Anna Tsykin

The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1

Epithelial to mesenchymal transition (EMT) facilitates tissue remodelling during embryonic development and is viewed as an essential early step in tumour metastasis. We found that all five members of the microRNA-200 family (miR-200a, miR-200b, miR-200c, miR-141 and miR-429) and miR-205 were markedly downregulated in cells that had undergone EMT in response to transforming growth factor (TGF)-β or to ectopic expression of the protein tyrosine phosphatase Pez. Enforced expression of the miR-200 family alone was sufficient to prevent TGF-β-induced EMT. Together, these microRNAs cooperatively regulate expression of the E-cadherin transcriptional repressors ZEB1 (also known as δEF1) and SIP1 (also known as ZEB2), factors previously implicated in EMT and tumour metastasis. Inhibition of the microRNAs was sufficient to induce EMT in a process requiring upregulation of ZEB1 and/or SIP1. Conversely, ectopic expression of these microRNAs in mesenchymal cells initiated mesenchymal to epithelial transition (MET). Consistent with their role in regulating EMT, expression of these microRNAs was found to be lost in invasive breast cancer cell lines with mesenchymal phenotype. Expression of the miR-200 family was also lost in regions of metaplastic breast cancer specimens lacking E-cadherin. These data suggest that downregulation of the microRNAs may be an important step in tumour progression.

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.

Genome‐wide identification of miR‐200 targets reveals a regulatory network controlling cell invasion

The microRNAs of the miR‐200 family maintain the central characteristics of epithelia and inhibit tumor cell motility and invasiveness. Using the Ago‐HITS‐CLIP technology for transcriptome‐wide identification of direct microRNA targets in living cells, along with extensive validation to verify the reliability of the approach, we have identified hundreds of miR‐200a and miR‐200b targets, providing insights into general features of miRNA target site selection. Gene ontology analysis revealed a predominant effect of miR‐200 targets in widespread coordinate control of actin cytoskeleton dynamics. Functional characterization of the miR‐200 targets indicates that they constitute subnetworks that underlie the ability of cancer cells to migrate and invade, including coordinate effects on Rho‐ROCK signaling, invadopodia formation, MMP activity, and focal adhesions. Thus, the miR‐200 family maintains the central characteristics of the epithelial phenotype by acting on numerous targets at multiple levels, encompassing both cytoskeletal effectors that control actin filament organization and dynamics, and upstream signals that locally regulate the cytoskeleton to maintain cell morphology and prevent cell migration.

Induction of miR-21 by Retinoic Acid in Estrogen Receptor-positive Breast Carcinoma Cells BIOLOGICAL CORRELATES AND MOLECULAR TARGETS

Retinoids are promising agents for the treatment/prevention of breast carcinoma. We examined the role of microRNAs in mediating the effects of all-trans-retinoic acid (ATRA), which suppresses the proliferation of estrogen receptor-positive (ERα+) breast carcinoma cells, such as MCF-7, but not estrogen receptor-negative cells, such as MDA-MB-231. We found that pro-oncogenic miR-21 is selectively induced by ATRA in ERα+ cells. Induction of miR-21 counteracts the anti-proliferative action of ATRA but has the potentially beneficial effect of reducing cell motility. In ERα+ cells, retinoid-dependent induction of miR-21 is due to increased transcription of the MIR21 gene via ligand-dependent activation of the nuclear retinoid receptor, RARα. RARα is part of the transcription complex present in the 5′-flanking region of the MIR21 gene. The receptor binds to two functional retinoic acid-responsive elements mapping upstream of the transcription initiation site. Silencing of miR-21 enhances ATRA-dependent growth inhibition and senescence while reverting suppression of cell motility afforded by the retinoid. Up-regulation of miR-21 results in retinoid-dependent inhibition of the established target, maspin. Knockdown and overexpression of maspin in MCF-7 cells indicates that the protein is involved in ATRA-induced growth inhibition and contributes to the ATRA-dependent anti-motility responses. Integration between whole genome analysis of genes differentially regulated by ATRA in MCF-7 and MDA-MB-231 cells, prediction of miR-21 regulated genes, and functional studies led to the identification of three novel direct miR-21 targets: the pro-inflammatory cytokine IL1B, the adhesion molecule ICAM-1 and PLAT, the tissue-type plasminogen activator. Evidence for ICAM-1 involvement in retinoid-dependent inhibition of MCF-7 cell motility is provided.

Gene expression profile of the bone microenvironment in human fragility fracture bone.

Osteoporosis (OP) is a common age-related systemic skeletal disease, with a strong genetic component, characterised by loss of bone mass and strength, which leads to increased bone fragility and susceptibility to fracture. Although some progress has been made in identifying genes that may contribute to OP disease, much of the genetic component of OP has yet to be accounted for. Therefore, to investigate the molecular basis for the changes in bone causally involved in OP and fragility fracture, we have used a microarray approach. We have analysed altered gene expression in human OP fracture bone by comparing mRNA in bone from individuals with fracture of the neck of the proximal femur (OP) with that from age-matched individuals with osteoarthritis (OA), and control (CTL) individuals with no known bone pathology. The OA sample set was included because an inverse association, with respect to bone density, has been reported between OA and the OP individuals. Compugen H19K oligo human microarray slides were used to compare the gene expression profiles of three sets of female samples comprising, 10 OP-CTL, 10 OP-OA, and 10 OA-CTL sample pairs. Using linear models for microarray analysis (Limma), 150 differentially expressed genes in OP bone with t scores >5 were identified. Differential expression of 32 genes in OP bone was confirmed by real time PCR analysis (p<0.01). Many of the genes identified have known or suspected roles in bone metabolism and in some cases have been implicated previously in OP pathogenesis. Three major sets of differentially expressed genes in OP bone were identified with known or suspected roles in either osteoblast maturation (PRRX1, ANXA2, ST14, CTSB, SPARC, FST, LGALS1, SPP1, ADM, and COL4A1), myelomonocytic differentiation and osteoclastogenesis (TREM2, ANXA2, IL10, CD14, CCR1, ADAM9, CCL2, CTGF, and KLF10), or adipogenesis, lipid and/or glucose metabolism (IL10, MARCO, CD14, AEBP1, FST, CCL2, CTGF, SLC14A1, ANGPTL4, ADM, TAZ, PEA15, and DOK4). Altered expression of these genes and others in these groups is consistent with previously suggested underlying molecular mechanisms for OP that include altered osteoblast and osteoclast differentiation and function, and an imbalance between osteoblastogenesis and adipogenesis.

Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy

BackgroundmicroRNAs (miRNAs) regulate target gene expression by controlling their mRNAs post-transcriptionally. Increasing evidence demonstrates that miRNAs play important roles in various biological processes. However, the functions and precise regulatory mechanisms of most miRNAs remain elusive. Current research suggests that miRNA regulatory modules are complicated, including up-, down-, and mix-regulation for different physiological conditions. Previous computational approaches for discovering miRNA-mRNA interactions focus only on down-regulatory modules. In this work, we present a method to capture complex miRNA-mRNA interactions including all regulatory types between miRNAs and mRNAs.ResultsWe present a method to capture complex miRNA-mRNA interactions using Bayesian network structure learning with splitting-averaging strategy. It is designed to explore all possible miRNA-mRNA interactions by integrating miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. We also present an analysis of data sets for epithelial and mesenchymal transition (EMT). Our results show that the proposed method identified all possible types of miRNA-mRNA interactions from the data. Many interactions are of tremendous biological significance. Some discoveries have been validated by previous research, for example, the miR-200 family negatively regulates ZEB1 and ZEB2 for EMT. Some are consistent with the literature, such as LOX has wide interactions with the miR-200 family members for EMT. Furthermore, many novel interactions are statistically significant and worthy of validation in the near future.ConclusionsThis paper presents a new method to explore the complex miRNA-mRNA interactions for different physiological conditions using Bayesian network structure learning with splitting-averaging strategy. The method makes use of heterogeneous data including miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. Results on EMT data sets show that the proposed method uncovers many known miRNA targets as well as new potentially promising miRNA-mRNA interactions. These interactions could not be achieved by the normal Bayesian network structure learning.

Inferring microRNA–mRNA causal regulatory relationships from expression data

MOTIVATION microRNAs (miRNAs) are known to play an essential role in the post-transcriptional gene regulation in plants and animals. Currently, several computational approaches have been developed with a shared aim to elucidate miRNA-mRNA regulatory relationships. Although these existing computational methods discover the statistical relationships, such as correlations and associations between miRNAs and mRNAs at data level, such statistical relationships are not necessarily the real causal regulatory relationships that would ultimately provide useful insights into the causes of gene regulations. The standard method for determining causal relationships is randomized controlled perturbation experiments. In practice, however, such experiments are expensive and time consuming. Our motivation for this study is to discover the miRNA-mRNA causal regulatory relationships from observational data. RESULTS We present a causality discovery-based method to uncover the causal regulatory relationship between miRNAs and mRNAs, using expression profiles of miRNAs and mRNAs without taking into consideration the previous target information. We apply this method to the epithelial-to-mesenchymal transition (EMT) datasets and validate the computational discoveries by a controlled biological experiment for the miR-200 family. A significant portion of the regulatory relationships discovered in data is consistent with those identified by experiments. In addition, the top genes that are causally regulated by miRNAs are highly relevant to the biological conditions of the datasets. The results indicate that the causal discovery method effectively discovers miRNA regulatory relationships in data. Although computational predictions may not completely replace intervention experiments, the accurate and reliable discoveries in data are cost effective for the design of miRNA experiments and the understanding of miRNA-mRNA regulatory relationships.

Gene expression analysis of multiple gastrointestinal regions reveals activation of common cell regulatory pathways following cytotoxic chemotherapy

Gastrointestinal mucositis involves many changes at the gene level, affecting epithelial/subepithelial interactions and leading to overt damage. The regional specificity and time course of these changes, and how they relate to subsequent mucositis development however remain unknown. The aim of this study was to determine the early time course of gene expression changes along the gastrointestinal tract of the DA rat following chemotherapy. Female DA rats were treated with a single dose of 200 mg/kg irinotecan to induce mucositis, and were killed at short intervals following treatment. Small sections of stomach, jejunum and colon were harvested for analysis of genetic profiles. RNA was hybridised to high density Affymetrix oligonucleotide microarrays. Data analysis was carried out with software package, TimeCourse, freely available through Bioconductor. As early as 1 hr following chemotherapy, expression of hundreds of genes was altered, including those for transcription factors, stress response proteins and protein turnover. These genes are involved in cell proliferation, differentiation and apoptosis along with other cellular processes. At early time points, there was a significant response involving the mitogen‐activated protein kinase pathway, cell cycle regulation and cytokine receptor signalling. At later time points, changes to the complement cascade became prominent. We have shown that changes in gene expression following chemotherapy occur by 1 hr, and persist for at least 72 hr after treatment. Many of these changes are highly likely to be specifically related to the subsequent development of gastrointestinal mucositis.

Inferring microRNA and transcription factor regulatory networks in heterogeneous data

BackgroundTranscription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA.ResultsWe propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then, in order to produce more meaningful results for further biological experimentation and research, the method searches the learnt network to identify the interplay between miRNAs and TFs and applies a network motif finding algorithm to further infer the network.We apply the proposed framework to the data sets of epithelial-to-mesenchymal transition (EMT). The results elucidate the complex gene regulatory mechanism for EMT which involves both TFs and miRNAs. Several discovered interactions and molecular functions have been confirmed by literature. In addition, many other discovered interactions and bio-markers are of high statistical significance and thus can be good candidates for validation by experiments. Moreover, the results generated by our method are compact, involving a small number of interactions which have been proved highly relevant to EMT.ConclusionsWe have designed a framework to infer gene regulatory networks involving both TFs and miRNAs from multiple sources of data, including gene expression data, target information, and sample categories. Results on the EMT data sets have shown that the proposed approach is able to produce compact and meaningful gene regulatory networks that are highly relevant to the biological conditions of the data sets. This framework has the potential for application to other heterogeneous datasets to reveal the complex gene regulatory relationships.

Genetic regulators of myelopoiesis and leukemic signaling identified by gene profiling and linear modeling

Mechanisms controlling the balance between proliferation and self‐renewal versus growth suppression and differentiation during normal and leukemic myelopoiesis are not understood. We have used the bi‐potent FDB1 myeloid cell line model, which is responsive to myelopoietic cytokines and activated mutants of the granulocyte macrophage‐colony stimulating factor (GM‐CSF) receptor, having differential signaling and leukemogenic activity. This model is suited to large‐scale gene‐profiling, and we have used a factorial time‐course design to generate a substantial and powerful data set. Linear modeling was used to identify gene‐expression changes associated with continued proliferation, differentiation, or leukemic receptor signaling. We focused on the changing transcription factor profile, defined a set of novel genes with potential to regulate myeloid growth and differentiation, and demonstrated that the FDB1 cell line model is responsive to forced expression of oncogenes identified in this study. We also identified gene‐expression changes associated specifically with the leukemic GM‐CSF receptor mutant, V449E. Signaling from this receptor mutant down‐regulates CCAAT/enhancer‐binding protein α (C/EBPα) target genes and generates changes characteristic of a specific acute myeloid leukemia signature, defined previously by gene‐expression profiling and associated with C/EBPα mutations.