Universidad Pública de Navarra

Although the majority of Amaranthus palmeri populations resistant to ALS inhibitors are due to alterations in the enzyme (target-site resistance mechanism), there are some cases of resistance due to herbicide metabolization (which is a non-target site (NTS) mechanism). In this study, we investigated the physiology of a sensitive (S) population and a sulfonylurea-resistant population due to NTSR of A. palmeri (R). Both populations were grown hydroponically and treated with the ALS inhibitor nicosulfuron, alone or combined with NBD-Cl (GST inhibitor). Seven days later, free amino acid profile, glutathione (GSH) content and GST activity and expression were measured in the leaves. Only S population leaves showed the typical amino acid profile previously described after sulfonylureas; R population showed no change, suggesting that such physiological disruption is prevented as ALS activity is maintained through herbicide metabolism. In the S population, the herbicide-induced oxidative stress triggered an increase in GST activity and accumulation of glutathione-related compounds (GSH and its precursors). R population showed a higher basal GST activity than S population and GST activity was induced by the herbicide. Three Phy and three Tau GSTs were evaluated, but none were clearly linked to increased activity. Root length in agar plates was used as a marker of herbicide sensitivity in the presence or absence of NBD-Cl and malathion (P450s inhibitor). The results provide evidence that P450s are involved in NTSR in the population of A. palmeri of this study, as previously reported, and they suggest, for the first time in a dicot weed, the involvement of GSTs in sulfonylurea metabolism.

Selective cleavage of oligonucleotide probes by bacterial nucleases has emerged as a promising strategy for detecting foodborne pathogens. In this study, we applied this approach to enable rapid, colorimetric detection of Listeria monocytogenes in fresh leafy vegetables and raw sheep milk, two common sources of foodborne human listeriosis. We optimized (i) culture conditions to maximize nuclease production; (ii) probe design, including oligonucleotide sequence and chemical modifications, for selective gold nanoparticle aggregation; and (iii) experimental parameters of pH, temperature, and ionic strength for efficient reaction kinetics. Proteomic and size-exclusion chromatography analyses identified L.monocytogenes Ribonuclease II (29.349 kDa, encoded by rnhB) as the nuclease responsible for probe cleavage. Results from L.monocytogenes-spiked samples of ready-to-eat lettuce (fourth-range) and high-fat, high-Mg⁺⁺ sheep milk indicate that this method represents a rapid, easy-to-perform, and cost-effective diagnostic tool for pathogen screening in food and environmental samples. Overall, these findings highlight the enormous potential of nucleases as bacterial biomarkers in the fields of food security and health.

While the transition from optical fiber to planar waveguide substrates in LMR-based sensors has allowed for the development of more robust, cost-effective, and easily manufactured platforms, it has also presented challenges in optimizing critical sensor parameters such as resolution and sensitivity in biosensing. In this work, we introduce the application of gold nanoparticles (AuNP) to enhance the sensitivity of a Lossy Mode Resonance (LMR)-based biosensor for the detection of vascular endothelial growth factor (VEGF) protein. The sensor was developed by depositing a nanometric TiO₂ film on a planar waveguide, and its performance was assessed using three detection approaches: label-free, sandwich assay, and AuNP-labeled sandwich assay. The integration of AuNP significantly improved sensitivity, enabling detection at concentrations as low as 0.1 ng mL^-1, surpassing the sensitivity of traditional label-free LMR-based sensors. These results demonstrate the potential of AuNP-enhanced LMR sensors for detecting low concentrations of biomarkers with high specificity and sensitivity, positioning them as promising tools for biosensing applications.