Two Forms of Tyrosyl-tRNA Synthetase from Pseudomonas aeruginosa: Characterization and Discovery of Inhibitory Compounds

The evolution of heteroresistance via small colony variants in Escherichia coli following long term exposure to bacteriostatic antibiotics

Traditionally, bacteriostatic antibiotics are agents able to arrest bacterial growth. Despite being traditionally viewed as unable to kill bacterial cells, when they are used clinically the outcome of these drugs is frequently as effective as when a bactericidal drug is used. We explore the dynamics of Escherichia coli after exposure to two ribosome-targeting bacteriostatic antibiotics, chloramphenicol and azithromycin, for thirty days. The results of our experiments provide evidence that bacteria exposed to these drugs replicate, evolve, and generate a sub-population of small colony variants (SCVs) which are resistant to multiple drugs. These SCVs contribute to the evolution of heteroresistance and rapidly revert to a susceptible state once the antibiotic is removed. Stated another way, exposure to bacteriostatic drugs selects for the evolution of heteroresistance in populations previously lacking this trait. More generally, our results question the definition of bacteriostasis as populations exposed to bacteriostatic drugs are replicating despite the lack of net growth.

The Evolution of Heteroresistance via Small Colony Variants in Escherichia coli Following Long Term Exposure to Bacteriostatic Antibiotics

Traditionally, bacteriostatic antibiotics are agents able to arrest bacterial growth. Despite being traditionally viewed as unable to kill bacterial cells, when they are used clinically the outcome of these drugs is frequently as effective as when a bactericidal drug is used. We explore the dynamics of Escherichia coli after exposure to two ribosome-targeting bacteriostatic antibiotics, chloramphenicol and azithromycin, for thirty days. The results of our experiments provide evidence that bacteria exposed to these drugs replicate, evolve, and generate a sub-population of small colony variants (SCVs) which are resistant to multiple drugs. These SCVs contribute to the evolution of heteroresistance and rapidly revert to a susceptible state once the antibiotic is removed. Stated another way, exposure to bacteriostatic drugs selects for the evolution of heteroresistance in populations previously lacking this trait. More generally, our results question the definition of bacteriostasis as populations exposed to bacteriostatic drugs are replicating despite the lack of net growth.

In silico study of antibacterial tyrosyl-tRNA synthetase and toxicity of main phytoconstituents from three active essential oils

The misuse and overuse of antibiotics have resulted in antibiotic resistance. However, there are alternative approaches that could either substitute antibiotics or enhance their effectiveness without harmful side effects. One such approach is the use of terpene-rich essential oils. In this study, we aimed to demonstrate the antibacterial activity of the main components of three plant essential oils, namely Anthemis punctata, Anthemis pedunculata and Daucus crinitus. Specifically, we targeted bacterial tyrosyl-tRNA synthetase, an enzyme that plays a critical role in bacterial protein synthesis. To investigate how the phytocompounds interact with the enzyme's active sites, we employed a molecular docking study using Autodock Software Tools 1.5.7. Our findings revealed that all 28 phytocompounds bound to the enzyme's active sites with binding energies ranging from -6.96 to -4.03 kcal/mol. These results suggest that terpene-rich essential oils could be a potential source of novel antimicrobial agents.Communicated by Ramaswamy H. Sarma.

Molecular docking, dynamics simulation and pharmacokinetic studies of Cyperus articulatus essential oil metabolites as inhibitors of Staphylococcus aureus

Cyperus articulatus has been extensively studied for its essential oil (EO), active components and antibacterial activities against a wide range of bacteria such as Bacillus megaterium, Streptococcus pyogenes, Staphylococcus epidermidis, Escherichia coli and Staphylococcus aureus. However, knowledge of the biomolecular interaction of the individual EO metabolites responsible for its inhibition activities is lacking. The multi-drug-resistant bacteria S. aureus, which is of prime concern, has been reported to be inhibited by Cyperus articulatus rhizome EO. The present work analyzed the molecular interactions of the major Cyperus articulatus rhizome EO metabolites with the target enzyme TyrRS of S. aureus and studied the conformational dynamics and stability of the protein-ligand complexes. Molecular docking studies of selected EO metabolites such as mustakone, longifolenaldehyde, cyperotundone, α-copaene, β-calacorene, α-calacorene and khusinol were conducted along with standard drug chloramphenicol for comparative analysis of their binding affinity with S. aureus TyrRS. The metabolites khusinol, mustakone, β-calacorene and α-calacorene generated comparable docking scores (-6.4, -6.2, -6.1 and -6.2 kcal/mol, respectively) with that of the drug chloramphenicol (-6.3 kcal/mol). Most EO metabolites did not exhibit H-bonding with the S. aureus TyrRS residues and were stabilized through pi-interactions. The MD simulation study illustrated that compounds like mustakone could effectively bind to the receptors of S. aureus TyrRS with high stability and integrity. Pharmacokinetic, drug-like properties and toxicity analysis of the EO metabolites supported the candidature of mustakone and khusinol as pharmacologically important antibacterial drug ingredients. The study envisaged the structural framework of the EO metabolites for antibacterial drug design.Communicated by Ramaswamy H. Sarma.

Evidence of episodic positive selection in Corynebacterium diphtheriae complex of species and its implementations in identification of drug and vaccine targets

BACKGROUND

Within the pathogenic bacterial species Corynebacterium genus, six species that can produce diphtheria toxin (C. belfantii, C. diphtheriae, C. pseudotuberculosis, C. rouxii, C. silvaticum and C. ulcerans) form a clade referred to as the C. diphtheria complex. These species have been found in humans and other animals, causing diphtheria or other diseases. Here we show the results of a genome scale analysis to identify positive selection in protein-coding genes that may have resulted in the adaptations of these species to their ecological niches and suggest drug and vaccine targets.

METHODS

Forty genomes were sampled to represent species, subspecies or biovars of Corynebacterium. Ten phylogenetic groups were tested for positive selection using the PosiGene pipeline, including species and biovars from the C. diphtheria complex. The detected genes were tested for recombination and had their sequences alignments and homology manually examined. The final genes were investigated for their function and a probable role as vaccine or drug targets.

RESULTS

Nineteen genes were detected in the species C. diphtheriae (two), C. pseudotuberculosis (10), C. rouxii (one), and C. ulcerans (six). Those were found to be involved in defense, translation, energy production, and transport and in the metabolism of carbohydrates, amino acids, nucleotides, and coenzymes. Fourteen were identified as essential genes, and six as virulence factors. Thirteen from the 19 genes were identified as potential drug targets and four as potential vaccine candidates. These genes could be important in the prevention and treatment of the diseases caused by these bacteria.

Image-based profiling for drug discovery: due for a machine-learning upgrade?

Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug's activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.