Network-based approaches to explore complex biological systems towards network medicine

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes

SPINNAKER: an R-based tool to highlight key RNA interactions in complex biological networks

Background: Recently, we developed a mathematical model for identifying putative competing endogenous RNA (ceRNA) interactions. This methodology has aroused a broad acknowledgment within the scientific community thanks to the encouraging results achieved when applied to breast invasive carcinoma, leading to the identification of PVT1, a long non-coding RNA functioning as ceRNA for the miR-200 family. The main shortcoming of the model is that it is no freely available and implemented in MATLAB®, a proprietary programming platform requiring a paid license for installing, operating, manipulating, and running the software. Results: Breaking through these model limitations demands to distribute it in an open-source, freely accessible environment, such as R, designed for an ordinary audience of users that are not able to afford a proprietary solution. Here, we present SPINNAKER (SPongeINteractionNetworkmAKER), the open-source version of our widely established mathematical model for predicting ceRNAs crosstalk, that is released as an exhaustive collection of R functions. SPINNAKER has been even designed for providing many additional features that facilitate its usability, make it more efficient in terms of further implementation and extension, and less intense in terms of computational execution time. Conclusions: SPINNAKER source code is freely available at https://github.com/sportingCode/SPINNAKER.git together with a thoroughgoing PPT-based guideline. In order to help users get the key points more conveniently, also a practical R-styled plain-text guideline is provided. Finally, a short movie is available to help the user to set the own directory, properly

Closing the circle : current state and perspectives of circular RNA databases

Circular RNAs (circRNAs) are covalently closed RNA molecules that have been linked to various diseases, including cancer. However, a precise function and working mechanism are lacking for the larger majority. Following many different experimental and computational approaches to identify circRNAs, multiple circRNA databases were developed as well. Unfortunately, there are several major issues with the current circRNA databases, which substantially hamper progression in the field. First, as the overlap in content is limited, a true reference set of circRNAs is lacking. This results from the low abundance and highly specific expression of circRNAs, and varying sequencing methods, data-analysis pipelines, and circRNA detection tools. A second major issue is the use of ambiguous nomenclature. Thus, redundant or even conflicting names for circRNAs across different databases contribute to the reproducibility crisis. Third, circRNA databases, in essence, rely on the position of the circRNA back-splice junction, whereas alternative splicing could result in circRNAs with different length and sequence. To uniquely identify a circRNA molecule, the full circular sequence is required. Fourth, circRNA databases annotate circRNAs' microRNA binding and protein-coding potential, but these annotations are generally based on presumed circRNA sequences. Finally, several databases are not regularly updated, contain incomplete data or suffer from connectivity issues. In this review, we present a comprehensive overview of the current circRNA databases and their content, features, and usability. In addition to discussing the current issues regarding circRNA databases, we come with important suggestions to streamline further research in this growing field

Aberrantly Expressed CeRNAs Account for Missing Genomic Variability of Cancer Genes via MicroRNA-Mediated Interactions

There is growing evidence that RNAs compete for binding and regulation by a finite pool of microRNAs (miRs), thus regulating each other through a competing endogenous RNA (ceRNA) mechanism. My dissertation work focused on systematically studying ceRNA interactions in cancer by reverse-engineering context-specific miR-RNA interactions and ceRNA regulatory interactions across multiple tumor types and study the effects of these interactions in cancer. I attempted to use ceRNA interactions to explain how genetic and epigenetic alterations are propagated to target established drivers of tumorigenesis. Using bioinformatics analysis of primary tumor samples and experimental validation in cell lines, I have investigated the roles that mRNAs and noncoding RNAs can play in tumorigenesis via ceRNA interactions. Specifically, I studied how RNAs target tumor-suppressors and oncogenes as ceRNAs, and attempted to accounting for some of the missing genomic variability in tumors

New technologies accelerate the exploration of non-coding RNAs in horticultural plants.

Non-coding RNAs (ncRNAs), that is, RNAs not translated into proteins, are crucial regulators of a variety of biological processes in plants. While protein-encoding genes have been relatively well-annotated in sequenced genomes, accounting for a small portion of the genome space in plants, the universe of plant ncRNAs is rapidly expanding. Recent advances in experimental and computational technologies have generated a great momentum for discovery and functional characterization of ncRNAs. Here we summarize the classification and known biological functions of plant ncRNAs, review the application of next-generation sequencing (NGS) technology and ribosome profiling technology to ncRNA discovery in horticultural plants and discuss the application of new technologies, especially the new genome-editing tool clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) systems, to functional characterization of plant ncRNAs

Unveiling ncRNA regulatory axes in atherosclerosis progression

Completion of the human genome sequencing project highlighted the richness of the cellular RNA world, and opened the door to the discovery of a plethora of short and long non-coding RNAs (the dark transcriptome) with regulatory or structural potential, which shifted the balance of pathological gene alterations from coding to non-coding RNAs. Thus, disease risk assessment currently has to also evaluate the expression of new RNAs such as small micro RNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), competing endogenous RNAs (ceRNAs), retrogressed elements, 3'UTRs of mRNAs, etc. We are interested in the pathogenic mechanisms of atherosclerosis (ATH) progression in patients suffering Chronic Kidney Disease, and in this review, we will focus in the role of the dark transcriptome (non-coding RNAs) in ATH progression. We will focus in miRNAs and in the formation of regulatory axes or networks with their mRNA targets and with the lncRNAs that function as miRNA sponges or competitive inhibitors of miRNA activity. In this sense, we will pay special attention to retrogressed genomic elements, such as processed pseudogenes and Alu repeated elements, that have been recently seen to also function as miRNA sponges, as well as to the use or miRNA derivatives in gene silencing, anti-ATH therapies. Along the review, we will discuss technical developments associated to research in lncRNAs, from sequencing technologies to databases, repositories and algorithms to predict miRNA targets, as well as new approaches to miRNA function, such as integrative or enrichment analysis and their potential to unveil RNA regulatory networks

TRANSCRIPTOMIC PROFILING OF POSTMORTEM PREFRONTAL CORTEX AND NUCLEUS ACCUMBENS FROM CHRONIC ALCOHOL ABUSERS.

Alcohol use disorder (AUD) is a debilitating psychiatric illness that develops from a combination of genetic and environmental factors. While it is well documented that AUD is heritable, the shift from recreational alcohol use to abuse/dependence is poorly understood. In this dissertation, using postmortem brain tissue from individuals with alcohol dependence (AD), we profiled the genome-wide expression of circular RNA (circRNA), microRNA (miRNA), and messenger RNA (mRNA) to better understand the impact of gene expression on the development of AUD. To achieve this, we performed two independent studies that explore transcriptome differences between AD cases and controls. The first of which examines differentially expressed gene (DEG) networks associated with AD that show either high or low levels of network preservation between two key areas of the mesocorticolimbic system (MCL), the prefrontal cortex (PFC) and nucleus accumbens (NAc). The second is a pilot study that interrogates the function of circRNA as miRNA sponges to impact the expression of mRNA. Overall, our findings corroborate results from recent studies while also providing novel evidence for biological processes that are differentially expressed between the PFC and NAc. Additionally, the second study is the first to explore circRNA:miRNA:mRNA interactions in the brains of chronic alcohol abusers and the role of circRNA as potential regulators of known AUD risk genes. Finally, we integrate genetic information in the form of eQTL analyses to determine the clinical relevance of these findings within the context of recent GWAS of AUD and other addiction phenotypes

Discovering the pathways and GO terms associated with Mettl3 modified circular RNAs in the embryonic cerebral cortex of mice.

Circular RNAs (cirRNAs) are a class of RNA molecules that result from the alternative back-splicing events that join the 3’ and 5’ ends normally present in the linear RNA molecules. It has been published that cirRNAs can function as gene regulators and as “microRNA sponges” to negatively control the functions of microRNAs. While many studies have been conducted to understand the regulatory roles of Mettl3 in linear messenger RNAs, fewer contributions were applied to understand the impact of Mettl3 modified cirRNAs on gene expression and on the regulation of different KEGG biological pathways and GO terms. This thesis was conducted to identify the role of Mettl3 modification of cirRNAs in regulating gene expression and controlling different KEGG biological pathways and GO terms in the embryonic cerebral cortex of mice using high-throughput data sequencing. We constructed a generalized framework that led us to the identification of the cirRNA sequences that are significantly enriched in miRNA binding motifs and ultimately to the associated KEGG pathways and GO terms related to these interactions. It has been found by this study that Mettl3 modification in cirRNAs can regulate gene expression by controlling different KEGG biological pathways and GO terms in a manner that is similar, but not identical, to their corresponding linear mRNAs. While some KEGG pathways and GO terms appeared to be regulated by the Mettl3 modification of both linear mRNAs and cirRNAs, few GO terms were regulated in mRNAs but not in cirRNAs. Interestingly, it has been found that Mettl3 modification in cirRNAs can promote the regulation of unique KEGG biological pathways and GO processes (not being regulated by the Mettl3 modified mRNAs) that are significant to the regulation of the neurological diseases’ progressions such as brain tumors and intellectual disabilities in the embryonic cerebral cortex of mice