A method for detecting chiral substances using a luminescent bacterial array sensor

Abstract

This invention discloses a method for detecting chiral substances using a luminescent bacteria array sensor. The method involves mixing the luminescent bacteria array sensor with the sample to be tested, followed by absorption spectral scanning to obtain the spectral data of the test solution. Linear discriminant analysis (LDA) is then performed on the spectral data. By comparing the positions of unknown sample data points in the LDA spectrum with data points of known chiral types, the type, composition, and content of chiral substances in the test solution can be determined. The luminescent bacteria array sensor is composed of multiple luminescent bacteria. This invention also discloses its application in chiral substance detection. The method provided by this invention is used for chiral identification. Through LDA processing of matrix data, it correctly identifies chiral substances of different chiralities and concentrations, and the mechanism of this identification is preliminarily explored. This method is simple to operate, can be used for rapid chiral detection, and has certain practicality and application prospects.

Description

Technical Field

[0001] This invention belongs to the field of analytical detection technology, specifically relating to a method for detecting chiral substances using a luminescent bacteria array sensor. Background Technology

[0002] Currently, chiral separation and chiral sensing technologies are the main methods for chiral analysis. Chiral separation techniques include crystallization, enzyme kinetics, chromatography, and capillary electrophoresis. These techniques have proven to be effective chiral analysis methods, but they also have significant drawbacks, such as the potential for causing conformational changes or inactivation of biological chiral selectors and analytes, high instrument costs, cumbersome operation, long analysis times, and difficulty in achieving in-situ and online detection. In contrast to the shortcomings of chiral separation techniques, chiral sensing technologies offer advantages such as simplicity, economy, speed, and high throughput, better meeting the requirements of drug development, clinical diagnostics, and chiral catalyst screening. Like all chiral recognition methods, chiral sensors require chiral selectors for enantiomeric recognition. Typically, chiral selectors can be conventional chiral molecules such as chiral crown ethers, cyclodextrins, and proteins, or they may be chiral supramolecular assemblies and nanocomposites. While chiral sensing technologies have developed rapidly and made significant progress, their application in chiral drug analysis is far less widespread than that of classical chiral chromatographic separation methods. The bottleneck restricting the widespread application of chiral sensors lies in the fact that, regardless of whether the chiral sensor is based on chiral molecules, supramolecular assemblies, or even nanocomposite materials, the signals generated by different enantiomers often cannot be distinguished and interfere with each other when they coexist. Therefore, a major approach to promoting the practical application of chiral sensors in the field of drug analysis is to improve their ability to recognize enantiomer differences.

[0003] As is well known, the main biomolecules that make up organisms exist in a strictly single chiral form. Therefore, biological systems possess a powerful chiral recognition capability, and related physiological processes can exhibit 100% enantioselectivity. The importance of chirality in drug research stems from this powerful enantioselectivity of organisms. This also indicates that biological systems possess enantioselectivity recognition effects that are completely unmatched by artificially synthesized chiral selectors and other biomolecules such as proteins. Therefore, if living organisms can be used as chiral selectors in chiral sensing technology, their chiral analysis capabilities will be greatly enhanced. Microorganisms such as bacteria are also precise and complex single-chiral systems and are continuously consumed resources. In particular, luminescent bacteria possess both chiral recognition and signal transduction functions. Therefore, luminescent bacteria are a complete chiral bioluminescent sensor. Different chiral enantioselectors will cause different biological effects on luminescent bacteria, thereby altering certain biochemical processes and thus affecting their bioluminescent system—either promoting or inhibiting it.

[0004] Currently, numerous instrument companies have developed various environmental toxicology detection instruments using naturally occurring bioluminescent bacteria, playing a vital role in environmental monitoring. However, toxicology detection based on naturally occurring bioluminescent bacteria lacks molecular selectivity and is merely a vague metric. The primary reason for this is the limited functionality of naturally occurring bioluminescent bacteria; despite their potential, they cannot yet meet the demands for more refined qualitative and quantitative analysis. Summary of the Invention

[0005] This invention aims to at least partially solve one of the technical problems in related technologies. Therefore, the main objective of this invention is to provide a method for detecting chiral substances using a luminescent bacterial array sensor, enabling simultaneous qualitative and quantitative analysis of chiral substances, and providing a new analytical method for the detection of chiral substances.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] In a first aspect, a method for detecting chiral substances using a luminescent bacteria array sensor is provided. The method involves mixing the luminescent bacteria array sensor with a test solution and then performing an absorption spectrum scan to obtain spectral data of the test solution. Linear discriminant analysis (LDA) is then performed on the spectral data. By comparing the positions of unknown sample data points in the LDA spectrum with data points of known types of chiral substances, the type, composition, and content of the chiral substances in the test solution can be determined. The luminescent bacteria array sensor is composed of at least two types of luminescent bacteria.

[0008] In some specific embodiments, the luminescent bacteria are recombinant Escherichia coli, Agrobacterium tumefaciens / c58, Bacillus subtilis, and the naturally luminescent Vibrio fischeri.

[0009] In some specific implementations, the LDA spectrum of the known type of chiral substance is obtained by scanning the spectral data of the test solution after mixing the luminescent bacteria array sensor with different concentrations of the known type of chiral substance, and then performing linear discriminant LDA analysis on the spectral data.

[0010] In some specific embodiments, the chiral substance is one of a monosaccharide, a chiral catalyst, and a drug.

[0011] Furthermore, the monosaccharide is one or more of glucose, ribose, xylose, mannose, galactose, and arabinose.

[0012] Furthermore, the monosaccharide is one or more of glucose, xylose, and mannose.

[0013] In some specific embodiments, the monosaccharide concentration is 10-1000 μM.

[0014] Furthermore, the concentration of the monosaccharide is 50-500 μM.

[0015] Secondly, based on the application of the aforementioned methods in the detection of chiral substances.

[0016] Compared with the prior art, the present invention has at least the following advantages:

[0017] The method of this invention is used for chiral identification. By processing matrix data through LDA, it can simultaneously and correctly identify chiral substances (monosaccharides, sweeteners) with different chiralities and concentrations, and conduct a preliminary investigation into the identification mechanism. This method is simple to operate, can be used for rapid chiral detection, and has certain practicality and application prospects. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below.

[0019] Figure 1 Normalized bioluminescent spectra (a) and bioluminescent intensities (b) of four luminescent bacteria in the method provided by this invention;

[0020] Figure 2 This is a scatter plot showing the classification of different monosaccharides in Example 2 of the present invention;

[0021] Figure 3 Linear regression diagram of monosaccharides with different L / D configuration ratios in Example 3;

[0022] Figure 4 This is a scatter plot showing the classification of different sweeteners in Example 4;

[0023] Figure 5 Bar chart showing the MTT crystallization amount of four luminescent bacteria when testing ribose using the luminescent bacteria array sensor provided by this invention for detecting chiral substances. Detailed Implementation

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. These embodiments are merely descriptive and not limiting, and should not be construed as limiting the scope of protection of the present invention. Unless otherwise specified in the embodiments, conventional conditions or conditions recommended by the manufacturer should be followed. Reagents or instruments whose manufacturers are not specified are all commercially available conventional products.

[0025] In the following embodiments, the applicant uses chiral substances—monosaccharides—as an example to illustrate the method for detecting chiral substances using a luminescent bacteria array sensor.

[0026] In the following examples, the concentration of luminescent bacteria used was 1.3 × 10⁻⁶.7 cfu / mL -1 Meanwhile, the volume ratio of luminescent bacteria to monosaccharides was 1:1.

[0027] Example 1: Construction of luminescent bacteria

[0028] The luminescent bacteria in this application are recombinant *Escherichia coli* (E. coli), *Agrobacterium tumefaciens* / c58, *Bacillus subtilis*, and the naturally luminescent bacterium *Vibrio fischeri*. The construction method of the recombinant *E. coli* (EC-lux), *Agrobacterium tumefaciens* / c58 (AT-lux), and *Bacillus subtilis* (BS-lux) is as follows: A commercially available plasmid containing the Lux luminescent gene was transformed into the corresponding bacteria using a CaCl2 chemical induction method, successfully constructing the three recombinant luminescent bacteria: *E. coli*, *Agrobacterium tumefaciens* / c58, and *Bacillus subtilis*. The three recombinant bacteria and the naturally luminescent bacterium *Vibrio fischeri* (V. fischeri) were then analyzed using an enzyme-linked immunosorbent assay (ELISA) reader at the same cell concentration (1.3 × 10⁻⁶). 7 cfu / mL -1 The luminescence properties of the luminescent bacteria were characterized, and their luminescence spectra were obtained, as shown in the figure below. Figure 1 As shown in the figure, the emission spectrum is distributed between 400nm and 650nm, with the maximum emission intensity at 490nm, indicating that the plasmid was successfully introduced into the bacteria and the recombinant bacteria have good luminescence performance, which can be used for subsequent experiments.

[0029] Of course, the construction of recombinant Escherichia coli, Agrobacterium tumefaciens / c58, and Bacillus subtilis in this application is a conventional technique in the art, which can be directly purchased by those skilled in the art or prepared by known methods disclosed in the art.

[0030] Example 2: Differential detection of different types of enantiomeric monosaccharides by luminescent bacteria

[0031] Based on Example 1, in this example, the test solution was mixed with recombinant Escherichia coli, Agrobacterium tumefaciens / c58, Bacillus subtilis, and Vibrio fischeri, and then scanned to obtain the spectral data of the test solution. Linear discriminant analysis (LDA) was performed on the spectral data, and the positions of unknown sample data points in the LDA spectrum were compared with the data points of known types of metal ions to determine the type composition of monosaccharides in the test solution.

[0032] Specifically, we selected six commonly used monosaccharides—glucose, ribose, xylose, mannose, galactose, and arabinose—as standard samples for luminescent bacteria detection. We first used the luminescent bacteria to respond to different concentrations of monosaccharides to find the optimal recognition concentration. We prepared L- and D-form solutions of the six monosaccharides, setting seven concentration gradients: 10 μM, 50 μM, 100 μM, 500 μM, 1 mM, 5 mM, and 10 mM, and added them to 96-well plates, performing six parallel experiments. Then, equal volumes of the four concentrations (1.3 × 10⁻⁶) from Example 1 were added. 7 cfu / mL -1 The luminescent bacteria (recombinant Escherichia coli, Agrobacterium tumefaciens / c58, Bacillus subtilis, and naturally occurring Vibrio fischeri) were used to identify six monosaccharides at different concentrations and configurations. The luminescence intensity was measured after the reaction using an ELISA reader, and the results were normalized by comparing the data with a blank control under the same conditions. LDA discriminant analysis was performed using SPSS, and the data for each concentration were plotted using Origin software. Figure 2 As shown, from Figure 2 The 3D scatter plots show that the data for various monosaccharides can be correctly classified, with all six monosaccharides completely distinguished and falling into different regions. Furthermore, the sample points in each 3D plot are dispersed, and the classification accuracy in the SPSS output is 100%. This indicates that the luminescence intensity of luminescent bacteria differs for different types and chiral monosaccharides. Therefore, these four bacteria can be used to construct arrays for the analysis of the types and chiralities of monosaccharides and other substances. Simultaneously, comparative analysis of the obtained data images revealed that the four recombinant luminescent bacteria showed the greatest differences in luminescence intensity for glucose, xylose, and mannose at concentrations of 50 μM, 1 mM, and 50 μM, demonstrating the best recognition effect and the ability to best distinguish different types of enantiomers.

[0033] Example 3: Differential detection of mixtures of different enantiomeric monosaccharides using a luminescent bacterial array sensor

[0034] Based on Example 2, this application uses a luminescent bacterial array sensor composed of recombinant Escherichia coli, Agrobacterium tumefaciens / c58, Bacillus subtilis, and Vibrio fischeri to detect the simultaneous presence of multiple different enantiomers of monosaccharides. As shown in Example 2, glucose, xylose, and mannose, which exhibit the greatest differences in luminescence intensity, were selected, with their corresponding optimal recognition concentrations of 50 μM, 1 mM, and 50 μM, respectively. Then, at the optimal recognition concentrations, glucose, xylose, and mannose solutions with different L / D configuration ratios were prepared as follows: 100% L, 80% L + 20% D, 60% L + 40% D, 40% L + 60% D, 20% L + 80% D, and 100% D. These solutions were then added in equal volumes to 96-well plates, followed by an equal volume of the luminescent bacterial array sensor. The luminescent bacterial array sensor, composed of four luminescent bacteria, was used for reaction recognition, and the luminescence intensity after the reaction was measured using a microplate reader. After normalizing the measured data by comparing them with a blank control under the same conditions, discriminant analysis was performed using SPSS, and Origin was used for plotting. Figure 3 , Figure 3 The linear plots are obtained by performing LDA processing on the data using SPSS software, and the linear projection coordinates of the dataset corresponding to the best discriminant factor. The linear analysis of each group shows good linearity, indicating that the constructed luminescent bacteria array can correctly classify and identify solutions with different proportions of mixed configurations, that is, it can identify chiral monosaccharide molecules with different configurations.

[0035] Example 4: Application of luminescent bacteria array sensor in the identification of real samples - sweeteners

[0036] To evaluate the potential of this method in practical applications, the method for detecting chiral substances using this luminescent bacteria array sensor was applied to the detection of actual sugar samples. Specifically:

[0037] This application selected five commonly used sweeteners as actual samples for array recognition: aspartame, sorbitol, xylitol, sodium saccharin, and cyclamate. To find the optimal recognition concentration and sugar type for the chiral enantiomers of monosaccharide molecules, we first used four types of luminescent bacteria to detect the response of different concentrations of sweeteners to find the optimal concentration. We first set up seven concentration gradients: 10 μM, 50 μM, 100 μM, 500 μM, 1 mM, 5 mM, and 10 mM, and prepared solutions of the five sweeteners for each. These solutions were added to 96-well plates, and six parallel experiments were performed. Then, an equal volume of a luminescent bacterial array sensor (composed of recombinant E. coli, A. tumefaciens / c58, B. subtilis, and the naturally luminescent Vibrio fischeri) was added to detect the five sweeteners. The luminescence intensity was then measured using a microplate reader after the reaction. The measured luminescence data were normalized by comparing them with a blank control under the same conditions. LDA discriminant analysis was then performed using SPSS, and the data were analyzed and plotted using Origin software. The resulting graphs for each concentration are shown below. Figure 4 As shown (the scatter plot in the figure is a 3D plot generated after each data point has been analyzed using LDA, transforming the data into discriminant scores), from... Figure 4 As can be seen, the data obtained from various sweeteners can be correctly classified into groups, indicating that the luminescent bacteria array sensor in this application can identify different types of sweeteners.

[0038] Example 5: Application of luminescent bacteria array sensor in the identification of actual samples - beverages

[0039] This embodiment selected two types of commercially available beverages—sweetened and sugar-free—for testing: regular cola, sugar-free cola, regular Sprite, sugar-free Sprite, regular green tea, and sugar-free green tea. Two concentration gradients, 50 μM and 500 μM, were used to prepare solutions for each of the six beverages, and six parallel experiments were performed, all added to 96-well plates. An array sensor composed of recombinant *E. coli* and *V. fischeri* was then added in equal volumes to identify the six beverage types. The luminescence intensity was measured after the reaction using an ELISA reader. The luminescence data were normalized by comparing the results with a blank control under the same conditions. The average values ​​of the data from different beverages showed that the measured values ​​for sugary beverages were significantly higher than those for sugar-free beverages, indicating that the constructed array can distinguish beverages with different sugar contents.

[0040] Example 6: Mechanism of monosaccharide chirality recognition by a luminescent bacterial array sensor

[0041] Bacteria utilize monosaccharides primarily through glycolysis and the TCA cycle. The production of NADH and DADPH affects bacterial luminescence intensity. Therefore, the preliminary assessment is that the mechanism primarily involves monosaccharides influencing bacterial sugar metabolism pathways, thereby affecting luminescence intensity. Bacteria utilize different configurations of monosaccharides to varying degrees, resulting in different luminescence intensities. The MTT assay can be used to detect succinate dehydrogenase in the mitochondria of live cells, and succinate dehydrogenase is an important enzyme in sugar metabolism. Therefore, assuming consistent bacterial content, the amount of MTT crystals formed is detected using a microplate reader to indirectly reflect succinate dehydrogenase activity, thereby examining whether different chiral monosaccharides have different effects on the metabolic extent of recombinant luminescent bacteria, thus affecting their luminescence intensity. We selected ribose, which is relatively readily apparent, for experimental verification. We prepared three concentrations: 500 μM, 50 mM, and 100 mM. First, we conducted preliminary experiments by reacting different concentrations of bacterial suspension to determine the optimal reaction concentration for each bacterium. Then, we added the sugar solution, bacterial suspension, and MTT solution to a 96-well plate for reaction. The amount of MTT crystals was then detected at 570 nm using a microplate reader. The data were compared with a control under the same conditions. The measured data were processed and analyzed, and the results are as follows: Figure 5 As shown in the figure, the horizontal axis represents the ribose concentration, and the vertical axis represents the MTT crystallization amount in the experimental group equivalent to the control group. The data of the four luminescent bacteria show that the content and activity of succinate dehydrogenase are different for the L and D configurations of sugar. This indicates that the four bacteria have different degrees of utilization of monosaccharides with different chiralities, with higher utilization of the D configuration. This suggests that monosaccharides may affect the luminescence intensity by influencing the bacterial respiratory metabolism.

[0042] This application provides a method for detecting chiral substances using a luminescent bacterial array sensor. By analyzing the influence of different chiral configurations and types of chiral substances on the luminescence intensity of recombinant luminescent bacteria, the method obtains response data. LDA (Linear Discriminant Analysis) is then used for identification and analysis, successfully distinguishing the substances and demonstrating the array's good recognition capability. This method can be used for chiral recognition of monosaccharides. The recognition mechanism was also preliminarily explored, and sweeteners and beverages in actual samples were detected.

[0043] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method of detecting a chiral substance using a photobacterium array sensor, characterized by, The method is that after mixing the luminescent bacteria array sensor with the liquid to be measured, the absorption spectrum is scanned to obtain the spectral data of the liquid to be measured, the linear discriminant LDA analysis is performed on the spectral data, the position of the unknown sample data point in the LDA atlas is compared with the data points of the known types of chiral substances, so that the chirality, type composition and content of the chiral substances in the liquid to be measured are determined; the luminescent bacteria array sensor is composed of four kinds of luminescent bacteria; The luminescent bacteria are recombinant E. coli, A. tumefaciens c58, B. subtilis and natural luminescent bacteria V. fischeri. The chiral substance is monosaccharide, the monosaccharide is one or more of glucose, ribose, xylose, mannose, galactose and arabinose, and the concentration of the monosaccharide is 50-500 μM.

2. The method of claim 1, wherein the chiral substance is detected by the photobacterium array sensor. The LDA atlas of the known types of chiral substances is obtained by scanning the luminescent bacteria array sensor mixed with different concentrations of the known types of chiral substances.

3. The method for detecting chiral substances using a luminescent bacteria array sensor according to claim 1, characterized in that, The monosaccharide is one or more of glucose, xylose and mannose.

4. The use of the method according to any one of claims 1-3 in the detection of chiral substances.

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