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 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 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 Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks

eLife ◽  
2014 ◽  
Vol 3 ◽  
Author(s):  
Luca Albergante ◽  
J Julian Blow ◽  
Timothy J Newman
Keyword(s):  
Large Scale ◽  
Human Cancer ◽  
Transcriptional Response ◽  
Cancer Cell Line ◽  
Transcriptional Networks ◽  
E Coli ◽  
Evolutionary Changes ◽  
Cancer Initiation ◽  
The Face ◽  
Scale Properties

The gene regulatory network (GRN) is the central decision‐making module of the cell. We have developed a theory called Buffered Qualitative Stability (BQS) based on the hypothesis that GRNs are organised so that they remain robust in the face of unpredictable environmental and evolutionary changes. BQS makes strong and diverse predictions about the network features that allow stable responses under arbitrary perturbations, including the random addition of new connections. We show that the GRNs of E. coli, M. tuberculosis, P. aeruginosa, yeast, mouse, and human all verify the predictions of BQS. BQS explains many of the small- and large‐scale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response. BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation.

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 Bacterial interspecies interactions modulate pH-mediated antibiotic tolerance

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Andrés Aranda-Díaz ◽  
Benjamin Obadia ◽  
Ren Dodge ◽  
Tani Thomsen ◽  
Zachary F Hallberg ◽  
...  
Keyword(s):  
Fruit Fly ◽  
Lag Time ◽  
Antibiotic Tolerance ◽  
Interspecies Interactions ◽  
Bacterial Physiology ◽  
E Coli ◽  
The Core ◽  
Erythromycin A ◽  
Antibiotic Efficacy ◽  
Translational Inhibitor

Predicting antibiotic efficacy within microbial communities remains highly challenging. Interspecies interactions can impact antibiotic activity through many mechanisms, including alterations to bacterial physiology. Here, we studied synthetic communities constructed from the core members of the fruit fly gut microbiota. Co-culturing of Lactobacillus plantarum with Acetobacter species altered its tolerance to the transcriptional inhibitor rifampin. By measuring key metabolites and environmental pH, we determined that Acetobacter species counter the acidification driven by L. plantarum production of lactate. Shifts in pH were sufficient to modulate L. plantarum tolerance to rifampin and the translational inhibitor erythromycin. A reduction in lag time exiting stationary phase was linked to L. plantarum tolerance to rifampicin, opposite to a previously identified mode of tolerance to ampicillin in E. coli. This mechanistic understanding of the coupling among interspecies interactions, environmental pH, and antibiotic tolerance enables future predictions of growth and the effects of antibiotics in more complex communities.

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