The effects of kanamycin concentration on gene transcription levels in Escherichia coli

Ketoprofen promotes the conjugative transfer of antibiotic resistance among antibiotic resistant bacteria in natural aqueous environments

The emergence and spread of antibiotic resistance in the environment pose a serious threat to global public health. It is acknowledged that non-antibiotic stresses, including disinfectants, pharmaceuticals and organic pollutants, play a crucial role in horizontal transmission of antibiotic resistance genes (ARGs). Despite the widespread presence of non-steroidal anti-inflammatory drugs (NSAIDs), notably in surface water, their contributions to the transfer of ARGs have not been systematically explored. Furthermore, previous studies have primarily concentrated on model strains to investigate whether contaminants promote the conjugative transfer of ARGs, leaving the mechanisms of ARG transmission among antibiotic resistant bacteria in natural aqueous environments under the selective pressures of non-antibiotic contaminants remains unclear. In this study, the Escherichia coli (E. coli) K12 carrying RP4 plasmid was used as the donor strain, indigenous strain Aeromonas veronii containing rifampicin resistance genes in Taihu Lake, and E. coli HB101 were used as receptor strains to establish inter-genus and intra-genus conjugative transfer systems, examining the conjugative transfer frequency under the stress of ketoprofen. The results indicated that ketoprofen accelerated the environmental spread of ARGs through several mechanisms. Ketoprofen promoted cell-to-cell contact by increasing cell surface hydrophobicity and reducing cell surface charge, thereby mitigating cell-to-cell repulsion. Furthermore, ketoprofen induced increased levels of reactive oxygen species (ROS) production, activated the DNA damage-induced response (SOS), and enhanced cell membrane permeability, facilitating ARG transmission in intra-genus and inter-genus systems. The upregulation of outer membrane proteins, oxidative stress, SOS response, mating pair formation (Mpf) system, and DNA transfer and replication (Dtr) system related genes, as well as the inhibition of global regulatory genes, all contributed to higher transfer efficiency under ketoprofen treatment. These findings served as an early warning for a comprehensive assessment of the roles of NSAIDs in the spread of antibiotic resistance in natural aqueous environments.

Antibacterial efficacy, mode of action, and safety of a novel nano-antibiotic against antibiotic-resistant Escherichia coli strains

Globally rising antibiotic-resistant (AR) and multi-drug resistant (MDR) bacterial infections are of public health concern due to treatment failure with current antibiotics. Enterobacteria, particularly Escherichia coli, cause infections of surgical wound, bloodstream, and urinary tract, including pneumonia and sepsis. Herein, we tested in vitro antibacterial efficacy, mode of action (MoA), and safety of novel amino-functionalized silver nanoparticles (NH2-AgNP) against the AR bacteria. Two AR E. coli strains (i.e., ampicillin- and kanamycin-resistant E. coli), including a susceptible strain of E. coli DH5α, were tested for susceptibility to NH2-AgNP using Kirby-Bauer disk diffusion and standard growth assays. Dynamic light scattering (DLS) was used to determine cell debris and relative conductance was used as a measure of cell leakage, and results were confirmed with transmission electron microscopy (TEM). Multiple oxidative stress assays were used for in vitro safety evaluation of NH2-AgNP in human lung epithelial cells. Results showed that ampicillin and kanamycin did not inhibit growth in either AR bacterial strain with doses up to 160 μg/mL tested. NH2-AgNP exhibited broad-spectrum bactericidal activity, inhibiting the growth of all three bacterial strains at doses ≥1 μg/mL. DLS and TEM revealed cell debris formation and cell leakage upon NH2-AgNP treatment, suggesting two possible MoAs: electrostatic interactions followed by cell wall damage. Safety evaluation revealed NH2-AgNP as noncytotoxic and antioxidative to human lung epithelial cells. Taken together, these results suggest that NH2-AgNP may serve as an effective and safer bactericidal therapy against AR bacterial infections compared to common antibiotics.

Codon optimization of a gene encoding DNA polymerase from Pyrococcus furiosus and its expression in Escherichia coli

BACKGROUND

DNA polymerase is an essential component in PCR assay for DNA synthesis. Improving DNA polymerase with characteristics indispensable for a powerful assay is crucial because it can be used in wide-range applications. Derived from Pyrococcus furiosus, Pfu DNA polymerase (Pfu pol) is one of the excellent polymerases due to its high fidelity. Therefore, we aimed to develop Pfu pol from a synthetic gene with codon optimization to increase its protein yield in Escherichia coli.

RESULTS

Recombinant Pfu pol was successfully expressed and purified with a two-step purification process using nickel affinity chromatography, followed by anion exchange chromatography. Subsequently, the purified Pfu pol was confirmed by Western blot analysis, resulting in a molecular weight of approximately 90 kDa. In the final purification process, we successfully obtained a large amount of purified enzyme (26.8 mg/L). Furthermore, the purified Pfu pol showed its functionality and efficiency when tested for DNA amplification using the standard PCR.

CONCLUSIONS

Overall, a high-level expression of recombinant Pfu pol was achieved by employing our approach in the present study. In the future, our findings will be useful for studies on synthesizing recombinant DNA polymerase in E. coli expression system.

Machine-Learning Single-Stranded DNA Nanoparticles for Bacterial Analysis

A two-dimensional nanoparticle-single-stranded DNA (ssDNA) array has been assembled for the detection of bacterial species using machine-learning (ML) algorithms. Out of 60 unknowns prepared from bacterial lysates, 54 unknowns were predicted correctly. Furthermore, the nanosensor array, supported by ML algorithms, was able to distinguish wild-type Escherichia coli from its mutant by a single gene difference. In addition, the nanosensor array was able to distinguish untreated wild-type E. coli from those treated with antimicrobial drugs. This work demonstrates the potential of nanoparticle-ssDNA arrays and ML algorithms for the discrimination and identification of complex biological matrixes.