The Regulatory Genome: Gene Regulatory Networks In Development And Evolution

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His concept of the significant roles of gene regulatory networks in development and evolution can be clearly understood using the aforementioned key terms I strongly recommend this book to young scientists with multidisciplinary talents, who will advance the author's idea of the regulatory genome into its next phase, a voyage into the sea of genome complexity Davidson has developed his idea of the gene regulatory network in development and evolution for more than 35 years and has raised it finally to the level of a real paradigm. Isabelle S. Over the last seven years they have co-authored a series of works on experimental, conceptual and computational analyses of developmental gene regulatory networks, including their evolutionary significance.

The discussions and conceptual explorations occasioned by this collaboration produced the new synthetic views encompassed in this book, building on decades of earlier work summarized in the and Academic Press books by Eric H. Du kanske gillar. Lifespan David Sinclair Inbunden.

Inbunden Engelska, Spara som favorit. Skickas inom vardagar. Laddas ned direkt. Gene regulatory networks are the most complex, extensive control systems found in nature. The interaction between biology and evolution has been the subject of great interest in recent years. The author, Eric Davidson, has been instrumental in elucidating this relationship.

He is a world renowned scientist and a major contributor to the field of developmental biology. New insights into the mechanisms of body plan evolution are derived from considerations of the consequences of change in developmental gene regulatory networks. Adv Bioinformatics. The visualization of the corresponding network is challenging due to the size and density of edges.

In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs.

Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs. Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps.

Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way.

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Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types e. Third, Escher harnesses the strengths of web technologies SVG, D3, developer tools so that visualizations can be rapidly adapted, extended, shared, and embedded.

This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools. The molecular biology revolution led to an intense focus on the study of interactions between DNA, RNA and protein biosynthesis in order to develop a more comprehensive understanding of the cell.

One consequence of this focus was a reduced attention to whole-system physiology, making it difficult to link molecular biology to clinical medicine. Equipped with the tools emerging from the genomics revolution, we are now in a position to link molecular states to physiological ones through the reverse engineering of molecular networks that sense DNA and environmental perturbations and, as a result, drive variations in physiological states associated with disease.

Swanhild U.

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Theis and Michael W. However, in silico predictions of mRNA—microRNA interactions do not take into account the specifi c transcriptomic status of the biological system and are biased by false positives. This chapter addresses the workfl ow and methods one can apply for expression profi ling and the integrative analysis of mRNA and miRNA data, as well as how to analyze and interpret results, and how to build up models of posttranscriptional regulatory networks.

In the long evolutionary history, plant has evolved elaborate regulatory network to control functional gene expression for surviving and thriving, such as transcription factor-regulated transcriptional programming. However, plenty of evidences from the past decade studies demonstrate that the nucleotides small RNA molecules, majorly microRNAs miRNAs play dominant roles in post-transcriptional gene regulation through base pairing with their complementary mRNA targets, especially prefer to target transcription factors in plants.

Here, we review current progresses on miRNA-controlled plant development, from miRNA biogenesis dysregulation-caused pleiotropic developmental defects to specific developmental processes, such as SAM regulation, leaf and root system regulation, and plant floral transition. We also summarize some miRNAs that are experimentally proved to greatly affect crop plant productivity and quality.

In addition, recent reports show that a single miRNA usually displays multiple regulatory roles, such as organ development, phase transition, and stresses responses. Thus, we infer that miRNA may act as a node molecule to coordinate the balance between plant development and environmental clues, which may shed the light on finding key regulator or regulatory pathway for uncovering the mysterious molecular network. Identifying key miRNAs, defined by their functional activities, can provide a deeper understanding of biology of miRNAs in cancer. The functional activities of key miRNAs were further demonstrated to be associated with clinical outcomes for other cancer types using independent datasets.

Our work provides a novel scheme to facilitate our understanding of miRNA. In summary, inferred activity of key miRNA provided a functional link to its mediated regulatory network, and can be used to robustly predict patient's survival. Inferring the evolutionary history of the group requires understanding the architecture of the developmental programs that constrain the vertebrate anatomy. Here, I review recent comparative genomic and epigenomic studies, based on ChIP-seq and chromatin accessibility, which focus on the identification of functionally equivalent cis-regulatory modules among species.

This pioneer work, primarily centered in the mammalian lineage, has set the groundwork for further studies in representative vertebrate and chordate species. Mapping of active regulatory regions across lineages will shed new light on the evolutionary forces stabilizing ancestral developmental programs, as well as allowing their variation to sustain morphological adaptations on the inherited vertebrate body plan.

High-throughput studies of biological systems are rapidly accumulating a wealth of 'omics'-scale data. Visualization is a key aspect of both the analysis and understanding of these data, and users now have many visualization methods and tools to choose from. The challenge is to create clear, meaningful and integrated visualizations that give biological insight, without being overwhelmed by the intrinsic complexity of the data. In this review, we discuss how visualization tools are being used to help interpret protein interaction, gene expression and metabolic profile data, and we highlight emerging new directions.

Thus, it is challenging for users to perform such analyses, highlighting the need for a single tool for such purposes. The 3Omics one-click web tool was developed to visualize and rapidly integrate multiple human inter- or intra-transcriptomic, proteomic, and metabolomic data by combining five commonly used analyses: correlation networking, coexpression, phenotyping, pathway enrichment, and GO Gene Ontology enrichment. RESULTS: 3Omics generates inter-omic correlation networks to visualize relationships in data with respect to time or experimental conditions for all transcripts, proteins and metabolites.

If only two of three omics datasets are input, then 3Omics supplements the missing transcript, protein or metabolite information related to the input data by text-mining the PubMed database. Although the principal application of 3Omics is the integration of multiple omics datasets, it is also capable of analyzing individual omics datasets.

The information obtained from the analyses of 3Omics in Case Studies 1 and 2 are also in accordance with comprehensive findings in the literature. Visualization and analysis results are downloadable for further user customization and analysis. Regulation of gene expression is central to many biological processes.

Although reconstruction of regulatory circuits from genomic data alone is therefore desirable, this remains a major computational challenge. Comparative approaches that examine the conservation and divergence of circuits and their components across strains and species can help reconstruct circuits as well as provide insights into the evolution of gene regulatory processes and their adaptive contribution.

In recent years, advances in genomic and computational tools have led to a wealth of methods for such analysis at the sequence, expression, pathway, module, and entire network level. Here, we review computational methods developed to study transcriptional regulatory networks using comparative genomics, from sequences to functional data. We highlight how these methods use evolutionary conservation and divergence to reliably detect regulatory components as well as estimate the extent and rate of divergence. Finally, we discuss the promise and open challenges in linking regulatory divergence to phenotypic divergence and adaptation.

The characterisation of these networks to reveal regulatory mechanisms is a long-term goal of many laboratories. However compiling, visualising and interacting with such networks is non-trivial. Current tools and databases typically focus on GRNs within simple, single celled organisms. However, data is available within the literature describing regulatory interactions in multi-cellular organisms, although not in any systematic form.

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This is particularly true within the field of developmental biology, where regulatory interactions should also be tagged with information about the time and anatomical location of development in which they occur. Users can submit interaction and gene expression data, either curated from published sources or derived from their own unpublished data. All interactions associated with publications are publicly visible, and unpublished interactions can only be shared between collaborating labs prior to publication.

Users can group interactions into discrete networks based on specific biological processes. Various filters allow dynamic production of network diagrams based on a range of information including tissue location, developmental stage or basic topology.

The Regulatory Genome

Individual networks can be viewed using myGRV, a tool focused on displaying developmental networks, or exported in a range of formats compatible with third party tools. Networks can also be analysed for the presence of common network motifs. We demonstrate the capabilities of myGRN using a network of zebrafish interactions integrated with expression data from the zebrafish database, ZFIN. Sexual dimorphism is one of the most pervasive and diverse features of animal morphology, physiology, and behavior.

Despite the generality of the phenomenon itself, the mechanisms controlling how sex is determined differ considerably among various organismic groups, have evolved repeatedly and independently, and the underlying molecular pathways can change quickly during evolution.

Even within closely related groups of organisms for which the development of gonads on the morphological, histological, and cell biological level is undistinguishable, the molecular control and the regulation of the factors involved in sex determination and gonad differentiation can be substantially different. The biological meaning of the high molecular plasticity of an otherwise common developmental program is unknown. While comparative studies suggest that the downstream effectors of sex-determining pathways tend to be more stable than the triggering mechanisms at the top, it is still unclear how conserved the downstream networks are and how all components work together.

After many years of stasis, when the molecular basis of sex determination was amenable only in the few classical model organisms fly, worm, mouse , recently, sex-determining genes from several animal species have been identified and new studies have elucidated some novel regulatory interactions and biological functions of the downstream network, particularly in vertebrates.

These data have considerably changed our classical perception of a simple linear developmental cascade that makes the decision for the embryo to develop as male or female, and how it evolves. However, the molecular mechanism behind HCC metastasis is not fully understood. Study of regulatory networks may help investigate HCC metastasis in the way of systems biology profiling. Differential regulation patterns, classifying marker modules, and key regulatory miRNAs were analyzed by comparing non-metastatic and metastatic networks.

However miRNAs displayed a more active role in the metastatic network than in the non-metastatic one.

Structure, function, and evolution of gene regulatory networks by Erik van Nimwegen

Seventeen differential regulatory modules discriminative of the metastatic status were identified as cumulative-module classifier, which could also distinguish survival time. Our results proposed possible transcriptional regulatory patterns underlying the different metastatic subgroups of HCC. The workflow in this study can be applied in similar context of cancer research and could also be extended to other clinical topics.

Brief Funct Genomics. An important group is long noncoding RNAs lncRNAs , which are typically longer than nt, and whose members originate from thousands of loci across genomes. We review progress in understanding the biogenesis and regulatory mechanisms of lncRNAs.

We describe diverse computational and high throughput technologies for identifying and studying lncRNAs.

Endomesoderm & Ectoderm Models

We discuss the current knowledge of functional elements embedded in lncRNAs as well as insights into the lncRNA-based regulatory network in animals. Mammalian blastocyst formation is characterized by two lineage segregations resulting in the formation of the trophectoderm, the hypoblast and the epiblast cell lineages. Cell fate determination during these early lineage segregations is associated with changes in the expression of specific transcription factors.

In addition to transcription factor based control, it has become clear that also microRNAs miRNAs play an important role in the posttranscriptional regulation of pluripotency and differentiation.

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To elucidate the role of miRNAs in early lineage segregation, we compared the miRNA expression in early bovine blastocysts with the more advanced stage of hatched blastocysts. The results of this study expand our knowledge about the miRNA signature of the bovine blastocyst and of the interactions between miRNAs and cell fate regulating transcription factors.

Gene-centered regulatory network mapping. The C. Most C. A major goal in C. Such regulators can act at transcriptional or post-transcriptional levels. Here, I will discuss the methods that can be used to delineate gene regulatory networks in C. I will mostly focus on gene-centered yeast one-hybrid Y1H assays that are used to map interactions between non-coding genic regions, such as promoters, and regulatory TFs.

The approaches discussed here are not only relevant to C.