The Experts below are selected from a list of 119787 Experts worldwide ranked by ideXlab platform
Eric H. Davidson - One of the best experts on this subject based on the ideXlab platform.
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A gene Regulatory Network controlling the embryonic specification of endoderm
Nature, 2011Co-Authors: Isabelle S. Peter, Eric H. DavidsonAbstract:Specification of endoderm is the prerequisite for gut formation in the embryogenesis of bilaterian organisms. Modern lineage labelling studies have shown that in the sea urchin embryo model system, descendants of the veg1 and veg2 cell lineages produce the endoderm, and that the veg2 lineage also gives rise to mesodermal cell types. It is known that Wnt/β-catenin signalling is required for endoderm specification and Delta/Notch signalling is required for mesoderm specification. Some direct cis-Regulatory targets of these signals have been found and various phenomenological patterns of gene expression have been observed in the pre-gastrular endomesoderm. However, no comprehensive, causal explanation of endoderm specification has been conceived for sea urchins, nor for any other deuterostome. Here we propose a model, on the basis of the underlying genomic control system, that provides such an explanation, built at several levels of biological organization. The hardwired core of the control system consists of the cis-Regulatory apparatus of endodermal Regulatory genes, which determine the relationship between the inputs to which these genes are exposed and their outputs. The architecture of the Network circuitry controlling the dynamic process of endoderm specification then explains, at the system level, a sequence of developmental logic operations, which generate the biological process. The control system initiates non-interacting endodermal and mesodermal gene Regulatory Networks in veg2-derived cells and extinguishes the endodermal gene Regulatory Network in mesodermal precursors. It also generates a cross-Regulatory Network that specifies future anterior endoderm in veg2 descendants and institutes a distinct Network specifying posterior endoderm in veg1-derived cells. The Network model provides an explanatory framework that relates endoderm specification to the genomic Regulatory code.
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gene Regulatory Network controlling embryonic specification in the sea urchin
Current Opinion in Genetics & Development, 2004Co-Authors: Paola Oliveri, Eric H. DavidsonAbstract:The current state of the gene Regulatory Network for endomesoderm specification in sea urchin embryos is reviewed. The Network was experimentally defined, and is presented as a predictive map of cis-Regulatory inputs and functional Regulatory gene interconnections (updated versions of the Network and most of the underlying data are at http://sugp.caltech.edu/endomes/). The Network illuminates the ‘whys’ of many aspects of zygotic control in early sea urchin development, both spatial and temporal. The Network includes almost 50 genes, and these are organized in subcircuits, each of which executes a particular Regulatory function.
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a genomic Regulatory Network for development
Science, 2002Co-Authors: Eric H. Davidson, Paola Oliveri, Jonathan P Rast, Andrew Ransick, Cristina Calestani, Chiouhwa Yuh, Takuya Minokawa, Gabriele Amore, Veronica F Hinman, Cesar ArenasmenaAbstract:Development of the body plan is controlled by large Networks of Regulatory genes. A gene Regulatory Network that controls the specification of endoderm and mesoderm in the sea urchin embryo is summarized here. The Network was derived from large-scale perturbation analyses, in combination with computational methodologies, genomic data, cis-Regulatory analysis, and molecular embryology. The Network contains over 40 genes at present, and each node can be directly verified at the DNA sequence level by cis-Regulatory analysis. Its architecture reveals specific and general aspects of development, such as how given cells generate their ordained fates in the embryo and why the process moves inexorably forward in developmental time.
Malcolm Whiteway - One of the best experts on this subject based on the ideXlab platform.
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evolutionary tinkering with conserved components of a transcriptional Regulatory Network
PLOS Biology, 2010Co-Authors: Hugo Lavoie, Herve Hogues, Jaideep Mallick, Adnane Sellam, Andre Nantel, Malcolm WhitewayAbstract:Gene expression variation between species is a major contributor to phenotypic diversity, yet the underlying flexibility of transcriptional Regulatory Networks remains largely unexplored. Transcription of the ribosomal regulon is a critical task for all cells; in S. cerevisiae the transcription factors Rap1, Fhl1, Ifh1, and Hmo1 form a multi-subunit complex that controls ribosomal gene expression, while in C. albicans this regulation is under the control of Tbf1 and Cbf1. Here, we analyzed, using full-genome transcription factor mapping, the roles, in both S. cerevisiae and C. albicans, of each orthologous component of this complete set of regulators. We observe dramatic changes in the binding profiles of the generalist regulators Cbf1, Hmo1, Rap1, and Tbf1, while the Fhl1-Ifh1 dimer is the only component involved in ribosomal regulation in both fungi: it activates ribosomal protein genes and rDNA expression in a Tbf1-dependent manner in C. albicans and a Rap1-dependent manner in S. cerevisiae. We show that the transcriptional Regulatory Network governing the ribosomal expression program of two related yeast species has been massively reshaped in cis and trans. Changes occurred in transcription factor wiring with cellular functions, movements in transcription factor hierarchies, DNA-binding specificity, and Regulatory complexes assembly to promote global changes in the architecture of the fungal transcriptional Regulatory Network.
Ritsert C Jansen - One of the best experts on this subject based on the ideXlab platform.
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Regulatory Network construction in arabidopsis by using genome wide gene expression quantitative trait loci
Proceedings of the National Academy of Sciences of the United States of America, 2007Co-Authors: Joost J B Keurentjes, Inez Terpstra, Juan M Garcia, Guido Van Den Ackerveken, Basten L Snoek, Anton J M Peeters, Dick Vreugdenhil, Maarten Koornneef, Ritsert C JansenAbstract:Accessions of a plant species can show considerable genetic differences that are analyzed effectively by using recombinant inbred line (RIL) populations. Here we describe the results of genome-wide expression variation analysis in an RIL population of Arabidopsis thaliana. For many genes, variation in expression could be explained by expression quantitative trait loci (eQTLs). The nature and consequences of this variation are discussed based on additional genetic parameters, such as heritability and transgression and by examining the genomic position of eQTLs versus gene position, polymorphism frequency, and gene ontology. Furthermore, we developed an approach for genetic Regulatory Network construction by combining eQTL mapping and regulator candidate gene selection. The power of our method was shown in a case study of genes associated with flowering time, a well studied Regulatory Network in Arabidopsis. Results that revealed clusters of coregulated genes and their most likely regulators were in agreement with published data, and unknown relationships could be predicted.
Kazuko Yamaguchishinozaki - One of the best experts on this subject based on the ideXlab platform.
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transcriptional Regulatory Network of plant heat stress response
Trends in Plant Science, 2017Co-Authors: Naohiko Ohama, Hikaru Sato, Kazuo Shinozaki, Kazuko YamaguchishinozakiAbstract:Heat stress (HS) is becoming an increasingly significant problem for food security as global warming progresses. Recent studies have elucidated the complex transcriptional Regulatory Networks involved in HS. Here, we provide an overview of current knowledge regarding the transcriptional Regulatory Network and post-translational regulation of the transcription factors involved in the HS response. Increasing evidence suggests that epigenetic regulation and small RNAs are important in heat-induced transcriptional responses and stress memory. It remains to be elucidated how plants sense and respond to HS. Several recent reports have discussed the heat sensing and signaling that activate transcriptional cascades; thus, we also highlight future directions of promoting crop tolerance to HS using these factors or other strategies for agricultural applications.
Arnaud Bonnaffoux - One of the best experts on this subject based on the ideXlab platform.
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wasabi a dynamic iterative framework for gene Regulatory Network inference
BMC Bioinformatics, 2019Co-Authors: Arnaud Bonnaffoux, Ulysse Herbach, Angelique Richard, Anissa Guillemin, Sandrine Goningiraud, Pierrealexis Gros, Olivier GandrillonAbstract:Background Inference of gene Regulatory Networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene Regulatory Network inference, with both successes and limitations.
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WASABI: a dynamic iterative framework for gene Regulatory Network inference
BMC Bioinformatics, 2019Co-Authors: Arnaud Bonnaffoux, Ulysse Herbach, Angelique Richard, Anissa Guillemin, Pierrealexis Gros, Sandrine Gonin-giraud, Olivier GandrillonAbstract:Background Inference of gene Regulatory Networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene Regulatory Network inference, with both successes and limitations. Results In the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical Network from time-stamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that the information provided by an external stimulus will affect genes one-by-one through a cascade, like waves spreading through a Network. This concept allows us to infer the Network one gene at a time, after genes have been ordered regarding their time of regulation. We then demonstrate the ability of WASABI to correctly infer small Networks, which have been simulated in silico using a mechanistic model consisting of coupled piecewise-deterministic Markov processes for the proper description of gene expression at the single-cell level. We finally apply WASABI on in vitro generated data on an avian model of erythroid differentiation. The structure of the resulting gene Regulatory Network sheds a new light on the molecular mechanisms controlling this process. In particular, we find no evidence for hub genes and a much more distributed Network structure than expected. Interestingly, we find that a majority of genes are under the direct control of the differentiation-inducing stimulus. Conclusions Together, these results demonstrate WASABI versatility and ability to tackle some general gene Regulatory Networks inference issues. It is our hope that WASABI will prove useful in helping biologists to fully exploit the power of time-stamped single-cell data.
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wasabi a dynamic iterative framework for gene Regulatory Network inference
bioRxiv, 2018Co-Authors: Arnaud Bonnaffoux, Ulysse Herbach, Angelique Richard, Anissa Guillemin, Pierrealexis Gros, Sandrine Giraud, Olivier GandrillonAbstract:Abstract Inference of gene Regulatory Networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene Regulatory Network inference, with both successes and limitations. In the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical Network from time-stamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that the information provided by an external stimulus will affect genes one-by-one through a cascade, like waves spreading through a Network. This concept allows us to infer the Network one gene at a time, after genes have been ordered regarding their time of regulation. We then demonstrate the ability of WASABI to correctly infer small Networks, which have been simulated in silico using a mechanistic model consisting of coupled piecewise-deterministic Markov processes for the proper description of gene expression at the single-cell level. We finally apply WASABI on in vitro generated data on an avian model of erythroid differentiation. The structure of the resulting gene Regulatory Network sheds a fascinating new light on the molecular mechanisms controlling this process. In particular, we find no evidence for hub genes and a much more distributed Network structure than expected. Interestingly, we find that a majority of genes are under the direct control of the differentiation-inducing stimulus. In conclusion, WASABI is a versatile algorithm which should help biologists to fully exploit the power of time-stamped single-cell data.
