Silver nanoparticles embedded in zeolite membranes: release of silver ions and mechanism of antibacterial action

Abstract

Background

The focus of this study is on the antibacterial properties of silver nanoparticles embedded within a zeolite membrane (AgNP-ZM).

Methods and Results

These membranes were effective in killing Escherichia coli and were bacteriostatic against methicillin-resistant Staphylococcus aureus. E. coli suspended in Luria Bertani (LB) broth and isolated from physical contact with the membrane were also killed. Elemental analysis indicated slow release of Ag⁺ from the AgNP-ZM into the LB broth. The E. coli killing efficiency of AgNP-ZM was found to decrease with repeated use, and this was correlated with decreased release of silver ions with each use of the support. Gene expression microarrays revealed upregulation of several antioxidant genes as well as genes coding for metal transport, metal reduction, and ATPase pumps in response to silver ions released from AgNP-ZM. Gene expression of iron transporters was reduced, and increased expression of ferrochelatase was observed. In addition, upregulation of multiple antibiotic resistance genes was demonstrated. The expression levels of multicopper oxidase, glutaredoxin, and thioredoxin decreased with each support use, reflecting the lower amounts of Ag⁺ released from the membrane. The antibacterial mechanism of AgNP-ZM is proposed to be related to the exhaustion of antioxidant capacity.

Conclusion

These results indicate that AgNP-ZM provide a novel matrix for gradual release of Ag⁺.

Keywords:

• silver nanoparticles
• zeolite
• antibacterial agent
• oxidative stress

Supplemental Methods

RNA isolation

Bacteria were collected from each well and pelleted at 4°C in 15 mL centrifuge tubes at 3250 × g for 15 minutes. Supernatants were discarded and the pellets were homogenized in 5 mL Trizol for five minutes. Each tube was shaken vigorously for 30 seconds after the addition of 1 mL of chloroform. The tubes were incubated at room temperature for three minutes prior to centrifugation at 4°C and 3250 × g for 15 minutes. The organic layer was then removed and placed into clean RNase free microfuge tubes. Equal amounts of 100% ethanol were added to each tube and mixed by pipetting. RNA purification was then performed using RNeasy mini kits as per the manufacturer’s instructions, during which DNase was added to remove contaminating DNA. At the final elution step, RNA was resuspended in 20 μL of RNase-free H₂O and stored at −80°C until further use in gene expression arrays and quantitative reverse transcription polymerase chain reaction experiments. The concentration of the samples provided was determined using the NanoDrop^® ND-1000 ultraviolet-visible spectrophotometer.

Gene arrays

Microarray slides were hybridized overnight, washed, and then scanned with an Agilent G2505C microarray scanner. This high-resolution scanner features an industry-leading extended dynamic range of 10⁶ (20-bits) for high sensitivity scanning without saturation, low-level detection resulting from optimized precision optics, broad dynamic range, and minimal spectral cross talk that enables detection of weak features. The information about each probe on the array was extracted from the image data using Agilent Feature Extraction 10.9. This data was stored in Feature Extraction “.txt” files. The raw intensity values from these files were imported into the mathematical software package “R”, which is used for all data input, diagnostic plots, normalization, and quality checking steps of the analysis process using scripts developed inhouse specifically for this analysis. These scripts call on several Bioconductor packages (http://www.bioconductor.org/). Bioconductor is an open source and open development software project that provides tools for the analysis and comprehension of genomic data.¹ Significance analyses of microarrays (SAM) is a powerful tool for analyzing microarray gene expression data useful for identifying differentially expressed genes between two conditions.² SAM was used to calculate a test statistic for relative difference in gene expression based on permutation analysis of expression data and calculated a false discovery rate using the q-value method presented by Storey and Tibshirani.³ In outline, SAM identified statistically significant genes by carrying out gene-specific t-tests and computed a statistic for each gene. This test statistic measured the strength of the relationship between gene expression and a five-response variable. This analysis used no-parametric statistics, given that the data may not follow a normal distribution. The response variable described and grouped the data based on experimental conditions. In this method, repeated permutations of the data were used to determine if the expression of any gene is significantly related to the response. The use of permutation-based analysis accounted for correlations in genes and avoided parametric assumptions about the distribution of individual genes. For this experiment, SAM analysis was implemented in R using the Bioconductor Siggenes package. Also, Relative Log Expression values were computed for each probe set by comparing the expression value in each array against the median expression value for that probe set across all arrays. Gene expression arrays were analyzed using a 10% false discovery rate to generate the list of significantly differentially expressed genes. The q-values (false discovery rate) for each gene are provided in the results table, and the lower the value the more significant the result.

Figure S1 Schematic of fabrication of zeolite support containing silver nanoparticles. Alumina supports were used as the substrate for zeolite membrane synthesis. Zeolite was grown into a continuous membrane by hydrothermal synthesis. Zeolite membranes were then ion-exchanged with 0.005 M AgNO³ solution, washed, and then reduced by hydrazine.

Figure S2 Viability of Escherichia coli after exposure to zeolite support containing silver nanoparticles for 30 minutes. The viability of E. coli was determined after exposure to zeolite support containing silver nanoparticles for 30 minutes. RNA was harvested from these experiments and used for the gene expression microarray analyses. Viability was significantly reduced after incubation with zeolite support containing silver nanoparticles for 30 minutes, compared with zeolite controls. Statistical significance was determined using the Student’s t-test (n = 4 for zeolite controls and zeolite support containing silver nanoparticles, P < 0.02).

Table S1 E. coli primer sequences for quantitative real-time PCR (QRT-PCR)

Table S2 Increases in E. coli gene expression in response to 30-minute exposures to four independent zeolite supports containing AgNPs versus E. coli exposed to four independent zeolite controls

Table S3 Decreases in Escherichia coli gene expression in response to 30-minute exposures to four independent zeolite supports containing silver nanoparticles versus E. coli exposed to four independent zeolite controls

Supplemental References

• GentlemanRCCareyVJBatesDMBioconductor: Open software development for computational biology and bioinformaticsGenome Biol2004510R8015461798
   PubMed Web of Science ®Google Scholar
• TusherVGTibshiraniRChuGSignificance analysis of microarrays applied to the ionizing radiation responseProc Natl Acad Sci U S A20019895116512111309499
   PubMed Web of Science ®Google Scholar
• StoreyJDTibshiraniRStatistical significance for genomewide studiesProc Natl Acad Sci U S A2003100169440944512883005
   PubMed Web of Science ®Google Scholar

Acknowledgments

Support for this research was obtained through grants from the National Institute for Occupational Safety and Health and US Department of Agriculture/National Institute of Food and Agriculture. We are grateful to Drs Vijay Pancholi and Joanne Trgovcich for donating the bacterial cultures used in this study. We thank David Newsom and Dr Peter White at the Nationwide Children’s Hospital’s Biomedical Genomics Core for their assistance with the gene expression microarray assays and analyses.

Disclosure

The authors report no conflicts of interest in this work.