An acetylatable lysine controls CRP function in <i>E. coli</i>

Davis, Robert; Écija-Conesa, Ana; Gallego‐Jara, Julia; Diego, Teresa De; Filippova, E.V.; Kuffel, Gina; Anderson, W.F.; Gibson, Bradford W.; Schilling, Birgit; Cánovas, Manuel Fernández; Wolfe, Alan J.

doi:10.1111/mmi.13874

Cited by 24 publications

(26 citation statements)

References 77 publications

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“…While the precise delay identified by our analysis was not consistent with that observed in experiments, studies confirmed that a delay existed between crp induction and the induction of several target genes (36). This delay can possibly be attributed to posttranslational modification of crp (37). Of 32 edges in the gold standard, SWING identified 27 true-positive (TP) edges and 5 false-positive (FP) edges (85% TP), while the ensemble model predicted 24 TP edges and 8 FP edges (75% TP).…”

Section: Swing Accurately Infers Regulondb Modules With Time-delayedcontrasting

confidence: 78%

Windowed Granger causal inference strategy improves discovery of gene regulatory networks

Finkle

Bagheri

2018

Proc. Natl. Acad. Sci. U.S.A.

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Accurate inference of regulatory networks from experimental data facilitates the rapid characterization and understanding of biological systems. High-throughput technologies can provide a wealth of time-series data to better interrogate the complex regulatory dynamics inherent to organisms, but many network inference strategies do not effectively use temporal information. We address this limitation by introducing Sliding Window Inference for Network Generation (SWING), a generalized framework that incorporates multivariate Granger causality to infer network structure from time-series data. SWING moves beyond existing Granger methods by generating windowed models that simultaneously evaluate multiple upstream regulators at several potential time delays. We demonstrate that SWING elucidates network structure with greater accuracy in both in silico and experimentally validated in vitro systems. We estimate the apparent time delays present in each system and demonstrate that SWING infers time-delayed, gene-gene interactions that are distinct from baseline methods. By providing a temporal framework to infer the underlying directed network topology, SWING generates testable hypotheses for gene-gene influences.

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Section: Swing Accurately Infers Regulondb Modules With Time-delayedcontrasting

confidence: 78%

Windowed Granger causal inference strategy improves discovery of gene regulatory networks

Finkle

Bagheri

2018

Proc. Natl. Acad. Sci. U.S.A.

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“…In this case, we observed that the transcriptional response of the strain ΔN differs markedly from the one observed in the wild type strain when tested along 8,862 genes ( R 2 = 0.4406, see Figure S1 ). In any case, beyond of the critical experimental details mentioned, the theory presented by Davis et al ( 2018 ) does not seem to assign any role to the Δ crp (R-type) mutant (i.e., the CRP deacetylated ) for which we have also observed specific, significant effects. Neither does the proposal account for the group of genes that we have herein detected as being equally influenced by both CRP acetylated and CRP deacetylated .…”

Section: Discussioncontrasting

confidence: 63%

“…Recently, it has been suggested that the Δ crp (Q-type) mutant (i.e., the CRP acetylated ) exerts an inhibitory effect on the CRP-driven genes containing the type II promoters, while possibly also simultaneously exerting a positive effect on the CRP-driven genes containing the type I promoters (Davis et al, 2018 ). However, the hypothesis of the opposed effect of CRP acetylated depending on the type of promoter still remains to be experimentally verified.…”

Section: Discussionmentioning

confidence: 99%

“…The second set concerns the overall transcriptional responses of E. coli to mutations in the crp gene, which codifies the DNA-binding dual regulator CRP. This information has been gathered by Écija-Conesa and de Diego (2007) (GEO database, access code: 96955) 1 and published latter (Davis et al, 2018 ). These set of data are highly consistent since they came from the same laboratory, uses the same host E. coli strain, culture media, operational conditions, and analytics.…”

Section: Methodsmentioning

confidence: 99%

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Influence of Glucose Availability and CRP Acetylation on the Genome-Wide Transcriptional Response of Escherichia coli: Assessment by an Optimized Factorial Microarray Analysis

Guebel¹,

Torres

2018

Front. Microbiol.

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Background: While in eukaryotes acetylation/deacetylation regulation exerts multiple pleiotropic effects, in Escherichia coli it seems to be more limited and less known. Hence, we aimed to progress in the characterization of this regulation by dealing with three convergent aspects: the effector enzymes involved, the master regulator CRP, and the dependence on glucose availability.Methods: The transcriptional response of E. coli BW25113 was analyzed across 14 relevant scenarios. These conditions arise when the wild type and four isogenic mutants (defective in deacetylase CobB, defective in N(ε)-lysine acetyl transferase PatZ, Q- and R-type mutants of protein CRP) are studied under three levels of glucose availability (glucose-limited chemostat and glucose-excess or glucose-exhausted in batch culture). The Q-type emulates a permanent stage of CRPacetylated, whereas the R-type emulates a permanent stage of CRPdeacetylated. The data were analyzed by an optimized factorial microarray method (Q-GDEMAR).Results: (a) By analyzing one mutant against the other, we were able to unravel the true genes that participate in the interaction between ΔcobB/ΔpatZ mutations and glucose availability; (b) Increasing stages of glucose limitation appear to be associated with the up-regulation of specific sets of target genes rather than with the loss of genes present when glucose is in excess; (c) Both CRPdeacetylated and CRPacetylated produce extensive changes in specific subsets of genes, but their number and identity depend on the glucose availability; (d) In other sub-sets of genes, the transcriptional effect of CRP seems to be independent of its acetylation or deacetylation; (e) Some specific ontology functions can be associated with each of the different sets of genes detected herein.Conclusions: CRP cannot be thought of only as an effector of catabolite repression, because it acts along all the glucose conditions tested (excess, limited, and exhausted), exerting both positive and negative effects through different sets of genes. Acetylation of CRP does not seem to be a binary form of regulation, as there is not a univocal relationship between its activation/inhibitory effect and its acetylation/deacetylation stage. All the combinatorial possibilities are observed. During the exponential growth phase, CRP also exerts a very significant transcriptional effect, mainly on flagellar assembly and chemotaxis (FDR = 7.2 × 10−44).

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“…Once formed, Ac-P could acetylate a variety of different proteins during diauxic shifts ( Figure 6 ) that are related to the glucose metabolism ( Kuhn et al, 2014 ; Venkat et al, 2017 ). Another possibility arises from a recent study ( Davis et al, 2018 ) that show that acetylation of CRP can also control its activity. Although ppGpp is not controlling the diauxic shift through changes in CRP expression, it might control it by changing its activity through acetylation.…”

Section: Discussionmentioning

confidence: 99%

Contributions of SpoT Hydrolase, SpoT Synthetase, and RelA Synthetase to Carbon Source Diauxic Growth Transitions in Escherichia coli

Fernández-Coll

Cashel

2018

Front. Microbiol.

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During the diauxic shift, Escherichia coli exhausts glucose and adjusts its expression pattern to grow on a secondary carbon source. Transcriptional profiling studies of glucose–lactose diauxic transitions reveal a key role for ppGpp. The amount of ppGpp depends on RelA synthetase and the balance between a strong SpoT hydrolase and its weak synthetase. In this study, mutants are used to search for synthetase or hydrolase specific regulation. Diauxic shifts experiments were performed with strains containing SpoT hydrolase and either RelA or SpoT synthetase as the sole source of ppGpp. Here, the length of the diauxic lag times is determined by the presence of ppGpp, showing contributions of both ppGpp synthetases (RelA and SpoT) as well as its hydrolase (SpoT). A balanced ppGpp response is key for a proper adaptation during diauxic shift. The effects of one or the other ppGpp synthetase on diauxic shifts are abolished by addition of amino acids or succinate, although by different mechanisms. While amino acids control the RelA response, succinate blocks the uptake of the excreted acetate via SatP. Acetate is converted to Acetyl-CoA through the ackA-pta pathway, producing Ac-P as intermediate. Evidence of control of the ackA-pta operon as well as a correlation between ppGpp and Ac-P is shown. Finally, acetylation of proteins is shown to occur during a diauxic glucose–lactose shift.

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An acetylatable lysine controls CRP function in E. coli

Cited by 24 publications

References 77 publications

Windowed Granger causal inference strategy improves discovery of gene regulatory networks

Windowed Granger causal inference strategy improves discovery of gene regulatory networks

Influence of Glucose Availability and CRP Acetylation on the Genome-Wide Transcriptional Response of Escherichia coli: Assessment by an Optimized Factorial Microarray Analysis

Contributions of SpoT Hydrolase, SpoT Synthetase, and RelA Synthetase to Carbon Source Diauxic Growth Transitions in Escherichia coli

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