Probing the cellular damage in bacteria induced by GaN nanoparticles using confocal laser Raman spectroscopy
Source abstract
Abstract
Understanding the mechanism of nanoparticle (NP) induced toxicity in microbes is of potential importance to a variety of disciplines including disease diagnostics, biomedical implants, and environmental analysis. In this context, toxicity to bacterial cells and inhibition of biofilm formation by GaN NPs and their functional derivatives have been investigated against gram positive and gram negative bacterial species down to single cellular level. High levels of inhibition of biofilm formation (>80 %) was observed on treatments with GaN NPs at sub-micro molar concentrations. These results were substantiated with morphological features investigated with field emission scanning electron microscope, and the observed changes in vibrational modes of microbial cells using Raman spectroscopy. Raman spectra provided molecular interpretation of cell damage by registering signatures of molecular vibrations of individual living microbial cells and mapping the interplay of proteins at the cell membrane. As compared to the untreated cells, Raman spectra of NP-treated cells showed an increase in the intensities of characteristic protein bands, which confirmed membrane damage and subsequent release of cellular contents outside the cells. Raman spectral mapping at single cellular level can facilitate understanding of the mechanistic aspect of toxicity of GaN NPs. The effect may be correlated to passive diffusion causing mechanical damage to the membrane or ingress of Ga3+ (ionic radius ~0.076 nm) which can potentially interfere with bacterial metabolism, as it resembles Fe2+ (ionic radius ~0.077 nm), which is essential for energy metabolism.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Nanoparticles: synthesis and applications · Bacterial biofilms and quorum sensing · Spectroscopy Techniques in Biomedical and Chemical Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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