Pseudomonas Aeruginosa AmpR Transcriptional Regulatory Network

Balasubramanian, Deepak

doi:10.25148/etd.fi13042330

Cited by 4 publications

(4 citation statements)

References 571 publications

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“…Interestingly, sequence variations in regulators such as AmpR, AmpG, AmpD (including AmpD homologs), and mpl and alteration in penicillin-binding proteins such as PBP4 (dacB) have been described to trigger constitutive ampC overexpression (Bagge et al, 2002;Juan et al, 2005Juan et al, , 2006Schmidtke & Hanson, 2008;Moya et al, 2009;Balasubramanian et al, 2012;Cabot et al, 2018). AmpR, however, does not only control ampC expression but has also been described to be a global regulator of resistance and virulence in P. aeruginosa and to be an important acute-chronic switch regulator (Balasubramanian et al, 2015). As such, AmpR is also involved in the regulation of alginate production as well as iron acquisition via siderophores.…”

Section: Discussionmentioning

confidence: 99%

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics

et al. 2020

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Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug‐resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and expression profiles, we generated predictive models and identified biomarkers of resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8–0.9) or very high (> 0.9) sensitivity and predictive values. For all drugs except for ciprofloxacin, gene expression information improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.

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Section: Discussionmentioning

confidence: 99%

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics

et al. 2020

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“…carbon and nitrogen sources) and O 2 (Palmer et al, 2007;La Rosa et al, 2018) and bacterial cells often have to face other challenges simultaneously, such as the presence of multispecies microbiota and several types of stress factors, for example, immune responses, antibiotics and oxidative and osmotic stress (Moradali et al, 2017). Sophisticated regulatory networks in Pseudomonads control the physiological responses to these stressful conditions, including both local and global regulators that adjust transcriptional levels according to environmental cues (Tribelli et al, 2013;Balasubramanian et al, 2015;Arce-Rodríguez et al, 2016;Udaondo et al, 2018). Carbon catabolite repression, governed by the Crc protein, is a major player among these regulatory systems in Pseudomonas species (Wolff et al, 1991;Collier et al, 1996;Velázquez et al, 2004;Rojo, 2010;Grenga et al, 2017;Liu et al, 2017;Wirebrand et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

The global regulator Crc orchestrates the metabolic robustness underlying oxidative stress resistance in Pseudomonas aeruginosa

Corona

Martínez

Nikel

2018

Environmental Microbiology

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Summary The remarkable metabolic versatility of bacteria of the genus Pseudomonas enable their survival across very diverse environmental conditions. P. aeruginosa, one of the most relevant opportunistic pathogens, is a prime example of this adaptability. The interplay between regulatory networks that mediate these metabolic and physiological features is just starting to be explored in detail. Carbon catabolite repression, governed by the Crc protein, controls the availability of several enzymes and transporters involved in the assimilation of secondary carbon sources. Yet, the regulation exerted by Crc on redox metabolism of P. aeruginosa (hence, on the overall physiology) had hitherto been unexplored. In this study, we address the intimate connection between carbon catabolite repression and metabolic robustness of P. aeruginosa PAO1. In particular, we explored the interplay between oxidative stress, metabolic rearrangements in central carbon metabolism and the cellular redox state. By adopting a combination of quantitative physiology experiments, multiomic analyses, transcriptional patterns of key genes, measurement of metabolic activities in vitro and direct quantification of redox balances both in the wild‐type strain and in an isogenic Δcrc derivative, we demonstrate that Crc orchestrates the overall response of P. aeruginosa to oxidative stress via reshaping of the core metabolic architecture in this bacterium.

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“…Mutations in the gene encoding the key quorum-sensing regulator, lasR , are especially common, and lead to defects in the production of key virulence effector compounds (12). Loss-of-function adaptations in gacS , retS, mutS, mucA and ampR (50–53) also increase in prevalence as infections progress and reflect the transition of P. aeruginosa from an acute to chronic infection phenotype. Many of these examples are mutated in only a small fraction of infections (15), which is consistent with the prevalence of aguA mutations (~5%) among our isolate library.…”

Section: Discussionmentioning

confidence: 99%

Agmatine accumulation byPseudomonas aeruginosaclinical isolates confers antibiotic tolerance and dampens host inflammation

McCurtain

Gilbertsen

Evert

et al. 2018

Preprint

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Pseudomonas Aeruginosa AmpR Transcriptional Regulatory Network

Cited by 4 publications

References 571 publications

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics

The global regulator Crc orchestrates the metabolic robustness underlying oxidative stress resistance in Pseudomonas aeruginosa

Agmatine accumulation byPseudomonas aeruginosaclinical isolates confers antibiotic tolerance and dampens host inflammation

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