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

Model-guided design of ligand-regulated RNAi for programmable control of gene expression

Progress in constructing biological networks will rely on the development of more advanced components that can be predictably modified to yield optimal system performance. We have engineered an RNA-based platform, which we call an shRNA switch, that provides for integrated ligand control of RNA interference (RNAi) by modular coupling of an aptamer, competing strand, and small hairpin (sh) RNA stem into a single component that links ligand concentration and target gene expression levels. A combined experimental and mathematical modelling approach identified multiple tuning strategies and moves towards a predictable framework for the forward design of shRNA switches. The utility of our platform is highlighted by the demonstration of fine-tuning, multi-input control, and model-guided design of shRNA switches with an optimized dynamic range. Thus, shRNA switches can serve as an advanced component for the construction of complex biological systems and offer a controlled means of activating RNAi in disease therapeutics

Whole transcriptome analysis reveals non-coding RNA's competing endogenous gene pairs as novel form of motifs in serous ovarian cancer

Publisher Copyright: © 2022The non-coding RNA (ncRNA) regulation appears to be associated to the diagnosis and targeted therapy of complex diseases. Motifs of non-coding RNAs and genes in the competing endogenous RNA (ceRNA) network would probably contribute to the accurate prediction of serous ovarian carcinoma (SOC). We conducted a microarray study profiling the whole transcriptomes of eight human SOCs and eight controls and constructed a ceRNA network including mRNAs, long ncRNAs, and circular RNAs (circRNAs). Novel form of motifs (mRNA-ncRNA-mRNA) were identified from the ceRNA network and defined as non-coding RNA's competing endogenous gene pairs (ceGPs), using a proposed method denoised individualized pair analysis of gene expression (deiPAGE). 18 cricRNA's ceGPs (cceGPs) were identified from multiple cohorts and were fused as an indicator (SOC index) for SOC discrimination, which carried a high predictive capacity in independent cohorts. SOC index was negatively correlated with the CD8+/CD4+ ratio in tumour-infiltration, reflecting the migration and growth of tumour cells in ovarian cancer progression. Moreover, most of the RNAs in SOC index were experimentally validated involved in ovarian cancer development. Our results elucidate the discriminative capability of SOC index and suggest that the novel competing endogenous motifs play important roles in expression regulation and could be potential target for investigating ovarian cancer mechanism or its therapy.Peer reviewe

A new procedure to analyze RNA non-branching structures

RNA structure prediction and structural motifs analysis are challenging tasks in the investigation of RNA function. We propose a novel procedure to detect structural motifs shared between two RNAs (a reference and a target). In particular, we developed two core modules: (i) nbRSSP_extractor, to assign a unique structure to the reference RNA encoded by a set of non-branching structures; (ii) SSD_finder, to detect structural motifs that the target RNA shares with the reference, by means of a new score function that rewards the relative distance of the target non-branching structures compared to the reference ones. We integrated these algorithms with already existing software to reach a coherent pipeline able to perform the following two main tasks: prediction of RNA structures (integration of RNALfold and nbRSSP_extractor) and search for chains of matches (integration of Structator and SSD_finder)

Cancer cells exploit an orphan RNA to drive metastatic progression.

Here we performed a systematic search to identify breast-cancer-specific small noncoding RNAs, which we have collectively termed orphan noncoding RNAs (oncRNAs). We subsequently discovered that one of these oncRNAs, which originates from the 3' end of TERC, acts as a regulator of gene expression and is a robust promoter of breast cancer metastasis. This oncRNA, which we have named T3p, exerts its prometastatic effects by acting as an inhibitor of RISC complex activity and increasing the expression of the prometastatic genes NUPR1 and PANX2. Furthermore, we have shown that oncRNAs are present in cancer-cell-derived extracellular vesicles, raising the possibility that these circulating oncRNAs may also have a role in non-cell autonomous disease pathogenesis. Additionally, these circulating oncRNAs present a novel avenue for cancer fingerprinting using liquid biopsies

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

The ever-evolving concept of the gene: The use of RNA/Protein experimental techniques to understand genome functions

The completion of the human genome sequence together with advances in sequencing technologies have shifted the paradigm of the genome, as composed of discrete and hereditable coding entities, and have shown the abundance of functional noncoding DNA. This part of the genome, previously dismissed as "junk" DNA, increases proportionally with organismal complexity and contributes to gene regulation beyond the boundaries of known protein-coding genes. Different classes of functionally relevant nonprotein-coding RNAs are transcribed from noncoding DNA sequences. Among them are the long noncoding RNAs (lncRNAs), which are thought to participate in the basal regulation of protein-coding genes at both transcriptional and post-transcriptional levels. Although knowledge of this field is still limited, the ability of lncRNAs to localize in different cellular compartments, to fold into specific secondary structures and to interact with different molecules (RNA or proteins) endows them with multiple regulatory mechanisms. It is becoming evident that lncRNAs may play a crucial role in most biological processes such as the control of development, differentiation and cell growth. This review places the evolution of the concept of the gene in its historical context, from Darwin's hypothetical mechanism of heredity to the post-genomic era. We discuss how the original idea of protein-coding genes as unique determinants of phenotypic traits has been reconsidered in light of the existence of noncoding RNAs. We summarize the technological developments which have been made in the genome-wide identification and study of lncRNAs and emphasize the methodologies that have aided our understanding of the complexity of lncRNA-protein interactions in recent years

Interaction and cross-talk between non-coding RNAs.

Non-coding RNA (ncRNA) has been shown to regulate diverse cellular processes and functions through controlling gene expression. Long non-coding RNAs (lncRNAs) act as a competing endogenous RNAs (ceRNAs) where microRNAs (miRNAs) and lncRNAs regulate each other through their biding sites. Interactions of miRNAs and lncRNAs have been reported to trigger decay of the targeted lncRNAs and have important roles in target gene regulation. These interactions form complicated and intertwined networks. Certain lncRNAs encode miRNAs and small nucleolar RNAs (snoRNAs), and may regulate expression of these small RNAs as precursors. SnoRNAs have also been reported to be precursors for PIWI-interacting RNAs (piRNAs) and thus may regulate the piRNAs as a precursor. These miRNAs and piRNAs target messenger RNAs (mRNAs) and regulate gene expression. In this review, we will present and discuss these interactions, cross-talk, and co-regulation of ncRNAs and gene regulation due to these interactions

Integration of bioinformatic predictions and experimental data to identify circRNA-miRNA associations

Circular RNAs (circRNAs) have recently emerged as a novel class of transcripts, characterized by covalently linked 3′–5′ ends that result in the so-called backsplice junction. During the last few years, thousands of circRNAs have been identified in different organisms. Yet, despite their role as disease biomarker started to emerge, depicting their function remains challenging. Different studies have shown that certain circRNAs act as miRNA sponges, but any attempt to generalize from the single case to the “circ-ome” has failed so far. In this review, we explore the potential to define miRNA “sponging” as a more general function of circRNAs and describe the different approaches to predict miRNA response elements (MREs) in known or novel circRNA sequences. Moreover, we discuss how experiments based on Ago2-IP and experimentally validated miRNA:target duplexes can be used to either prioritize or validate putative miRNA-circRNA associations