MicroRNA target prediction by expression analysis of host genes

Refining transcriptional programs in kidney development by integration of deep RNA-sequencing and array-based spatial profiling

Background

The developing mouse kidney is currently the best-characterized model of organogenesis at a transcriptional level. Detailed spatial maps have been generated for gene expression profiling combined with systematic in situ screening. These studies, however, fall short of capturing the transcriptional complexity arising from each locus due to the limited scope of microarray-based technology, which is largely based on "gene-centric" models.

Results

To address this, the polyadenylated RNA and microRNA transcriptomes of the 15.5 dpc mouse kidney were profiled using strand-specific RNA-sequencing (RNA-Seq) to a depth sufficient to complement spatial maps from pre-existing microarray datasets. The transcriptional complexity of RNAs arising from mouse RefSeq loci was catalogued; including 3568 alternatively spliced transcripts and 532 uncharacterized alternate 3' UTRs. Antisense expressions for 60% of RefSeq genes was also detected including uncharacterized non-coding transcripts overlapping kidney progenitor markers, Six2 and Sall1, and were validated by section in situ hybridization. Analysis of genes known to be involved in kidney development, particularly during mesenchymal-to-epithelial transition, showed an enrichment of non-coding antisense transcripts extended along protein-coding RNAs.

Conclusion

The resulting resource further refines the transcriptomic cartography of kidney organogenesis by integrating deep RNA sequencing data with locus-based information from previously published expression atlases. The added resolution of RNA-Seq has provided the basis for a transition from classical gene-centric models of kidney development towards more accurate and detailed "transcript-centric" representations, which highlights the extent of transcriptional complexity of genes that direct complex development events.

Impact of host genes and strand selection on miRNA and miRNA* expression

Dysregulation of miRNAs expression plays a critical role in the pathogenesis of genetic, multifactorial disorders and in human cancers. We exploited sequence, genomic and expression information to investigate two main aspects of post-transcriptional regulation in miRNA biogenesis, namely strand selection regulation and expression relationships between intragenic miRNAs and host genes. We considered miRNAs expression profiles, measured in five sizeable microarray datasets, including samples from different normal cell types and tissues, as well as different tumours and disease states. First, the study of expression profiles of “sister” miRNA pairs (miRNA/miRNA*, 5′ and 3′ strands of the same hairpin precursor) showed that the strand selection is highly regulated since it shows tissue-/cell-/condition-specific modulation. We used information about the direction and the strength of the strand selection bias to perform an unsupervised cluster analysis for the sample classification evidencing that is able to distinguish among different tissues, and sometimes between normal and malignant cells. Then, considering a minimum expression threshold, in few miRNA pairs only one mature miRNA is always present in all considered cell types, whereas the majority of pairs were concurrently expressed in some cell types and alternatively in others. In a significant fraction of concurrently expressed pairs, the major and the minor forms found at comparable levels may contribute to post-transcriptional gene silencing, possibly in a coordinate way. In the second part of the study, the behaved tendency to co-expression of intragenic miRNAs and their “host” mRNA genes was confuted by expression profiles examination, suggesting that the expression profile of a given host gene can hardly be a good estimator of co-transcribed miRNA(s) for post-transcriptional regulatory networks inference. Our results point out the regulatory importance of post-transcriptional phases of miRNAs biogenesis, reinforcing the role of such layer of miRNA biogenesis in miRNA-based regulation of cell activities.

Characterization of the CLEAR network reveals an integrated control of cellular clearance pathways

In metazoans, lysosomes are the center for the degradation of macromolecules and play a key role in a variety of cellular processes, such as autophagy, exocytosis and membrane repair. Defects of lysosomal pathways are associated with lysosomal storage disorders and with several late onset neurodegenerative diseases. We recently discovered the CLEAR (Coordinated Lysosomal Expression and Regulation) gene network and its master gene transcription factor EB (TFEB), which regulates lysosomal biogenesis and function. Here, we used a combination of genomic approaches, including ChIP-seq (sequencing of chromatin immunoprecipitate) analysis, profiling of TFEB-mediated transcriptional induction, genome-wide mapping of TFEB target sites and recursive expression meta-analysis of TFEB targets, to identify 471 TFEB direct targets that represent essential components of the CLEAR network. This analysis revealed a comprehensive system regulating the expression, import and activity of lysosomal enzymes that control the degradation of proteins, glycosaminoglycans, sphingolipids and glycogen. Interestingly, the CLEAR network appears to be involved in the regulation of additional lysosome-associated processes, including autophagy, exo- and endocytosis, phagocytosis and immune response. Furthermore, non-lysosomal enzymes involved in the degradation of essential proteins such as hemoglobin and chitin are also part of the CLEAR network. Finally, we identified nine novel lysosomal proteins by using the CLEAR network as a tool for prioritizing candidates. This study provides potential therapeutic targets to modulate cellular clearance in a variety of disease conditions.

A Lasso regression model for the construction of microRNA-target regulatory networks

MOTIVATION

MicroRNAs have recently emerged as a major class of regulatory molecules involved in a broad range of biological processes and complex diseases. Construction of miRNA-target regulatory networks can provide useful information for the study and diagnosis of complex diseases. Many sequence-based and evolutionary information-based methods have been developed to identify miRNA-mRNA targeting relationships. However, as the amount of available miRNA and gene expression data grows, a more statistical and systematic method combining sequence-based binding predictions and expression-based correlation data becomes necessary for the accurate identification of miRNA-mRNA pairs.

RESULTS

We propose a Lasso regression model for the identification of miRNA-mRNA targeting relationships that combines sequence-based prediction information, miRNA co-regulation, RISC availability and miRNA/mRNA abundance data. By comparing this modelling approach with two other known methods applied to three different datasets, we found that the Lasso regression model has considerable advantages in both sensitivity and specificity. The regression coefficients in the model can be used to determine the true regulatory efficacies in tissues and was demonstrated using the miRNA target site type data. Finally, by constructing the miRNA regulatory networks in two stages of prostate cancer (PCa), we found the several significant miRNA-hubbed network modules associated with PCa metastasis. In conclusion, the Lasso regression model is a robust and informative tool for constructing the miRNA regulatory networks for diagnosis and treatment of complex diseases.

AVAILABILITY

The R program for predicting miRNA-mRNA targeting relationships using the Lasso regression model is freely available, along with the described datasets and resulting regulatory network, at http://biocompute.bmi.ac.cn/CZlab/alarmnet/. The source code is open for modification and application to other miRNA/mRNA expression datasets.

CONTACT

zhangcg@bmi.ac.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational prediction of intronic microRNA targets using host gene expression reveals novel regulatory mechanisms

Approximately half of known human miRNAs are located in the introns of protein coding genes. Some of these intronic miRNAs are only expressed when their host gene is and, as such, their steady state expression levels are highly correlated with those of the host gene's mRNA. Recently host gene expression levels have been used to predict the targets of intronic miRNAs by identifying other mRNAs that they have consistent negative correlation with. This is a potentially powerful approach because it allows a large number of expression profiling studies to be used but needs refinement because mRNAs can be targeted by multiple miRNAs and not all intronic miRNAs are co-expressed with their host genes.

Here we introduce InMiR, a new computational method that uses a linear-Gaussian model to predict the targets of intronic miRNAs based on the expression profiles of their host genes across a large number of datasets. Our method recovers nearly twice as many true positives at the same fixed false positive rate as a comparable method that only considers correlations. Through an analysis of 140 Affymetrix datasets from Gene Expression Omnibus, we build a network of 19,926 interactions among 57 intronic miRNAs and 3,864 targets. InMiR can also predict which host genes have expression profiles that are good surrogates for those of their intronic miRNAs. Host genes that InMiR predicts are bad surrogates contain significantly more miRNA target sites in their 3′ UTRs and are significantly more likely to have predicted Pol II and Pol III promoters in their introns.

We provide a dataset of 1,935 predicted mRNA targets for 22 intronic miRNAs. These prediction are supported both by sequence features and expression. By combining our results with previous reports, we distinguish three classes of intronic miRNAs: Those that are tightly regulated with their host gene; those that are likely to be expressed from the same promoter but whose host gene is highly regulated by miRNAs; and those likely to have independent promoters.

g:Profiler--a web server for functional interpretation of gene lists (2011 update)

Functional interpretation of candidate gene lists is an essential task in modern biomedical research. Here, we present the 2011 update of g:Profiler (http://biit.cs.ut.ee/gprofiler/), a popular collection of web tools for functional analysis. g:GOSt and g:Cocoa combine comprehensive methods for interpreting gene lists, ordered lists and list collections in the context of biomedical ontologies, pathways, transcription factor and microRNA regulatory motifs and protein–protein interactions. Additional tools, namely the biomolecule ID mapping service (g:Convert), gene expression similarity searcher (g:Sorter) and gene homology searcher (g:Orth) provide numerous ways for further analysis and interpretation. In this update, we have implemented several features of interest to the community: (i) functional analysis of single nucleotide polymorphisms and other DNA polymorphisms is supported by chromosomal queries; (ii) network analysis identifies enriched protein–protein interaction modules in gene lists; (iii) functional analysis covers human disease genes; and (iv) improved statistics and filtering provide more concise results. g:Profiler is a regularly updated resource that is available for a wide range of species, including mammals, plants, fungi and insects.

A simple Bayesian estimate of direct RNAi gene regulation events from differential gene expression profiles

BACKGROUND

Microarrays are commonly used to investigate both the therapeutic potential and functional effects of RNA interfering (RNAi) oligonucleotides such as microRNA (miRNA) and small interfering RNA (siRNA). However, the resulting datasets are often challenging to interpret as they include extensive information relating to both indirect transcription effects and off-target interference events.

METHOD

In an attempt to refine the utility of microarray expression data when evaluating the direct transcriptional affects of an RNAi agent we have developed SBSE (Simple Bayesian Seed Estimate). The key assumption implemented in SBSE is that both direct regulation of transcription by miRNA, and siRNA off-target interference, can be estimated using the differential distribution of an RNAi sequence (seed) motif in a ranked 3' untranslated region (3' UTR) sequence repository. SBSE uses common microarray summary statistics (i.e. fold change) and a simple Bayesian analysis to estimate how the RNAi agent dictated the observed differential expression profile. On completion a trace of the estimate and the location of the optimal partitioning of the dataset are plotted within a simple graphical representation of the 3'UTR landscape. The combined estimates define the differential distribution of the query motif within the dataset and by inference are used to quantify the magnitude of the direct RNAi transcription effect.

RESULTS

SBSE has been evaluated using five diverse human RNAi microarray focused investigations. In each instance SBSE unambiguously identified the most likely location of the direct RNAi effects for each of the differential gene expression profiles.

CONCLUSION

These analyses indicate that miRNA with conserved seed regions may share minimal biological activity and that SBSE can be used to differentiate siRNAs of similar efficacy but with different off-target signalling potential.

MicroRNA expression in the livers of inbred mice

MicroRNAs are short, non-coding RNA sequences that regulate genes at the post-transcriptional level and have been shown to be important in development, tissue differentiation, and disease. Limited attention has been given to the natural variation in miRNA expression across genetically diverse populations even though it is well established that genetic polymorphisms can have a profound effect on mRNA levels. Expression level of 577 miRNAs in the livers of 70 strains of inbred mice was assessed, and we found that miRNA expression is highly stable across different strains. Globally, the expression of miRNA target transcripts does not correlate with miRNA expression, primarily due to the low variance of miRNA but high variance of mRNA expression across strains. Our results show that there is little genetic effect on the baseline miRNA levels in murine liver. The stability of mouse liver miRNA expression in a genetically diverse population suggests that treatment-induced disruptions in liver miRNA expression, a phenomenon established for a large number of toxicants, may indicate an important mechanism for the disturbance of normal liver function, and may prove to be a useful genetic background-independent biomarker of toxicant effect.

Enhancing miRNA annotation confidence in miRBase by continuous cross dataset analysis

The immaculate annotation of all microRNAs (miRNAs) is a prerequisite to study their biological function on a genome-wide scale. However, the original criteria for proper miRNA annotation seem unsuited for the automated analysis of the immense number of small RNA reads available in next generation sequencing (NGS) datasets. Here we analyze the confidence of past miRNA annotation in miRBase by cross-analyzing publicly available NGS datasets using strengthened annotation requirements. Our analysis highlights that a large number of annotated human miRNAs in miRBase seems to require more experimental validation to be confidently annotated. Notably, our dataset analysis also identified almost 300 currently non-annotated miRNA*s and 28 novel miRNAs. These observations hereby greatly increase the confidence of past miRNA annotation in miRBase but also illustrate the usefulness of continuous re-evaluating NGS datasets in the identification of novel miRNAs.

Predicting the target genes of intronic microRNAs using large-scale gene expression data

Current microRNA target prediction techniques provide long lists of putative miRNA-target interactions, many of which are false positives. The goal of this paper is to identify functional targets in these lists based on biological evidence obtained from the expression profiles of the host genes of intronic miRNAs and those of their targets. We propose a scoring strategy for each interaction based on the combinatorial effect of miRNAs. In particular, the change in expression level of a target gene is expressed in terms of a linear combination of the host gene data which are used as surrogates for expression data of the intronic miRNAs. The parameters of this linear model give an estimate of the contribution of each intronic miRNA in down-regulating the target gene. The experimental results show that our prediction technique is able to detect several functional interactions. In addition, the analysis of mRNA microarrays after intronic miRNA transfection confirms that significantly down-regulated genes are among targets detected by our technique.

HOCTAR database: a unique resource for microRNA target prediction

microRNAs (miRNAs) are the most abundant class of small RNAs in mammals. They play an important role in regulation of gene expression by inducing mRNA cleavage or translational inhibition. Each miRNA targets an average of 100–200 genes by binding, preferentially, to their 3′ UTRs by means of partial sequence complementarity. Most miRNAs are localized within transcriptional units, termed host genes, and show similar expression behavior with respect to their corresponding host genes. Considering the impact of miRNA in the regulation of gene expression and their involvement in a growing number of human disorders, it is vital to develop sensitive computational approaches able to identify miRNA target genes. The HOCTAR database (db) is a publicly available resource collecting ranked list of predicted target genes for 290 intragenic miRNAs annotated in human. HOCTARdb is a unique resource that integrates miRNA target prediction genes and transcriptomic data to score putative miRNA targets looking at the expression behavior of their host genes. We demonstrated, by testing 135 known validated target genes (either at the translational or transcriptional level) for different miRNAs, that the miRNA target prediction lists present in HOCTARdb are highly reliable. Moreover, HOCTARdb associates biological roles to each miRNA-controlled transcriptional network by means of Gene Ontology analysis. This information is easily accessible through a user-friendly query page. The HOCTARdb is available at http://hoctar.tigem.it/. We believe that a detailed relationship between miRNAs and their target genes and a constant update of the information contained in HOCTARdb will provide an extremely valuable resource to assist the researcher in the discovery of miRNA target genes.

mRNA turnover rate limits siRNA and microRNA efficacy

Based on a simple model of the mRNA life cycle, we predict that mRNAs with high turnover rates in the cell are more difficult to perturb with RNAi.

We test this hypothesis using a luciferase reporter system and obtain additional evidence from a variety of large-scale data sets, including microRNA overexpression experiments and RT–qPCR-based efficacy measurements for thousands of siRNAs.

Our results suggest that mRNA half-lives will influence how mRNAs are differentially perturbed whenever small RNA levels change in the cell, not only after transfection but also during differentiation, pathogenesis and normal cell physiology.

What determines how strongly an mRNA responds to a microRNA or an siRNA? We know that properties of the sequence match between the small RNA and the mRNA are crucial. However, large-scale validations of siRNA efficacies have shown that certain transcripts remain recalcitrant to perturbation even after repeated redesign of the siRNA (Krueger et al, 2007). Weak response to RNAi may thus be an inherent property of the mRNA, but the underlying factors have proven difficult to uncover.

siRNAs induce degradation by sequence-specific cleavage of their target mRNAs (Elbashir et al, 2001). MicroRNAs, too, induce mRNA degradation, and ∼80% of their effect on protein levels can be explained by changes in transcript abundance (Hendrickson et al, 2009; Guo et al, 2010). Given that multiple factors act simultaneously to degrade individual mRNAs, we here consider whether variable responses to micro/siRNA regulation may, in part, be explained simply by the basic dynamics of mRNA turnover. If a transcript is already under strong destabilizing regulation, it is theoretically possible that the relative change in abundance after the addition of a novel degrading factor would be less pronounced compared with a stable transcript (Figure 1). mRNA turnover is achieved by a multitude of factors, and the influence of such factors on targetability can be explored. However, their combined action, including yet unknown factors, is summarized into a single property: the mRNA decay rate.

First, we explored the theoretical relationship between the pre-existing turnover rate of an mRNA, and its expected susceptibility to perturbation by a small RNA. We assumed a basic model of the mRNA life cycle, in which the rate of transcription is constant and the rate of degradation is described by first-order kinetics. Under this model, the relative change in steady-state expression level will become smaller as the pre-existing decay rate grows larger, independent of the transcription rate. This relationship persists also if we assume various degrees of synergy and antagonism between the pre-existing factors and the external factor, with increasing synergism leading to transcripts being more equally targetable, regardless of their pre-existing decay rate.

We next generated a series of four luciferase reporter constructs with destabilizing AU-rich elements (AREs) of various strengths incorporated into their 3′ UTRs. To evaluate how the different constructs would respond to perturbation, we performed co-transfections with an siRNA targeted at the coding region of the luciferase gene. This reduced the signal of the non-destabilized construct to 26% compared with a control siRNA. In contrast, the most destabilized construct showed 42% remaining reporter activity, and we could observe a dose–response relationship across the series.

The reporter experiment encouraged an investigation of this effect on real-world mRNAs. We analyzed a set of 2622 siRNAs, for which individual efficacies were determined using RT–qPCR 48 h post-transfection in HeLa cells (www.appliedbiosystems.com). Of these, 1778 could be associated with an experimentally determined decay rate (Figure 4A). Although the overall correlation between the two variables was modest (Spearman's rank correlation r_s=0.22, P<1e−20), we found that siRNAs directed at high-turnover (t_1/2<200 min) and medium-turnover (200<t_1/2<1000 min) mRNAs caused significantly less repression than those targeting long-lived (t_1/2>1000 min) transcripts (P<8e−11 and 4e−9, respectively, two-tailed KS-test, Figure 4B). While 41.6% (498/1196) of the siRNAs directed at low-turnover transcripts reached 10% remaining expression or better, only 16.7% (31/186) of the siRNAs that targeted high-turnover mRNAs reached this high degree of silencing (Figure 4B). Reduced targetability (25.2%, 100/396) was also seen for transcripts with medium-turnover rate.

Our results based on siRNA data suggested that turnover rates could also influence microRNA targeting. By assembling genome-wide mRNA expression data from 20 published microRNA transfections in HeLa cells, we found that predicted target mRNAs with short and medium half-life were significantly less repressed after transfection than their long-lived counterparts (P<8e−5 and P<0.03, respectively, two-tailed KS-test). Specifically, 10.2% (293/2874) of long-lived targets versus 4.4% (41/942) of short-lived targets were strongly (z-score <−3) repressed. siRNAs are known to cause off-target effects that are mediated, in part, by microRNA-like seed complementarity (Jackson et al, 2006). We analyzed changes in transcript levels after transfection of seven different siRNAs, each with a unique seed region (Jackson et al, 2006). Putative ‘off-targets' were identified by mapping of non-conserved seed matches in 3′ UTRs. We found that low-turnover mRNAs (t_1/2 >1000 min) were more affected by seed-mediated off-target silencing than high-turnover mRNAs (t_1/2 <200 min), with twice as many long-lived seed-containing transcripts (3.8 versus 1.9%) being strongly (z-score <−3) repressed.

In summary, mRNA turnover rates have an important influence on the changes exerted by small RNAs on mRNA levels. It can be assumed that mRNA half-lives will influence how mRNAs are differentially perturbed whenever small RNA levels change in the cell, not only after transfection but also during differentiation, pathogenesis and normal cell physiology.

The microRNA pathway participates in basic cellular processes and its discovery has enabled the development of si/shRNAs as powerful investigational tools and potential therapeutics. Based on a simple kinetic model of the mRNA life cycle, we hypothesized that mRNAs with high turnover rates may be more resistant to RNAi-mediated silencing. The results of a simple reporter experiment strongly supported this hypothesis. We followed this with a genome-wide scale analysis of a rich corpus of experiments, including RT–qPCR validation data for thousands of siRNAs, siRNA/microRNA overexpression data and mRNA stability data. We find that short-lived transcripts are less affected by microRNA overexpression, suggesting that microRNA target prediction would be improved if mRNA turnover rates were considered. Similarly, short-lived transcripts are more difficult to silence using siRNAs, and our results may explain why certain transcripts are inherently recalcitrant to perturbation by small RNAs.

NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs

Background

MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome – referred to as the micronome – to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal — mirDIP (http://ophid.utoronto.ca/mirDIP).

Results

mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05), suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001), to be more studied (p<0.0002), and to have higher degree in the KEGG cancer pathway (p<0.0001), compared to intra-pathway microRNAs.

Conclusions

Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level.

Vax2 regulates retinoic acid distribution and cone opsin expression in the vertebrate eye

Vax2 is an eye-specific homeobox gene, the inactivation of which in mouse leads to alterations in the establishment of a proper dorsoventral eye axis during embryonic development. To dissect the molecular pathways in which Vax2 is involved, we performed a transcriptome analysis of Vax2(-/-) mice throughout the main stages of eye development. We found that some of the enzymes involved in retinoic acid (RA) metabolism in the eye show significant variations of their expression levels in mutant mice. In particular, we detected an expansion of the expression domains of the RA-catabolizing enzymes Cyp26a1 and Cyp26c1, and a downregulation of the RA-synthesizing enzyme Raldh3. These changes determine a significant expansion of the RA-free zone towards the ventral part of the eye. At postnatal stages of eye development, Vax2 inactivation led to alterations of the regional expression of the cone photoreceptor genes Opn1sw (S-Opsin) and Opn1mw (M-Opsin), which were significantly rescued after RA administration. We confirmed the above described alterations of gene expression in the Oryzias latipes (medaka fish) model system using both Vax2 gain- and loss-of-function assays. Finally, a detailed morphological and functional analysis of the adult retina in mutant mice revealed that Vax2 is necessary for intraretinal pathfinding of retinal ganglion cells in mammals. These data demonstrate for the first time that Vax2 is both necessary and sufficient for the control of intraretinal RA metabolism, which in turn contributes to the appropriate expression of cone opsins in the vertebrate eye.

The integrative analysis of microRNA and mRNA expression in mouse uterus under delayed implantation and activation

Background

Delayed implantation is a developmental arrest at the blastocyst stage and a good model for embryo implantation. MicroRNAs (miRNAs) have been shown to be involved in mouse embryo implantation through regulating uterine gene expression. This study was to have an integrative analysis on global miRNA and mRNA expression in mouse uterus under delayed implantation and activation through Illumina sequencing.

Methodology/Principal Findings

By deep sequencing and analysis, we found that there are 20 miRNAs up-regulated and 42 miRNAs down-regulated at least 1.2 folds, and 268 genes up-regulated and 295 genes down-regulated at least 2 folds under activation compared to delayed implantation, respectively. Many different forms of editing in mature miRNAs are detected. The percentage of editing at positions 4 and 5 of mature miRNAs is significantly higher under delayed implantation than under activation. Although the number of miR-21 reference sequence under activation is slightly lower than that under delayed implantation, the total level of miR-21 under activation is higher than that under delayed implantation. Six novel miRNAs are predicted and confirmed. The target genes of significantly up-regulated miRNAs under activation are significantly enriched.

Conclusions

miRNA and mRNA expression patterns are closely related. The target genes of up-regulated miRNAs are significantly enriched. A high level of editing at positions 4 and 5 of mature miRNAs is detected under delayed implantation than under activation. Our data should be valuable for future study on delayed implantation.

Integrated analysis of mutations, miRNA and mRNA expression in glioblastoma

BACKGROUND

Glioblastoma arises from complex interactions between a variety of genetic alterations and environmental perturbations. Little attention has been paid to understanding how genetic variations, altered gene expression and microRNA (miRNA) expression are integrated into networks which act together to alter regulation and finally lead to the emergence of complex phenotypes and glioblastoma.

RESULTS

We identified association of somatic mutations in 14 genes with glioblastoma, of which 8 genes are newly identified, and association of loss of heterozygosity (LOH) is identified in 11 genes with glioblastoma, of which 9 genes are newly discovered. By gene coexpression network analysis, we identified 15 genes essential to the function of the network, most of which are cancer related genes. We also constructed miRNA coexpression networks and found 19 important miRNAs of which 3 were significantly related to glioblastoma patients' survival. We identified 3,953 predicted miRNA-mRNA pairs, of which 14 were previously verified by experiments in other groups. Using pathway enrichment analysis we also found that the genes in the target network of the top 19 important miRNAs were mainly involved in cancer related signaling pathways, synaptic transmission and nervous systems processes. Finally, we developed new methods to decipher the pathway connecting mutations, expression information and glioblastoma. We identified 4 cis-expression quantitative trait locus (eQTL): TP53, EGFR, NF1 and PIK3C2G; 262 trans eQTL and 26 trans miRNA eQTL for somatic mutation; 2 cis-eQTL: NRAP and EGFR; 409 trans- eQTL and 27 trans- miRNA eQTL for lost of heterozygosity (LOH) mutation.

CONCLUSIONS

Our results demonstrate that integrated analysis of multi-dimensional data has the potential to unravel the mechanism of tumor initiation and progression.

Identification of common microRNA-mRNA regulatory biomodules in human epithelial cancers

The complex regulatory network between microRNAs and gene expression remains unclear domain of active research. We proposed to address in part this complex regulation with a novel approach for the genome-wide identification of biomodules derived from paired microRNA and mRNA profiles, which could reveal correlations associated with a complex network of de-regulation in human cancer. Two published expression datasets for 68 samples with 11 distinct types of epithelial cancers and 21 samples of normal tissues were used, containing microRNA expression (Lu et al. Nature Letters 2005) and gene expression (Ramaswarmy et al. PNAS 2001) profiles, respectively. As results, the microRNA expression used jointly with mRNA expression can provide better classifiers of epithelial cancers against normal epithelial tissue than either dataset alone (p=1×10(-10), F-Test). We identified a combination of six microRNA-mRNA biomodules that optimally classified epithelial cancers from normal epithelial tissue (total accuracy = 93.3%; 95% confidence intervals: 86% - 97%), using penalized logistic regression (PLR) algorithm and three-fold cross-validation. Three of these biomodules are individually sufficient to cluster epithelial cancers from normal tissue using mutual information distance. The biomodules contain 10 distinct microRNAs and 98 distinct genes, including well known tumor markers such as miR-15a, miR-30e, IRAK1, TGFBR2, DUSP16, CDC25B and PDCD2. In addition, there is a significant enrichment (Fisher's exact test p=3×10(-10)) between putative microRNA-target gene pairs reported in five microRNA target databases and the inversely correlated micro-RNA-mRNA pairs in the biomodules. Further, microRNAs and genes in the biomodules were found in abstracts mentioning epithelial cancers (Fisher Exact Test, unadjusted p<0.05). Taken together, these results strongly suggest that the discovered microRNA-mRNA biomodules correspond to regulatory mechanisms common to human epithelial cancer samples. In conclusion, we developed and evaluated a novel comprehensive method to systematically identify, on a genome scale, microRNA-mRNA expression biomodules common to distinct cancers of the same tissue. These biomodules also comprise novel microRNA and genes as well as an imputed regulatory network, which may accelerate the work of cancer biologists as large regulatory maps of cancers can be drawn efficiently for hypothesis generation.

A potential role for intragenic miRNAs on their hosts' interactome

Background

miRNAs are small, non-coding RNA molecules that mainly act as negative regulators of target gene messages. Due to their regulatory functions, they have lately been implicated in several diseases, including malignancies. Roughly half of known miRNA genes are located within previously annotated protein-coding regions ("intragenic miRNAs"). Although a role of intragenic miRNAs as negative feedback regulators has been speculated, to the best of our knowledge there have been no conclusive large-scale studies investigating the relationship between intragenic miRNAs and host genes and their pathways.

Results

miRNA-containing host genes were three times longer, contained more introns and had longer 5' introns compared to a randomly sampled gene cohort. These results are consistent with the observation that more than 60% of intronic miRNAs are found within the first five 5' introns. Host gene 3'-untranslated regions (3'-UTRs) were 40% longer and contained significantly more adenylate/uridylate-rich elements (AREs) compared to a randomly sampled gene cohort. Coincidentally, recent literature suggests that several components of the miRNA biogenesis pathway are required for the rapid decay of mRNAs containing AREs. A high-confidence set of predicted mRNA targets of intragenic miRNAs also shared many of these features with the host genes. Approximately 20% of intragenic miRNAs were predicted to target their host mRNA transcript. Further, KEGG pathway analysis demonstrated that 22 of the 74 pathways in which host genes were associated showed significant overrepresentation of proteins encoded by the mRNA targets of associated intragenic miRNAs.

Conclusions

Our findings suggest that both host genes and intragenic miRNA targets may potentially be subject to multiple layers of regulation. Tight regulatory control of these genes is likely critical for cellular homeostasis and absence of disease. To this end, we examined the potential for negative feedback loops between intragenic miRNAs, host genes, and miRNA target genes. We describe, how higher-order miRNA feedback on hosts' interactomes may at least in part explain correlation patterns observed between expression of host genes and intragenic miRNA targets in healthy and tumor tissue.

Strategies to identify microRNA targets: new advances

MicroRNAs (miRNAs) are small regulatory RNA molecules functioning to modulate gene expression at the post-transcriptional level, and playing an important role in many developmental and physiological processes. Ten thousand miRNAs have been discovered in various organisms. Although considerable progress has been made in computational methodology to identify miRNA targets, most predicted miRNA targets may be false positive. Due to the lack of effective tools to identify miRNA targets, the study of miRNAs is seriously retarded. In recent years, some molecular cloning strategies of miRNA targets have been developed, including RT-PCR using miRNAs as endogenous primers, labeled miRNA pull-down assay (LAMP) and RNA ligase-mediated amplification of cDNA end (RLM-RACE). The identified miRNA targets should be further validated via effects of miRNA alteration on the target protein levels and bioactivity. This review summarizes advances in strategies to identify miRNA targets and methods by which miRNA targets are validated.

miR-204 is required for lens and retinal development via Meis2 targeting

MicroRNAs (miRNAs) are small noncoding RNAs that have important roles in the regulation of gene expression. The roles of individual miRNAs in controlling vertebrate eye development remain, however, largely unexplored. Here, we show that a single miRNA, miR-204, regulates multiple aspects of eye development in the medaka fish (Oryzias latipes). Morpholino-mediated ablation of miR-204 expression resulted in an eye phenotype characterized by microphthalmia, abnormal lens formation, and altered dorsoventral (D-V) patterning of the retina, which is associated with optic fissure coloboma. Using a variety of in vivo and in vitro approaches, we identified the transcription factor Meis2 as one of the main targets of miR-204 function. We show that, together with altered regulation of the Pax6 pathway, the abnormally elevated levels of Meis2 resulting from miR-204 inactivation are largely responsible for the observed phenotype. These data provide an example of how a specific miRNA can regulate multiple events in eye formation; at the same time, they uncover an as yet unreported function of Meis2 in the specification of D-V patterning of the retina.

Systematic analysis of regulation and functions of co-expressed microRNAs in humans

MicroRNAs (miRNAs) are a class of small non-coding RNA genes that post-transcriptionally regulate gene expression. With the development of high-throughput miRNA detection technology, researchers have begun to investigate the relationships between miRNA expression and its functions. In this study, we systematically analyzed the underlying molecular mechanisms and biological functions of co-expressed miRNAs. By integrating miRNA expression profiles, miRNA genome locations, transcriptional factors (TFs) of miRNAs and their target genes, we concluded that co-expressed miRNAs are more likely to be located in the same miRNA cluster (p = 6.05 x 10(-30)), are regulated by more common TFs (p = 9.17 x 10(-17)) and have consistent functions (p = 1.01 x 10(-6)). Moreover, among the top 10% (84) co-expressed miRNA pairs that are located on the same chromosomes, 37 miRNA pairs are located in the same cluster. Of the remaining 47 pairs, 36 miRNA pairs share more common TFs (>7). They account for 73/84 (86.9%) of total miRNA pairs. Finally, we further analyzed the top 10 co-expressed miRNA pairs. Almost all of these miRNA pairs are located in the same cluster, are regulated by many common TFs and have highly consistent functions, in agreement with previous reports. Thus, our study may provide an important reference for miRNA regulations and functions.

Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases

MOTIVATION

It is popular to explore meaningful molecular targets and infer new functions of genes through gene functional similarity measuring and gene functional network construction. However, little work is available in this field for microRNA (miRNA) genes due to limited miRNA functional annotations. With the rapid accumulation of miRNAs, it is increasingly needed to uncover their functional relationships in a systems level.

RESULTS

It is known that genes with similar functions are often associated with similar diseases, and the relationship of different diseases can be represented by a structure of directed acyclic graph (DAG). This is also true for miRNA genes. Therefore, it is feasible to infer miRNA functional similarity by measuring the similarity of their associated disease DAG. Based on the above observations and the rapidly accumulated human miRNA-disease association data, we presented a method to infer the pairwise functional similarity and functional network for human miRNAs based on the structures of their disease relationships. Comparisons showed that the calculated miRNA functional similarity is well associated with prior knowledge of miRNA functional relationship. More importantly, this method can also be used to predict novel miRNA biomarkers and to infer novel potential functions or associated diseases for miRNAs. In addition, this method can be easily extended to other species when sufficient miRNA-associated disease data are available for specific species.

AVAILABILITY

The online tool is available at http://cmbi.bjmu.edu.cn/misim

CONTACT

cuiqinghua@hsc.pku.edu.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational methods for the identification of microRNA targets

MicroRNAs are pivotal regulators of development and cellular homeostasis. They act as post-transcriptional regulators, which control the stability and translation efficiency of their target mRNAs. The prediction of microRNA targets and detection of microRNA-mRNA regulatory modules (MRMs) are crucial components for understanding of microRNA functions. Numerous computational methods for microRNA target prediction have been developed. Computationally-predicted targets have been recently used in the integrative analysis of microRNA and mRNA expression analysis to identify microRNA targets and MRMs. In this article we review these recent developments in the integrative analysis methods. We also discuss the remaining challenges and our insights on future directions.

Computational methodologies for studying non-coding RNAs relevant to central nervous system function and dysfunction

Non-coding RNAs (ncRNAs) are a large and diverse group of transcripts that span the eukaryotic genome, of which less than 2% encodes proteins. Several distinct families of ncRNAs have been described and implicated in many aspects of central nervous system (CNS) function including translation, RNA metabolism, gene regulation, and development. The need to distinguish ncRNAs from sequence data, as well as potentially uncovering novel ncRNA families, has ignited the development of customized computational approaches and bioinformatic resources to handle these tasks. In this review, we provide an overview of the numerous procedures developed to predict ncRNAs based on their primary sequence and predicted secondary structure. These methodologies are broadly grouped into genome scanning algorithms, mixed approaches, and machine learning algorithms. Regulatory ncRNAs, particularly microRNAs (miRNAs), are a major focus of current research efforts and this review will therefore center on the prediction of miRNAs and the putative gene targets they act upon. With the advent of ultra high-throughput sequencing technologies 'deep sequencing' has emerged as the cutting-edge method for ncRNA identification and we will also touch on some computational resources that play a key role in analysis of this type of data.

Investigation of post-transcriptional gene regulatory networks associated with autism spectrum disorders by microRNA expression profiling of lymphoblastoid cell lines

Background

Autism spectrum disorders (ASD) are neurodevelopmental disorders characterized by abnormalities in reciprocal social interactions and language development and/or usage, and by restricted interests and repetitive behaviors. Differential gene expression of neurologically relevant genes in lymphoblastoid cell lines from monozygotic twins discordant in diagnosis or severity of autism suggested that epigenetic factors such as DNA methylation or microRNAs (miRNAs) may be involved in ASD.

Methods

Global miRNA expression profiling using lymphoblasts derived from these autistic twins and unaffected sibling controls was therefore performed using high-throughput miRNA microarray analysis. Selected differentially expressed miRNAs were confirmed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis, and the putative target genes of two of the confirmed miRNA were validated by knockdown and overexpression of the respective miRNAs.

Results

Differentially expressed miRNAs were found to target genes highly involved in neurological functions and disorders in addition to genes involved in gastrointestinal diseases, circadian rhythm signaling, as well as steroid hormone metabolism and receptor signaling. Novel network analyses of the putative target genes that were inversely expressed relative to the relevant miRNA in these same samples further revealed an association with ASD and other co-morbid disorders, including muscle and gastrointestinal diseases, as well as with biological functions implicated in ASD, such as memory and synaptic plasticity. Putative gene targets (ID3 and PLK2) of two RT-PCR-confirmed brain-specific miRNAs (hsa-miR-29b and hsa-miR-219-5p) were validated by miRNA overexpression or knockdown assays, respectively. Comparisons of these mRNA and miRNA expression levels between discordant twins and between case-control sib pairs show an inverse relationship, further suggesting that ID3 and PLK2 are in vivo targets of the respective miRNA. Interestingly, the up-regulation of miR-23a and down-regulation of miR-106b in this study reflected miRNA changes previously reported in post-mortem autistic cerebellum by Abu-Elneel et al. in 2008. This finding validates these differentially expressed miRNAs in neurological tissue from a different cohort as well as supports the use of the lymphoblasts as a surrogate to study miRNA expression in ASD.

Conclusions

Findings from this study strongly suggest that dysregulation of miRNA expression contributes to the observed alterations in gene expression and, in turn, may lead to the pathophysiological conditions underlying autism.

Intronic microRNAs support their host genes by mediating synergistic and antagonistic regulatory effects

BACKGROUND

MicroRNA-mediated control of gene expression via translational inhibition has substantial impact on cellular regulatory mechanisms. About 37% of mammalian microRNAs appear to be located within introns of protein coding genes, linking their expression to the promoter-driven regulation of the host gene. In our study we investigate this linkage towards a relationship beyond transcriptional co-regulation.

RESULTS

Using measures based on both annotation and experimental data, we show that intronic microRNAs tend to support their host genes by regulation of target gene expression with significantly correlated expression patterns. We used expression data of three differentiating cell types and compared gene expression profiles of host and target genes. Many microRNA target genes show expression patterns significantly correlated with the expressions of the microRNA host genes. By calculating functional similarities between host and predicted microRNA target genes based on GO annotations, we confirm that many microRNAs link host and target gene activity in an either synergistic or antagonistic manner.

CONCLUSIONS

These two regulatory effects may result from fine tuning of target gene expression functionally related to the host or knock-down of remaining opponent target gene expression. This finding allows to extend the common practice of mapping large scale gene expression data to protein associated genes with functionality of co-expressed intronic microRNAs.

MicroRNAs--targeting and target prediction

MicroRNAs (miRNAs) are a class of small noncoding RNAs that can regulate many genes by base pairing to sites in mRNAs. The functionality of miRNAs overlaps that of short interfering RNAs (siRNAs), and many features of miRNA targeting have been revealed experimentally by studying miRNA-mimicking siRNAs. This review outlines the features associated with animal miRNA targeting and describes currently available prediction tools.

Computational methods to identify miRNA targets

MicroRNAs (miRNAs) are short RNA molecules that regulate the post-transcriptional expression of their target genes. This regulation may take the form of stable translational or degradation of the target transcript, although the mechanisms governing the outcome of miRNA-mediated regulation remain largely unknown. While it is becoming clear that miRNAs are core components of gene regulatory networks, elucidating precise roles for each miRNA within these networks will require an accurate means of identifying target genes and assessing the impact of miRNAs on individual targets. Numerous computational methods for predicting targets are currently available. These methods vary widely in their emphasis, accuracy, and ease of use for researchers. This review will focus on a comparison of the available computational methods in animals, with an emphasis on approaches that are informed by experimental analysis of microRNA:target complexes.

Impact of probe annotation on the integration of miRNA-mRNA expression profiles for miRNA target detection

MicroRNAs (miRNAs) are small non-coding RNAs that mediate gene expression at the post-transcriptional and translational levels by an imperfect binding to target mRNA 3′UTR regions. While the ab-initio computational prediction of miRNA–mRNA interactions still poses significant challenges, it is possible to overcome some of its limitations by carefully integrating into the analysis the paired expression profiles of miRNAs and mRNAs. In this work, we show how the choice of a proper probe annotation for microarray platforms is an essential requirement to achieve good sensitivity in the identification of miRNA–mRNA interactions. We compare the results obtained from the analysis of the same expression profiles using both gene and transcript based custom CDFs that we have developed for a number of different annotations (ENSEMBL, RefSeq, AceView). In all cases, transcript-based annotations clearly improve the effectiveness of data integration and thus provide a more reliable confirmation of computationally predicted miRNA–mRNA interactions.

Identification of microRNA expression patterns and definition of a microRNA/mRNA regulatory network in distinct molecular groups of multiple myeloma

To date, little evidence of miRNA expression/deregulation in multiple myeloma has been reported. To characterize miRNA in the context of the major multiple myeloma molecular types, we generated miRNA expression profiles of highly purified malignant plasma cells from 40 primary tumors. Furthermore, transcriptional profiles, available for all patients, were used to investigate the occurrence of miRNA/predicted target mRNA pair anticorrelations, and the miRNA and genome-wide DNA data were integrated in a subset of patients to evaluate the influence of allelic imbalances on miRNA expression. Differential miRNA expression patterns were identified, which were mainly associated with the major IGH translocations; particularly, t(4;14) patients showed specific overexpression of let-7e, miR-125a-5p, and miR-99b belonging to a cluster at 19q13.33. The occurrence of other lesions (ie, 1q gain, 13q and 17p deletions, and hyperdiploidy) was slightly characterized by specific miRNA signatures. Furthermore, the occurrence of several allelic imbalances or loss of heterozygosity was found significantly associated with the altered expression of miRNAs located in the involved regions, such as let-7b at 22q13.31 or miR-140-3p at 16q22. Finally, the integrative analysis based on computational target prediction and miRNA/mRNA profiling defined a network of putative functional miRNA-target regulatory relations supported by expression data.

Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods

The MEM web resource allows users to search for co-expressed genes across all microarray datasets in the ArrayExpress database.

We present a web resource MEM (Multi-Experiment Matrix) for gene expression similarity searches across many datasets. MEM features large collections of microarray datasets and utilizes rank aggregation to merge information from different datasets into a single global ordering with simultaneous statistical significance estimation. Unique features of MEM include automatic detection, characterization and visualization of datasets that includes the strongest coexpression patterns. MEM is freely available at http://biit.cs.ut.ee/mem/.

Patrocles: a database of polymorphic miRNA-mediated gene regulation in vertebrates

The Patrocles database (http://www.patrocles.org/) compiles DNA sequence polymorphisms (DSPs) that are predicted to perturb miRNA-mediated gene regulation. Distinctive features include: (i) the coverage of seven vertebrate species in its present release, aiming for more when information becomes available, (ii) the coverage of the three compartments involved in the silencing process (i.e. targets, miRNA precursors and silencing machinery), (iii) contextual information that enables users to prioritize candidate ‘Patrocles DSPs’, including graphical information on miRNA-target coexpression and eQTL effect of genotype on target expression levels, (iv) the inclusion of Copy Number Variants and eQTL information that affect miRNA precursors as well as genes encoding components of the silencing machinery and (v) a tool (Patrocles finder) that allows the user to determine whether her favorite DSP may perturb miRNA-mediated gene regulation of custom target sequences. To support the biological relevance of Patrocles' content, we searched for signatures of selection acting on ‘Patrocles single nucleotide polymorphisms (pSNPs)’ in human and mice. As expected, we found a strong signature of purifying selection against not only SNPs that destroy conserved target sites but also against SNPs that create novel, illegitimate target sites, which is reminiscent of the Texel mutation in sheep.

Identification of microRNA activity by Targets' Reverse EXpression

MOTIVATION

Non-coding microRNAs (miRNAs) act as regulators of global protein output. While their major effect is on protein levels of target genes, it has been proven that they also specifically impact on the messenger RNA level of targets. Prominent interest in miRNAs strongly motivates the need for increasing the options available to detect their cellular activity.

RESULTS

We used the effect of miRNAs over their targets for the detection of miRNA activity using mRNAs expression profiles. Here we describe the method, called T-REX (from Targets' Reverse EXpression), compare it to other similar applications, show its effectiveness and apply it to build activity maps. We used six different target predictions from each of four algorithms: TargetScan, PicTar, DIANA-microT and DIANA Union. First, we proved the sensitivity and specificity of our technique in miRNA over-expression and knock-out animal models. Then, we used whole transcriptome data from acute myeloid leukemia to show that we could identify critical miRNAs in a real life, complex, clinically relevant dataset. Finally, we studied 66 different cellular conditions to confirm and extend the current knowledge on the role of miRNAs in cellular physiology and in cancer.

AVAILABILITY

Software is available at http://aqua.unife.it and is free for all users with no login requirement.

microRNAs and genetic diseases

microRNAs (miRNAs) are a class of small RNAs (19-25 nucleotides in length) processed from double-stranded hairpin precursors. They negatively regulate gene expression in animals, by binding, with imperfect base pairing, to target sites in messenger RNAs (usually in 3' untranslated regions) thereby either reducing translational efficiency or determining transcript degradation. Considering that each miRNA can regulate, on average, the expression of approximately several hundred target genes, the miRNA apparatus can participate in the control of the gene expression of a large quota of mammalian transcriptomes and proteomes. As a consequence, miRNAs are expected to regulate various developmental and physiological processes, such as the development and function of many tissue and organs. Due to the strong impact of miRNAs on the biological processes, it is expected that mutations affecting miRNA function have a pathogenic role in human genetic diseases, similar to protein-coding genes. In this review, we provide an overview of the evidence available to date which support the pathogenic role of miRNAs in human genetic diseases. We will first describe the main types of mutation mechanisms affecting miRNA function that can result in human genetic disorders, namely: (1) mutations affecting miRNA sequences; (2) mutations in the recognition sites for miRNAs harboured in target mRNAs; and (3) mutations in genes that participate in the general processes of miRNA processing and function. Finally, we will also describe the results of recent studies, mostly based on animal models, indicating the phenotypic consequences of miRNA alterations on the function of several tissues and organs. These studies suggest that the spectrum of genetic diseases possibly caused by mutations in miRNAs is wide and is only starting to be unravelled.

UTRdb and UTRsite (RELEASE 2010): a collection of sequences and regulatory motifs of the untranslated regions of eukaryotic mRNAs

The 5' and 3' untranslated regions of eukaryotic mRNAs (UTRs) play crucial roles in the post-transcriptional regulation of gene expression through the modulation of nucleo-cytoplasmic mRNA transport, translation efficiency, subcellular localization and message stability. UTRdb is a curated database of 5' and 3' untranslated sequences of eukaryotic mRNAs, derived from several sources of primary data. Experimentally validated functional motifs are annotated and also collated as the UTRsite database where more specific information on the functional motifs and cross-links to interacting regulatory protein are provided. In the current update, the UTR entries have been organized in a gene-centric structure to better visualize and retrieve 5' and 3'UTR variants generated by alternative initiation and termination of transcription and alternative splicing. Experimentally validated miRNA targets and conserved sequence elements are also annotated. The integration of UTRdb with genomic data has allowed the implementation of an efficient annotation system and a powerful retrieval resource for the selection and extraction of specific UTR subsets. All internet resources implemented for retrieval and functional analysis of 5' and 3' untranslated regions of eukaryotic mRNAs are accessible at http://utrdb.ba.itb.cnr.it/.

Mechanism-anchored profiling derived from epigenetic networks predicts outcome in acute lymphoblastic leukemia

Background

Current outcome predictors based on "molecular profiling" rely on gene lists selected without consideration for their molecular mechanisms. This study was designed to demonstrate that we could learn about genes related to a specific mechanism and further use this knowledge to predict outcome in patients – a paradigm shift towards accurate "mechanism-anchored profiling". We propose a novel algorithm, PGnet, which predicts a tripartite mechanism-anchored network associated to epigenetic regulation consisting of phenotypes, genes and mechanisms. Genes termed as GEMs in this network meet all of the following criteria: (i) they are co-expressed with genes known to be involved in the biological mechanism of interest, (ii) they are also differentially expressed between distinct phenotypes relevant to the study, and (iii) as a biomodule, genes correlate with both the mechanism and the phenotype.

Results

This proof-of-concept study, which focuses on epigenetic mechanisms, was conducted in a well-studied set of 132 acute lymphoblastic leukemia (ALL) microarrays annotated with nine distinct phenotypes and three measures of response to therapy. We used established parametric and non parametric statistics to derive the PGnet tripartite network that consisted of 10 phenotypes and 33 significant clusters of GEMs comprising 535 distinct genes. The significance of PGnet was estimated from empirical p-values, and a robust subnetwork derived from ALL outcome data was produced by repeated random sampling. The evaluation of derived robust network to predict outcome (relapse of ALL) was significant (p = 3%), using one hundred three-fold cross-validations and the shrunken centroids classifier.

Conclusion

To our knowledge, this is the first method predicting co-expression networks of genes associated with epigenetic mechanisms and to demonstrate its inherent capability to predict therapeutic outcome. This PGnet approach can be applied to any regulatory mechanisms including transcriptional or microRNA regulation in order to derive predictive molecular profiles that are mechanistically anchored. The implementation of PGnet in R is freely available at .

Expression of microRNAs is not related to increased expression of ZDHHC9 in hind leg muscles of splay leg piglets

ZDHHC9 (zinc finger, DHHC-type containing 9) is a protein acyl transferase involved in palmitoylation of basic signaling molecules. We found ZDHHC9 expression increased in hind leg muscles of newborn splay leg piglets. In order to elucidate the background of this increased expression we determined the structure of the porcine gene, including sequence variation, and analyzed the structure and expression of microRNAs potentially targeting the gene. We confirmed the expression results by RT Real-time PCR. The porcine ZDHHC9 gene has a similar structure to the human gene with two transcripts resulting in an identical protein. None of the 17 single nucleotide polymorphisms (SNPs) identified in the porcine gene affects the protein or putative microRNA binding sites, respectively. Two microRNAs (93 [minor] and 106b) were assayed in the muscles. Their expression variation proved to be independent from ZDHHC9 expression thus eliminating them as causally related to congenital splay leg.

Reciprocal regulation of microRNA and mRNA profiles in neuronal development and synapse formation

Background

Synapse formation and the development of neural networks are known to be controlled by a coordinated program of mRNA synthesis. microRNAs are now recognized to be important regulators of mRNA translation and stability in a wide variety of organisms. While specific microRNAs are known to be involved in neural development, the extent to which global microRNA and mRNA profiles are coordinately regulated in neural development is unknown.

Results

We examined mouse primary neuronal cultures, analyzing microRNA and mRNA expression. Three main developmental patterns of microRNA expression were observed: steady-state levels, up-regulated and down-regulated. Co-expressed microRNAs were found to have related target recognition sites and to be encoded in distinct genomic locations. A number of 43 differentially expressed miRNAs were located in five genomic clusters. Their predicted mRNA targets show reciprocal levels of expression. We identified a set of reciprocally expressed microRNAs that target mRNAs encoding postsynaptic density proteins and high-level steady-state microRNAs that target non-neuronal low-level expressed mRNAs.

Conclusion

We characterized hundreds of miRNAs in neuronal culture development and identified three major modes of miRNA expression. We predict these miRNAs to regulate reciprocally expressed protein coding genes, including many genes involved in synaptogenesis. The identification of miRNAs that target mRNAs during synaptogenesis indicates a new level of regulation of the synapse.

Computational identification of hepatitis C virus associated microRNA-mRNA regulatory modules in human livers

BACKGROUND

Hepatitis C virus (HCV) is a major cause of chronic liver disease by infecting over 170 million people worldwide. Recent studies have shown that microRNAs (miRNAs), a class of small non-coding regulatory RNAs, are involved in the regulation of HCV infection, but their functions have not been systematically studied. We propose an integrative strategy for identifying the miRNA-mRNA regulatory modules that are associated with HCV infection. This strategy combines paired expression profiles of miRNAs and mRNAs and computational target predictions. A miRNA-mRNA regulatory module consists of a set of miRNAs and their targets, in which the miRNAs are predicted to coordinately regulate the level of the target mRNA.

RESULTS

We simultaneously profiled the expression of cellular miRNAs and mRNAs across 30 HCV positive or negative human liver biopsy samples using microarray technology. We constructed a miRNA-mRNA regulatory network, and using a graph theoretical approach, identified 38 miRNA-mRNA regulatory modules in the network that were associated with HCV infection. We evaluated the direct miRNA regulation of the mRNA levels of targets in regulatory modules using previously published miRNA transfection data. We analyzed the functional roles of individual modules at the systems level by integrating a large-scale protein interaction network. We found that various biological processes, including some HCV infection related canonical pathways, were regulated at the miRNA level during HCV infection.

CONCLUSION

Our regulatory modules provide a framework for future experimental analyses. This report demonstrates the utility of our approach to obtain new insights into post-transcriptional gene regulation at the miRNA level in complex human diseases.

MirZ: an integrated microRNA expression atlas and target prediction resource

MicroRNAs (miRNAs) are short RNAs that act as guides for the degradation and translational repression of protein-coding mRNAs. A large body of work showed that miRNAs are involved in the regulation of a broad range of biological functions, from development to cardiac and immune system function, to metabolism, to cancer. For most of the over 500 miRNAs that are encoded in the human genome the functions still remain to be uncovered. Identifying miRNAs whose expression changes between cell types or between normal and pathological conditions is an important step towards characterizing their function as is the prediction of mRNAs that could be targeted by these miRNAs. To provide the community the possibility of exploring interactively miRNA expression patterns and the candidate targets of miRNAs in an integrated environment, we developed the MirZ web server, which is accessible at www.mirz.unibas.ch. The server provides experimental and computational biologists with statistical analysis and data mining tools operating on up-to-date databases of sequencing-based miRNA expression profiles and of predicted miRNA target sites in species ranging from Caenorhabditis elegans to Homo sapiens.