Detecting microRNAs of high influence on protein functional interaction networks: a prostate cancer case study

ABSTRACT: BACKGROUND: The use of biological molecular network information for diagnostic and prognostic purposes and elucidation of molecular disease mechanism is a key objective in systems biomedicine. The network of regulatory miRNA-target and functional protein interactions is a rich source of information to elucidate the function and the prognostic value of miRNAs in cancer. The objective of this study is to identify miRNAs that have high influence on target protein complexes in prostate cancer as a case study. This could provide biomarkers or therapeutic targets relevant for prostate cancer treatment. RESULTS: Our findings demonstrate that a miRNA's functional role can be explained by its target protein connectivity within a physical and functional interaction network. To detect miRNAs with high influence on target protein modules, we integrated miRNA and mRNA expression profiles with a sequence based miRNA-target network and human functional and physical protein interactions (FPI). miRNAs with high influence on target protein complexes play a role in prostate cancer progression and are promising diagnostic or prognostic biomarkers. We uncovered several miRNA-regulated protein modules which were enriched in focal adhesion and prostate cancer genes. Several miRNAs such as miR-96, miR-182, and miR-143 demonstrated high influence on their target protein complexes and could explain most of the gene expression changes in our analyzed prostate cancer data set. CONCLUSIONS: We describe a novel method to identify active miRNA-target modules relevant to prostate cancer progression and outcome. miRNAs with high influence on protein networks are valuable biomarkers that can be used in clinical investigations for prostate cancer treatment.     

Lessons from microRNA mutants in worms,flies and mice

It is apparent that microRNAs (miRNAs) are important components in the regulation of genetic networks in many biological contexts. Based on computational analysis, typical miRNAs are inferred to have tens to hundreds of conserved targets. Many miRNA-target interactions have been validated by various means, including heterologous tests in cultured cells and gain-of-function approaches that can yield striking phenotypes in whole animals. However, these strategies do not report on the endogenous importance of such miRNA activities. Likewise, studies of miRNA pathway mutants can suggest an endogenous role for miRNAs in a given setting, but do not identify roles for specific miRNAs. Therefore, these approaches must be complemented with the analysis of miRNA mutant alleles. In this review, we describe some of the lessons learned from studying miRNA gene deletions in worms, flies and mice, and discuss their implications for the control of endogenous regulatory networks.     

Network reconfiguration and neuronal plasticity in rhythm-generating networks

Neuronal networks are highly plastic and reconfigure in a state-dependent manner. The plasticity at the network level emerges through multiple intrinsic and synaptic membrane properties that imbue neurons and their interactions with numerous nonlinear properties. These properties are continuously regulated by neuromodulators and homeostatic mechanisms that are critical to maintain not only network stability and also adapt networks in a short- and long-term manner to changes in behavioral, developmental, metabolic, and environmental conditions. This review provides concrete examples from neuronal networks in invertebrates and vertebrates, and illustrates that the concepts and rules that govern neuronal networks and behaviors are universal.     

On the Relationships between Generative Encodings,Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks

A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT). Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1) in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2) whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.     

Diverse Ways to Control p27Kip1 Function: miRNAs Come into Play

microRNAs (miRNAs) are small RNA molecules controlling messenger RNA (mRNA) and protein abundance. Since their discovery, research has been aimed at identifying the functions and target genes of miRNAs. A number of computer algorithms have been developed capable of predicting putative targets for any given miRNA. However, they might predict many false-positive targets and on top of that some true targets could be missed. This reflects the incomplete knowledge we still have concerning the rules governing true and effective miRNA-mRNA interactions. To experimentally identify miRNA-target genes, we have recently developed a genetic approach and employed it on p27Kip1, a hapo-insufficient tumor suppressor and cell cycle inhibitor. Here we review the difficulties interpreting the data from available computer algorithms, and critically address the pros and cons of our genetic screening method. Additionally, we focus on the different ways in which p27 is managed, argue how miRNAs could be involved in the regulation of p27 in both normal and malignant conditions, and discuss possible use of this knowledge for cancer therapy.     

A network-based method for target selection in metabolic networks

MOTIVATION: The lack of new antimicrobials, combined with increasing microbial resistance to old ones, poses a serious threat to public health. With hundreds of genomes sequenced, systems biology promises to help in solving this problem by uncovering new drug targets. RESULTS: Here, we propose an approach that is based on the mapping of the interactions between biochemical agents, such as proteins and metabolites, onto complex networks. We report that nodes and links in complex biochemical networks can be grouped into a small number of classes, based on their role in connecting different functional modules. Specifically, for metabolic networks, in which nodes represent metabolites and links represent enzymes, we demonstrate that some enzyme classes are more likely to be essential, some are more likely to be species-specific and some are likely to be both essential and specific. Our network-based enzyme classification scheme is thus a promising tool for the identification of drug targets. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Highly covarying residues have a functional role in antibody constant domains

The ability to generate and design antibodies recognizing specific targets has revolutionized the pharmaceutical industry and medical imaging. Engineering antibody therapeutics in some cases requires modifying their constant domains to enable new and altered interactions. Engineering novel specificities into antibody constant domains has proved challenging due to the complexity of inter‐domain interactions. Covarying networks of residues that tend to cluster on the protein surface and near binding sites have been identified in some proteins. However, the underlying role these networks play in the protein resulting in their conservation remains unclear in most cases. Resolving their role is crucial, because residues in these networks are not viable design targets if their role is to maintain the fold of the protein. Conversely, these networks of residues are ideal candidates for manipulating specificity if they are primarily involved in binding, such as the myriad interdomain interactions maintained within antibodies. Here, we identify networks of evolutionarily‐related residues in C‐class antibody domains by evaluating covariation, a measure of propensity with which residue pairs vary dependently during evolution. We computationally test whether mutation of residues in these networks affects stability of the folded antibody domain, determining their viability as design candidates. We find that members of covarying networks cluster at domain‐domain interfaces, and that mutations to these residues are diverse and frequent during evolution, precluding their importance to domain stability. These results indicate that networks of covarying residues exist in antibody domains for functional reasons unrelated to thermodynamic stability, making them ideal targets for antibody design. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

miRTissue: a web application for the analysis of miRNA-target interactions in human tissues

Background

microRNAs act as regulators of gene expression interacting with their gene targets. Current bioinformatics services, such as databases of validated miRNA-target interactions and prediction tools, usually provide interactions without any information about what tissue that interaction is more likely to appear nor information about the type of interactions, causing mRNA degradation or translation inhibition respectively.

Results

In this work, we introduce miRTissue, a web application that combines validated miRNA-target interactions with statistical correlation among expression profiles of miRNAs, genes and proteins in 15 different human tissues. Validated interactions are taken from the miRTarBase database, while expression profiles are downloaded from The Cancer Genome Atlas repository. As a result, the service provides a tissue-specific characterisation of each couple of miRNA and gene together with its statistical significance (p-value). The inclusion of protein data also allows providing the type of interaction. Moreover, miRTissue offers several views for analysing interactions, focusing for example on the comparison between different cancer types or different tissue conditions. All the results are freely downloadable in the most common formats.

Conclusions

miRTissue fills a gap concerning current bioinformatics services related to miRNA-target interactions because it provides a tissue-specific context to each validated interaction and the type of interaction itself. miRTissue is easily browsable allowing the user to select miRNAs, genes, cancer types and tissue conditions. The results can be sorted according to p-values to immediately identify those interactions that are more likely to occur in a given tissue. miRTissue is available at http://tblab.pa.icar.cnr.it/mirtissue.html.

     

New friends for Ago2 in neuronal plasticity

MicroRNAs have emerged as central regulators of cellular homeostasis and increasing evidence suggests that they play a key role in neuronal plasticity. Major efforts are made to define microRNA networks and their targets in the brain. The mechanisms by which microRNA activity is regulated are, however, relatively unexplored. In this issue of The EMBO Journal, Störchel et al ( 2015 ) screened for proteins that affect microRNA function in neurons. They identify Nova1 and Ncoa3 as novel regulators of miRNA activity and demonstrate that both proteins are essential for neuronal plasticity in a microRNA‐dependent manner.     

Potent effect of target structure on microRNA function

MicroRNAs (miRNAs) are small noncoding RNAs that repress protein synthesis by binding to target messenger RNAs. We investigated the effect of target secondary structure on the efficacy of repression by miRNAs. Using structures predicted by the Sfold program, we model the interaction between an miRNA and a target as a two-step hybridization reaction: nucleation at an accessible target site followed by hybrid elongation to disrupt local target secondary structure and form the complete miRNA-target duplex. This model accurately accounts for the sensitivity to repression by let-7 of various mutant forms of the Caenorhabditis elegans lin-41 3' untranslated region and for other experimentally tested miRNA-target interactions in C. elegans and Drosophila melanogaster. These findings indicate a potent effect of target structure on target recognition by miRNAs and establish a structure-based framework for genome-wide identification of animal miRNA targets.     

Structure of Protein Interaction Networks and Their Implications on Drug Design

Protein-protein interaction networks (PINs) are rich sources of information that enable the network properties of biological systems to be understood. A study of the topological and statistical properties of budding yeast and human PINs revealed that they are scale-rich and configured as highly optimized tolerance (HOT) networks that are similar to the router-level topology of the Internet. This is different from claims that such networks are scale-free and configured through simple preferential-attachment processes. Further analysis revealed that there are extensive interconnections among middle-degree nodes that form the backbone of the networks. Degree distributions of essential genes, synthetic lethal genes, synthetic sick genes, and human drug-target genes indicate that there are advantageous drug targets among nodes with middle- to low-degree nodes. Such network properties provide the rationale for combinatorial drugs that target less prominent nodes to increase synergetic efficacy and create fewer side effects.     

Prioritizing cancer-related key miRNA-target interactions by integrative genomics

Accumulating evidence indicates that microRNAs (miRNAs) can function as oncogenes or tumor suppressor genes by controlling few key targets, which in turn contribute to the pathogenesis of cancer. The identification of cancer-related key miRNA-target interactions remains a challenge. We performed a systematic analysis of known cancer-related key interactions manually curated from published papers based on different aspects including sequence, expression and function. Known cancer-related key interactions show more miRNA binding sites (especially for 8mer binding sites), more reliable binding of miRNA to the target region, higher expression associations and broader functional coverage when compared to non-disease-related interactions. Through integrating these sequence, expression and function features, we proposed a bioinformatics approach termed PCmtI to prioritize cancer-related key interactions. Ten-fold cross-validation of our approach revealed that it can achieve an area under the receiver operating characteristic curve of 93.9%. Subsequent leave-one-miRNA-out cross-validation also demonstrated the performance of our approach. Using miR-155 as a case, we found that the top ranked interactions can account for most functions of miR-155. In addition, we further demonstrated the power of our approach by 23 recently identified cancer-related key interactions. The approach described here offers a new way for the discovery of novel cancer-related key miRNA-target interactions.