The regulatory network architecture of cardiometabolic diseases

2022 ◽  
Vol 54 (1) ◽  
pp. 2-3
Author(s):  
Harald H. H. W. Schmidt ◽  
Jörg Menche
2021 ◽  
Vol 118 (51) ◽  
pp. e2110550118
Author(s):  
Xing Zhao ◽  
Jiliang Hu ◽  
Yiwei Li ◽  
Ming Guo

Recent studies have revealed that extensive heterogeneity of biological systems arises through various routes ranging from intracellular chromosome segregation to spatiotemporally varying biochemical stimulations. However, the contribution of physical microenvironments to single-cell heterogeneity remains largely unexplored. Here, we show that a homogeneous population of non–small-cell lung carcinoma develops into heterogeneous subpopulations upon application of a homogeneous physical compression, as shown by single-cell transcriptome profiling. The generated subpopulations stochastically gain the signature genes associated with epithelial–mesenchymal transition (EMT; VIM, CDH1, EPCAM, ZEB1, and ZEB2) and cancer stem cells (MKI67, BIRC5, and KLF4), respectively. Trajectory analysis revealed two bifurcated paths as cells evolving upon the physical compression, along each path the corresponding signature genes (epithelial or mesenchymal) gradually increase. Furthermore, we show that compression increases gene expression noise, which interplays with regulatory network architecture and thus generates differential cell-fate outcomes. The experimental observations of both single-cell sequencing and single-molecule fluorescent in situ hybridization agrees well with our computational modeling of regulatory network in the EMT process. These results demonstrate a paradigm of how mechanical stimulations impact cell-fate determination by altering transcription dynamics; moreover, we show a distinct path that the ecology and evolution of cancer interplay with their physical microenvironments from the view of mechanobiology and systems biology, with insight into the origin of single-cell heterogeneity.


2012 ◽  
Vol 28 (5) ◽  
pp. 221-232 ◽  
Author(s):  
Guilhem Chalancon ◽  
Charles N.J. Ravarani ◽  
S. Balaji ◽  
Alfonso Martinez-Arias ◽  
L. Aravind ◽  
...  

2017 ◽  
Vol 114 (31) ◽  
pp. E6466-E6474 ◽  
Author(s):  
Francesca Spiga ◽  
Eder Zavala ◽  
Jamie J. Walker ◽  
Zidong Zhao ◽  
John R. Terry ◽  
...  

The hypothalamic–pituitary–adrenal axis is a dynamic system regulating glucocorticoid hormone synthesis in the adrenal glands. Many key factors within the adrenal steroidogenic pathway have been identified and studied, but little is known about how these factors function collectively as a dynamic network of interacting components. To investigate this, we developed a mathematical model of the adrenal steroidogenic regulatory network that accounts for key regulatory processes occurring at different timescales. We used our model to predict the time evolution of steroidogenesis in response to physiological adrenocorticotropic hormone (ACTH) perturbations, ranging from basal pulses to larger stress-like stimulations (e.g., inflammatory stress). Testing these predictions experimentally in the rat, our results show that the steroidogenic regulatory network architecture is sufficient to respond to both small and large ACTH perturbations, but coupling this regulatory network with the immune pathway is necessary to explain the dissociated dynamics between ACTH and glucocorticoids observed under conditions of inflammatory stress.


PLoS Genetics ◽  
2018 ◽  
Vol 14 (5) ◽  
pp. e1007375 ◽  
Author(s):  
Sebastian Kittelmann ◽  
Alexandra D. Buffry ◽  
Franziska A. Franke ◽  
Isabel Almudi ◽  
Marianne Yoth ◽  
...  

2009 ◽  
Vol 5 (1) ◽  
pp. 294 ◽  
Author(s):  
Raja Jothi ◽  
S Balaji ◽  
Arthur Wuster ◽  
Joshua A Grochow ◽  
Jörg Gsponer ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thanakorn Jaemthaworn ◽  
Saowalak Kalapanulak ◽  
Treenut Saithong

AbstractRobustness, a naïve property of biological systems, enables organisms to maintain functions during perturbation and is crucial for improving the resilience of crops to prevailing stress conditions and diseases, guaranteeing food security. Most studies of robustness in crops have focused on genetic superiority based upon individual genes, overlooking the collaborative actions of multiple responsive genes and the regulatory network topology. This research aims to uncover patterns of gene cooperation leading to organismal robustness by studying the topology of gene co-expression networks (GCNs) of both CBSV virus resistant and susceptible cassava cultivars. The resulting GCNs show higher topological clustering of cooperative genes in the resistant cultivar, suggesting that the network architecture is central to attaining robustness. Despite a reduction in the number of hub genes in the resistant cultivar following the perturbation, essential biological functions contained in the network were maintained through neighboring genes that withstood the shock. The susceptible cultivar seemingly coped by inducing more gene actions in the network but could not maintain the functions required for plant growth. These findings underscore the importance of regulatory network architecture in ensuring phenotypic robustness and deepen our understanding of transcriptional regulation.


2017 ◽  
Author(s):  
Sebastian Kittelmann ◽  
Alexandra D. Buffry ◽  
Franziska A. Franke ◽  
Isabel Almudi ◽  
Marianne Yoth ◽  
...  

AbstractConvergent phenotypic evolution is often caused by recurrent changes at particular nodes in the underlying gene regulatory networks (GRNs). The genes at such evolutionary ‘hotspots’ are thought to maximally affect the phenotype with minimal pleiotropic consequences. This has led to the suggestion that if a GRN is understood in sufficient detail, the path of evolution may be predictable. The repeated evolutionary loss of larval trichomes among Drosophila species is caused by the loss of shavenbaby (svb) expression. svb is also required for development of leg trichomes, but the evolutionary gain of trichomes in the ‘naked valley’ on T2 femurs in Drosophila melanogaster is caused by the loss of microRNA-92a (miR-92a) expression rather than changes in svb. We compared the expression and function of components between the larval and leg trichome GRNs to investigate why the genetic basis of trichome pattern evolution differs in these developmental contexts. We found key differences between the two networks in both the genes employed, and in the regulation and function of common genes. These differences in the GRNs reveal why mutations in svb are unlikely to contribute to leg trichome evolution and how instead miR-92a represents the key evolutionary switch in this context. Our work shows that variability in GRNs across different developmental contexts, as well as whether a morphological feature is lost versus gained, influence the nodes at which a GRN evolves to cause morphological change. Therefore, our findings have important implications for understanding the pathways and predictability of evolution.Author SummaryA major goal of biology is to identify the genetic cause of organismal diversity. Convergent evolution of traits is often caused by changes in the same genes – evolutionary ‘hotspots’. shavenbaby is a ‘hotspot’ for larval trichome loss in Drosophila, however microRNA-92a underlies the gain of leg trichomes. To understand this difference in the genetics of phenotypic evolution, we compared the expression and function of genes in the underlying regulatory networks. We found that the pathway of evolution is influenced by differences in gene regulatory network architecture in different developmental contexts, as well as by whether a trait is lost or gained. Therefore, hotspots in one context may not readily evolve in a different context. This has important implications for understanding the genetic basis of phenotypic change and the predictability of evolution.


2021 ◽  
Author(s):  
Tianyu Lu ◽  
Anjali Silva

Methods for gene regulatory network inference focus on network architecture identification but neglect model selection and simulation. We implement an extension to the dynGENIE3 algorithm that accounts for model uncertainty as an R package, providing users with an easy to use interface for model selection and gene expression profile simulation. Source code is available at https://github.com/tianyu-lu/dynUGENE with a detailed user guide. A webserver with interactive controls is available at https://tianyulu.shinyapps.io/dynUGENE/.


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