scholarly journals Input dose differentiation by NF-κB

2019 ◽  
Author(s):  
Minjun Son ◽  
Andrew Wang ◽  
Hsiung-Lin Tu ◽  
Marie O Metzig ◽  
Parthiv Patel ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Short Term Memory ◽  
Single Cells ◽  
Absolute Difference ◽  
Transcriptional Responses ◽  
Wide Range ◽  
Negative Feedbacks ◽  
Gene Regulatory ◽  
Complex Signaling ◽  
Simple Network

AbstractCells receive a wide range of dynamic signaling inputs during immune regulation, but how gene regulatory networks measure and interpret such dynamic inputs is not understood. Here, we used microfluidic live-cell analysis and mathematical modeling to study how NF-κB pathway in single-cells responds to time-varying immune inputs such as increasing, decreasing or fluctuating cytokine signals. Surprisingly, we found that NF-κB acts as a differentiator, responding strictly to the absolute difference in cytokine concentration, and not to the concentration itself. Our analyses revealed that negative feedbacks by the regulatory proteins A20 and IκBα enable dose differentiation by providing short-term memory of prior cytokine level and continuously resetting kinase cycling and receptor levels. Investigation of NF-κB target gene expression showed that cells create unique transcriptional responses under different dynamic cytokine profiles. Our results demonstrate how cells use simple network motifs and transcription factor dynamics to efficiently extract information from complex signaling environments.

scholarly journals Ultrabithorax Is a Micromanager of Hindwing Identity in Butterflies and Moths

2021 ◽  
Vol 9 ◽  
Author(s):  
Amruta Tendolkar ◽  
Aaron F. Pomerantz ◽  
Christa Heryanto ◽  
Paul D. Shirk ◽  
Nipam H. Patel ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Targeted Mutagenesis ◽  
Junonia Coenia ◽  
Wing Venation ◽  
Loss Of Function ◽  
Color Patterns ◽  
Wide Range ◽  
Range Of Functions ◽  
Gene Regulatory ◽  
Pyralid Moth

The forewings and hindwings of butterflies and moths (Lepidoptera) are differentiated from each other, with segment-specific morphologies and color patterns that mediate a wide range of functions in flight, signaling, and protection. The Hox gene Ultrabithorax (Ubx) is a master selector gene that differentiates metathoracic from mesothoracic identities across winged insects, and previous work has shown this role extends to at least some of the color patterns from the butterfly hindwing. Here we used CRISPR targeted mutagenesis to generate Ubx loss-of-function somatic mutations in two nymphalid butterflies (Junonia coenia, Vanessa cardui) and a pyralid moth (Plodia interpunctella). The resulting mosaic clones yielded hindwing-to-forewing transformations, showing Ubx is necessary for specifying many aspects of hindwing-specific identities, including scale morphologies, color patterns, and wing venation and structure. These homeotic phenotypes showed cell-autonomous, sharp transitions between mutant and non-mutant scales, except for clones that encroached into the border ocelli (eyespots) and resulted in composite and non-autonomous effects on eyespot ring determination. In the pyralid moth, homeotic clones converted the folding and depigmented hindwing into rigid and pigmented composites, affected the wing-coupling frenulum, and induced ectopic scent-scales in male androconia. These data confirm Ubx is a master selector of lepidopteran hindwing identity and suggest it acts on many gene regulatory networks involved in wing development and patterning.

scholarly journals Evolutionary strategies applied to artificial gene regulatory networks

2021 ◽  
Author(s):  
André Luiz de Lucena Moreira ◽  
César Rennó-Costa
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Task Complexity ◽  
Single Cells ◽  
Single Gene ◽  
Evolutionary Strategy ◽  
Evolutionary Strategies ◽  
Gene Regulatory ◽  
Artificial Gene ◽  
The Impact

Evolution optimizes cellular behavior throughout sequential generations by selecting the successful individual cells in a given context. As gene regulatory networks (GRNs) determine the behavior of single cells by ruling the activation of different processes - such as cell differentiation and death - how GRNs change from one generation to the other might have a relevant impact on the course of evolution. It is not clear, however, which mechanisms that affect GRNs effectively favor evolution and how. Here, we use a population of computational robotic models controlled by artificial gene regulatory networks (AGRNs) to evaluate the impact of different genetic modification strategies in the course of evolution. The virtual agent senses the ambient and acts on it as a bacteria in different phototaxis-like tasks - orientation to light, phototaxis, and phototaxis with obstacles. We studied how the strategies of gradual and abrupt changes on the AGRNs impact evolution considering multiple levels of task complexity. The results indicated that a gradual increase in the complexity of the performed tasks is beneficial for the evolution of the model. Furthermore, we have seen that larger gene regulatory networks are needed for more complex tasks, with single-gene duplication being an excellent evolutionary strategy for growing these networks, as opposed to full-genome duplication. Studying how GRNs evolved in a biological environment allows us to improve the computational models produced and provide insights into aspects and events that influenced the development of life on earth.

scholarly journals Neuron subtype-specific effector gene expression in the Motor Ganglion of Ciona

2019 ◽  
Author(s):  
Susanne Gibboney ◽  
Kwantae Kim ◽  
Christopher J. Johnson ◽  
Jameson Orvis ◽  
Paula Martínez-Feduchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Gene Expression Analysis ◽  
Transcriptome Profiling ◽  
Matrix Proteins ◽  
Wide Range ◽  
Specific Effector ◽  
The Central Nervous System ◽  
Differential Gene ◽  
Gene Regulatory

AbstractThe central nervous system of the Ciona larva contains only 177 neurons. The precise regulation of neuron subtype-specific morphogenesis and differentiation observed in during the formation of this minimal connectome offers a unique opportunity to dissect gene regulatory networks underlying chordate neurodevelopment. Here we compare the transcriptomes of two very distinct neuron types in the hindbrain/spinal cord homolog of Ciona, the Motor Ganglion (MG): the Descending decussating neuron (ddN, proposed homolog of Mauthner Cells in vertebrates) and the MG Interneuron 2 (MGIN2). Both types are invariantly represented by a single bilaterally symmetric left/right pair of cells in every larva. Supernumerary ddNs and MGIN2s were generated in synchronized embryos and isolated by fluorescence-activated cell sorting for transcriptome profiling. Differential gene expression analysis revealed ddN- and MGIN2-specific enrichment of a wide range of genes, including many encoding potential “effectors” of subtype-specific morphological and functional traits. More specifically, we identified the upregulation of centrosome-associated, microtubule-stabilizing/bundling proteins and extracellular matrix proteins and axon guidance cues as part of a single intrinsic regulatory program that might underlie the unique polarization of the ddNs, the only descending MG neurons that cross the midline.

scholarly journals Integration of a Computational Pipeline for Dynamic Inference of Gene Regulatory Networks in Single Cells

2019 ◽  
Author(s):  
Kyung Dae Ko ◽  
Stefania Dell’Orso ◽  
Aster H. Juan ◽  
Vittorio Sartorelli
Keyword(s):  
Prior Knowledge ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Developmental Stages ◽  
Individual Cell ◽  
Single Cells ◽  
Rna Seq ◽  
Gene Regulatory ◽  
Molecular Expression

SUMMARYSingle-cell RNA-seq permits the characterization of the molecular expression states of individual cells. Several methods have been developed to spatially and temporally resolve individual cell populations. However, these methods are not always integrated and some of them are constrained by prior knowledge. Here, we present an integrated pipeline for inference of gene regulatory networks. The pipeline does not rely on prior knowledge, it improves inference accuracy by integrating signatures from different data dimensions and facilitates tracing variation of gene expression by visualizing gene-interacting patterns of co-expressed gene regulatory networks at distinct developmental stages.

Dynamics of Bacterial Gene Regulatory Networks

2018 ◽  
Vol 47 (1) ◽  
pp. 447-467 ◽  
Author(s):  
David L. Shis ◽  
Matthew R. Bennett, ◽  
Oleg A. Igoshin
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Network Architecture ◽  
Regulatory Networks ◽  
Single Cells ◽  
Cell Physiology ◽  
Bacterial Cells ◽  
Bacterial Gene ◽  
Gene Expression Dynamics ◽  
Gene Regulatory

The ability of bacterial cells to adjust their gene expression program in response to environmental perturbation is often critical for their survival. Recent experimental advances allowing us to quantitatively record gene expression dynamics in single cells and in populations coupled with mathematical modeling enable mechanistic understanding on how these responses are shaped by the underlying regulatory networks. Here, we review how the combination of local and global factors affect dynamical responses of gene regulatory networks. Our goal is to discuss the general principles that allow extrapolation from a few model bacteria to less understood microbes. We emphasize that, in addition to well-studied effects of network architecture, network dynamics are shaped by global pleiotropic effects and cell physiology.

Prioritizing candidate disease-related long non-coding RNAs by walking on the heterogeneous lncRNA and disease network

2015 ◽  
Vol 11 (3) ◽  
pp. 760-769 ◽  
Author(s):  
Meng Zhou ◽  
Xiaojun Wang ◽  
Jiawei Li ◽  
Dapeng Hao ◽  
Zhenzhen Wang ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Biological Processes ◽  
Disease Network ◽  
Wide Range ◽  
Gene Regulatory ◽  
Non Coding Rnas

Accumulated evidence has shown that long non-coding RNAs (lncRNA) act as a widespread layer in gene regulatory networks and are involved in a wide range of biological processes.

scholarly journals On the Study of Reverse Degree-Based Topological Properties for the Third Type of p  th Chain Hex-Derived Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ali N. A. Koam ◽  
Ali Ahmad ◽  
Ashfaq Ahmed Qummer
Keyword(s):  
Computer Science ◽  
Regulatory Networks ◽  
Metabolic Networks ◽  
Environmental Chemistry ◽  
Global Network ◽  
Network Characteristics ◽  
The Third ◽  
Degree Of A Vertex ◽  
Wide Range ◽  
Gene Regulatory

Vertices and edges are made from a network, with the degree of a vertex referring to the number of connected edges. The chance of every vertex possessing a given degree is represented by a network’s degree appropriation, which reveals important global network characteristics. Many fields, including sociology, public health, business, medicine, engineering, computer science, and basic sciences, use network theory. Logistical networks, gene regulatory networks, metabolic networks, social networks, and driven networks are some of the most significant networks. In physical, theoretical, and environmental chemistry, a topological index is a numerical value assigned to a molecular structure/network that is used for correlation analysis. Hexagonal networks of dimension t are used to build hex-derived networks, which have a wide range of applications in computer science, medicine, and engineering. For the third type of hex-derived networks, topological indices of reverse degree based are discussed in this study.

scholarly journals ENDOGENOUS RETROVIRUSES AS GENETIC MODULES THAT SHAPE THE GENOME REGULATORY NETWORKS DURING EVOLUTION

2018 ◽  
Keyword(s):  
Regulatory Networks ◽  
Gene Activation ◽  
Cell Types ◽  
Regulatory Elements ◽  
Endogenous Retroviruses ◽  
Protein Coding ◽  
Transcriptional Enhancers ◽  
Wide Range ◽  
Gene Regulatory Elements ◽  
Gene Regulatory

Endogenous retroviruses (ERV) are the descendants of exogenous retroviruses that integrated into the germ cells genome, fixed and became inheritable. ERVs have evolved transcriptional enhancers and promoters that allow their replication in a wide range of tissue. Because ERVs comprise the regulatory elements it could be assume that ERVs capable to shape and reshape genomic regulatory networks by inserting their promoters and enhancers in new genomic loci upon retrotransposition. Thus retroransposition events can build new regulatory regions and lead to a new pattern of gene activation in the cell. In this review we summarize evidence which revealed that ERVs provide a plethora of novel gene regulatory elements, including tissue specific promoters and enhancers for protein-coding genes or long noncoding RNAs in a wide range of cell types. The accumulated findings support the hypothesis that the ERVs have rewired the gene regulatory networks and act as a major source of genomic regulatory innovation during evolution.

scholarly journals Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self-Organizing Maps

2018 ◽  
Author(s):  
Camden Jansen ◽  
Ricardo N. Ramirez ◽  
Nicole C. El-Ali ◽  
David Gomez-Cabrero ◽  
Jesper Tegner ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Time Course ◽  
Single Cells ◽  
Emergent Properties ◽  
Data Types ◽  
Self Organizing Maps ◽  
Cell Assays ◽  
Gene Regulatory ◽  
Self Organizing

AbstractRapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using self-organizing maps (SOM) to link scATAC-seq and scRNA-seq data that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of single-cells.

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