Long non-coding RNAs and their implications in cancer epigenetics

LncRNAs (long non-coding RNAs) have emerged as key molecular players in the regulation of gene expression in different biological processes. Their involvement in epigenetic processes includes the recruitment of histone-modifying enzymes and DNA methyltransferases, leading to the establishment of chromatin conformation patterns that ultimately result in the fine control of genes. Some of these genes are related to tumorigenesis and it is well documented that the misregulation of epigenetic marks leads to cancer. In this review, we highlight how some of the lncRNAs implicated in cancer are involved in the epigenetic control of gene expression. While very few lncRNAs have already been identified as players in determining the cancer-survival outcome in a number of different cancer types, for most of the lncRNAs associated with epigenetic regulation only their altered pattern of expression in cancer is demonstrated. Thanks to their tissue-specificity features, lncRNAs have already been proposed as diagnostic markers in specific cancer types. We envision the discovery of a wealth of novel spliced and unspliced intronic lncRNAs involved in epigenetic networks or in highly location-specific epigenetic control, which might be predominantly altered in specific cancer subtypes. We expect that the characterization of new lncRNA (long non-coding RNA)–protein and lncRNA–DNA interactions will contribute to the discovery of potential lncRNA targets for use in therapies against cancer.     

Dynamic properties of network motifs contribute to biological network organization

Biological networks, such as those describing gene regulation, signal transduction, and neural synapses, are representations of large-scale dynamic systems. Discovery of organizing principles of biological networks can be enhanced by embracing the notion that there is a deep interplay between network structure and system dynamics. Recently, many structural characteristics of these non-random networks have been identified, but dynamical implications of the features have not been explored comprehensively. We demonstrate by exhaustive computational analysis that a dynamical property—stability or robustness to small perturbations—is highly correlated with the relative abundance of small subnetworks (network motifs) in several previously determined biological networks. We propose that robust dynamical stability is an influential property that can determine the non-random structure of biological networks.     

Gene regulatory network inference: Data integration in dynamic models—A review

Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein–DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.     

Inference of scale-free networks from gene expression time series

Quantitative time-series observation of gene expression is becoming possible, for example by cell array technology. However, there are no practical methods with which to infer network structures using only observed time-series data. As most computational models of biological networks for continuous time-series data have a high degree of freedom, it is almost impossible to infer the correct structures. On the other hand, it has been reported that some kinds of biological networks, such as gene networks and metabolic pathways, may have scale-free properties. We hypothesize that the architecture of inferred biological network models can be restricted to scale-free networks. We developed an inference algorithm for biological networks using only time-series data by introducing such a restriction. We adopt the S-system as the network model, and a distributed genetic algorithm to optimize models to fit its simulated results to observed time series data. We have tested our algorithm on a case study (simulated data). We compared optimization under no restriction, which allows for a fully connected network, and under the restriction that the total number of links must equal that expected from a scale free network. The restriction reduced both false positive and false negative estimation of the links and also the differences between model simulation and the given time-series data.     

A survey on methods for modeling and analyzing integrated biological networks

Understanding how cellular systems build up integrated responses to their dynamically changing environment is one of the open questions in Systems Biology. Despite their intertwinement, signaling networks, gene regulation and metabolism have been frequently modeled independently in the context of well-defined subsystems. For this purpose, several mathematical formalisms have been developed according to the features of each particular network under study. Nonetheless, a deeper understanding of cellular behavior requires the integration of these various systems into a model capable of capturing how they operate as an ensemble. With the recent advances in the "omics" technologies, more data is becoming available and, thus, recent efforts have been driven toward this integrated modeling approach. We herein review and discuss methodological frameworks currently available for modeling and analyzing integrated biological networks, in particular metabolic, gene regulatory and signaling networks. These include network-based methods and Chemical Organization Theory, Flux-Balance Analysis and its extensions, logical discrete modeling, Petri Nets, traditional kinetic modeling, Hybrid Systems and stochastic models. Comparisons are also established regarding data requirements, scalability with network size and computational burden. The methods are illustrated with successful case studies in large-scale genome models and in particular subsystems of various organisms.     

Long non-coding RNAs and chromatin modifiers

The emergence of long non-coding RNAs (lncRNAs) has shaken up our conception of gene expression regulation, as lncRNAs take prominent positions as components of cellular networks. Several cellular processes involve lncRNAs, and a significant number of them have been shown to function in cooperation with chromatin modifying enzymes to promote epigenetic activation or silencing of gene expression. Different model mechanisms have been proposed to explain how lncRNAs achieve regulation of gene expression by interacting with the epigenetic machinery. Here we describe these models in light of the current knowledge of lncRNAs, such as Xist and HOTAIR, and discuss recent literature on the role of the three-dimensional structure of the genome in the mechanism of action of lncRNAs and chromatin modifiers.     

DNA methylation of nuclear receptor genes--possible role in malignancy

The members of the nuclear receptor superfamily are known to mediate a wide array of basic biological processes, such as regulation of cell growth and differentiation, and induction of apoptosis. In several human malignancies, this central control function of nuclear receptors is disturbed, which seems to play an important role in tumor development and progression. Many nuclear receptor genes have been reported to be downregulated in malignancies; however, only a few mutations, gene arrangements, deletions or similar genetic changes have been shown to occur in these tumors.During the last decade, increasing attention has been directed towards epigenetic mechanisms of gene regulation such as DNA methylation. Many nuclear receptor genes can be silenced through aberrant methylation in tumors; epigenetic silencing, therefore, represents an additional mechanism that modifies expression of key genes during carcinogenesis.This review will give insights into the role of DNA methylation in the silencing of nuclear receptor genes and its involvement in human malignancies.     

Epigenetics in C. elegans: facts and challenges

Epigenetics is defined as the study of heritable changes in gene expression that are not accompanied by changes in the DNA sequence. Epigenetic mechanisms include histone post-translational modifications, histone variant incorporation, non-coding RNAs, and nucleosome remodeling and exchange. In addition, the functional compartmentalization of the nucleus also contributes to epigenetic regulation of gene expression. Studies on the molecular mechanisms underlying epigenetic phenomena and their biological function have relied on various model systems, including yeast, plants, flies, and cultured mammalian cells. Here we will expose the reader to the current understanding of epigenetic regulation in the roundworm C. elegans. We will review recent models of nuclear organization and its impact on gene expression, the biological role of enzymes modifying core histones, and the function of chromatin-associated factors, with special emphasis on Polycomb (PcG) and Trithorax (Trx-G) group proteins. We will discuss how the C. elegans model has provided novel insight into mechanisms of epigenetic regulation as well as suggest directions for future research.     

Programming and engineering biological networks

Synthetic biology aims to build new functions in living organisms. Recent work has addressed the creation of synthetic epigenetic switches in mammalian cells and synthetic intracellular communication. Fundamentally new, and potentially scaleable, modes of gene regulation have been created that enable expansion of the scope of synthetic circuits. Increasingly sophisticated models of gene regulation that include stochastic effects are beginning to predict the behaviour of small synthetic networks. Overall, these advances suggest that a combination of molecular engineering and systems engineering should allow the creation of living matter capable of performing many useful and novel functions.     

植物根尖干细胞的表观遗传调控

干细胞是一类具有特化为不同细胞类型能力的多能性细胞,他为多细胞生物的器官发生、损伤修复和再生源源不断提供新细胞。干细胞的特化和维持需要复杂的基因调控网络来有序调控。此外,表观遗传调控在包括干细胞命运决定在内的许多生物学过程中发挥极其重要的作用。本文归纳了近年来对植物,主要是模式植物拟南芥（Arabidopsis thaliana（L.）Heynh.）根尖干细胞表观遗传调控方面的研究进展,重点论述了表观调控因子与控制干细胞的关键转录因子之间如何互作、调控植物根尖干细胞的自我更新和分化,并对今后研究的突破方向进行了展望。