease. Instead, the gene expression profiles have been widely used to forecast clinical outcomes. Many genes related to survival have been identified, and recently miRNA expression signatures predicting patient survival have been also investigated for several cancers. However, miRNAs and their target genes associated with clinical outcomes have remained largely unexplored.

Methods

Here, we demonstrate a survival analysis based on the regulatory relationships of miRNAs and their target genes. The patient survivals for the two major cancers, ovarian cancer and glioblastoma multiforme (GBM), are investigated through the integrated analysis of miRNA-mRNA interaction pairs.

Results

We found that there is a larger survival difference between two patient groups with an inversely correlated expression profile of miRNA and mRNA. It supports the idea that signatures of miRNAs and their targets related to cancer progression can be detected via this approach.

Conclusions

This integrated analysis can help to discover coordinated expression signatures of miRNAs and their target mRNAs that can be employed for therapeutics in human cancers.

     

Joint Genome-Wide Profiling of miRNA and mRNA Expression in Alzheimer's Disease Cortex Reveals Altered miRNA Regulation

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.     

Identifying MicroRNA and mRNA Expression Profiles in Embryonic Stem Cells Derived from Parthenogenetic, Androgenetic and Fertilized Blastocysts

MicroRNAs (miRNAs) are a class of highly conserved small non-coding RNA molecules that play a pivotal role in several cellular functions. In this study, miRNA and messenger RNA (mRNA) profiles were examined by Illumina microarray in mouse embryonic stem cells (ESCs) derived from parthenogenetic, androgenetic, and fertilized blastocysts. The global analysis of miRNA-mRNA target pairs provided insight into the role of miRNAs in gene expression. Results showed that a total of 125 miRNAs and 2394 mRNAs were differentially expressed between androgenetic ESCs (aESCs) and fertilized ESCs (fESCs), a total of 42 miRNAs and 87 mRNAs were differentially expressed between parthenogenetic ESCs (pESCs) and fESCs, and a total of 99 miRNAs and 1788 mRNAs were differentially expressed between aESCs and pESCs. In addition, a total of 575, 5 and 376 miRNA-mRNA target pairs were observed in aESCs vs. fESCs, pESCs vs. fESCs, and aESCs vs. pESCs, respectively. Furthermore, 15 known imprinted genes and 16 putative uniparentally expressed miRNAs with high expression levels were confirmed by both microarray and real-time RT-PCR. Finally, transfection of miRNA inhibitors was performed to validate the regulatory relationship between putative maternally expressed miRNAs and target mRNAs. Inhibition of miR-880 increased the expression of Peg3, Dyrk1b, and Prrg2 mRNA, inhibition of miR-363 increased the expression of Nfat5 and Soat1 mRNA, and inhibition of miR-883b-5p increased Nfat5, Tacstd2, and Ppapdc1 mRNA. These results warrant a functional study to fully understand the underlying regulation of genomic imprinting in early embryo development.     

Retracted: Deep integrative analysis of microRNA-mRNA regulatory networks for biomarker and target discovery in chondrosarcoma

Chondrosarcoma (CHS) is a common malignant bone sarcoma and its occurrence increases with age. microRNAs (miRNAs) are a class of noncoding RNAs that participate in various biological processes and disease pathogenesis by targeting functional messenger RNA (mRNA). However, the modulation of miRNAs in CHS remains largely unknown. In this study, we performed integrative analysis to explore the expression profiles of miRNAs and mRNAs, together with their interaction networks in human CHS tissues and cell lines by RNA-seq (miRNA and mRNA). A total of 133 and 796 differentially expressed miRNAs and mRNAs were identified (|Fold change| ≥ 2 and P-value ≤ 0.5). miRNA-mRNA regulatory interactions between 55 miRNAs and 242 mRNAs were screened by the Pearson correlation analysis and target prediction. mRNAs in the network were enriched to 145 Gene Ontology terms and 35 Kyoto Encyclopedia of Genes and Genomes pathways. Specifically, some key regulators (hub-miRNAs) in the network (miR-622, miR-4539, miR-145, miR-25, and miR-96) were suggested to play important regulatory roles in the pathogenesis of CHS. In addition, functional experiments validated that miR-622 regulated CHS cell proliferation by targeting bone morphogenetic protein 1 (BMP1).     

Consistent probabilistic outputs for protein function prediction

Background:

The wide availability of genome-scale data for several organisms has stimulated interest in computational approaches to gene function prediction. Diverse machine learning methods have been applied to unicellular organisms with some success, but few have been extensively tested on higher level, multicellular organisms. A recent mouse function prediction project (MouseFunc) brought together nine bioinformatics teams applying a diverse array of methodologies to mount the first large-scale effort to predict gene function in the laboratory mouse.

Results:

In this paper, we describe our contribution to this project, an ensemble framework based on the support vector machine that integrates diverse datasets in the context of the Gene Ontology hierarchy. We carry out a detailed analysis of the performance of our ensemble and provide insights into which methods work best under a variety of prediction scenarios. In addition, we applied our method to Saccharomyces cerevisiae and have experimentally confirmed functions for a novel mitochondrial protein.

Conclusion:

Our method consistently performs among the top methods in the MouseFunc evaluation. Furthermore, it exhibits good classification performance across a variety of cellular processes and functions in both a multicellular organism and a unicellular organism, indicating its ability to discover novel biology in diverse settings.

     

Methods for predicting single-cell miRNA in breast cancer

It has been demonstrated that miRNAs are involved in many biological processes including cell proliferation and differentiation, apoptosis, and stress responses. Although single-cell RNA sequencing technology is prevailing nowadays, it still remains challenging in quantifying miRNA at the single-cell level. Herein, we present the computational methods to infer the single-cell miRNA expression level using its target gene abundances. Firstly, we developed an enrichment-based approach in estimating miRNA expression considering miRNA-mRNA regulation information and miRNA-mRNA correlation signal captured from existing TCGA datasets. Further efforts were made to infer the miRNA expression with machine learning models. The methods were applied to compare the accuracy and robustness with the simulated single-cell data. Finally, we applied the method in single-cell RNA-seq triple negative breast cancer (TNBC) patients to further discover miRNA marker at the single-cell level for the malignant cells. Our tool is available online at: https://github.com/ChengkuiZhao/Single-cell-miRNA-prediction.     

小菜蛾幼虫偏好表达的microRNA鉴定及功能研究

[目的]microRNA(miRNA)在昆虫生长发育中发挥重要功能,本研究拟通过鉴定小菜蛾不同发育阶段的miRNA,挖掘幼虫偏好表达的miRNA及其潜在功能.[方法]对小菜蛾卵、3龄幼虫、蛹和成虫的miRNA开展高通量测序,结合生物信息学分析方法,筛选在幼虫期偏好表达的miRNA;借助实时荧光定量PCR技术,验证候选m...