scholarly journals Evo-engineering and the Cellular and Molecular Origins of the Vertebrate Spinal Cord

2016 ◽  
Author(s):  
Ben Steventon ◽  
Alfonso Martinez Arias
Keyword(s):  
Spinal Cord ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Systems Approach ◽  
Continuous Process ◽  
Neural Plate ◽  
Model Organisms ◽  
Self Renewal ◽  
Gene Regulatory ◽  
Vertebrate Embryos

AbstractThe formation of the spinal cord during early embryonic development in vertebrate embryos is a continuous process that begins at gastrulation and continues through to the completion of somitogenesis. Despite the conserved usage of patterning mechanisms and gene regulatory networks that act to generate specify spinal cord progenitors, there now exists two seemingly disparate models to account for their action. In the first, a posterior localized signalling source transforms previously anterior-specified neural plate into the spinal cord. In the second, a population of bipotent stem cells undergo continuous self-renewal and differentiation to progressively lay down the spinal cord and axial mesoderm by posterior growth. Whether this represents fundamental differences between the experimental model organisms utilised in the generation of these models remains to be addressed. Here we review lineage studies across four key vertebrate models: mouse, chicken, Xenopus and zebrafish and relate this to the underlying gene regulatory networks that are known to be required for spinal cord formation. We propose that by applying a dynamical systems approach to understanding how distinct neural and mesodermal fates arise from a bipotent progenitor pool, it is possible to begin to understand how differences in the dynamical cell behaviours such as proliferation rates and cell movements can map onto conserved regulatory networks to generate diversity in the timing of tissue generation and patterning during development.

scholarly journals Multiple morphogens and rapid elongation promote segmental patterning during development

2021 ◽  
Vol 17 (6) ◽  
pp. e1009077
Author(s):  
Yuchi Qiu ◽  
Lianna Fung ◽  
Thomas F. Schilling ◽  
Qing Nie
Keyword(s):  
Cell Fate ◽  
Gene Regulatory Networks ◽  
Cell Sorting ◽  
Short Range ◽  
Regulatory Networks ◽  
Neural Plate ◽  
Convergent Extension ◽  
Simultaneous Formation ◽  
Gene Regulatory ◽  
Rapid Elongation

The vertebrate hindbrain is segmented into rhombomeres (r) initially defined by distinct domains of gene expression. Previous studies have shown that noise-induced gene regulation and cell sorting are critical for the sharpening of rhombomere boundaries, which start out rough in the forming neural plate (NP) and sharpen over time. However, the mechanisms controlling simultaneous formation of multiple rhombomeres and accuracy in their sizes are unclear. We have developed a stochastic multiscale cell-based model that explicitly incorporates dynamic morphogenetic changes (i.e. convergent-extension of the NP), multiple morphogens, and gene regulatory networks to investigate the formation of rhombomeres and their corresponding boundaries in the zebrafish hindbrain. During pattern initiation, the short-range signal, fibroblast growth factor (FGF), works together with the longer-range morphogen, retinoic acid (RA), to specify all of these boundaries and maintain accurately sized segments with sharp boundaries. At later stages of patterning, we show a nonlinear change in the shape of rhombomeres with rapid left-right narrowing of the NP followed by slower dynamics. Rapid initial convergence improves boundary sharpness and segment size by regulating cell sorting and cell fate both independently and coordinately. Overall, multiple morphogens and tissue dynamics synergize to regulate the sizes and boundaries of multiple segments during development.

scholarly journals Six2 and Wnt Regulate Self-Renewal and Commitment of Nephron Progenitors through Shared Gene Regulatory Networks

2012 ◽  
Vol 23 (3) ◽  
pp. 637-651 ◽  
Author(s):  
Joo-Seop Park ◽  
Wenxiu Ma ◽  
Lori L. O'Brien ◽  
Eunah Chung ◽  
Jin-Jin Guo ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Self Renewal ◽  
Nephron Progenitors ◽  
Gene Regulatory ◽  
Shared Gene

scholarly journals PCL2 modulates gene regulatory networks controlling self-renewal and commitment in embryonic stem cells

Cell Cycle ◽  
2011 ◽  
Vol 10 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Emily Walker ◽  
Janet L. Manias ◽  
Wing Y. Chang ◽  
William L. Stanford
Keyword(s):  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Embryonic Stem ◽  
Self Renewal ◽  
Gene Regulatory

scholarly journals Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes

2020 ◽  
Author(s):  
Francis C. Motta ◽  
Robert C. Moseley ◽  
Bree Cummins ◽  
Anastasia Deckard ◽  
Steven B. Haase
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Fraction ◽  
Regulatory Elements ◽  
Natural World ◽  
Model Systems ◽  
Model Organisms ◽  
Core Network ◽  
The Core ◽  
Gene Regulatory

AbstractCell and circadian cycles control a large fraction of cell and organismal physiology by regulating large periodic transcriptional programs that encompass anywhere from 15-80% of the genome. The gene-regulatory networks (GRNs) controlling these programs were largely identified by genetics and chromosome mapping approaches in model systems, yet it is unlikely that we have identified all of the core GRN components. Moreover, large periodic transcriptional programs controlling a variety of processes certainly exist in important non-model organisms where genetic approaches to identifying networks are expensive, time-consuming or intractable. Ideally, the core network components could be identified using data-driven approaches on the transcriptome dynamics data already available. Previous work used dynamic gene expression features to identify sets of genes with periodic behavior; our work goes further to distinguish genes by role: core versus their non-regulatory outputs. Here we present a quantitative approach that can identify nodes of GRNs controlling cell or circadian cycles across taxa. There are practical applications of the approach for network biologists, but our findings reveal something unexpected—that there are quantifiable and fundamental shared features of these unrelated GRNs controlling disparate periodic phenotypes.Author summaryCircadian rhythms, cellular division, and the developmental cycles of a multitude of living creatures, including those responsible for infectious diseases, are among the many dynamic phenomena in the natural world that are known to be the eventual output of gene regulatory networks. Identifying the small number of specialized genes that control these dynamic behaviors is of fundamental importance to our understanding of life, and our treatment of disease, but is difficult because of the sheer size of the genomes. We show that the core genes in organisms separated by millions of years of evolution have remarkable similarities that can be used to identify them.

scholarly journals A probabilistic graphical model for system-wide analysis of gene regulatory networks

2020 ◽  
Vol 36 (10) ◽  
pp. 3192-3199 ◽  
Author(s):  
Stephen Kotiang ◽  
Ali Eslami
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Graphical Model ◽  
Expression Profiles ◽  
Biological Data ◽  
Model Organisms ◽  
Network Systems ◽  
Experimental Conditions ◽  
Global Consistency ◽  
Gene Regulatory

Abstract Motivation The inference of gene regulatory networks (GRNs) from DNA microarray measurements forms a core element of systems biology-based phenotyping. In the recent past, numerous computational methodologies have been formalized to enable the deduction of reliable and testable predictions in today’s biology. However, little focus has been aimed at quantifying how well existing state-of-the-art GRNs correspond to measured gene-expression profiles. Results Here, we present a computational framework that combines the formulation of probabilistic graphical modeling, standard statistical estimation, and integration of high-throughput biological data to explore the global behavior of biological systems and the global consistency between experimentally verified GRNs and corresponding large microarray compendium data. The model is represented as a probabilistic bipartite graph, which can handle highly complex network systems and accommodates partial measurements of diverse biological entities, e.g. messengerRNAs, proteins, metabolites and various stimulators participating in regulatory networks. This method was tested on microarray expression data from the M3D database, corresponding to sub-networks on one of the best researched model organisms, Escherichia coli. Results show a surprisingly high correlation between the observed states and the inferred system’s behavior under various experimental conditions. Availability and implementation Processed data and software implementation using Matlab are freely available at https://github.com/kotiang54/PgmGRNs. Full dataset available from the M3D database.

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