scholarly journals Posttranscriptional Gene Regulatory Networks in Chronic Airway Inflammatory Diseases: In silico Mapping of RNA-Binding Protein Expression in Airway Epithelium

2020 ◽  
Vol 11 ◽  
Author(s):  
Luca Ricciardi ◽  
Giorgio Giurato ◽  
Domenico Memoli ◽  
Mariagrazia Pietrafesa ◽  
Jessica Dal Col ◽  
...  
Keyword(s):  
Protein Expression ◽  
Gene Regulatory Networks ◽  
Airway Epithelium ◽  
In Silico ◽  
Regulatory Networks ◽  
Rna Binding ◽  
Inflammatory Diseases ◽  
Rna Binding Protein ◽  
In Silico Mapping ◽  
Gene Regulatory

scholarly journals Expression pattern determines regulatory logic

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244864
Author(s):  
Carlos Mora-Martinez
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Dna Sequences ◽  
In Silico ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Cell Types ◽  
Evolutionary Strategy ◽  
Specific Gene ◽  
Gene Regulatory

Large amounts of effort have been invested in trying to understand how a single genome is able to specify the identity of hundreds of cell types. Inspired by some aspects of Caenorhabditis elegans biology, we implemented an in silico evolutionary strategy to produce gene regulatory networks (GRNs) that drive cell-specific gene expression patterns, mimicking the process of terminal cell differentiation. Dynamics of the gene regulatory networks are governed by a thermodynamic model of gene expression, which uses DNA sequences and transcription factor degenerate position weight matrixes as input. In a version of the model, we included chromatin accessibility. Experimentally, it has been determined that cell-specific and broadly expressed genes are regulated differently. In our in silico evolved GRNs, broadly expressed genes are regulated very redundantly and the architecture of their cis-regulatory modules is different, in accordance to what has been found in C. elegans and also in other systems. Finally, we found differences in topological positions in GRNs between these two classes of genes, which help to explain why broadly expressed genes are so resilient to mutations. Overall, our results offer an explanatory hypothesis on why broadly expressed genes are regulated so redundantly compared to cell-specific genes, which can be extrapolated to phenomena such as ChIP-seq HOT regions.

AU-rich RNA binding proteins in hematopoiesis and leukemogenesis

Blood ◽  
2011 ◽  
Vol 118 (22) ◽  
pp. 5732-5740 ◽  
Author(s):  
Maria Baou ◽  
John D. Norton ◽  
John J. Murphy
Keyword(s):  
Cell Growth ◽  
Regulatory Networks ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Binding Protein ◽  
Rna Binding Protein ◽  
Growth And Survival ◽  
Hematopoietic Malignancies ◽  
Gene Regulatory

Abstract Posttranscriptional mechanisms are now widely acknowledged to play a central role in orchestrating gene-regulatory networks in hematopoietic cell growth, differentiation, and tumorigenesis. Although much attention has focused on microRNAs as regulators of mRNA stability/translation, recent data have highlighted the role of several diverse classes of AU-rich RNA-binding protein in the regulation of mRNA decay/stabilization. AU-rich elements are found in the 3′-untranslated region of many mRNAs that encode regulators of cell growth and survival, such as cytokines and onco/tumor-suppressor proteins. These are targeted by a burgeoning number of different RNA-binding proteins. Three distinct types of AU-rich RNA binding protein (ARE poly-U–binding degradation factor-1/AUF1, Hu antigen/HuR/HuA/ELAVL1, and the tristetraprolin/ZFP36 family of proteins) are essential for normal hematopoiesis. Together with 2 further AU-rich RNA-binding proteins, nucleolin and KHSRP/KSRP, the functions of these proteins are intimately associated with pathways that are dysregulated in various hematopoietic malignancies. Significantly, all of these AU-rich RNA-binding proteins function via an interconnected network that is integrated with microRNA functions. Studies of these diverse types of RNA binding protein are providing novel insight into gene-regulatory mechanisms in hematopoiesis in addition to offering new opportunities for developing mechanism-based targeted therapeutics in leukemia and lymphoma.

scholarly journals Improved Reconstruction of In Silico Gene Regulatory Networks by Integrating Knockout and Perturbation Data

PLoS ONE ◽  
2010 ◽  
Vol 5 (1) ◽  
pp. e8121 ◽  
Author(s):  
Kevin Y. Yip ◽  
Roger P. Alexander ◽  
Koon-Kiu Yan ◽  
Mark Gerstein
Keyword(s):  
Gene Regulatory Networks ◽  
In Silico ◽  
Regulatory Networks ◽  
Perturbation Data ◽  
Gene Regulatory

scholarly journals In Silico Design of Self-Optimizing Integrated Metabolic and Gene Regulatory Networks

2014 ◽  
Author(s):  
Timo Maarleveld ◽  
Bennet K NG ◽  
Herbert Sauro ◽  
Kyung Kim
Keyword(s):  
Gene Regulatory Networks ◽  
Environmental Conditions ◽  
In Silico ◽  
Regulatory Networks ◽  
Metabolic Networks ◽  
Self Regulation ◽  
Protein Signaling ◽  
Target Network ◽  
Gene Regulatory ◽  
Protein Signaling Networks

Biological organisms acclimatize to varying environmental conditions via active self-regulation of internal gene regulatory networks, metabolic networks, and protein signaling networks. While much work has been done to elucidate the topologies of individual networks in isolation, understanding of inter-network regulatory mechanisms remains limited. This shortcoming is of particular relevance to synthetic biology. Synthetic biological circuits tend to lose their engineered functionality over generational time, primarily due to the deleterious stress that they exert on their host organisms. To reduce this stress (and thus minimize loss of functionality) synthetic circuits must be sensitive to the health of the host organism. Development of integrated regulatory systems is therefore essential to robust synthetic biological systems. The aim of this study was to develop integrated gene-regulatory and metabolic networks which self-optimize in response to varying environmental conditions. We performed \emph{in silico} evolution to develop such networks using a two-step approach: (1) We optimized metabolic networks given a constrained amount of available enzyme. Here, we found that a proportional relationship between flux control coefficients and enzyme mass holds in all linear sub-networks of branched networks, except those sub-networks which contain allosteric regulators. Network optimization was performed by iteratively redistributing enzyme until flux through the network was maximized. Optimization was performed for a range of boundary metabolite conditions to develop a profile of optimal enzyme distributions as a function of environmental conditions. (2) We generated and evolved randomized gene regulatory networks to modulate the enzymes of a target metabolic pathway. The objective of the gene regulatory networks was to produce the optimal distribution of metabolic network enzymes given specific boundary metabolite conditions of the target network. Competitive evolutionary algorithms were applied to optimize the specific structures and kinetic parameters of the gene regulatory networks. With this method, we demonstrate the possibility of algorithmic development of integrated adaptive gene and metabolic regulatory networks which dynamically self-optimize in response to changing environmental conditions.

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