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A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models.

ABSTRACT: Pseudomonas aeruginosa is an opportunistic human pathogen that has been a constant global health problem due to its ability to cause infection at different body sites and its resistance to a broad spectrum of clinically available antibiotics. The World Health Organization classified multidrug-resistant Pseudomonas aeruginosa among the top-ranked organisms that require urgent research and development of effective therapeutic options. Several approaches have been taken to achieve these goals, but they all depend on discovering potential drug targets. The large amount of data obtained from sequencing technologies has been used to create computational models of organisms, which provide a powerful tool for better understanding their biological behavior. In the present work, we applied a method to integrate transcriptome data with genome-scale metabolic networks of Pseudomonas aeruginosa. We submitted both metabolic and integrated models to dynamic simulations and compared their performance with published in vitro growth curves. In addition, we used these models to identify potential therapeutic targets and compared the results to analyze the assumption that computational models enriched with biological measurements can provide more selective and (or) specific predictions. Our results demonstrate that dynamic simulations from integrated models result in more accurate growth curves and flux distribution more coherent with biological observations. Moreover, identifying drug targets from integrated models is more selective as the predicted genes were a subset of those found in the metabolic models. Our analysis resulted in the identification of 26 non-host homologous targets. Among them, we highlighted five top-ranked genes based on lesser conservation with the human microbiome. Overall, some of the genes identified in this work have already been proposed by different approaches and (or) are already investigated as targets to antimicrobial compounds, reinforcing the benefit of using integrated models as a starting point to selecting biologically relevant therapeutic targets.

SUBMITTER: Medeiros Filho F

PROVIDER: S-EPMC8488468 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:As one of the most common pathogens of opportunistic and hospital-acquired infections, Pseudomonas aeruginosa is associated with resistance to diverse antibiotics, which represents a significant challenge to current treatment modalities. Phage therapy is considered a promising alternative to conventional antimicrobials. The characterization and isolation of new bacteriophages and the concurrent evaluation of their therapeutic potential are fundamental for phage therapy. In this study, we employed an enrichment method and a double-layer agar overlay to isolate bacteriophages that infect P. aeruginosa strains PAO1 and PA14. Three phages (named PA_LZ01, PA_LZ02, and PA_LZ03) were isolated and showed icosahedral heads and contractile tails. Following full-genome sequencing, we found that phage PA_LZ01 contained a genome of 65,367 bp in size and harbored 90 predicted open reading frames (ORFs), phage PA_LZ02 contained a genome of 57,243 bp in size and harbored 75 predicted ORFs, and phage PA_LZ03 contained a genome of 57,367 bp in size and carried 77 predicted ORFs. Further comparative analysis showed that phage PA_LZ01 belonged to the genus Pbunavirus genus, phage PA_LZ02 belonged to the genus Pamexvirus, and phage PA_LZ03 belonged to the family Mesyanzhinovviridae. Next, we demonstrated that these phages were rather stable at different temperatures and pHs. One-step growth curves showed that the burst size of PA_LZ01 was 15 PFU/infected cell, and that of PA_LZ02 was 50 PFU/infected cell, while the titer of PA_LZ03 was not elevated. Similarly, the biofilm clearance capacities of PA_LZ01 and PA_LZ02 were also higher than that of PA_LZ03. Therapeutically, PA_LZ01 and PA_LZ02 treatment led to decreased bacterial loads and inflammatory responses in a mouse model. In conclusion, we isolated three phages that can infect P. aeruginosa, which were stable in different environments and could reduce bacterial biofilms, suggesting their potential as promising candidates to treat P. aeruginosa infections. IMPORTANCE Phage therapy is a promising therapeutic option for treating bacterial infections that do not respond to common antimicrobial treatments. Biofilm-mediated infections are particularly difficult to treat with traditional antibiotics, and the emergence of antibiotic-resistant strains has further complicated the situation. Pseudomonas aeruginosa is a bacterial pathogen that causes chronic infections and is highly resistant to many antibiotics. The library of phages that target P. aeruginosa is expanding, and the isolation of new bacteriophages is constantly required. In this study, three bacteriophages that could infect P. aeruginosa were isolated, and their biological characteristics were investigated. In particular, the isolated phages are capable of reducing biofilms formed by P. aeruginosa. Further analysis indicates that treatment with PA_LZ01 and PA_LZ02 phages reduces bacterial loads and inflammatory responses in vivo. This study isolated and characterized bacteriophages that could infect P. aeruginosa, which offers a resource for phage therapy.

Project description:Opioids are widely used for treating different types of pains, but overuse and abuse of prescription opioids have led to opioid epidemic in the United States. Besides analgesic effects, chronic use of opioid can also cause tolerance, dependence, and even addiction. Effective treatment of opioid addiction remains a big challenge today. Studies on addictive effects of opioids focus on striatum, a main component in the brain responsible for drug dependence and addiction. Some transcription regulators have been associated with opioid addiction, but relationship between analgesic effects of opioids and dependence behaviors mediated by them at the molecular level has not been thoroughly investigated. In this paper, we developed a new computational strategy that identifies novel targets and potential therapeutic molecular compounds for opioid dependence and addiction. We employed several statistical and machine learning techniques and identified differentially expressed genes over time which were associated with dependence-related behaviors after exposure to either morphine or heroin, as well as potential transcription regulators that regulate these genes, using time course gene expression data from mouse striatum. Moreover, our findings revealed that some of these dependence-associated genes and transcription regulators are known to play key roles in opioid-mediated analgesia and tolerance, suggesting that an intricate relationship between opioid-induce pain-related pathways and dependence may develop at an early stage during opioid exposure. Finally, we determined small compounds that can potentially target the dependence-associated genes and transcription regulators. These compounds may facilitate development of effective therapy for opioid dependence and addiction. We also built a database (http://daportals.org) for all opioid-induced dependence-associated genes and transcription regulators that we discovered, as well as the small compounds that target those genes and transcription regulators.

Project description:BackgroundThe ability of bacteria to rapidly evolve resistance to antibiotics is a critical public health problem. Resistance leads to increased disease severity and death rates, as well as imposes pressure towards the discovery and development of new antibiotic therapies. Improving understanding of the evolution and genetic basis of resistance is a fundamental goal in the field of microbiology.ResultsWe have applied a new genomic method, Scalar Analysis of Library Enrichments (SCALEs), to identify genomic regions that, given increased copy number, may lead to aminoglycoside resistance in Pseudomonas aeruginosa at the genome scale. We report the result of selections on highly representative genomic libraries for three different aminoglycoside antibiotics (amikacin, gentamicin, and tobramycin). At the genome-scale, we show significant (p<0.05) overlap in genes identified for each aminoglycoside evaluated. Among the genomic segments identified, we confirmed increased resistance associated with an increased copy number of several genomic regions, including the ORF of PA5471, recently implicated in MexXY efflux pump related aminoglycoside resistance, PA4943-PA4946 (encoding a probable GTP-binding protein, a predicted host factor I protein, a delta 2-isopentenylpyrophosphate transferase, and DNA mismatch repair protein mutL), PA0960-PA0963 (encoding hypothetical proteins, a probable cold shock protein, a probable DNA-binding stress protein, and aspartyl-tRNA synthetase), a segment of PA4967 (encoding a topoisomerase IV subunit B), as well as a chimeric clone containing two inserts including the ORFs PA0547 and PA2326 (encoding a probable transcriptional regulator and a probable hypothetical protein, respectively).ConclusionsThe studies reported here demonstrate the application of new a genomic method, SCALEs, which can be used to improve understanding of the evolution of antibiotic resistance in P. aeruginosa. In our demonstration studies, we identified a significant number of genomic regions that increased resistance to multiple aminoglycosides. We identified genetic regions that include open reading frames that encode for products from many functional categories, including genes related to O-antigen synthesis, DNA repair, and transcriptional and translational processes.

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A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models.

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