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

Background

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

Methods

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

Results

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

Conclusions

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

     

Grouping miRNAs of similar functions via weighted information content of gene ontology

Background

Regulation mechanisms between miRNAs and genes are complicated. To accomplish a biological function, a miRNA may regulate multiple target genes, and similarly a target gene may be regulated by multiple miRNAs. Wet-lab knowledge of co-regulating miRNAs is limited. This work introduces a computational method to group miRNAs of similar functions to identify co-regulating miRNAsfrom a similarity matrix of miRNAs.

Results

We define a novel information content of gene ontology (GO) to measure similarity between two sets of GO graphs corresponding to the two sets of target genes of two miRNAs. This between-graph similarity is then transferred as a functional similarity between the two miRNAs. Our definition of the information content is based on the size of a GO term’s descendants, but adjusted by a weight derived from its depth level and the GO relationships at its path to the root node or to the most informative common ancestor (MICA). Further, a self-tuning technique and the eigenvalues of the normalized Laplacian matrix are applied to determine the optimal parameters for the spectral clustering of the similarity matrix of the miRNAs.

Conclusions

Experimental results demonstrate that our method has better clustering performance than the existing edge-based, node-based or hybrid methods. Our method has also demonstrated a novel usefulness for the function annotation of new miRNAs, as reported in the detailed case studies.

     

Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph

Background

Current knowledge and data on miRNA-lncRNA interactions is still limited and little effort has been made to predict target lncRNAs of miRNAs. Accumulating evidences suggest that the interaction patterns between lncRNAs and miRNAs are closely related to relative expression level, forming a titration mechanism. It could provide an effective approach for characteristic feature extraction. In addition, using the coding non-coding co-expression network and sequence data could also help to measure the similarities among miRNAs and lncRNAs. By mathematically analyzing these types of similarities, we come up with two findings that (i) lncRNAs/miRNAs tend to collaboratively interact with miRNAs/lncRNAs of similar expression profiles, and vice versa, and (ii) those miRNAs interacting with a cluster of common target genes tend to jointly target at the common lncRNAs.

Methods

In this work, we developed a novel group preference Bayesian collaborative filtering model called GBCF for picking up a top-k probability ranking list for an individual miRNA or lncRNA based on the known miRNA-lncRNA interaction network.

Results

To evaluate the effectiveness of GBCF, leave-one-out and k-fold cross validations as well as a series of comparison experiments were carried out. GBCF achieved the values of area under ROC curve of 0.9193, 0.8354+/??0.0079, 0.8615+/??0.0078, and 0.8928+/??0.0082 based on leave-one-out, 2-fold, 5-fold, and 10-fold cross validations respectively, demonstrating its reliability and robustness.

Conclusions

GBCF could be used to select potential lncRNA targets of specific miRNAs and offer great insights for further researches on ceRNA regulation network.

     

SAGE detects microRNA precursors

Background

MicroRNAs (miRNAs) have been shown to play important roles in regulating gene expression. Since miRNAs are often evolutionarily conserved and their precursors can be folded into stem-loop hairpins, many miRNAs have been predicted. Yet experimental confirmation is difficult since miRNA expression is often specific to particular tissues and developmental stages.

Results

Analysis of 29 human and 230 mouse longSAGE libraries revealed the expression of 22 known and 10 predicted mammalian miRNAs. Most were detected in embryonic tissues. Four SAGE tags detected in human embryonic stem cells specifically match a cluster of four human miRNAs (mir-302a, b, c&d) known to be expressed in embryonic stem cells. LongSAGE data also suggest the existence of a mouse homolog of human and rat mir-493.

Conclusion

The observation that some orphan longSAGE tags uniquely match miRNA precursors provides information about the expression of some known and predicted miRNAs.

     

The miRNAome of ramie (<Emphasis Type="Italic">Boehmeria nivea</Emphasis> L.): identification,expression, and potential roles of novel microRNAs in regulation of cadmium stress response

Background

MicroRNAs (miRNAs) regulate numerous crucial abiotic stress processes in plants. However, information is limited on their involvement in cadmium (Cd) stress response and tolerance mechanisms in plants, including ramie (Boehmeria nivea L.) that produces a number of economic valuable as an important natural fibre crop and an ideal crop for Cd pollution remediation.

Results

Four small RNA libraries of Cd-stressed and non-stressed leaves and roots of ramie were constructed. Using small RNA-sequencing, 73 novel miRNAs were identified. Genome-wide expression analysis revealed that a set of miRNAs was differentially regulated in response to Cd stress. In silico target prediction identified 426 potential miRNA targets that include several uptake or transport factors for heavy metal ions. The reliability of small RNA sequencing and the relationship between the expression levels of miRNAs and their target genes were confirmed by quantitative PCR (q-PCR). We showed that the expression patterns of miRNAs obtained by q-PCR were consistent with those obtained from small RNA sequencing. Moreover, we demonstrated that the expression of six randomly selected target genes was inversely related to that of their corresponding miRNAs, indicating that the miRNAs regulate Cd stress response in ramie.

Conclusions

This study enriches the number of Cd-responsive miRNAs and lays a foundation for the elucidation of the miRNA-mediated regulatory mechanism in ramie during Cd stress.

     

A novel two-layer SVM model in miRNA Drosha processing site detection

Background

MicroRNAs (miRNAs) are a large class of non-coding RNAs with important functions wide spread in animals, plants and viruses. Studies showed that an RNase III family member called Drosha recognizes most miRNAs, initiates their processing and determines the mature miRNAs. The Drosha processing sites identification will shed some light on both miRNA identification and understanding the mechanism of Drosha processing.

Methods

We developed a computational method for Drosha processing site predicting, named as DroshaPSP, which employs a two-layer mathematical model to integrate structure feature in the first layer and sequence features in the second layer. The performance of DroshaPSP was estimated by 5-fold cross-validation and measured by ACC (accuracy), Sn (sensitivity), Sp (specificity), P (precision) and MCC (Matthews correlation coefficient).

Results

The results of testing DroshaPSP on the miRNA data of Drosophila melanogaster indicated that the Sn, Sp, and MCC thereof reach to 0.86, 0.99 and 0.86 respectively.

Conclusions

We found the Shannon entropy, a chemical kinetics feature, is a significant feature in telling the true sites among the nearby sites and improving the performance.

     

miRACA: A database for miRNAs associated with cancers and age related disorders (ARD)

Background

With the given diversity and abundance of several targets of miRNAs, they functionally appear to interact with several elements of the multiple cellular networks to maintain physiologic homeostasis. They can function as tumor suppressors or oncogenes, whose under or overexpression has both diagnostic and prognostic significance in various cancers while being implicated as prospective regulators of age-related disorders (ARD) as well. Establishing a concatenate between ARD and cancers by looking into the insights of the shared miRNAs may have a practical relevance.

Methods

In the present work, we performed network analysis of miRNA-disease association and miRNA-target gene interaction to prioritize miRNAs that play significant roles in the manifestation of cancer as well as ARD. Also, we developed a repository that stores miRNAs common to both ARD and cancers along with their target genes.

Results

We have comprehensively curated all miRNAs that we found to be shared in both the diseases in the human genome and established a database, miRACA (Database for microRNAs Associated with Cancers and ARD) that currently houses information of 1648 miRNAs that are significantly associated with 38 variants supported with pertinent data. It has been made available online at http://genomeinformatics.dtu.ac.in/miraca/ for easy retrieval and utilization of data by the scientific community.

Conclusion

To the best of our knowledge, our database is the first attempt at compilation of such data. We believe this work may serve as a significant resource and facilitate the analysis of miRNA regulatory mechanisms shared between cancers and ARD to apprehend disease etiology.

     

Bioavailability of transgenic microRNAs in genetically modified plants

Background

Transgenic expression of small RNAs is a prevalent approach in agrobiotechnology for the global enhancement of plant foods. Meanwhile, emerging studies have, on the one hand, emphasized the potential of transgenic microRNAs (miRNAs) as novel dietary therapeutics and, on the other, suggested potential food safety issues if harmful miRNAs are absorbed and bioactive. For these reasons, it is necessary to evaluate the bioavailability of transgenic miRNAs in genetically modified crops.

Results

As a pilot study, two transgenic Arabidopsis lines ectopically expressing unique miRNAs were compared and contrasted with the plant bioavailable small RNA MIR2911 for digestive stability and serum bioavailability. The expression levels of these transgenic miRNAs in Arabidopsis were found to be comparable to that of MIR2911 in fresh tissues. Assays of digestive stability in vitro and in vivo suggested the transgenic miRNAs and MIR2911 had comparable resistance to degradation. Healthy mice consuming diets rich in Arabidopsis lines expressing these miRNAs displayed MIR2911 in the bloodstream but no detectable levels of the transgenic miRNAs.

Conclusions

These preliminary results imply digestive stability and high expression levels of miRNAs in plants do not readily equate to bioavailability. This initial work suggests novel engineering strategies be employed to enhance miRNA bioavailability when attempting to use transgenic foods as a delivery platform.

     

Measuring disease similarity and predicting disease-related ncRNAs by a novel method

Background

Similar diseases are always caused by similar molecular origins, such as diasease-related protein-coding genes (PCGs). And the molecular associations reflect their similarity. Therefore, current methods for calculating disease similarity often utilized functional interactions of PCGs. Besides, the existing methods have neglected a fact that genes could also be associated in the gene functional network (GFN) based on intermediate nodes.

Methods

Here we presented a novel method, InfDisSim, to deduce the similarity of diseases. InfDisSim utilized the whole network based on random walk with damping to model the information flow. A benchmark set of similar disease pairs was employed to evaluate the performance of InfDisSim.

Results

The region beneath the receiver operating characteristic curve (AUC) was calculated to assess the performance. As a result, InfDisSim reaches a high AUC (0.9786) which indicates a very good performance. Furthermore, after calculating the disease similarity by the InfDisSim, we reconfirmed that similar diseases tend to have common therapeutic drugs (Pearson correlation γ²?=?0.1315, p?=?2.2e-16). Finally, the disease similarity computed by infDisSim was employed to construct a miRNA similarity network (MSN) and lncRNA similarity network (LSN), which were further exploited to predict potential associations of lncRNA-disease pairs and miRNA-disease pairs, respectively. High AUC (0.9893, 0.9007) based on leave-one-out cross validation shows that the LSN and MSN is very appropriate for predicting novel disease-related lncRNAs and miRNAs, respectively.

Conclusions

The high AUC based on benchmark data indicates the method performs well. The method is valuable in the prediction of disease-related lncRNAs and miRNAs.

     

Comprehensive annotation of microRNA expression profiles

Background

MicroRNAs (miRNAs) regulate many biological processes by post-translational gene silencing. Analysis of miRNA expression profiles is a reliable method for investigating particular biological processes due to the stability of miRNA and the development of advanced sequencing methods. However, this approach is limited by the broad specificity of miRNAs, which may target several mRNAs.

Result

In this study, we developed a method for comprehensive annotation of miRNA array or deep sequencing data for investigation of cellular biological effects. Using this method, the specific pathways and biological processes involved in Alzheimer’s disease were predicted with high correlation in four independent samples. Furthermore, this method was validated for evaluation of cadmium telluride (CdTe) nanomaterial cytotoxicity. As a result, apoptosis pathways were selected as the top pathways associated with CdTe nanoparticle exposure, which is consistent with previous studies.

Conclusions

Our findings contribute to the validation of miRNA microarray or deep sequencing results for early diagnosis of disease and evaluation of the biological safety of new materials and drugs.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

Identifying functional relationships within sets of co-expressed genes by combining upstream regulatory motif analysis and gene expression information

Background

Existing clustering approaches for microarray data do not adequately differentiate between subsets of co-expressed genes. We devised a novel approach that integrates expression and sequence data in order to generate functionally coherent and biologically meaningful subclusters of genes. Specifically, the approach clusters co-expressed genes on the basis of similar content and distributions of predicted statistically significant sequence motifs in their upstream regions.

Results

We applied our method to several sets of co-expressed genes and were able to define subsets with enrichment in particular biological processes and specific upstream regulatory motifs.

Conclusions

These results show the potential of our technique for functional prediction and regulatory motif identification from microarray data.

     

The use of metabolome analysis to identify the cause of an unexplained disease of Japanese gentians (<Emphasis Type="Italic">Gentiana triflora</Emphasis>)

Introduction

Gentian spotted bleaching disease (GSBD), a novel disease of unknown etiology, affects Gentiana triflora plants that are cultivated as ornamental flowers in Japan. This disease leads to the production of necrotic leaf spots, a delay in flowering, and has thus become a serious problem for gentian production.

Objectives

The objective of this study was to identify the cause of GSBD in G. triflora by analyzing differences between healthy and GSBD-affected leaves.

Method

Selected metabolite concentrations in healthy and GSBD-affected leaves were quantified using capillary electrophoresis and liquid chromatography-mass spectrometry, and statistically significant differences in metabolite concentrations were assessed. GSBD-affected metabolic pathways were identified followed by examination of pathway-related gene expression and enzyme activities. Furthermore, the effects of root hypoxia on metabolite concentrations and gene expression were investigated.

Results

We found that concentrations of Calvin cycle intermediates and ribulose 1,5-bisphosphate carboxylase/oxygenase (RubisCO) activity were significantly lower in GSBD-affected leaves, whereas sucrose cleavage and Ala accumulation were enhanced. Since these metabolic changes are frequently observed in plants exposed to hypoxia, the expression of hypoxia-responsive genes was investigated. Expression levels of hypoxia-responsive genes were higher in GSBD-affected plants than in the controls. Furthermore, root hypoxia induced similar symptoms and metabolic changes as those observed in GSBD-affected plants.

Conclusion

Our results indicate that GSBD was likely induced by root hypoxia and that metabolome analysis is an effective tool for identifying the cause of plant disease with unknown etiologies.

     

KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data

Introduction

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives

Merge in the same platform the steps required for metabolomics data processing.

Methods

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.

     

Responses of switchgrass soil respiration and its components to precipitation gradient in a mesocosm study

Aims

The objective of this study was to investigate the effects of the precipitation changes on soil, microbial and root respirations of switchgrass soils, and the relationships between soil respiration and plant growth, soil moisture and temperature.

Methods

A mesocosm experiment was conducted with five precipitation treatments over two years in a greenhouse in Nashville, Tennessee. The treatments included ambient precipitation, ?50%, ?33%, +33% and +50% of ambient precipitation. Soil, microbial, and root respirations were quantified during the growing seasons.

Results

Mean soil and root respirations in the +50% treatment were the highest (2.48 and 0.93 μmol CO₂ m^?2 s^?1, respectively) among all treatments. Soil microbial respiration contributed more to soil respiration, and had higher precipitation sensitivity mostly than root respiration. Increases in precipitation mostly enhanced microbial respiration while decreases in precipitation reduced both microbial and root respirations. Across precipitation treatments, soil respiration was significantly influenced by soil moisture, soil temperature, and aboveground biomass.

Conclusions

Our results showed that microbial respiration was more sensitive to precipitation changes, and precipitation regulated the response of soil respiration to soil temperature. The information generated in this study will be useful for model simulation of soil respiration in switchgrass fields under precipitation changes.

     

The role of laterally transferred genes in adaptive evolution

Background

Bacterial genomes develop new mechanisms to tide them over the imposing conditions they encounter during the course of their evolution. Acquisition of new genes by lateral gene transfer may be one of the dominant ways of adaptation in bacterial genome evolution. Lateral gene transfer provides the bacterial genome with a new set of genes that help it to explore and adapt to new ecological niches.

Methods

A maximum likelihood analysis was done on the five sequenced corynebacterial genomes to model the rates of gene insertions/deletions at various depths of the phylogeny.

Results

The study shows that most of the laterally acquired genes are transient and the inferred rates of gene movement are higher on the external branches of the phylogeny and decrease as the phylogenetic depth increases. The newly acquired genes are under relaxed selection and evolve faster than their older counterparts. Analysis of some of the functionally characterised LGTs in each species has indicated that they may have a possible adaptive role.

Conclusion

The five Corynebacterial genomes sequenced to date have evolved by acquiring between 8 – 14% of their genomes by LGT and some of these genes may have a role in adaptation.

     

Mitochondrial aconitase is a key regulator of energy production for growth and protein expression in Chinese hamster ovary cells

Introduction

Mammalian cells like Chinese hamster ovary (CHO) cells are routinely used for production of recombinant therapeutic proteins. Cells require a continuous supply of energy and nutrients to sustain high cell densities whilst expressing high titres of recombinant proteins. Cultured mammalian cells are primarily dependent on glucose and glutamine metabolism for energy production.

Objectives

The TCA cycle is the main source of energy production and its continuous flow is essential for cell survival. Modulated regulation of TCA cycle can affect ATP production and influence CHO cell productivity.

Methods

To determine the key metabolic reactions of the cycle associated with cell growth in CHO cells, we transiently silenced each gene of the TCA cycle using RNAi.

Results

Silencing of at least four TCA cycle genes was detrimental to CHO cell growth. With an exception of mitochondrial aconitase (or Aco2), all other genes were associated with ATP production reactions of the TCA cycle and their resulting substrates can be supplied by other anaplerotic and cataplerotic reactions. This study is the first of its kind to have established key role of aconitase gene in CHO cells. We further investigated the temporal effects of aconitase silencing on energy production, CHO cell metabolism, oxidative stress and recombinant protein production.

Conclusion

Transient silencing of mitochondrial aconitase inhibited cell growth, reduced ATP production, increased production of reactive oxygen species and reduced cell specific productivity of a recombinant CHO cell line by at least twofold.

     

Characterizing biomarkers in osteosarcoma metastasis based on an ego-network

Objectives

To characterize biomarkers that underlie osteosarcoma (OS) metastasis based on an ego-network.

Results

From the microarray data, we obtained 13,326 genes. By combining PPI data and microarray data, 10,520 shared genes were found and constructed into ego-networks. 17 significant ego-networks were identified with p < 0.05. In the pathway enrichment analysis, seven ego-networks were identified with the most significant pathway.

Conclusions

These significant ego-modules were potential biomarkers that reveal the potential mechanisms in OS metastasis, which may contribute to understanding cancer prognoses and providing new perspectives in the treatment of cancer.