Dataset Information

Structure-based Druggability Assessment of Anti-virulence Targets from Pseudomonas aeruginosa.

ABSTRACT: Antimicrobial Resistance (AMR) represents a serious threat to health and the global economy. However, interest in antibacterial drug development has decreased substantially in recent decades. Meanwhile, anti-virulence drug development has emerged as an attractive alternative to fight AMR. Although several macromolecular targets have been explored for this goal, their druggability is a vital piece of information that has been overlooked. This review explores this subject by showing how structure- based freely available in silico tools, such as PockDrug and FTMap, might be useful for designing novel inhibitors of the pyocyanin biosynthesis pathway and improving the potency/selectivity of compounds that target the Pseudomonas aeruginosa quorum sensing mechanism. The information provided by hotspot analysis, along with binding site features, reveals novel druggable targets (PhzA and PhzS) that remain largely unexplored. However, it also highlights that in silico druggability prediction tools have several limitations that might be overcome in the near future. Meanwhile, anti-virulence drug targets should be assessed by complementary methods, such as the combined use of FTMap/PockDrug, once the consensus druggability classification reduces the risk of wasting resources on undruggable proteins.

SUBMITTER: Froes TQ

PROVIDER: S-EPMC7067568 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Structure-based Druggability Assessment of Anti-virulence Targets from Pseudomonas aeruginosa.

Froes Thamires Q TQ Baldini Regina L RL Vajda Sandor S Castilho Marcelo S MS

Current protein & peptide science 20190101 12

Antimicrobial Resistance (AMR) represents a serious threat to health and the global economy. However, interest in antibacterial drug development has decreased substantially in recent decades. Meanwhile, anti-virulence drug development has emerged as an attractive alternative to fight AMR. Although several macromolecular targets have been explored for this goal, their druggability is a vital piece of information that has been overlooked. This review explores this subject by showing how structure- ...[more]

PMID: 31038064

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Project description:In the opportunistic pathogen Pseudomonas aeruginosa, quorum sensing (QS) via acyl-homoserine lactone (AHL) signals coordinates virulence gene expression. AHL signals must reach a critical threshold before enough is bound by cognate regulators LasR and RhlR to drive transcription of target genes. In addition, three anti-activator proteins, QteE, QscR, and QslA, sequester QS regulators to increase the threshold for induction and delay expression of QS target genes. It remains unclear how multiple anti-activators work together to achieve the quorum threshold. Here, we employed a combination of mutational, kinetic, phenotypic, and transcriptomic analysis to examine regulatory effects and interactions of the three distinct anti-activators. We observed combinatorial, additive effects on QS gene expression. As measured by reporter gene fusion, individual deletion of each anti-activator gene increased lasB expression and QS-controlled virulence factor production. Deletion of qslA in combination with the deletion of any other anti-activator gene resulted in the greatest increase and earliest activation of lasB gene expression. Western analysis revealed that relative increases in soluble LasR in anti-activator mutants correlate with increased lasB expression and QS-controlled virulence factor production. RNA-seq of the previously uncharacterized QslA and QteE regulons revealed overlapping, yet distinct groups of differentially expressed genes. Simultaneous inactivation of qteE and qslA had the largest effect on gene expression with 999 genes induced and 798 genes repressed in the double mutant vs. wild-type. We found that LasR and RhlR-activated QS genes formed a subset of the genes induced in the qteE, qslA, and double mutant. The activation of almost all of these QS genes was advanced from stationary phase to log phase in the qteE qslA double mutant. Taken together, our results identify additive effects of anti-activation on QS gene expression, likely via LasR and RhlR, but do not rule out QS-independent effects.

Project description:Variation in the genome of Pseudomonas aeruginosa, an important pathogen, can have dramatic impacts on the bacterium's ability to cause disease. We therefore asked whether it was possible to predict the virulence of P. aeruginosa isolates based on their genomic content. We applied a machine learning approach to a genetically and phenotypically diverse collection of 115 clinical P. aeruginosa isolates using genomic information and corresponding virulence phenotypes in a mouse model of bacteremia. We defined the accessory genome of these isolates through the presence or absence of accessory genomic elements (AGEs), sequences present in some strains but not others. Machine learning models trained using AGEs were predictive of virulence, with a mean nested cross-validation accuracy of 75% using the random forest algorithm. However, individual AGEs did not have a large influence on the algorithm's performance, suggesting instead that virulence predictions are derived from a diffuse genomic signature. These results were validated with an independent test set of 25 P. aeruginosa isolates whose virulence was predicted with 72% accuracy. Machine learning models trained using core genome single-nucleotide variants and whole-genome k-mers also predicted virulence. Our findings are a proof of concept for the use of bacterial genomes to predict pathogenicity in P. aeruginosa and highlight the potential of this approach for predicting patient outcomes.IMPORTANCE Pseudomonas aeruginosa is a clinically important Gram-negative opportunistic pathogen. P. aeruginosa shows a large degree of genomic heterogeneity both through variation in sequences found throughout the species (core genome) and through the presence or absence of sequences in different isolates (accessory genome). P. aeruginosa isolates also differ markedly in their ability to cause disease. In this study, we used machine learning to predict the virulence level of P. aeruginosa isolates in a mouse bacteremia model based on genomic content. We show that both the accessory and core genomes are predictive of virulence. This study provides a machine learning framework to investigate relationships between bacterial genomes and complex phenotypes such as virulence.

Dataset Information

Structure-based Druggability Assessment of Anti-virulence Targets from Pseudomonas aeruginosa.

Publications

Structure-based Druggability Assessment of Anti-virulence Targets from Pseudomonas aeruginosa.

Similar Datasets

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