Ultrasensitive Electrochemiluminescence Sensor Utilizing Aggregation-Induced Emission Active Probe for Accurate Arsenite Quantification in Rice Grains.

Arsenic (As) constitutes a substantial threat to global ecosystems and public health. An accurate quantification of inorganic arsenite (As(III)) in rice grains is crucial for ensuring food safety and human well-being. Herein, we constructed an electrochemiluminescence (ECL) biosensor utilizing aggregation-induced emission (AIE) active Pdots for the sensitive detection of As(III) in rice. We synthesized tetraphenylethylene-based AIE-active Pdots, exhibiting stable and highly efficient ECL emission in their aggregated states. Owing to the overlap of spectra, we employed an electrochemiluminescence resonance energy transfer (ECL-RET) system, with the Pdots as the donor and black hole quencher (BHQ) as the acceptor. Upon the introduction of As(III), the conformational changes of As(III)-specific aptamer could trigger the detachment of BHQ-labeled DNA aptamer from the electrode surface, leading to the recovery of the ECL signal. The target-induced "signal-on" bioassay enabled the sensitive and specific detection of As(III) with a linear range of 10 pM to 500 nM, with an ultralow limit of detection (LOD) of 5.8 pM/0.4 ppt. These values significantly surpass those of existing sensors designed for As(III) quantification in rice. Furthermore, by employing amylase hydrolysis for efficient extraction, we successfully applied our sensor to measure As(III) in actual rice samples sourced from diverse regions of China. The results obtained using our sensor were in close agreement with those derived from the reference method of HPLC-ICP-MS. This study not only presents a sensitive and reliable method for detecting arsenite but also underscores its potential applications in enhancing food safety, agriculture practices, and environmental monitoring.

Two-tiered mutualism improves survival and competitiveness of cross-feeding soil bacteria.

Metabolic cross-feeding is a pervasive microbial interaction type that affects community stability and functioning and directs carbon and energy flows. The mechanisms that underlie these interactions and their association with metal/metalloid biogeochemistry, however, remain poorly understood. Here, we identified two soil bacteria, Bacillus sp. BP-3 and Delftia sp. DT-2, that engage in a two-tiered mutualism. Strain BP-3 has low utilization ability of pyruvic acid while strain DT-2 lacks hexokinase, lacks a phosphotransferase system, and is defective in glucose utilization. When strain BP-3 is grown in isolation with glucose, it releases pyruvic acid to the environment resulting in acidification and eventual self-killing. However, when strain BP-3 is grown together with strain DT-2, strain DT-2 utilizes the released pyruvic acid to meet its energy requirements, consequently rescuing strain BP-3 from pyruvic acid-induced growth inhibition. The two bacteria further enhance their collective competitiveness against other microbes by using arsenic as a weapon. Strain DT-2 reduces relatively non-toxic methylarsenate [MAs(V)] to highly toxic methylarsenite [MAs(III)], which kills or suppresses competitors, while strain BP-3 detoxifies MAs(III) by methylation to non-toxic dimethylarsenate [DMAs(V)]. These two arsenic transformations are enhanced when strains DT-2 and BP-3 are grown together. The two strains, along with their close relatives, widely co-occur in soils and their abundances increase with the soil arsenic concentration. Our results reveal that these bacterial types employ a two-tiered mutualism to ensure their collective metabolic activity and maintain their ecological competitive against other soil microbes. These findings shed light on the intricateness of bacterial interactions and their roles in ecosystem functioning.

Natural Microbial Reactor-Based Sensing Platform for Highly Sensitive Detection of Inorganic Arsenic in Rice Grains.

Rice is a major dietary source of inorganic arsenic (iAs), a highly toxic arsenical that accumulates in rice and poses health risks to rice-based populations. However, the availability of detection methods for iAs in rice grains is limited. In this study, we developed a novel approach utilizing a natural bacterial biosensor, Escherichia coli AW3110 (pBB-ArarsR-mCherry), in conjunction with amylase hydrolysis for efficient extraction, enabling high-throughput and quantitative detection of iAs in rice grains. The biosensor exhibits high specificity for arsenic and distinguishes between arsenite [As(III)] and arsenate [As(V)] by modulating the concentration of PO43- in the detection system. We determined the iAs concentrations in 19 rice grain samples with varying total As concentrations and compared our method with the standard technique of microwave digestion coupled with HPLC-ICP-MS. Both methods exhibited comparable results, without no significant bias in the concentrations of As(III) and As(V). The whole-cell biosensor demonstrated excellent reproducibility and a high signal-to-noise ratio, achieving a limit of detection of 16 µg kg-1 [As(III)] and 29 µg kg-1 [As(V)]. These values are considerably lower than the maximum allowable level (100 µg kg-1) for infant rice supplements established by the European Union. Our straightforward sensing strategy presents a promising tool for detecting iAs in other food samples.