Complexity Of Gene Regulatory Networks Research Articles

Genome-Wide Identification and Characterization of miRNAs and Natural Antisense Transcripts Show the Complexity of Gene Regulatory Networks for Secondary Metabolism in Aristolochia contorta.

Aristolochia contorta Bunge is an academically and medicinally important plant species. It belongs to the magnoliids, with an uncertain phylogenetic position, and is one of the few plant species lacking a whole-genome duplication (WGD) event after the angiosperm-wide WGD. A. contorta has been an important traditional Chinese medicine material. Since it contains aristolochic acids (AAs), chemical compounds with nephrotoxity and carcinogenicity, the utilization of this plant has attracted widespread attention. Great efforts are being made to increase its bioactive compounds and reduce or completely remove toxic compounds. MicroRNAs (miRNAs) and natural antisense transcripts (NATs) are two classes of regulators potentially involved in metabolism regulation. Here, we report the identification and characterization of 223 miRNAs and 363 miRNA targets. The identified miRNAs include 51 known miRNAs belonging to 20 families and 172 novel miRNAs belonging to 107 families. A negative correlation between the expression of miRNAs and their targets was observed. In addition, we identified 441 A. contorta NATs and 560 NAT-sense transcript (ST) pairs, of which 12 NATs were targets of 13 miRNAs, forming 18 miRNA-NAT-ST modules. Various miRNAs and NATs potentially regulated secondary metabolism through the modes of miRNA-target gene-enzyme genes, NAT-STs, and NAT-miRNA-target gene-enzyme genes, suggesting the complexity of gene regulatory networks in A. contorta. The results lay a solid foundation for further manipulating the production of its bioactive and toxic compounds.

DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks

BackgroundKnowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes.ResultsIn this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ uses a gene regulatory network model to identify direct perturbations to the transcription of genes using gene expression data. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course expression profiles, as well as leverage information on the gene network structure. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains.ConclusionDeltaNeTS+ is a powerful network analysis tool for inferring gene targets from gene expression profiles. As demonstrated in the case studies, by incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~ 10 s). Integrating static and dynamic expression data with transcriptional network structure extracted from genomic information, as enabled by DeltaNeTS+, is crucial toward personalized medicine, where treatments can be tailored to individual patients. DeltaNeTS+ can be freely downloaded from http://www.github.com/cabsel/deltanetsplus.

Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states.

The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Our study provides a framework to infer the dynamics of differentiation from single cell transcriptomics data and to build predictive models of the gene regulatory networks that drive the sequence of cell fate decisions during development.

Computer simulation of the ontogeny of organisms with different types of symmetry

The ontogeny of multicellular organisms is based on self-assembly of ordered structures due to coherent behavior of cells, each of which is basically “programmed” in the same way. All embryonic cells follow the same set of rules encoded in the structure of a gene regulatory network (GRN), which in its turn is encoded in the genome. This principle formed the basis for the Evo-Devo software designed by the authors for modeling the ontogeny of multicellular organisms. Modeling revealed the absence of unambiguous correlation between the type of symmetry of the organism and the complexity of GRNs that are needed to produce such symmetry. The developmental evolution of symmetry can occur without increasing complexity of the developmental program. However, the complexity of GRNs should increase in order to make the phenotype more robust, stable and ordered. This conclusion is concordant with Schmalhausen’s concepts concerning the formative role of stabilizing selection. Selection for more robust phenotype results in the development of additional regulatory mechanisms based on negative feedback, a process that necessarily implies the increase in complexity of the genetic developmental program. New elements of GRNs, in their turn, open new paths for evolutionary change.

MicroRNAs in the regeneration of skeletal muscle

MicroRNAs (miRNAs) have emerged as critical regulators of numerous biological processes by modulating gene expression at the post-transcriptional level. The discovery of miRNAs as new and important regulators of gene expression is expected to broaden our biological understanding of the regulatory mechanism in muscle by adding another dimension of regulation to the diversity and complexity of gene-regulatory networks. Exercise-induced skeletal muscle injury and repair have always been among the highlights of research in sports medicine. However, many mechanism problems that occur during regeneration and repair in the aftermath of skeletal muscle injury remain unsolved. It has become increasingly clear that the regeneration of skeletal muscle development involve regulation by miRNAs. In the last few years the field has seen a rapid expansion of our knowledge of miRNAs in the regeneration of skeletal muscle. This study reviews the miRNAs related with regeneration of skeletal muscle, and discuss the regulation of their expression in muscles, and emerging themes of miRNA regulation.

Data Integration in Functional Analysis of MicroRNAs

The discovery of microRNAs (miRNAs), about a decade ago, has completely changed our understanding of the complexity of gene regulatory networks. It has already been shown that they are abundantly found in many organisms and can regulate hundreds of genes in post-transcriptional level. To elucidate the individual or co-operative effects of miRNAs, it is required to place them in the overall network of gene regulation and link them to other pathways and systems-level processes. One key step in this effort is predicting targets of individual miRNAs. Although current tools are helpful in predicting miRNA-mRNA binding to a considerable extent, they are not able to model many-to-many relationships between miRNAs and their targets using solely sequence information. Therefore, other types of information sources have been employed for better prediction of these functional relationships. This report focuses on the state-of-theart solutions and current challenges on mining miRNA-related data to discover the systems-level role of miRNAs, with an emphasis on the integration of different information sources. We aim to provide new insights for fusion of different types of biochemical and experimental information sources which may facilitate functional analysis of miRNAs. Keywords: microRNA activity, gene regulatory networks, target prediction, miRNA and mRNA sequence, Context Sensitivity, Systems-Level Measurements, Sequence-based target predictions, mRNA expression, TargetBoost, MicroInspector

MiRNAS in normal and diseased skeletal muscle

The last 20 years have witnessed major advances in the understanding of muscle diseases and significant inroads are being made to treat muscular dystrophy. However, no curative therapy is currently available for any of the muscular dystrophies, despite the immense progress made using several approaches and only palliative and symptomatic treatment is available for patients. The discovery of miRNAs as new and important regulators of gene expression is expected to broaden our biological understanding of the regulatory mechanism in muscle by adding another dimension of regulation to the diversity and complexity of gene-regulatory networks. As important regulators of muscle development, unravelling the regulatory circuits involved may be challenging, given that a single miRNA can regulate the expression of many mRNA targets. Although the identification of the regulatory targets of miRNAs in muscle is a challenge, it will be critical for placing them in genetic pathways and biological contexts. Therefore, combining informatics, biochemical and genetic approaches will not only expected to reveal the elucidation of the miRNA regulatory network in skeletal muscle and to bring a better knowledge on muscle tissue regulation but will also raise new opportunities for therapeutic intervention in muscular dystrophies by identifying candidate miRNAs as potential targets for clinical application.

Structure of regulatory networks and diversity of gene expression patterns

Complexity of gene regulatory network has been considered to be responsible for diversity of cells. Different types of cells, characterized by the expression patterns of genes, are produced in early development through the dynamics of gene activities based on the regulatory network. However, very little is known about relationship between the structure of regulatory networks and the dynamics of gene activities. In this paper, I introduce new idea of “steady-state compatibility” by which the diversity of possible gene activities can be determined from the topological structure of gene regulatory networks. The basic premise is very simple: the activity of a gene should be a function of the controlling genes. Thus, a gene should always show unique expression activity if the activities of the controlling genes are unique. Based on this, the maximum possible diversity of steady states is determined using only information regarding regulatory linkages without knowing the regulatory functions of genes. By extending this idea, some general properties were derived. For example, multiple loop structures in regulatory networks are necessary for increasing the diversity of gene activity. On the other hand, connected multiple loops sharing the same genes do not increase the diversity. The method was applied to a gene regulatory network responsible for early development in a sea urchin species. A set of important genes responsible for generating diversities of gene activities was derived based on the concept of compatibility of steady states.

Inference of Gene Regulatory Networks and its Validation

Genes encode proteins, some of which in turn regulate other genes. Such interactions make up a gene regulatory network. The understanding and unraveling of gene regulatory networks have been proven very useful in disease diagnosis and genomic drug design. Due to the complexity of gene regulatory networks, the completely understanding of their dynamics is difficult to achieve only through biological experiments without any computational aids. As a consequence, computational models for gene regulatory networks are indispensable. Recently a wide variety of different computational models have been proposed for inferring gene regulatory networks. This paper surveys some of computational models for inferring large gene regulatory networks, in particular, Boolean network model, differential/ difference equation models, and state-space models. Some advantages and disadvantages of these models are commented on. Some criteria for validating the inferred gene regulatory networks are also discussed from the bioinformatics perspective. Finally, several directions of the future work for modeling gene regulatory networks are proposed.