Development of rapid and sensitive methods to detect pathogens is important to food and water safety. This study aimed to detect and discriminate important food- and waterborne bacteria (i.e., Escherichia coli O157:H7, Staphylococcus epidermidis, Listeria monocytogenes, and Enterococcus faecelis) by surface-enhanced Raman spectroscopy (SERS) coupled with intracellular nanosilver as SERS substrates. An in vivo molecular probing using intracellular nanosilver for the preparation of bacterial samples was established and assessed. Satisfactory SERS performance and characteristic SERS spectra were obtained from different bacterial samples. Distinctive differences were observed in SERS spectral data, specifically in the Raman shift region of 500-1,800 cm(-1), and between bacterial samples at the species and strain levels. The detection limit of SERS coupled with in vivo molecular probing using silver nanosubstrates could reach the level of single cells. Experiments with a mixture of E. coli O157:H7 and S. epidermidis for SERS measurement demonstrate that SERS could be used for classification of mixed bacterial samples. Transmission electron microscopy was used to characterize changes of morphology and cellular composition of bacterial cells after treatment of intracellular nanosilver. The results indicate that SERS coupled with intracellular silver nanosubstrates is a promising method for detection and characterization of food- and waterborne pathogenic and non-pathogenic bacterial samples.

Food- and waterborne viruses pose serious health risks to humans and were associated with many outbreaks worldwide. Rapid, accurate, and nondestructive methods for detection of viruses are of great importance to protect public health. In this study, surface-enhanced Raman spectroscopy (SERS) coupled with gold SERS-active substrates was used to detect and discriminate 7 food- and waterborne viruses, including norovirus, adenovirus, parvovirus, rotavirus, coronavirus, paramyxovirus, and herpersvirus. Virus samples were purified and dialyzed in phosphate buffered saline (8 to 9 log PFU/mL) and then further diluted in deionized water for SERS measurement. After capturing the characteristic SERS spectral patterns, multivariate statistical analyses, including soft independent modeling of class analogy (SIMCA) and principal component analysis (PCA), were employed to analyze SERS spectral data for characterization and identification of viruses. The results show that SIMCA was able to differentiate viruses with and without envelope with >95% of classification accuracy, while PCA presented clear spectral data segregations between different virus strains. The virus detection limit by SERS using gold substrates reached a titer of 10(2).

Root symbioses with arbuscular mycorrhizal fungi and rhizobial bacteria share a common signaling pathway in legumes. Among the common symbiosis genes are CASTOR and POLLUX, the twin homologous genes in Lotus japonicus that encode putative ion channel proteins. Here, we show that the orthologs of CASTOR and POLLUX are ubiquitously present and highly conserved in both legumes and nonlegumes. Using rice (Oryza sativa) as a study system, we employ reverse genetic tools (knockout mutants and RNA interference) to demonstrate that Os-CASTOR and Os-POLLUX are indispensable for mycorrhizal symbiosis in rice. Furthermore, a cross-species complementation test indicates that Os-POLLUX can restore nodulation, but not rhizobial infection, to a Medicago truncatula dmi1 mutant.

The presence of inorganic nitrogen species in water can be unsuitable for drinking and detrimental to the environment. In this study, a surface-enhanced Raman spectroscopy (SERS) method coupled with a commercially available gold nanosubstrate (a gold-coated silicon material) was evaluated for the detection of nitrate and nitrite in water and wastewater. Applications of SERS coupled with gold nanosubstrates resulted in an enhancement of Raman signals by a factor of ∼10(4) compared to that from Raman spectroscopy. The new method was able to detect nitrate with linear ranges of 1-10,000 mg NO3(-)/L (R(2)= 0.978) and 1-100 mg NO3(-)/L (R(2)= 0.919) for water and wastewater samples, respectively. Among the common anions, phosphate appeared to be the major interfering anion affecting nitrate measurement. Nevertheless, the percentage error of nitrate measurement in wastewater by the proposed SERS method was comparable to that by ion chromatography. The nitrate detection limits in water and wastewater samples were about 0.5 mg/L. The SERS method could simultaneously detect sulfate, which may serve as a reference standard in water. These results suggested that the SERS coupled with nanosubstrates is a promising method to determine nitrate concentrations in water and wastewater.