scholarly journals Computational design of genomic transcriptional networks with adaptation to varying environments

2012 ◽  
Vol 109 (38) ◽  
pp. 15277-15282 ◽  
Author(s):  
Javier Carrera ◽  
Santiago F. Elena ◽  
Alfonso Jaramillo
Keyword(s):  
De Novo ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Profiling ◽  
Computational Design ◽  
Computational Procedure ◽  
Transcriptional Networks ◽  
E Coli ◽  
Environmental Perturbations ◽  
Transcriptional Regulatory ◽  
Varying Environments

Transcriptional profiling has been widely used as a tool for unveiling the coregulations of genes in response to genetic and environmental perturbations. These coregulations have been used, in a few instances, to infer global transcriptional regulatory models. Here, using the large amount of transcriptomic information available for the bacterium Escherichia coli, we seek to understand the design principles determining the regulation of its transcriptome. Combining transcriptomic and signaling data, we develop an evolutionary computational procedure that allows obtaining alternative genomic transcriptional regulatory network (GTRN) that still maintains its adaptability to dynamic environments. We apply our methodology to an E. coli GTRN and show that it could be rewired to simpler transcriptional regulatory structures. These rewired GTRNs still maintain the global physiological response to fluctuating environments. Rewired GTRNs contain 73% fewer regulated operons. Genes with similar functions and coordinated patterns of expression across environments are clustered into longer regulated operons. These synthetic GTRNs are more sensitive and show a more robust response to challenging environments. This result illustrates that the natural configuration of E. coli GTRN does not necessarily result from selection for robustness to environmental perturbations, but that evolutionary contingencies may have been important as well. We also discuss the limitations of our methodology in the context of the demand theory. Our procedure will be useful as a novel way to analyze global transcription regulation networks and in synthetic biology for the de novo design of genomes.

scholarly journals Antibiotic tolerance is associated with a broad and complex transcriptional response in E. coli

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Heather S. Deter ◽  
Tahmina Hossain ◽  
Nicholas C. Butzin
Keyword(s):  
Transcriptional Regulatory Network ◽  
Transcriptional Response ◽  
Regulatory Proteins ◽  
Health Concern ◽  
Transcriptional Networks ◽  
Antibiotic Tolerance ◽  
Multiple Gene ◽  
E Coli ◽  
Transcriptional Regulatory ◽  
Antibiotic Efficacy

AbstractAntibiotic treatment kills a large portion of a population, while a small, tolerant subpopulation survives. Tolerant bacteria disrupt antibiotic efficacy and increase the likelihood that a population gains antibiotic resistance, a growing health concern. We examined how E. coli transcriptional networks changed in response to lethal ampicillin concentrations. We are the first to apply transcriptional regulatory network (TRN) analysis to antibiotic tolerance by leveraging existing knowledge and our transcriptional data. TRN analysis shows that gene expression changes specific to ampicillin treatment are likely caused by specific sigma and transcription factors typically regulated by proteolysis. These results demonstrate that to survive lethal concentration of ampicillin specific regulatory proteins change activity and cause a coordinated transcriptional response that leverages multiple gene systems.

scholarly journals Antibiotic tolerance is associated with a broad and complex transcriptional response in E. coli

2020 ◽  
Author(s):  
Heather S. Deter ◽  
Tahmina Hossain ◽  
Nicholas C. Butzin
Keyword(s):  
Gene Expression ◽  
Antibiotic Treatment ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Response ◽  
Regulatory Proteins ◽  
Transcriptional Networks ◽  
Antibiotic Tolerance ◽  
Multiple Gene ◽  
E Coli ◽  
Transcriptional Regulatory

SummaryAntibiotic treatment kills a large portion of a population, while a small, tolerant subpopulation survives. Tolerant bacteria disrupt the efficacy of antibiotics and increase the likelihood that a population gains antibiotic resistance, a growing concern. Using a systems biology approach to, we examine how transcriptional networks respond to antibiotic stress to survive and recover from antibiotic treatment. We are the first to apply transcriptional regulatory network (TRN) analysis to antibiotic tolerance in E. coli, by comparing gene expression with and without lethal concentrations of ampicillin and leveraging existing knowledge of transcriptional regulation. TRN analysis shows that changes in gene expression specific to ampicillin treatment are likely caused by specific sigma and transcription factors typically regulated by proteolysis. These results demonstrate that altered activity of specific regulatory proteins cause an active and coordinated transcriptional response that leverages multiple gene systems to survive and recover from ampicillin treatment.

scholarly journals The Escherichia coli transcriptome mostly consists of independently regulated modules

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Anand V. Sastry ◽  
Ye Gao ◽  
Richard Szubin ◽  
Ying Hefner ◽  
Sibei Xu ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Regulators ◽  
Specific Gene ◽  
E Coli ◽  
Gene Sets ◽  
Transcriptional Regulatory ◽  
Genetic Perturbations ◽  
Function Discovery ◽  
Multiple Strains

AbstractUnderlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome.

scholarly journals Modular analysis of the transcriptional regulatory network of E. coli

2005 ◽  
Vol 21 (1) ◽  
pp. 16-20 ◽  
Author(s):  
Osbaldo Resendis-Antonio ◽  
Julio A. Freyre-González ◽  
Ricardo Menchaca-Méndez ◽  
Rosa M. Gutiérrez-Ríos ◽  
Agustino Martínez-Antonio ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
E Coli ◽  
Transcriptional Regulatory ◽  
Modular Analysis

scholarly journals Demonstration of de novo chemotaxis in E. coli using a real-time, quantitative, and digital-like approach

2017 ◽  
Author(s):  
Tzila Davidov ◽  
Naor Granik ◽  
Sharbel Zahran ◽  
Inbal Adir ◽  
Ofek Elul ◽  
...  
Keyword(s):  
Real Time ◽  
De Novo ◽  
Bacterial Chemotaxis ◽  
Computational Design ◽  
High Threshold ◽  
Chemical Stimulus ◽  
Label Free ◽  
E Coli ◽  
Detection Approach ◽  
Time Changes

AbstractChemotaxis is the movement of an organism in response to an external chemical stimulus. This system enables bacteria to sense their immediate environment and adapt to changes in its chemical composition. Bacterial chemotaxis is mediated by chemoreceptors, membrane proteins that bind an effector and transduce the signal to the downstream proteins. From a synthetic biology perspective, the natural chemotactic repertoire is of little use since bacterial chemoreceptors have evolved to sense specific ligands that either benefit or harm the cell. Here we demonstrate that using a combined computational design approach together with a quantitative, real-time, and digital detection approach, we can rapidly design, manufacture, and characterize a synthetic chemoreceptor in E. coli for histamine (a ligand for which there are no known chemoreceptors). First, we employed a computational protocol that uses the Rosetta bioinformatics software together with high threshold filters to design mutational variants to the native Tar ligand binding domain that target histamine. Second, we tested different ligand-chemoreceptors pairs with a novel chemotaxis assay, based on optical reflectance interferometry of porous silicon (PSi) optical transducers, enabling label-free quantification of chemotaxis by monitoring real-time changes in the optical readout (expressed as the effective optical thickness, EOT). We found that different ligands can be characterized by an individual set of fingerprints in our assay. Namely, a binary, digital-like response in EOT change (i.e. positive or negative) that differentiates between attractants and repellants, the amplitude of change of EOT response, and the rate by which steady state in EOT change is reached. Using this assay, we were able to positively identify and characterize a single mutational chemoreceptor variant for histamine that mediated chemotaxis comparably to the natural Tar-aspartate system. Our results demonstrate the possibility of not only expanding the natural chemotaxis repertoire, but also provide a new quantitative assay by which to characterize the efficacy of the chemotactic response.

scholarly journals Anteroposterior patterning of the zebrafish ear through Fgf- and Hh-dependent regulation of hmx3a expression

2018 ◽  
Author(s):  
Ryan D. Hartwell ◽  
Samantha J. England ◽  
Nicholas A. M. Monk ◽  
Nicholas J. van Hateren ◽  
Sarah Baxendale ◽  
...  
Keyword(s):  
Regulatory Network ◽  
De Novo ◽  
Transcriptional Regulatory Network ◽  
Ectopic Expression ◽  
Rapid Expansion ◽  
Wild Type ◽  
Transcriptional Responses ◽  
Anteroposterior Patterning ◽  
Transcriptional Regulatory ◽  
Over Time

AbstractIn the zebrafish, Fgf and Hh signalling assign anterior and posterior identity, respectively, to the poles of the developing ear. Mis-expression of fgf3 or inhibition of Hh signalling results in double-anterior ears, including ectopic expression of hmx3a. To understand how this double-anterior pattern is established, we characterised transcriptional responses in Fgf gain-of-signalling or Hh loss-of-signalling backgrounds. Mis-expression of fgf3 resulted in rapid expansion of anterior otic markers, refining over time to give the duplicated pattern. Response to Hh inhibition was very different: initial anteroposterior asymmetry was retained, with de novo duplicate expression domains appearing later. We show that Hmx3a is required for normal anterior otic patterning, but neither loss nor gain of hmx3a function was sufficient to generate ear duplications. Using our data to infer a transcriptional regulatory network required for acquisition of otic anterior identity, we can recapitulate both the wild-type and the double-anterior pattern in a mathematical model.

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