Advances in the understanding of cancer cell biology and response to drug treatment have benefited from new molecular technologies and methods for integrating information from multiple sources. The NCI-60, a panel of 60 diverse human cancer cell lines, has been used by the National Cancer Institute to screen >100,000 chemical compounds and natural product extracts for anticancer activity. The NCI-60 has also been profiled for mRNA and protein expression, mutational status, chromosomal aberrations, and DNA copy number, generating an unparalleled public resource for integrated chemogenomic studies. Recently, microRNAs have been shown to target particular sets of mRNAs, thereby preventing translation or accelerating mRNA turnover. To complement the existing NCI-60 datasets, we have measured expression levels of microRNAs in the NCI-60. Cell line groupings based on microRNA expression were generally consistent with tissue type and with cell line clustering based on mRNA expression. Comparison of microRNA expression patterns and compound potency patterns showed significant correlations which suggests that microRNAs may play a role in chemoresistance. To assess the effect of altering microRNA levels on compound potency we selected three microRNAs and performed dose-response studies with a set of structurally diverse compounds with and without suppression or forced expression of the target microRNA. In several cases, microRNA level had a significant affect on compound potency. Combined with gene expression and other biological data using multivariate analysis, microRNA expression profiles may provide a critical link for understanding mechanisms involved in chemosensitivity and chemoresistance.
Microarrays have allowed high throughput study of gene expression. New microRNA microarrays allow to opportunity to study most known miRNAs in a single assay. It has been hoped that analysis of miRNA microarray data would mimic analysis for expression arrays, but there are some important differences between these two types of arrays.
Difficulties in obtaining reliable quantitative information from miRNA microarrays will be described. I will also present some examples from experiments carried out using miRNA microarrays from three different companies.
Combined computational/experimental approaches have played a significant role during recent years in the identification of novel microRNAs (miRNAs), as well as in the analysis of their function. We have developed several tools for analyzing the genomic organization and function of miRNAs: DIANA-microT, TarBase, and miRGen. Additionally, we have developed a microRNA gene finder based on a machine learning approach (Support Vector Machines). The specificity of the algorithm is first estimated computationally, and then evaluated through a customized microarray chip. Recently we have supported research on edited miRNAs in brain, and on the role of SNPs within miRNA targets.
Statistical methods have been used extensively to study the transcriptional regulation of genes. One particular area of success has been the use of statistical procedures for the prediction of transcription factor binding sites in the promoter region. These promoter elements are predicted either by sequence scanning algorithms for known transcription factor signals or de novo algorithms that find over-represented elements in promoter sequences. I will discuss some methodological issues with the application of statistical procedures to the prediction of promoter elements involved in the control of microRNAs in Arabidopsis. This is collaborative work with the laboratory of Artemis G. Hatzigeorgiou (Student: Molly Megraw) at the University of Pennsylvania.
The proto-oncogene c-MYC encodes a transcription factor that regulates cell proliferation, growth, and apoptosis. Dysregulated expression or function of c-Myc is one of the most common abnormalities in human malignancy. We have demonstrated that c-Myc activates the expression of a group of six miRNAs on human chromosome 13 known as the miR-17 cluster. Chromatin immunoprecipation showed that c-Myc binds directly to this locus to activate transcription of these miRNAs. Data from my laboratory and other laboratories has demonstrated that this group of miRNAs is widely overexpressed in human cancers and can promote tumorigenesis in animal models. These miRNAs are therefore likely to play an important role in c-Myc-mediated tumorigenesis. New data from my laboratory demonstrates that miRNA stability and subcellular localization can be regulated in a sequence-specified manner. Human miR-29a and miR-29b are two related miRNAs derived from a common primary transcript. In a screen for miRNAs that exhibit cell-cycle-phase specific expression patterns in HeLa cells, we observed that these miRNAs are discordantly expressed. Whereas miR-29a is expressed constitutively throughout the cell cycle, miR-29b is present at low levels except during mitosis. We demonstrated that the discordant expression of these co-transcribed miRNAs is due to rapid decay of miR-29b in all cell-cycle phases except mitosis. In contrast, miR-29a is stable throughout the cell cycle. Disassembly of the nuclear membrane is one feature that distinguishes mitosis from other phases of the cell cycle. All studied animal miRNAs, including miR-29a, are predominantly cytoplasmic in cycling cells. Remarkably, fractionation and in situ hybridization experiments demonstrate that miR-29b is partially localized to the nucleus. Furthermore, we show that the unique hexanucleotide terminal motif of miR-29b is a transferable element that specifies nuclear import of this and heterologous miRNAs and siRNAs. These findings demonstrate that related miRNAs, generally believed to be redundant, may have distinct functions due to the presence of cis-acting regulatory motifs. Moreover, this nuclear import motif may be useful for generating siRNAs that regulate nuclear steps in gene expression such as transcription or splicing.
Since the recent emergence of miRNAs as a new class of regulatory RNAs, rapid progress has been made in identifying new members of this class and in determining factors important for their expression and function. However, the mechanism by which these regulatory RNAs inhibit the expression of specific target mRNAs is still unclear. My lab primarily focuses on the founding miRNA genes, lin-4 and let-7, and their genetically defined targets in C. elegans to understand how these miRNAs control development of the animal. The majority of animal miRNAs, including lin-4 and let-7, recognize sites of partial complementarity in the 3' untranslated regions (UTRs) of target genes, and this is believed to result in translational repression. Recently my lab demonstrated that regulation by lin-4 and let-7 in C. elegans also results in degradation of their mRNA targets. Currently, we are exploring the generality of mRNA degradation as an outcome of regulation by miRNAs. We are also attempting to identify the mRNA degradation intermediates and the protein factors that participate in this mechanism of regulation by miRNAs. Studying miRNAs and their targets in C. elegans allows us to analyze the effect of authentic mutants in miRNA genes on endogenous gene expression. By elucidating the function of miRNAs in this organism, we hope to contribute to the general understanding of this newly discovered and widely conserved mode of gene regulation.
MicroRNAs (miRNAs) are short RNAs of ~22 nucleotides which post-transcriptionally regulate the expression of their target genes by binding to the target mRNAs. Although hundreds of miRNAs have been discovered in plants and animals, the number of experimentally validated targets is smaller by an order of magnitude. Computational prediction of miRNA targets is of outstanding importance in the generation of reliable hypotheses on possible miRNA/target interactions. RNAhybrid is an algorithmically and statistically sophisticated approach for this task and has been shown to predict miRNA targets with high sensitivity and specificity. RNAhybrid considers binding energies of microRNA/target duplexes, statistical significance of individual and multiple binding sites, and evolutionary conservation of microRNA/target relationships. Since its publication in October 2004, RNAhybrid has been downloaded more than 1,500 times, and the online version is used well over 2,000 times per month. Thanks to its versatility, which includes control of maximal mismatch sizes, position of seed regions, acceptance of G:U base pairs in these, and number of sites per target to look at, the program can be used in a variety of applications and for organisms ranging from animals to plants.
"Rna22" was recently proposed to address the questions of finding targets for a given microRNA and novel microRNA precursors in a given genome. The method is pattern-based and does not rely on cross-species conservation, thus allowing us to tackle these two questions in a rather unconstrained manner.
In this presentation, I will describe in detail the basic concepts behind the "rna22" method, discuss our key findings and also present some of our more recent results from the study of several types of cancer. Our experimental results suggest that the typical microRNA could have as many as several thousand targets. Also, our computational analyses indicate that, in addition to their 3'UTRs, microRNAs must be targeting transcripts through their 5'UTRs and CDSs. We currently estimate that more than 90% of the genes in mammals are likely under microRNA control. Finally, with respect to the number of microRNAs that are present in a given genome, our studies of vertebrate and invertebrate genomes using "rna22" indicate that the eventual number of microRNAs may be substantially higher than the current estimates suggest.
MicroRNAs (miRNA) are noncoding RNAs that induce cleavage of the message or inhibit translation by regulating gene expression through binding to target mRNAs. Many of them have recently emerged in a variety of cellular processes. Alterations of miRNA genes have also been detected in human tumors.
There is an increasing number of computational tools available for miRNA target prediction. The problem however is that the thus obtained results usually have little information overlap. Most recently there are first attempts to consolidate such results. The goal is to provide an improved information basis before costly genetic experiments are conducted. Approaches taken so far apply similarity score (Yang et al., 2005, JBCB 4, 693-708) and rank aggregation (Lin et al., forthcomming) methods. We propose a stochastic procedure to test for random degeneration of paired rank information.
Let us assume two assessors (i.e. tools for miRNA target prediction), one of which ranks N distinct objects according to the extent to which a particular attribute is present. The ranking is from 1 to N, without ties. The second assessor also ranks the objects from 1 to N. An indicator variable takes I_j=1 if the ranking given by the second assessor to the object ranked j by the first is not distant more than k, say, from j, and zero otherwise. Our aim is to determine how far into the two rankings one can go before the differences between them degenerate into noise. This allows us to identify a sequence of objects that is characterized by a high degree of assignment conformity.
For the estimation of the point of degeneration into noise we assume independent Bernoulli random variables. Under the condition of a general decrease of p_j for increasing j a formal inference model is developed based on moderate deviation arguments implicit in the work of Donoho et al. (1995, JRSS, Ser. B 57, 301-369). This idealized model is translated into an algorithm that allows to adjust for irregular rankings (i.e. handling of quite different rankings of some objects) typically occuring in real data. A regularization parameter needs to be specified to account for the closeness of the assessors' rankings and the degree of randomness in the assignments. Our approach can be directly applied to produce a short-cut composite target list for downstream experiments. It is also feasible to generalize it to the case of more than two assessors.
Collaborative work with Peter Hall (The University of Melbourne, Australia) and Eva Budinska (Masaryk University, Czech Republic).
Recent work in our laboratory has focused on miRNA profiling in human cancer using real-time, quantitative RT-PCR assays. A real-time PCR assay to precursor miRNAs was used to profile over 200 miRNAs in specimens of human pancreatic adenocarcinoma, paired benign tissue, normal pancreas, chronic pancreatitis and nine pancreatic cancer cell lines. One hundred miRNA precursors were aberrantly expressed in pancreatic cancer or desmoplasia (P < 0.01) including miRNAs previously reported as differentially expressed in other human cancers (miR-155, miR-21, miR-221 and miR-222) as well as those not previously reported in cancer (miR-376a and miR-301). Most of the top aberrantly expressed miRNAs displayed increased expression in the tumor. The PAM algorithm correctly classified 28 of 28 tumors, 6 of 6 normal pancreas and 11 of 15 adjacent benign tissues. Expression of the active, mature miRNA was validated using Northern blotting and RT-PCR. Reverse transcription in situ PCR showed that several of the top differentially expressed miRNAs were localized to tumor cells and not to stroma or normal acini or ducts. Our data demonstrate that a large number of miRNAs are differentially expressed in pancreas cancer and that quantifying miRNA expression has the potential to serve as a diagnostic for pancreatic cancer. More recently, we have profiled mature miRNA using a commercially available real-time PCR assay. Attempts to correlate the expression of the precursor and mature miRNA have been made for pancreas and liver cancer, a number of normal tissues and cancer cell lines. A strong correlation exists between the precursor and mature miRNA for a large number of miRNAs, however, several miRNAs are expressed at the precursor level but are not processed to the mature miRNA. Our data suggest that miRNA biogenesis may be regulated at the various steps in between expression of the primary transcript and generation of the active mature miRNA.
MicroRNAs (miRNAs) are small repressors of gene expression with roles in development, physiology and aging. MiRNAs are found across the metazoa with hundreds or thousands in each genome. A select dozen or so miRNAs are conserved in all bilateral animals. In my laboratory we are using the nematode C. elegans to determine the functions and expression patterns of these conserved miRNAs as a prelude to detailed studies of their roles in mammals. In particular we are interested in the developmental or disease effects of loss of these miRNAs in the mouse and human.
MicroRNAs (miRNAs) are short, noncoding RNAs that post-transcriptionally regulate gene expression. While hundreds of mammalian miRNA genes have been identified, little is known about the pathways that regulate the production of active miRNA species. We now know that a large fraction of miRNA genes are regulated post-transcriptionally. During early mouse development, many miRNA primary transcripts, including the Let-7 family, are present at high levels but are not processed by the enzyme Drosha. An analysis of gene expression in primary tumors indicates that the widespread down-regulation of miRNAs observed in cancer is due to a failure at the Drosha processing step. These data uncover a novel regulatory step in miRNA function and provide a mechanism for miRNA down-regulation in cancer.