Molecular
Regulatory Networks 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. Zavolan M 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 Schadt EE. 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. Martinez-Morales JR 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.twComparative 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. Walhout AJ 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.
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