Detecting microRNA activity from gene expression data

Structural Analysis of microRNAs in Myeloid Cancer Reveals Consensus Motifs

MicroRNAs (miRNAs) are short non-coding RNAs that function in post-transcriptional gene silencing and mRNA regulation. Although the number of nucleotides of miRNAs ranges from 17 to 27, they are mostly made up of 22 nucleotides. The expression of miRNAs changes significantly in cancer, causing protein alterations in cancer cells by preventing some genes from being translated into proteins. In this research, a structural analysis of 587 miRNAs that are differentially expressed in myeloid cancer was carried out. Length distribution studies revealed a mean and median of 22 nucleotides, with an average of 21.69 and a variance of 1.65. We performed nucleotide analysis for each position where Uracil was the most observed nucleotide and Adenine the least observed one with 27.8% and 22.6%, respectively. There was a higher frequency of Adenine at the beginning of the sequences when compared to Uracil, which was more frequent at the end of miRNA sequences. The purine content of each implicated miRNA was also assessed. A novel motif analysis script was written to detect the most frequent 3–7 nucleotide (3–7n) long motifs in the miRNA dataset. We detected CUG (42%) as the most frequent 3n motif, CUGC (15%) as a 4n motif, AGUGC (6%) as a 5n motif, AAGUGC (4%) as a 6n motif, and UUUAGAG (4%) as a 7n motif. Thus, in the second part of our study, we further characterized the motifs by analyzing whether these motifs align at certain consensus sequences in our miRNA dataset, whether certain motifs target the same genes, and whether these motifs are conserved within other species. This thorough structural study of miRNA sequences provides a novel strategy to study the implications of miRNAs in health and disease. A better understanding of miRNA structure is crucial to developing therapeutic settings.

Progesterone Receptor Signaling Selectively Modulates Cytokine-Induced Global Gene Expression in Human Cervical Stromal Cells

Preterm birth (PTB) is the leading cause of morbidity and mortality in infants <1 year of age. Intrauterine inflammation is a hallmark of preterm and term parturition; however, this alone cannot fully explain the pathobiology of PTB. For example, the cervix undergoes a prolonged series of biochemical and biomechanical events, including extracellular matrix (ECM) remodeling and mechanochemical changes, culminating in ripening. Vaginal progesterone (P₄) prophylaxis demonstrates great promise in preventing PTB in women with a short cervix (<25 mm). We used a primary culture model of human cervical stromal fibroblasts to investigate gene expression signatures in cells treated with interleukin-1β (IL-1β) in the presence or absence of P₄ following 17β-estradiol (17β-E₂) priming for 7–10 days. Microarrays were used to measure global gene expression in cells treated with cytokine or P₄ alone or in combination, followed by validation of select transcripts by semiquantitative polymerase chain reactions (qRT-PCR). Primary/precursor (MIR) and mature microRNAs (miR) were quantified by microarray and NanoString^® platforms, respectively, and validated by qRT-PCR. Differential gene expression was computed after data normalization followed by pathway analysis using Kyoto Encyclopedia Genes and Genomes (KEGG), Panther, Gene Ontology (GO), and Ingenuity Pathway Analysis (IPA) upstream regulator algorithm tools. Treatment of fibroblasts with IL-1β alone resulted in the differential expression of 1432 transcripts (protein coding and non-coding), while P₄ alone led to the expression of only 43 transcripts compared to untreated controls. Cytokines, chemokines, and their cognate receptors and prostaglandin endoperoxide synthase-2 (PTGS-2) were among the most highly upregulated transcripts following either IL-1β or IL-1β + P₄. Other prominent differentially expressed transcripts were those encoding ECM proteins, ECM-degrading enzymes, and enzymes involved in glycosaminoglycan (GAG) biosynthesis. We also detected differential expression of bradykinin receptor-1 and -2 transcripts, suggesting (prominent in tissue injury/remodeling) a role for the kallikrein–kinin system in cervical responses to cytokine and/or P₄ challenge. Collectively, this global gene expression study provides a rich database to interrogate stromal fibroblasts in the setting of a proinflammatory and endocrine milieu that is relevant to cervical remodeling/ripening during preparation for parturition.

The molecular effect of a polymorphic microRNA binding site of Wolfram syndrome 1 gene in dogs

Background

Although the molecular function of wolframin remains unclear, the lack of this protein is known to cause stress in the endoplasmic reticulum. Some variants in the Wolfram Syndrome 1 gene (WFS1) were associated with various neuropsychiatric disorders in humans, such as aggressiveness, impulsivity and anxiety.

Results

Here we present an in silico study predicting a single nucleotide polymorphism (rs852850348) in the canine WFS1 gene which was verified by direct sequencing and was genotyped by a PCR-based technique. We found that the rs852850348 polymorphism is located in a putative microRNA (cfa-miR-8834a and cfa-miR-1838) binding site. Therefore, the molecular effect of allelic variants was studied in a luciferase reporter system that allowed assessing gene expression. We demonstrated that the variant reduced the activity of the reporter protein expression in an allele-specific manner. Additionally, we performed a behavioral experiment and investigated the association with this locus to different performance in this test. Association was found between food possessivity and the studied WFS1 gene polymorphism in the Border collie breed.

Conclusions

Based on our findings, the rs852850348 locus might contribute to the genetic risk of possessivity behavior of dogs in at least one breed and might influence the regulation of wolframin expression.

MiRNA-BD: an evidence-based bioinformatics model and software tool for microRNA biomarker discovery

MicroRNAs (miRNAs) are small non-coding RNAs with the potential as biomarkers for disease diagnosis, prognosis and therapy. In the era of big data and biomedical informatics, computer-aided biomarker discovery has become the current frontier. However, most of the computational models are highly dependent on specific prior knowledge and training-testing procedures, very few are mechanism-guided or evidence-based. To the best of our knowledge, untill now no general rules have been uncovered and applied to miRNA biomarker screening. In this study, we manually collected literature-reported cancer miRNA biomarkers and analyzed their regulatory patterns, including the regulatory modes, biological functions and evolutionary characteristics of their targets in the human miRNA-mRNA network. Two evidences were statistically detected and used to distinguish biomarker miRNAs from others. Based on these observations, we developed a novel bioinformatics model and software tool for miRNA biomarker discovery ( http://sysbio.suda.edu.cn/MiRNA-BD/ ). In contrast to routine methods that focus on miRNA synergic functions, our method searches for vulnerable sites in the miRNA-mRNA network and considers the independent regulatory power of miRNAs, i.e., single-line regulations between miRNAs and mRNAs. The performance comparison demonstrates the generality and precision of our model, which identifies miRNA biomarkers for cancers as well as other complex diseases without training or specific prior knowledge.

Master Transcriptional Regulators in Cancer: Discovery via Reverse Engineering Approaches and Subsequent Validation

Reverse engineering of transcriptional networks using gene expression data enables identification of genes that underpin the development and progression of different cancers. Methods to this end have been available for over a decade and, with a critical mass of transcriptomic data in the oncology arena having been reached, they are ever more applicable. Extensive and complex networks can be distilled into a small set of key master transcriptional regulators (MTR), genes that are very highly connected and have been shown to be involved in processes of known importance in disease. Interpreting and validating the results of standardized bioinformatic methods is of crucial importance in determining the inherent value of MTRs. In this review, we briefly describe how MTRs are identified and focus on providing an overview of how MTRs can and have been validated for use in clinical decision making in malignant diseases, along with serving as tractable therapeutic targets. Cancer Res; 77(9); 2186-90. ©2017 AACR.

Network Consistency Projection for Human miRNA-Disease Associations Inference

Prediction and confirmation of the presence of disease-related miRNAs is beneficial to understand disease mechanisms at the miRNA level. However, the use of experimental verification to identify disease-related miRNAs is expensive and time-consuming. Effective computational approaches used to predict miRNA-disease associations are highly specific. In this study, we develop the Network Consistency Projection for miRNA-Disease Associations (NCPMDA) method to reveal the potential associations between miRNAs and diseases. NCPMDA is a non-parametric universal network-based method that can simultaneously predict miRNA-disease associations in all diseases but does not require negative samples. NCPMDA can also confirm the presence of miRNAs in isolated diseases (diseases without any known miRNA association). Leave-one-out cross validation and case studies have shown that the predictive performance of NCPMDA is superior over that of previous method.

Transcriptome-wide based identification of miRs in congenital anomalies of the kidney and urinary tract (CAKUT) in children: the significant upregulation of tissue miR-144 expression

BACKGROUND

The genetic cause of most congenital anomalies of the kidney and urinary tract (CAKUT) cases remains unknown, therefore the novel approaches in searching for the common disease denominators are required. miRs regulate gene expression in humans and therefore have potentially therapeutic and biomarker properties. No studies thus far have attempted to explore the miRs in human CAKUT. We applied a new strategy to identify most specific miRs associated with CAKUT, in pediatric patients.

METHODS

Data from the whole genome expression, gathered from ureter tissue samples of 19 patients and 7 controls, were used for the bioinformatic prediction of miRs activity in CAKUT. We integrated microarray gene expression data and miR target predictions from multiple prediction algorithms using Co-inertia analysis (CIA) in conjunction with correspondence analysis and between group analysis, to produce a ranked list of miRs associated with CAKUT. The CIA included five different sequence based miR target prediction algorithms and the Co-expression Meta-analysis of miR Targets. For the experimental validation of expression of miRs identified by the CIA we used tissue from 36 CAKUT patients and 9 controls. The results of gene ontology (GO) analysis on co-expressed targets of miRs associated with CAKUT were used for the selection of putative biological processes relevant to CAKUT.

RESULTS

We identified 7 miRs with a potential role in CAKUT. The top ranked miRs from miRCos communities 4, 1 and 7 were chosen for experimental validation of expression in CAKUT tissue. The 5.7 fold increase of hsa-miR-144 expression in human tissue from CAKUT patients compared to controls (p = 0.005) was observed. From the GO we selected 7 biological processes that could contribute to CAKUT, which genes are potentially influenced by hsa-miR-144. The hsa-miR-200a, hsa-miR-183 and hsa-miR-375 weren't differentially expressed in CAKUT.

CONCLUSIONS

This study shows that integrative approach applied here was useful in identification of the miRs associated with CAKUT. The hsa-miR-144, first time identified in CAKUT, could be connected with biological processes crucial for normal development of kidney and urinary tract. Further functional analysis must follow to reveal the impact of hsa-miR-144 on CAKUT occurrence.

Inferred miRNA activity identifies miRNA-mediated regulatory networks underlying multiple cancers

MOTIVATION

MicroRNAs (miRNAs) play a key role in regulating tumor progression and metastasis. Identifying key miRNAs, defined by their functional activities, can provide a deeper understanding of biology of miRNAs in cancer. However, miRNA expression level cannot accurately reflect miRNA activity.

RESULTS

We developed a computational approach, ActMiR, for identifying active miRNAs and miRNA-mediated regulatory mechanisms. Applying ActMiR to four cancer datasets in The Cancer Genome Atlas (TCGA), we showed that (i) miRNA activity was tumor subtype specific; (ii) genes correlated with inferred miRNA activities were more likely to enrich for miRNA binding motifs; (iii) expression levels of these genes and inferred miRNA activities were more likely to be negatively correlated. For the four cancer types in TCGA we identified 77-229 key miRNAs for each cancer subtype and annotated their biological functions. The miRNA-target pairs, predicted by our ActMiR algorithm but not by correlation of miRNA expression levels, were experimentally validated. The functional activities of key miRNAs were further demonstrated to be associated with clinical outcomes for other cancer types using independent datasets. For ER(-)/HER2(-) breast cancers, we identified activities of key miRNAs let-7d and miR-18a as potential prognostic markers and validated them in two independent ER(-)/HER2(-) breast cancer datasets. Our work provides a novel scheme to facilitate our understanding of miRNA. In summary, inferred activity of key miRNA provided a functional link to its mediated regulatory network, and can be used to robustly predict patient's survival.

AVAILABILITY AND IMPLEMENTATION

the software is freely available at http://research.mssm.edu/integrative-network-biology/Software.html.

CONTACT

jun.zhu@mssm.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Reconstruction of temporal activity of microRNAs from gene expression data in breast cancer cell line

Background

MicroRNAs (miRNAs) are small non-coding RNAs that regulate genes at the post-transcriptional level in spatiotemporal manner. Several miRNAs are identified as prognostic and diagnostic markers in many human cancers. Estimation of the temporal activities of the miRNAs is an important step in the way to understand the complex interactions of these important regulatory elements with transcription factors (TFs) and target genes (TGs). However, current research on miRNA activities excludes network dynamics from the studies, disregarding the important element of time in the regulatory network analysis.

Results

In the current study, we combined experimentally verified miRNA-TG interactions with breast cancer microarray TG expression data to identify key miRNAs and compute their temporal activity using network component analysis (NCA). The computed activities showed that miRNAs were regulated in a time dependent manner. Our results allowed constructing a synergistic network of miRNAs using the computed miRNA activities and their shared regulation of TGs. We further extended this network by incorporating miRNA-TG, miRNA-TF, TF-miRNA and TF-TG regulations in the context of breast cancer. Our integrated network identified several miRNAs known to be involved in breast cancer regulation and revealed several novel miRNAs. Our further analysis detected substantial involvement of the miRNAs miR-324, miR-93, miR-615 and miR-1 in breast cancer, which was not known previously. Next, combining our integrated networks with functional annotation of differentially expressed genes resulted in new sub-networks. These sub-networks allowed us to identify the key miRNAs and their interactions with TFs and TGs of several biological processes involved in breast cancer. The identified markers are validated for their potential as prognostic markers for breast cancer through survival analysis.

Conclusions

Our dynamical analysis of the miRNA interactions greatly helps to discover new network based markers, and is highly applicable (but not limited) to cancer research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2260-3) contains supplementary material, which is available to authorized users.

microRNAs: a new class of breast cancer biomarkers

MicroRNAs (miRNAs) are regulatory molecules known to be aberrantly expressed in cancer and contribute to numerous aspects of tumor biology including the initiation, growth and spread of the tumor. With such diverse roles, it is becoming apparent that some may also provide valuable information which may be of use in a clinical setting, demonstrating the potential to act as both screening tools for the stratification of high-risk patients, while informing the treatment decision-making process. There is mounting evidence to suggest that some miRNAs may even provide assistance in the diagnosis of patients with breast cancer. In addition, miRNAs may themselves be considered therapeutic targets, with inhibition or reintroduction of a particular miRNA capable of inducing a response in vivo. This review focuses on miRNAs that have prognostic, diagnostic or predictive potential in breast cancer as well as the possible challenges in the translation of such observations to the clinic.

Inferring condition-specific miRNA activity from matched miRNA and mRNA expression data

MOTIVATION

MicroRNAs (miRNAs) play crucial roles in complex cellular networks by binding to the messenger RNAs (mRNAs) of protein coding genes. It has been found that miRNA regulation is often condition-specific. A number of computational approaches have been developed to identify miRNA activity specific to a condition of interest using gene expression data. However, most of the methods only use the data in a single condition, and thus, the activity discovered may not be unique to the condition of interest. Additionally, these methods are based on statistical associations between the gene expression levels of miRNAs and mRNAs, so they may not be able to reveal real gene regulatory relationships, which are causal relationships.

RESULTS

We propose a novel method to infer condition-specific miRNA activity by considering (i) the difference between the regulatory behavior that an miRNA has in the condition of interest and its behavior in the other conditions; (ii) the causal semantics of miRNA-mRNA relationships. The method is applied to the epithelial-mesenchymal transition (EMT) and multi-class cancer (MCC) datasets. The validation by the results of transfection experiments shows that our approach is effective in discovering significant miRNA-mRNA interactions. Functional and pathway analysis and literature validation indicate that the identified active miRNAs are closely associated with the specific biological processes, diseases and pathways. More detailed analysis of the activity of the active miRNAs implies that some active miRNAs show different regulation types in different conditions, but some have the same regulation types and their activity only differs in different conditions in the strengths of regulation.

AVAILABILITY AND IMPLEMENTATION

The R and Matlab scripts are in the Supplementary materials.

The co-inertia approach in identification of specific microRNA in early and advanced atherosclerosis plaque

MicroRNAs (miRs) are short, non-coding RNAs that regulate gene expression by absolute or partial binding to mRNA, which results in transcript degradation and translation blocking. Atherosclerosis, as a complex and progressive disease, represents one of the main causes of cardiovascular clinical complications and even death. We applied co-inertia analysis (CIA) as a novel computation method, to determine which miRs are potentially associated with differences in gene expression levels originating from microarray data of early and advanced atherosclerotic plaque. As the CIA has not been applied in the field of atherosclerosis yet, we hypothesized that using CIA we can get novel information about the miRs that have significant role in early phase of disease or in severe phase of disease. The characteristic split in the data along the axes of performed CIA showed the difference in the gene expression pattern between early atherosclerosis and advanced atherosclerotic plaque. The advanced atherosclerotic plaques showed more homogenous gene expression pattern than early atherosclerosis samples. In early carotid lesions five out of five algorithms predicted miR-24, four out of five predicted miR-155, miR-145, and miR-100 as early active miRs. These miRs could be "protective" in plaque evolution context because they were not active in advanced plaques according to our results. They were reported previously as atheroprotective, which in a way represents confirmation of CIA application in atherosclerosis. We detected 13 new miRs which could be active in early plaque phenotype according to CIA prediction. In the advanced plaques we predicted miR-221, miR-222, miR-127 and miR-146 which were previously revealed to have atherogenic properties. In addition to miRs that have literature support, we also found new 8 miRs that, with described function so far, could present a novelty in research of atherosclerotic plaque evolution. All of these examples show that CIA results have a great potential to be of interest in future research in atherosclerotic plaque progression. We validated the applicability of CIA in the field of atherosclerosis, but we also found new interesting miR competitors that have strong potential to serve as markers and plaque development factors. These results should be experimentally confirmed in further research with ultimate goal to discover new mediators and blood markers, which could improve the prevention and therapy of this complex disease.

Identification of candidate miRNA biomarkers from miRNA regulatory network with application to prostate cancer

Background

MicroRNAs (miRNAs) are a class of non-coding regulatory RNAs approximately 22 nucleotides in length that play a role in a wide range of biological processes. Abnormal miRNA function has been implicated in various human cancers including prostate cancer (PCa). Altered miRNA expression may serve as a biomarker for cancer diagnosis and treatment. However, limited data are available on the role of cancer-specific miRNAs. Integrative computational bioinformatics approaches are effective for the detection of potential outlier miRNAs in cancer.

Methods

The human miRNA-mRNA target network was reconstructed by integrating multiple miRNA-mRNA interaction datasets. Paired miRNA and mRNA expression profiling data in PCa versus benign prostate tissue samples were used as another source of information. These datasets were analyzed with an integrated bioinformatics framework to identify potential PCa miRNA signatures. In vitro q-PCR experiments and further systematic analysis were used to validate these prediction results.

Results

Using this bioinformatics framework, we identified 39 miRNAs as potential PCa miRNA signatures. Among these miRNAs, 20 had previously been identified as PCa aberrant miRNAs by low-throughput methods, and 16 were shown to be deregulated in other cancers. In vitro q-PCR experiments verified the accuracy of these predictions. miR-648 was identified as a novel candidate PCa miRNA biomarker. Further functional and pathway enrichment analysis confirmed the association of the identified miRNAs with PCa progression.

Conclusions

Our analysis revealed the scale-free features of the human miRNA-mRNA interaction network and showed the distinctive topological features of existing cancer miRNA biomarkers from previously published studies. A novel cancer miRNA biomarker prediction framework was designed based on these observations and applied to prostate cancer study. This method could be applied for miRNA biomarker prediction in other cancers.

MicroRNAs with a role in gene regulation and in human diseases

MicroRNAs (miRNAs) are short 20-22 nucleotide non-coding RNA sequences. Recently identified, these are novel regulators of gene expression at translational level as well as transcriptional level. Alteration in miRNAs level has been observed in a number of human diseases and studies have been conducted on the effect of altered expression level of miRNAs on the development and progression of different diseases. The miRNAs can be used as molecular biomarkers in a number of diseases. Also, miRNAs are promising in providing a new platform for molecular therapeutics of previously incurable diseases. This review will focus on the introduction, recent advances in the field of miRNA and its importance in some human disorders.

Ensemble classifier based on context specific miRNA regulation modules: a new method for cancer outcome prediction

Background

Many calssifiers which are constructed with chosen gene markers have been proposed to forecast the prognosis of patients who suffer from breast cancer. However, few of them has been applied in clinical practice because of the bad generalization, which results from the situation that markers selected by one method are very different from those obtained by anohter mothod, and thus such markers always lack discriminative capability in the other data sets.

Methods

In this work, a new ensemble classifier, on the basis of context specific miRNA regulation modules, has been proposed to forecast the metastasis risk of cancer sufferers. First, we defined all of the miRNAs which regulate the same context as a module that contains miRNAs and their regulating context, and applied the CoMi (Context-specific miRNA activity) score in order to illustrate a miRNA's effect which happened in a particular background; then the miRNA regulation modules with distinguising abilities were detected and each of them was responsible for building a weak classifier separately; at last, by using majority voting strategy, we integrated all weak classifiers to establish an ensembled one that was applied to forecast the prognosis of patients who suffer from cancer.

Results

After comparing, the results on the cohorts containing over 1,000 samples showed that the proposed ensemble classifier is superior to other three classifiers based on miRNA expression profiles, mRNA expression profiles and CoMi activity patterns respectively. Significantly, our method outperforms the representative works. Moreover, the detected modules from different data sets show great stability (with p-value of 6.40e-08). For investigating the biological significance of those selected modules, case studies have been done by us and the results suggested that our method do help to reveal latent mechanism in metastasis of breast cancer.

Conclusions

One context specific miRNA regulation module can uncover one critical biological process and its involved miRNAs that are related to the cancer outcome, and several modules together can help to study the biological mechanism in cancer metastasis, thus the classifer based on ensembling multiple classifers which were built with different context specific miRNA regulation modules has showed promising performances in terms with both prediction accuracy and generalization.

Exploring the transcription factor activity in high-throughput gene expression data using RLQ analysis

Background

Interpretation of gene expression microarray data in the light of external information on both columns and rows (experimental variables and gene annotations) facilitates the extraction of pertinent information hidden in these complex data. Biologists classically interpret genes of interest after retrieving functional information from a subset of genes of interest. Transcription factors play an important role in orchestrating the regulation of gene expression. Their activity can be deduced by examining the presence of putative transcription factors binding sites in the gene promoter regions.

Results

In this paper we present the multivariate statistical method RLQ which aims to analyze microarray data where additional information is available on both genes and samples. As an illustrative example, we applied RLQ methodology to analyze transcription factor activity associated with the time-course effect of steroids on the growth of primary human lung fibroblasts. RLQ could successfully predict transcription factor activity, and could integrate various other sources of external information in the main frame of the analysis. The approach was validated by means of alternative statistical methods and biological validation.

Conclusions

RLQ provides an efficient way of extracting and visualizing structures present in a gene expression dataset by directly modeling the link between experimental variables and gene annotations.

miRNA target enrichment analysis reveals directly active miRNAs in health and disease

microRNAs (miRNAs) are short non-coding regulatory RNA molecules. The activity of a miRNA in a biological process can often be reflected in the expression program that characterizes the outcome of the activity. We introduce a computational approach that infers such activity from high-throughput data using a novel statistical methodology, called minimum-mHG (mmHG), that examines mutual enrichment in two ranked lists. Based on this methodology, we provide a user-friendly web application that supports the statistical assessment of miRNA target enrichment analysis (miTEA) in the top of a ranked list of genes or proteins. Using miTEA, we analyze several target prediction tools by examining performance on public miRNA constitutive expression data. We also apply miTEA to analyze several integrative biology data sets, including a novel matched miRNA/mRNA data set covering nine human tissue types. Our novel findings include proposed direct activity of miR-519 in placenta, a direct activity of the oncogenic miR-15 in different healthy tissue types and a direct activity of the poorly characterized miR-768 in both healthy tissue types and cancer cell lines. The miTEA web application is available at http://cbl-gorilla.cs.technion.ac.il/miTEA/.

Integrated miRNA, mRNA and protein expression analysis reveals the role of post-transcriptional regulation in controlling CHO cell growth rate

Background

To study the role of microRNA (miRNA) in the regulation of Chinese hamster ovary (CHO) cell growth, qPCR, microarray and quantitative LC-MS/MS analysis were utilised for simultaneous expression profiling of miRNA, mRNA and protein. The sample set under investigation consisted of clones with variable cellular growth rates derived from the same population. In addition to providing a systems level perspective on cell growth, the integration of multiple profiling datasets can facilitate the identification of non-seed miRNA targets, complement computational prediction tools and reduce false positive and false negative rates.

Results

51 miRNAs were associated with increased growth rate (35 miRNAs upregulated and 16 miRNAs downregulated). Gene ontology (GO) analysis of genes (n=432) and proteins (n=285) found to be differentially expressed (DE) identified biological processes driving proliferation including mRNA processing and translation. To investigate the influence of miRNA on these processes we combined the proteomic and transcriptomic data into two groups. The first set contained candidates where evidence of translational repression was observed (n=158). The second group was a mixture of proteins and mRNAs where evidence of translational repression was less clear (n=515). The TargetScan algorithm was utilised to predict potential targets within these two groups for anti-correlated DE miRNAs.

Conclusions

The evidence presented in this study indicates that biological processes such as mRNA processing and protein synthesis are correlated with growth rate in CHO cells. Through the integration of expression data from multiple levels of the biological system a number of proteins central to these processes including several hnRNPs and components of the ribosome were found to be post-transcriptionally regulated. We utilised the expression data in conjunction with in-silico tools to identify potential miRNA-mediated regulation of mRNA/proteins involved in CHO cell growth rate. These data have allowed us to prioritise candidates for cell engineering and/or biomarkers relevant to industrial cell culture. We also expect the knowledge gained from this study to be applicable to other fields investigating the role of miRNAs in mammalian cell growth.

microRNAs targeting DEAD-box helicases are involved in salinity stress response in rice (Oryza sativa L.)

BACKGROUND

Rice (Oryza sativa L.), one of the most important food crop in the world, is considered to be a salt-sensitive crop. Excess levels of salt adversely affect all the major metabolic activities, including cell wall damage, cytoplasmic lysis and genomic stability. In order to cope with salt stress, plants have evolved high degrees of developmental plasticity, including adaptation via cascades of molecular networks and changes in gene expression profiles. Posttranscriptional regulation, through the activity of microRNAs, also plays an important role in the plant response to salinity conditions. MicroRNAs are small endogenous RNAs that modulate gene expression and are involved in the most essential physiological processes, including plant development and adaptation to environmental changes.

RESULTS

In the present study, we investigated the expression profiles of osa-MIR414, osa-MIR408 and osa-MIR164e along with their targeted genes, under salinity stress conditions in wild type and transgenic rice plants ectopically expressing the PDH45 (Pea DNA Helicase) gene. The present miRNAs were predicted to target the OsABP (ATP-Binding Protein), OsDSHCT (DOB1/SK12/helY-like DEAD-box Helicase) and OsDBH (DEAD-Box Helicase) genes, included in the DEAD-box helicase family. An in silico characterization of the proteins was performed and the miRNAs predicted targets were validated by RLM-5'RACE. The qRT-PCR analysis showed that the OsABP, OsDBH and OsDSHCT genes were up-regulated in response to 100 and 200 mM NaCl treatments. The present study also highlighted an increased accumulation of the gene transcripts in wild type plants, with the exception of the OsABP mRNA which showed the highest level (15.1-fold change compared to control) in the transgenic plants treated with 200 mM NaCl. Salinity treatments also affected the expression of osa-MIR414, osa-MIR164e and osa-MIR408, found to be significantly down-regulated, although the changes in miRNA expression were limited.

CONCLUSIONS

Osa-MIR414, osa-MIR164e and osa-MIR408 were experimentally validated for the first time in plants as targeting the OsABP, OsDBH and OsDSHCT genes. Our data showed that that the genes were up-regulated and the miRNAs were down-regulated in relation to salt stress. The negative correlation between the miRNAs and their targets was proven.

Gene expression changes as markers of early lapatinib response in a panel of breast cancer cell lines

Background

Lapatinib, a tyrosine kinase inhibitor of HER2 and EGFR and is approved, in combination with capecitabine, for the treatment of trastuzumab-refractory metastatic breast cancer. In order to establish a possible gene expression response to lapatinib, a panel of breast cancer cell lines with varying sensitivity to lapatinib were analysed using a combination of microarray and qPCR profiling.

Methods

Co-inertia analysis (CIA), a data integration technique, was used to identify transcription factors associated with the lapatinib response on a previously published dataset of 96 microarrays. RNA was extracted from BT474, SKBR3, EFM192A, HCC1954, MDAMB453 and MDAMB231 breast cancer cell lines displaying a range of lapatinib sensitivities and HER2 expression treated with 1 μM of lapatinib for 12 hours and quantified using Taqman RT-PCR. A fold change ≥ ± 2 was considered significant.

Results

A list of 421 differentially-expressed genes and 8 transcription factors (TFs) whose potential regulatory impact was inferred in silico, were identified as associated with lapatinib response. From this group, a panel of 27 genes (including the 8 TFs) were selected for qPCR validation. 5 genes were determined to be significantly differentially expressed following the 12 hr treatment of 1 μM lapatinib across all six cell lines. Furthermore, the expression of 4 of these genes (RB1CC1, FOXO3A, NR3C1 and ERBB3) was directly correlated with the degree of sensitivity of the cell line to lapatinib and their expression was observed to “switch” from up-regulated to down-regulated when the cell lines were arranged in a lapatinib-sensitive to insensitive order. These included the novel lapatinib response-associated genes RB1CC1 and NR3C1. Additionally, Cyclin D1 (CCND1), a common regulator of the other four proteins, was also demonstrated to observe a proportional response to lapatinib exposure.

Conclusions

A panel of 5 genes were determined to be differentially expressed in response to lapatinib at the 12 hour time point examined. The expression of these 5 genes correlated directly with lapatinib sensitivity. We propose that the gene expression profile may represent both an early measure of the likelihood of sensitivity and the level of response to lapatinib and may therefore have application in early response detection.

Computational approaches for identifying cancer miRNA expressions

MicroRNAs (miRNAs) play a major role in cancer development and also act as a key factor in many other diseases. In this investigation, we propose three methods for handling miRNA expressions. The first two methods determine whether a miRNA is indicating normal or cancer condition, and the third one determines how many miRNAs are supporting the cancer sample/patient. While Method 1 acts as a two-class classifier and is based on normalized average expression value, Method 2 also does the same and is based on the normalized average intraclass distance. Method 3 checks whether a miRNA belongs to the cancer class or not, provides the percentage of supporting miRNAs for a cancer patient, and is based on weighted normalized average intraclass distance. The values of the weights are determined using exhaustive search by maximizing the accuracy in training samples. The proposed methods are tested on the differentially regulated miRNAs in three types of cancers (breast, colon, and melanoma cancer). The performances of Method 1 and Method 2 are evaluated by F score, Matthews Correlation Coefficient (MCC), and plotting "1--specificity versus sensitivity" in Receiver Operating Characteristic (ROC) space and are found to be superior to the kNN and SVM classifiers for breast, colon, and melanoma cancer data sets. It is also observed that both the sensitivity and the specificity of Method 1 and Method 2 are higher than 0.5. For the same data sets, Method 3 achieved an average accuracy of more than 98% in detecting the miRNAs, supporting the cancer condition.

A probabilistic approach to microRNA-target binding

Elucidation of microRNA activity is a crucial step in understanding gene regulation. One key problem in this effort is how to model the pairwise interactions of microRNAs with their targets. As this interaction is strongly mediated by their sequences, it is desired to set-up a probabilistic model to explain the binding preferences between a microRNA sequence and the sequence of a putative target. To this end, we introduce a new model of microRNA-target binding, which transforms an aligned duplex to a new sequence and defines the likelihood of this sequence using a Variable Length Markov Chain. It offers a complementary representation of microRNA-mRNA pairs for microRNA target prediction tools or other probabilistic frameworks of integrative gene regulation analysis. The performance of present model is evaluated by its ability to predict microRNA-target mRNA interaction given a mature microRNA sequence and a putative mRNA binding site. In regard to classification accuracy, it outperforms two recent methods based on thermodynamic stability and sequence complementarity. The experiments can also unveil the effects of base pairing types and non-seed region in duplex formation.

mESAdb: microRNA expression and sequence analysis database

microRNA expression and sequence analysis database (http://konulab.fen.bilkent.edu.tr/mirna/) (mESAdb) is a regularly updated database for the multivariate analysis of sequences and expression of microRNAs from multiple taxa. mESAdb is modular and has a user interface implemented in PHP and JavaScript and coupled with statistical analysis and visualization packages written for the R language. The database primarily comprises mature microRNA sequences and their target data, along with selected human, mouse and zebrafish expression data sets. mESAdb analysis modules allow (i) mining of microRNA expression data sets for subsets of microRNAs selected manually or by motif; (ii) pair-wise multivariate analysis of expression data sets within and between taxa; and (iii) association of microRNA subsets with annotation databases, HUGE Navigator, KEGG and GO. The use of existing and customized R packages facilitates future addition of data sets and analysis tools. Furthermore, the ability to upload and analyze user-specified data sets makes mESAdb an interactive and expandable analysis tool for microRNA sequence and expression data.

The activity and expression of microRNAs in prostate cancers

Recent studies have shown that microRNA (miRNA) inhibitory activity can be quantified by examining their target mRNA expression levels. The accumulated evidence of differential miRNA activities between cancer subtypes necessitates the systematical comparison of miRNA expressions and activities. In this study, we integrated 8 mRNA microarray datasets to infer and compare the miRNA activities between prostate cancers (PCs) and normal tissues (NTs). Gene expression analyses show that miRNA activity is stronger in PCs. This conclusion is consolidated by target protein expression. We simultaneously collected 6 independent miRNA expression datasets, where great inconsistency is present in the expression difference between PCs and NTs. The overall correlation between miRNA activity and expression is very weak. However, meta-analysis demonstrated that the expressions of 114 individual miRNAs agree with their activities. Additionally, we detected two other factors associated with higher miRNA activity in PCs. One is deregulation of some key miRNA-repression related genes, such as the over-expression of Dicer, TRBP and Ago2, and the under-expression of IRP1 in PCs. The other is that miRNA-mRNA binding site efficacy has significant positive correlation with miRNA activity, whereas no correlation with miRNA expression. Furthermore, miRNA activity is more reproducible than miRNA expression across different datasets, which allows miRNA activity to be a good feature for the classification of cancer subtypes. We expect our analysis can improve the methods for inferring miRNA activity and further, provide some clues to the role of miRNA in tumorigenesis.