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Plants have evolved intricate signaling pathways, which operate as networks governed by feedback to deal with stressors. Nevertheless, the sophisticated molecular mechanisms underlying these routes still need to be comprehended, and experimental validation poses significant challenges and expenses. Consequently, computational hypothesis evaluation gains prominence in understanding plant signaling dynamics. Biosensors are genetically modified to emit light when exposed to a particular hormone, such as abscisic acid (ABA), enabling quantification. We developed computational models to simulate the relationship between ABA concentrations and bioluminescent sensors utilizing the Hill equation and ordinary differential equations (ODEs), aiding better hypothesis development regarding plant signaling. Based on simulation results, the luminescence intensity was recorded for a concentration of 47.646 RLUs for 1.5 μmol, given the specified parameters and model assumptions. This method enhances our understanding of plant signaling pathways at the cellular level, offering significant benefits to the scientific community in a cost-effective manner. The alignment of these computational predictions with experimental results emphasizes the robustness of our approach, providing a cost-effective means to validate mathematical models empirically. The research intended to correlate the bioluminescence of biosensors with plant signaling and its mathematical models for quantified detection of specific plant hormone ABA. [ABSTRACT FROM AUTHOR]

Abscisic acid (ABA), as the most common plant hormone in the growth of wheat, can greatly affect the yield when its levels deviate from normal. Therefore, highly sensitive and selective detection of this hormone is greatly needed. In this work, we developed an aptamer sensor based on surface-enhanced Raman spectroscopy (SERS) and applied it for the high sensitivity detection of ABA. Biotin-modified ABA aptamer complement chains were modified on ferrosoferric oxide magnetic nanoparticles (Fe3O4MNPs) and acted as capture probes, and sulfhydryl aptamer (SH-Apt)-modified silver-coated gold nanospheres (Au@Ag NPs) were used as signal probes. Through the recognition of the ABA aptamer and its complementary chains, an aptamer sensor based on SERS was constructed. As SERS internal standard molecules of 4-mercaptobenzoic acid (4-MBA) were encapsulated between the gold core and silver shell of the signal probes; the constructed aptamer sensor generated a strong SERS signal of 4-MBA after magnetic separation. When there were ABA molecules in the detection system, with the preferential binding of ABA aptamer and ABA molecule, the signal probes were released from the capture probes, after magnetic separation, leading to a linear decrease in SERS intensity of 4-MBA. Thus, the detection response was linear over a logarithmic concentration range, with an ultra-low detection limit of 0.67 fM. In addition, the practical use of this assay method was demonstrated in ABA detection from fresh wheat leaves, with a relative error (RE) of 5.43–8.94% when compared with results from enzyme-linked immunosorbent assay (ELISA). The low RE value proves that the aptamer sensor will be a promising method for ABA detection. [ABSTRACT FROM AUTHOR]

A rapid, accurate and novel detection method of plant hormones abscisic acid at ultra-low concentrations in wheat leaves was proposed in this study. This surface-enhanced Raman spectroscopy based method developed the substrate with the silver coated gold nanocomposites. This substrate exhibited excellent detection of rhodamine6G with an ultra-low concentration of 1 × 10−12 M. And resulted in high-performance, well homogeneity and reproducibility against abscisic acid hormone detection with a limit of detection of 1 × 10−9 M. This assay further measured abscisic acid hormones from the fresh leaves extract of wheat with the relative error of 3.72–10.66% compared with enzyme-linked immunosorbent assay bioassay. [ABSTRACT FROM AUTHOR]

A multi-channel localized surface plasmon resonance system is described for absorptiometric determination of abscisic acid (ABA). The system is making use of gold nanoparticles and consists of a broadband light source, a multi-channel alignment device, and a fiber spectrometer. The method is based on the specific interaction between an ABA-binding aptamer and ABA. This induces the growth of gold nanoparticles (AuNPs) functionalized with a polyadenine-tailed aptamer that act as optical probes. Different concentrations of ABA give rise to varied morphologies of grown AuNPs. This causes a change of absorption spectra which is recorded by the system. ABA can be quantified by measurement of the peak wavelength shifts of grown AuNPs. Under optimized conditions, this method shows a linear relationship in the 1 nM to 10 μM ABA concentration range. The detection limit is 0.51 nM. The sensitivity of the ABA assay is strongly improved compared to the method based on salt-induced AuNP aggregation. This is attributed to the use of a poly-A-tailed aptamer and the catalytic ability of AuNPs. In the actual application, the ABA concentration of ABA in fresh leaves of rice is measured with the maximum relative error of 8.03% in comparison with the ELISA method. [ABSTRACT FROM AUTHOR]

Abscisic acid (ABA) plays an important role in abiotic stress response and physiological signal transduction resisting to the adverse environment. Therefore, it is very essential for the quantitative detection of abscisic acid (ABA) due to its indispensable role in plant physiological activities. Herein, a new detection method based on localized surface plasmon resonance (LSPR) using aptamer-functionalized gold nanoparticles (AuNPs) is developed without using expensive instrument and antibody. In the presence of ABA, ABA specifically bind with their aptamers to form the ABA-aptamer complexes with G-quadruplex-like structure and lose the ability to stabilize AuNPs against NaCl-induced aggregation. Meanwhile, the changes of the LSPR spectra of AuNP solution occur and therefore the detection of ABA achieved. Under optimized conditions, this method showed a good linear range covering from 5×10−7 M to 5×10−5 M with a detection limit of 0.33 μM. In practice, the usage of this novel method has been demonstrated by its application to detect ABA from fresh leaves of rice with the relative error of 6.59%-7.93% compared with ELISA bioassay. The experimental results confirmed that this LSPR-based biosensor is simple, selective and sensitive for the detection of ABA. The proposed LSPR method could offer a new analytical platform for the detection of other plant hormones by changing the corresponding aptamer. [ABSTRACT FROM AUTHOR]