Integrated analysis of microRNA and mRNA
Big Data in Transcriptomics & Molecular Biology
Intro -- Gene Regulatory Networks
Eric Davidson and Michael Levin
PNAS vol. 102 no. 14
The Special Feature on gene regulatory networks in this issue of PNAS highlights an emerging field in the biosciences: gene regulatory networks that control animal development. The complex control systems underlying development have probably been evolving for more than a billion years. They regulate the expression of thousands of genes in any given developmental process. They are essentially hardwired genomic regulatory codes, the role of which is to specify the sets of genes that must be expressed in specific spatial and temporal patterns. In physical terms, these control systems consist of many thousands of modular DNA sequences. Each such module receives and integrates multiple inputs, in the form of regulatory proteins (activators and repressors) that recognize specific sequences within them. The end result is the precise transcriptional control of the associated genes. Some regulatory modules control the activities of the genes encoding regulatory proteins. Functional linkages between these particular genes, and their associated regulatory modules, define the core networks underlying development.
Gene regulatory networks explicitly represent the causality of developmental processes. They explain exactly how genomic sequence encodes the regulation of expression of the sets of genes that progressively generate developmental patterns and execute the construction of multiple states of differentiation.
As this new field takes shape, the following are among the key emergent concepts:
Gene regulatory networks for development.
Michael Levine and Eric H. Davidson
PNAS (2005) 102 (14): 4936–4942
The genomic program for development operates primarily by the regulated expression of genes encoding transcription factors and components of cell signaling pathways. This program is executed by cis-regulatory DNAs (e.g., enhancers and silencers) that control gene expression. The regulatory inputs and functional outputs of developmental control genes constitute network-like architectures. In this PNAS Special Feature are assembled papers on developmental gene regulatory networks governing the formation of various tissues and organs in nematodes, flies, sea urchins, frogs, and mammals. Here, we survey salient points of these networks, by using as reference those governing specification of the endomesoderm in sea urchin embryos and dorsal–ventral patterning in the Drosophila embryo.
miRNA-miRNA crosstalk -- from genomics to phenomics.
Xu J, Shao T, Ding N, Li Y, Li X
Brief Bioinform. 2016 Aug 21
The discovery of microRNA (miRNA)-miRNA crosstalk has greatly improved our understanding of complex gene regulatory networks in normal and disease-specific physiological conditions. Numerous approaches have been proposed for modeling miRNA-miRNA networks based on genomic sequences, miRNA-mRNA regulation, functional information and phenomics alone, or by integrating heterogeneous data. In addition, it is expected that miRNA-miRNA crosstalk can be reprogrammed in different tissues or specific diseases. Thus, transcriptome data have also been integrated to construct context-specific miRNA-miRNA networks. In this review, we summarize the state-of-the-art miRNA-miRNA network modeling methods, which range from genomics to phenomics, where we focus on the need to integrate heterogeneous types of omics data. Finally, we suggest future directions for studies of crosstalk of noncoding RNAs. This comprehensive summarization and discussion elucidated in this work provide constructive insights into miRNA-miRNA crosstalk.
Developmental gene regulatory networks in sea urchins and what we can learn from them.
Martik ML, Lyons DC, McClay DR
F1000Res. 2016 Feb 22;5. pii: F1000 Faculty Rev-203 -- eCollection 2016
Sea urchin embryos begin zygotic transcription shortly after the egg is fertilized. Throughout the cleavage stages a series of transcription factors are activated and, along with signaling through a number of pathways, at least 15 different cell types are specified by the beginning of gastrulation. Experimentally, perturbation of contributing transcription factors, signals and receptors and their molecular consequences enabled the assembly of an extensive gene regulatory network model. That effort, pioneered and led by Eric Davidson and his laboratory, with many additional insights provided by other laboratories, provided the sea urchin community with a valuable resource. Here we describe the approaches used to enable the assembly of an advanced gene regulatory network model describing molecular diversification during early development. We then provide examples to show how a relatively advanced authenticated network can be used as a tool for discovery of how diverse developmental mechanisms are controlled and work.
Inferring gene expression regulatory networks from high-throughput measurements.
Methods. 2015 Sep 1;85: 1-2
While molecular biology has meticulously and successfully built the catalog of components for a large number of cell types, recent technological developments have broadened the spectrum and resolution of measurement techniques. These have led to a flourishing of a number of subfields, including mathematical biology, computational biology, systems biology, synthetic biology, etc. Although the precise definitions and boundaries of these partially overlapping subfields can be debated, it is clear that the general availability of high-throughput approaches of increasing quantitative accuracy has shifted the focus away from single components toward quantitative modeling of whole-cell behaviors. The vision behind this volume was to illustrate some of these approaches and the insights that they have brought to the field. We focused on gene expression, which in eukaryotic cells is a very complex process of many steps, all of which are subject to regulation. We hope that readers find this perspective motivating. I am grateful to the contributing authors that participated in this endeavor, to Dr. Adolf for the invitation to edit such an issue, and to Tiffany Hicks and Liz Weishaar for their great help in seeing the project to completion.
Regulatory networks of non-coding RNAs in brown/beige adipogenesis.
Xu S, Chen P, Sun L
Biosci Rep. 2015 35(5)
BAT (brown adipose tissue) is specialized to burn fatty acids for heat generation and energy expenditure to defend against cold and obesity. Accumulating studies have demonstrated that manipulation of BAT activity through various strategies can regulate metabolic homoeostasis and lead to a healthy phenotype. Two classes of ncRNA (non-coding RNA), miRNA and lncRNA (long non-coding RNA), play crucial roles in gene regulation during tissue development and remodelling. In the present review, we summarize recent findings on regulatory role of distinct ncRNAs in brown/beige adipocytes, and discuss how these ncRNA regulatory networks contribute to brown/beige fat development, differentiation and function. We suggest that targeting ncRNAs could be an attractive approach to enhance BAT activity for protecting the body against obesity and its pathological consequences.
ARMADA -- Using motif activity dynamics to infer gene regulatory networks from gene expression data.
Pemberton-Ross PJ, Pachkov M, van Nimwegen E
Methods. 2015 Sep 1;85: 62-74
Analysis of gene expression data remains one of the most promising avenues toward reconstructing genome-wide gene regulatory networks. However, the large dimensionality of the problem prohibits the fitting of explicit dynamical models of gene regulatory networks, whereas machine learning methods for dimensionality reduction such as clustering or principal component analysis typically fail to provide mechanistic interpretations of the reduced descriptions. To address this, we recently developed a general methodology called motif activity response analysis (MARA) that, by modeling gene expression patterns in terms of the activities of concrete regulators, accomplishes dramatic dimensionality reduction while retaining mechanistic biological interpretations of its predictions (Balwierz, 2014). Here we extend MARA by presenting ARMADA, which models the activity dynamics of regulators across a time course, and infers the causal interactions between the regulators that drive the dynamics of their activities across time. We have implemented ARMADA as part of our ISMARA webserver, ismara.unibas.ch, allowing any researcher to automatically apply it to any gene expression time course. To illustrate the method, we apply ARMADA to a time course of human umbilical vein endothelial cells treated with TNF. Remarkably, ARMADA is able to reproduce the complex observed motif activity dynamics using a relatively small set of interactions between the key regulators in this system. In addition, we show that ARMADA successfully infers many of the key regulatory interactions known to drive this inflammatory response and discuss several novel interactions that ARMADA predicts. In combination with ISMARA, ARMADA provides a powerful approach to generating plausible hypotheses for the key interactions between regulators that control gene expression in any system for which time course measurements are available.Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways.
Transcriptional regulatory network during development in the olfactory epithelium.
Im S and Moon C
BMB Rep. 2015 48(11): 599-608
Regeneration, a process of reconstitution of the entire tissue, occurs throughout life in the olfactory epithelium (OE). Regeneration of OE consists of several stages: proliferation of progenitors, cell fate determination between neuronal and non-neuronal lineages, their differentiation and maturation. How the differentiated cell types that comprise the OE are regenerated, is one of the central questions in olfactory developmental neurobiology. The past decade has witnessed considerable progress regarding the regulation of transcription factors (TFs) involved in the remarkable regenerative potential of OE. Here, we review current state of knowledge of the transcriptional regulatory networks that are powerful modulators of the acquisition and maintenance of developmental stages during regeneration in the OE. Advance in our understanding of regeneration will not only shed light on the basic principles of adult plasticity of cell identity, but may also lead to new approaches for using stem cells and reprogramming after injury or degenerative neurological diseases.
A multilevel gamma-clustering layout algorithm for visualization of biological networks.
Hruz T, Wyss M, Lucas C, Laule O, von Rohr P, Zimmermann P, Bleuler S.
Adv Bioinformatics. 2013: 920325
Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. 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. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ -clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. 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.
King ZA, Dräger A, Ebrahim A, Sonnenschein N, Lewis NE, Palsson BO
PLoS Comput Biol. 2015 Aug 27;11(8): e1004321 -- eCollection 2015.
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. 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.g. RNA-Seq, proteomics, ChIP)-in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics). 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.
Molecular networks as sensors and drivers of common human diseases
Nature. 2009 Sep 10;461(7261): 218-223
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.
Posttranscriptional Regulatory Networks: From Expression Profi ling to Integrative Analysis of mRNA and MicroRNA Data.
Swanhild U. Meyer, Katharina Stoecker, Steffen Sass, Fabian J. Theis and Michael W. Pfaffl
Chapter 15 in Quantitative Real-Time PCR: Methods and Protocols (Methods in Molecular Biology)
by Roberto Biassoni, Alessandro Raso
Protein coding RNAs are posttranscriptionally regulated by microRNAs, a class of small noncoding RNAs. Insights in messenger RNA (mRNA) and microRNA (miRNA) regulatory interactions facilitate the understanding of fi ne-tuning of gene expression and might allow better estimation of protein synthesis. 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. One possible solution to predict rather reliable mRNA-miRNA relations in the specifi c biological context is to integrate real mRNA and miRNA transcriptomic data as well as in silico target predictions. 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.
Integrative Analysis of MicroRNA and mRNA Data Reveals an Orchestrated Function of MicroRNAs in Skeletal Myocyte Differentiation in Response to TNF-α or IGF1.
Meyer SU, Sass S, Mueller NS, Krebs S, Bauersachs S, Kaiser S, Blum H, Thirion C, Krause S, Theis FJ, Pfaffl MW
PLoS One. 2015 Aug 13;10(8):e0135284 -- eCollection 2015
INTRODUCTION: Skeletal muscle cell differentiation is impaired by elevated levels of the inflammatory cytokine tumor necrosis factor-α (TNF-α) with pathological significance in chronic diseases or inherited muscle disorders. Insulin like growth factor-1 (IGF1) positively regulates muscle cell differentiation. Both, TNF-α and IGF1 affect gene and microRNA (miRNA) expression in this process. However, computational prediction of miRNA-mRNA relations is challenged by false positives and targets which might be irrelevant in the respective cellular transcriptome context. Thus, this study is focused on functional information about miRNA affected target transcripts by integrating miRNA and mRNA expression profiling data.
METHODOLOGY & PRINCIPAL FINDINGS: Murine skeletal myocytes PMI28 were differentiated for 24 hours with concomitant TNF-α or IGF1 treatment. Both, mRNA and miRNA expression profiling was performed. The data-driven integration of target prediction and paired mRNA/miRNA expression profiling data revealed that i) the quantity of predicted miRNA-mRNA relations was reduced, ii) miRNA targets with a function in cell cycle and axon guidance were enriched, iii) differential regulation of anti-differentiation miR-155-5p and miR-29b-3p as well as pro-differentiation miR-335-3p, miR-335-5p, miR-322-3p, and miR-322-5p seemed to be of primary importance during skeletal myoblast differentiation compared to the other miRNAs, iv) the abundance of targets and affected biological processes was miRNA specific, and v) subsets of miRNAs may collectively regulate gene expression.
CONCLUSIONS: Joint analysis of mRNA and miRNA profiling data increased the process-specificity and quality of predicted relations by statistically selecting miRNA-target interactions. Moreover, this study revealed miRNA-specific predominant biological implications in skeletal muscle cell differentiation and in response to TNF-α or IGF1 treatment. Furthermore, myoblast differentiation-associated miRNAs are suggested to collectively regulate gene clusters and targets associated with enriched specific gene ontology terms or pathways. Predicted miRNA functions of this study provide novel insights into defective regulation at the transcriptomic level during myocyte proliferation and differentiation due to inflammatory stimuli.
MicroRNAs in Control of Plant Development.
Li C and Zhang B
J Cell Physiol. 2016 231(2): 303-313
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 21-24 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.
Inferred miRNA activity identifies miRNA-mediated regulatory networks underlying multiple cancers.
Lee E, Ito K, Zhao Y, Schadt EE, Irie HY, Zhu J
Bioinformatics. 2015 Sep 10
MOTIVATION: MicroRNAs (miRNAs) play a key role in regulating tumor progression and metastasis. Identifying key miRNAs, defined by their functional activities, can provide a deeper understanding of biology of miRNAs in cancer. However, miRNA expression level can't accurately reflect miRNA activity.
RESULTS: We developed a computational approach, ActMiR, for identifying active miRNAs and miRNA-mediated regulatory mechanisms. Applying ActMiR to four cancer datasets in The Cancer Genome Atlas (TCGA), we showed that (1) miRNA activity was tumor subtype specific; (2) genes correlated with inferred miRNA activities were more likely to enrich for miRNA binding motifs; (3) expression levels of these genes and inferred miRNA activities were more likely to be negatively correlated. For the four cancer types in TCGA we identified 77~229 key miRNAs for each cancer subtype and annotated their biological functions. The miRNA-target pairs, predicted by our ActMiR algorithm but not by correlation of miRNA expression levels, were experimentally validated. The functional activities of key miRNAs were further demonstrated to be associated with clinical outcomes for other cancer types using independent datasets. For ER-/HER2- breast cancers, we identified activities of key miRNAs let-7d and miR-18a as potential prognostic markers and validated them in two independent ER-/HER2- breast cancer data sets. 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.AVAILABILITY: the software is freely available at http://research.mssm.edu/integrative-network-biology/Software.html
Toward understanding the evolution of vertebrate gene regulatory networks: comparative genomics and epigenomic approaches.
Brief Funct Genomics. 2015 Aug 20.
Vertebrates, as most animal phyla, originated >500 million years ago during the Cambrian explosion, and progressively radiated into the extant classes. 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.
Visualization of omics data for systems biology
Gehlenborg N, O'Donoghue SI, Baliga NS, Goesmann A, Hibbs MA, Kitano H, Kohlbacher O, Neuweger H, Schneider R, Tenenbaum D, Gavin AC.
Nat Methods. 2010 7(3 Suppl): S56-68
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.
Images made with R http://www.r-project.org/
3Omics -- a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data
Kuo TC, Tian TF, Tseng YJ.
BMC Syst Biol. 2013 Jul 23;7:64
BACKGROUND: Integrative and comparative analyses of multiple transcriptomics, proteomics and metabolomics datasets require an intensive knowledge of tools and background concepts. 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. 3Omics' coexpression analysis assists in revealing functions shared among different omics datasets. 3Omics' phenotype analysis integrates Online Mendelian Inheritance in Man with available transcript or protein data. Pathway enrichment analysis on metabolomics data by 3Omics reveals enriched pathways in the KEGG/HumanCyc database. 3Omics performs statistical Gene Ontology-based functional enrichment analyses to display significantly overrepresented GO terms in transcriptomic experiments. 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.
CONCLUSIONS: 3Omics incorporates the advantages and functionality of existing software into a single platform, thereby simplifying data analysis and enabling the user to perform a one-click integrated analysis. Visualization and analysis results are downloadable for further user customization and analysis.The 3Omics software can be freely accessed at http://3omics.cmdm.tw
Comparative Analysis of Gene Regulatory Networks: From Network Reconstruction to Evolution.
Thompson D, Regev A, Roy S
Annu Rev Cell Dev Biol. 2015 Sept 3rd
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. Expected final online publication date for the Annual Review of Cell and Developmental Biology Volume 31 is October 06, 2015. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.myGRN: a database and visualisation system for the storage and analysis of developmental genetic regulatory networks.
Bacha J, Brodie JS, Loose MW
BMC Dev Biol. 2009 Jun 6;9: 33
BACKGROUND: Biological processes are regulated by complex interactions between transcription factors and signalling molecules, collectively described as Genetic Regulatory Networks (GRNs). 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. 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.
DESCRIPTION: We have developed myGRN (http://www.myGRN.org), a web application for storing and interrogating interaction data, with an emphasis on developmental processes. 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. 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.CONCLUSION: Here we are launching myGRN as a community-based repository for interaction networks, with a specific focus on developmental networks. We plan to extend its functionality, as well as use it to study networks involved in embryonic development in the future.
Plasticity of gene-regulatory networks controlling sex determination: of masters, slaves, usual suspects, newcomers, and usurpators.
Herpin A and Schartl M
EMBO Rep. 2015 Sep 9. pii: e201540667
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.
Differential combinatorial regulatory network analysis related to venous metastasis of hepatocellular carcinoma.
Zeng L, Yu J, Huang T, Jia H, Dong Q, He F, Yuan W, Qin L, Li Y, Xie L.
School of Life Science and Technology, Tongji University, Shanghai 200092, PR China.
BMC Genomics. 2012;13 Suppl 8: S14
BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most fatal cancers in the world, and metastasis is a significant cause to the high mortality in patients with HCC. 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.
METHODS: By utilizing both sequence information and parallel microRNA(miRNA) and mRNA expression data on the same cohort of HBV related HCC patients without or with venous metastasis, we constructed combinatorial regulatory networks of non-metastatic and metastatic HCC which contain transcription factor(TF) regulation and miRNA regulation. Differential regulation patterns, classifying marker modules, and key regulatory miRNAs were analyzed by comparing non-metastatic and metastatic networks.
RESULTS: Globally TFs accounted for the main part of regulation while miRNAs for the minor part of regulation. However miRNAs displayed a more active role in the metastatic network than in the non-metastatic one. Seventeen differential regulatory modules discriminative of the metastatic status were identified as cumulative-module classifier, which could also distinguish survival time. MiR-16, miR-30a, Let-7e and miR-204 were identified as key miRNA regulators contributed to HCC metastasis.
CONCLUSION: In this work we demonstrated an integrative approach to conduct differential combinatorial regulatory network analysis in the specific context venous metastasis of HBV-HCC. 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.
RNA regulatory networks in animals and plants: a long noncoding RNA perspective.
Bai Y, Dai X, Harrison AP, Chen M.
Brief Funct Genomics. 2015 Mar;14(2):91-101
A recent highlight of genomics research has been the discovery of many families of transcripts which have function but do not code for proteins. An important group is long noncoding RNAs (lncRNAs), which are typically longer than 200 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. We discuss the current knowledge of functional elements embedded in lncRNAs as well as insights into the lncRNA-based regulatory network in animals. We also describe genome-wide studies of large amount of lncRNAs in plants, as well as knowledge of selected plant lncRNAs with a focus on biotic/abiotic stress-responsive lncRNAs.
Regulatory microRNA network identification in bovine blastocyst development.
Goossens K, Mestdagh P, Lefever S, Van Poucke M, Van Zeveren A, van Soom A, Vandesompele J, Peelman LJ.
Ghent university, Department of Nutrition, Genetics and Ethology, Merelbeke, Belgium
Stem Cells Dev. 2013 Feb 11
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. 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. RT-qPCR based miRNA expression profiling revealed 8 upregulated miRNAs (miR-127, miR-130a, miR-155, miR-196a, miR-203, miR-28, miR-29c, miR-376a) and 4 downregulated miRNAs (miR-135a, miR-218, miR-335, miR-449b) in hatched blastocysts. Through an integrative analysis of matching miRNA and mRNA expression data, candidate miRNA-mRNA interaction pairs were prioritized for validation. Using an in vitro luciferase reporter assay we confirmed a direct interaction between miR-218 and CDH2, miR-218 and NANOG, and miR-449b and NOTCH1. By interfering with the FGF signaling pathway, we found functional evidence that miR-218, mainly expressed in the ICM, regulates the NANOG expression in the bovine blastocyst in response to FGF signaling. 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.
Methods Cell Biol. 2011;106:2271-288
The Caenorhabditis elegans hermaphrodite is a complex multicellular animal that is composed of 959 somatic cells. The C. elegans genome contains ∼20,000 protein-coding genes, 940 of which encode regulatory transcription factors (TFs). In addition, the worm genome encodes more than 100 microRNAs and many other regulatory RNA and protein molecules. Most C. elegans genes are subject to regulatory control, most likely by multiple regulators, and combined, this dictates the activation or repression of the gene and corresponding protein in the relevant cells and under the appropriate conditions. A major goal in C. elegans research is to determine the spatiotemporal expression pattern of each gene throughout development and in response to different signals, and to determine how this expression pattern is accomplished. Gene regulatory networks describe physical and/or functional interactions between genes and their regulators that result in specific spatiotemporal gene expression. 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. elegans. 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. elegans biology, but can also be applied to other model organisms and humans.
Integrated microRNA-mRNA analyses reveal OPLL specific microRNA regulatory network using high-throughput sequencing.
Xu C, Chen Y, Zhang H, Chen Y, Shen X, Shi C, Liu Y, Yuan W
Sci Rep. 2016 6: 21580
Ossification of the posterior longitudinal ligament (OPLL) is a genetic disorder which involves pathological heterotopic ossification of the spinal ligaments. Although studies have identified several genes that correlated with OPLL, the underlying regulation network is far from clear. Through small RNA sequencing, we compared the microRNA expressions of primary posterior longitudinal ligament cells form OPLL patients with normal patients (PLL) and identified 218 dysregulated miRNAs (FDR < 0.01). Furthermore, assessing the miRNA profiling data of multiple cell types, we found these dysregulated miRNAs were mostly OPLL specific. In order to decipher the regulation network of these OPLL specific miRNAs, we integrated mRNA expression profiling data with miRNA sequencing data. Through computational approaches, we showed the pivotal roles of these OPLL specific miRNAs in heterotopic ossification of longitudinal ligament by discovering highly correlated miRNA/mRNA pairs that associated with skeletal system development, collagen fibril organization, and extracellular matrix organization. The results of which provide strong evidence that the miRNA regulatory networks we established may indeed play vital roles in OPLL onset and progression. To date, this is the first systematic analysis of the micronome in OPLL, and thus may provide valuable resources in finding novel treatment and diagnostic targets of OPLL.
MicroRNA Regulatory Network Revealing the Mechanism of Inflammation in Atrial Fibrillation.
Zhang H, Liu L, Hu J, Song L
Med Sci Monit. 2015 21: 3505-3513
BACKGROUND: Atrial fibrillation (AF) is a highly prevalent condition associated with high morbidity and mortality that can cause or exacerbate heart failure and is an important risk factor for stroke. AF is the disorganized propagation of electrical activity in the atrium, which prevents organized contractions. However, the effect of microRNAs and the patterns of the regulatory network of AF remain vague.
MATERIAL AND METHODS: The mRNA expression data of atrial tissue splices from 3 conditions - permanent atrial fibrillation (AF), sinus rhythm (SR), and human left ventricular non-failing myocardium (LV) - were downloaded from GSE2240 and the differentially expressed genes (DEGs) between the 3 kinds of samples were calculated. Then we constructed 3 miRNA-DEGs networks and these networks were integrated to construct the final merged AF-related microRNA regulatory network. Finally, we constructed the miRNA-inflammation networks to detect the roles of miRNAs in inflammation development of AF.
RESULTS: This network included 108 DEGs, and 27 microRNAs and DEGs are regulated by both microRNAs. We found that a sub-network composed by miR-124, miR-183, miR-215, miR-192, and a DEG of EGR1 were all represents in these 3 networks. Based on functional enrichment analysis, some biological process, such as energy and glucan metabolic process and heart and blood vessel development, were found to be regulated by miRNAs in AF. Some miRNAs, such as miR-26b and miR-355p, were involved in inflammation in AF.
CONCLUSIONS: In conclusion, the microRNA regulatory network sheds new light on the molecular mechanism of AF with this non-coding regulated model.
Methods of integrating data to uncover genotype-phenotype interactions.
Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D
Nat Rev Genet. 2015 16(2): 85-97
Recent technological advances have expanded the breadth of available omic data, from whole-genome sequencing data, to extensive transcriptomic, methylomic and metabolomic data. A key goal of analyses of these data is the identification of effective models that predict phenotypic traits and outcomes, elucidating important biomarkers and generating important insights into the genetic underpinnings of the heritability of complex traits. There is still a need for powerful and advanced analysis strategies to fully harness the utility of these comprehensive high-throughput data, identifying true associations and reducing the number of false associations. In this Review, we explore the emerging approaches for data integration - including meta-dimensional and multi-staged analyses - which aim to deepen our understanding of the role of genetics and genomics in complex outcomes. With the use and further development of these approaches, an improved understanding of the relationship between genomic variation and human phenotypes may be revealed.