A multi-component nuclease system based on self-assembly of cholesterol nanomicelles and application thereof in SNP detection

Abstract

This invention relates to a multi-component nuclease system based on the self-assembly of cholesterol nanomicelles and its application in SNP detection. The system comprises multi-component nuclease probes and cholesterol micelles. The multi-component nuclease probes are densely arranged on the surface of the cholesterol micelles through base pairing to form a stable lipid nanoassembly, C-MNAzyme. Using the above technical solution, this invention provides a multi-component nuclease system based on the self-assembly of cholesterol nanomicelles and its application in SNP detection. By directly encoding mutation sites into adjacent positions of the linker region of the catalytic core recombination of the multi-component nuclease, high-fidelity recognition of single-base mutations is achieved. It enables highly specific detection of KRAS G12D without protease under clinically relevant conditions, providing a general new strategy for developing high-fidelity SNP detection technology.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and in particular to a multi-component nuclease system based on the self-assembly of cholesterol nanomicelles and its application in SNP detection. Background Technology

[0002] Liquid biopsy has become an important minimally invasive strategy for cancer diagnosis and real-time disease monitoring. Among various circulating biomarkers, circulating tumor DNA (ctDNA) can directly reflect gene mutation information originating from tumors, and therefore has received widespread attention in the field of precision medicine. Among these, single-nucleotide polymorphisms (SNPs) are one of the most clinically significant mutation types, because a substitution of just one base can significantly alter tumor-related signaling pathways, treatment response, and disease prognosis.

[0003] KRAS oncogene mutations are typical examples, particularly common in colorectal cancer (CRC) and pancreatic cancer. Among them, the G12D mutation has significant clinical implications because it is closely associated with resistance to anti-epidermal growth factor receptor (EGFR) targeted therapy and poor clinical outcomes. Therefore, accurate identification of KRAS G12D mutations is crucial for early diagnosis, patient stratification, and individualized treatment decisions.

[0004] However, due to the extremely low abundance of mutant alleles in ctDNA and the fact that they differ from wild-type sequences by only a single base, especially in complex biological matrices such as serum, achieving reliable detection of SNPs still faces significant challenges.

[0005] Currently, various analytical techniques have been developed for SNP detection, including polymerase chain reaction (PCR)-based methods, next-generation sequencing, and CRISPR-related nuclease systems. These methods demonstrate excellent sensitivity and accuracy, and have greatly promoted the development of molecular diagnostics. However, most strategies rely on protease systems, complex instruments, and multi-step sample processing procedures, significantly increasing detection costs and operational complexity. Furthermore, enzyme-dependent systems are typically highly sensitive to reaction conditions and sample quality, often exhibiting decreased stability and reproducibility in complex clinical samples. Therefore, there is an urgent need to develop a molecular detection strategy that is simple to operate, stable, reliable, and does not require protease involvement, while achieving highly specific single-base recognition.

[0006] Functional nucleic acids (NCAs) have emerged as a promising alternative in molecular diagnostics in recent years due to their programmable sequence characteristics, excellent chemical stability, and protease-free operation. Compared to protein-based detection systems, NCA platforms can be adapted to different targets simply by redesigning the recognition sequence, demonstrating high modularity and low cost. However, most current SNP detection strategies based on NCAs still rely on hybridization-mediated recognition mechanisms, i.e., single-base discrimination is achieved through the weak thermodynamic difference between perfectly matched and mismatched double strands. This equilibrium-driven recognition method is inherently limited because the free energy change introduced by single-base mismatches is usually small, strongly dependent on sequence background, and easily masked by environmental factors under physiological or serum conditions, leading to decreased detection specificity. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of existing technologies by providing a multi-component nuclease system based on cholesterol nanomicelle self-assembly and its application in SNP detection. By directly encoding the mutation site into the adjacent position of the linker region of the catalytic core recombination of the multi-component nuclease, high-fidelity recognition of single-base mutations is achieved. Under clinically relevant conditions, it enables protease-free, high-specificity detection of KRAS G12D, providing a general new strategy for the development of high-fidelity SNP detection technology.

[0008] The technical solution of the present invention is a multi-component nuclease system based on the self-assembly of cholesterol nanomicelles, comprising a multi-component nuclease probe and cholesterol micelles. The multi-component nuclease probe is densely arranged on the surface of the cholesterol micelles through base pairing to form a stable lipid nanoassembly C-MNAzyme.

[0009] Further setup: The multicomponent nuclease probe is modified from DNAzyme. The DNAzyme splits into two inactive fragments via a catalytic core. These two inactive fragments are each linked to a target recognition arm. The KRAS G12D mutation site is designed at the first nucleotide position on the target sequence adjacent to the recombination linker region of the catalytic core. When the perfectly matched KRAS G12D target binds to the two target recognition arms via base pairing, the two inactive catalytic fragments are pulled closer and reassembled into an active conformation, restoring Mg... 2+ Dependent catalytic cleavage activity; when a wild-type KRAS sequence containing a G–T single base mismatch binds to the target recognition arm, the mismatched base is inserted into the catalytic core recombination region, causing local structural distortion and inhibiting catalytic cleavage activity.

[0010] Further configuration: The multi-component nuclease probe includes nucleic acid chains Partzyme1, Partzyme2, and Substrate, wherein the sequence of Partzyme1 is CTACGTGCATCTCTTCTCCGAGCCAGCTCCAACT, the sequence of Partzyme2 is TACGCCATCGGTCGAAATAGTGAGTCGCTCT, and the sequence of Substrate is AGTTAGTTTCCCTATAGAGCGACTCACTA / iBHQ1dT / / rA / GGAAGAGA / i6FAMdT / GCACGTAG.

[0011] Further settings: Partzyme1, Partzyme2, and Substrate are in equimolar proportions.

[0012] Further configuration: The cholesterol micelles are formed by cholesterol-modified DNA spontaneously forming micelle nanostructures through hydrophobic-driven self-assembly.

[0013] Further configuration: The sequence of the cholesterol-modified DNA is ATAGGGAAACTAACTGGGGGG-TEG-Cholesterol.

[0014] Another technical solution of the present invention is the application of a multi-component nuclease system based on cholesterol nanomicelle self-assembly in SNP detection.

[0015] The beneficial effects of this invention are as follows: By directly encoding the mutation site into the adjacent position of the linker region of the catalytic core recombination of a multi-component nuclease, high-fidelity recognition of single-base mutations is achieved. In this system, when a single-base mutation occurs in the target sequence, the mismatched base cannot be correctly paired with the recognition arm, thus being forced to embed into the catalytic core structure of the DNAzyme. Even such extremely small structural perturbations can be amplified into significant differences in catalytic cleavage efficiency, thereby achieving highly selective differentiation between mutant and wild-type sequences. Cholesterol-modified DNA is introduced to drive the micellar self-assembly of the MNAzyme. This cholesterol-mediated nanoassembly significantly enhances the stability of the system in the serum environment through nanoscale spatial constraints and further amplifies the signal. The detection platform constructed thereby can achieve high-specificity detection of KRAS G12D without protease under clinically relevant conditions, providing a general new strategy for developing high-fidelity SNP detection technology. Attached Figure Description

[0016] Figure 1 In the diagram, A represents the C-MNAzyme system using fluorescence to detect KRAS G12D single-base mutations. Figure 1B in the diagram is a schematic diagram of the C-MNAzyme system amplifying the detection signal. Figure 1 The C in the diagram represents the principle of the C-MNAzyme system for detecting single-base mutations. Figure 2 In the diagram, A is a flowchart of the C-MNAzyme system assembly process. Figure 2 In this context, B represents the particle size characterization of the C-MNAzme detection system. Figure 2 In this context, C represents the zeta potential characterization of the C-MNAzme detection system. Figure 2 In this context, D represents the electron microscopy characterization of Chol-micelle. Figure 2 In this context, E represents the electron microscopy characterization of the C-MNAzme detection system; Figure 2 In this context, F represents the assembly of the n-PAGE gel in the C-MNAzme detection system. Figure 2 In this context, G represents the magnesium ion concentration-dependent cutting effect. Figure 3 In the diagram, A represents the feasibility of fluorescence analysis for detecting KRAS G12D single-base mutations, where "●" and "○" indicate the presence and absence of the corresponding component, respectively. Figure 3 In the figure, B represents the typical fluorescence spectra of samples a, b, c, and d. Figure 3 In the figure, C represents the typical fluorescence spectra of samples a, e, c, and f. Figure 3 In this context, D represents the quantitative analysis and visualization of fluorescence data from samples a, b, c, and d. Figure 3 In this context, E represents the quantitative analysis and visualization of fluorescence data from samples a, e, c, and f. Figure 4 In the figure, A represents the sensitivity results of different concentrations of the target KRAS G12D during fluorescence measurement. Figure 4 In this context, B represents the fluorescence change value of different concentrations of KRAS G12D detected by the C-MNAzme detection system; Figure 4 In this context, C represents the linearity of the C-MNAzme detection system in detecting high concentrations of KAS G12D. Figure 4 In this context, D represents the linearity of the C-MNAzme detection system in detecting low concentrations of KAS G12D; Figure 5 The results of LOD detection limit are shown in a specific embodiment of the present invention; Figure 6 A in the diagram is a schematic diagram of the C-MNAzme detection system for detecting single-base mutations in the KRAS gene; Figure 6 In the figure, B represents the fluorescence change value detected for different KRAS mutations. Figure 6 In the figure, C represents the fluorescence change value detected for different KRAS mutations; Figure 6 In the image, D represents the fluorescence image; Figure 7In the diagram, A represents the detection flowchart. Figure 7 B in the figure represents the fluorescence change value detection result; Figure 8 In this context, A represents the reaction kinetics of the C-MNAzme detection system. Figure 8 In this context, B represents the signal-to-noise ratio at different times. Figure 8 In this context, C represents the reaction kinetics of the MNAzme detection system. Figure 8 In this context, D represents the signal-to-noise ratio at different times; Figure 9 In this figure, A represents the stability of the C-MNAzme detection system after 24 hours of reaction in 10% fetal bovine serum. Figure 9 In the figure, B represents the fluorescence leakage of the C-MNAzme detection system in 10% fetal bovine serum. Figure 9 In this context, C represents the stability of the MNAzme detection system after 24 hours of reaction in 10% fetal bovine serum. Figure 9 In this context, D represents the fluorescence leakage of the MNAzme detection system at 10% fetal bovine serum. Figure 10 In the figure, A represents a comparison of the detection performance of the C-MNAzme detection system and the MNAzme detection system at different concentrations of fetal bovine serum. Figure 10 In this context, B represents the fluorescence change of the C-MNAzyme system with or without the addition of the KRAS G12D target to different FBS concentrations. Figure 10 In this context, C represents the fluorescence change of the MNAzyme system with or without the KRAS G12D target. Figure 10 In this context, D represents the signal-to-noise ratio of the two methods at different concentrations of fetal bovine serum. Figure 11 In this context, A represents the clinical testing process; Figure 11 In the figure, B represents the relative fluorescence value of serum samples from 29 clinical patients. Figure 11 In the graph, C represents the t-test statistical analysis of whether a patient has the KRAS G12D mutation. Patients 1-8 represent mutation types other than KRAS G12D, while patients 9-29 represent KRAS G12D mutation patients. Figure 11 In this context, D represents a comparison between the results of the C-MNAzme detection system and the gold standard gene sequencing. Figure 11 E in the figure represents the clinical subject curve of the C-MNAzme detection system. Figure 11 F in the figure is a schematic diagram of the clinical subject confusion matrix of the C-MNAzme detection system; Figure 12 A in the diagram is a schematic diagram of the C-MNAzme detection system for detecting KRAS G12V mutations; Figure 12 In the figure, B represents the fluorescence curves of different samples. Figure 12 C in the graph represents the detection statistics. Figure 13 In the diagram, A represents the detection of two single-base mutations using the C-MNAzyme system. Figure 13 B in the diagram represents the AND logic gate of the C-MNAzme detection system for detecting two single-base mutations. Figure 13 C in the figure represents the d-PAGE gel result. Detailed Implementation

[0017] The technical solutions in this embodiment will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0019] Example 1: Reagents and Materials Custom-synthesized oligonucleotides were synthesized by Shanghai Sangon Biotech Co., Ltd. (Shanghai, China) and purified by high-performance liquid chromatography (HPLC). Each sequence was diluted in 1×TE buffer (10 mM Tris-HCl, 0.1 mM EDTA, pH 8.0) to prepare DNA stock solutions (10 μM) and stored at 4 °C. TE buffer was purchased from Beyotime Biotechnology Co., Ltd. (Shanghai, China). Low molecular weight DNA markers were purchased from New England Biolabs (Beijing) Co., Ltd. (Beijing, China). SYBR Green was purchased from Solarbio Biotechnology Co., Ltd. (Beijing, China). DNA / RNA loading buffer (2×, for denaturing polyacrylamide gel electrophoresis) was purchased from Beyotime Biotechnology Co., Ltd. (Shanghai, China). Fetal bovine serum (FBS, catalog number S711-001S) was purchased from Lonza Biotechnology Co., Ltd. (Suzhou, China). All chemical reagents used were of analytical grade. Ultrapure water (resistivity ≥18.2 MΩ•cm) prepared using an Avidity Science purification system (Watford, Wisconsin, USA) was used throughout the experiments. The oligonucleotide sequences are shown in Table 1 below: Table 1

[0020] In this context, the underlined bases represent mutant bases.

[0021] instrument Fluorescence spectroscopy was performed using a HORIBA Duetta fluorescence and absorption spectrometer (Horiba, China). Zeta potential and dynamic light scattering (DLS) analysis were performed using a Litesizer DLS 500 dynamic light scattering system (Antonpah, Austria). Polyacrylamide gel electrophoresis was conducted using a Bio-Rad ChemDoc XRS system (Bio-Rad, USA) and imaging was performed using a ChemiScope 6200 imaging system (Clinez, China). Transmission electron microscopy (TEM) scanning was performed using a transmission electron microscope (TEM, 200 kV, JEM-F200). All instruments were operated according to the manufacturer's operating procedures.

[0022] Example 2: System Detection Principle and Process like Figure 1 As shown, fluorescence signals are used to identify single-base mutations in KRAS G12D and KRAS WT; as Figure 2 As shown, multi-component nuclease (MNAzyme) probes are densely arranged on the surface of cholesterol micelles. Through a hydrophobically driven self-assembly process, cholesterol-modified DNA (Chol-DNA) spontaneously forms micelle nanostructures. The multi-component nuclease probes are densely loaded onto the micelle surface via base pairing, forming stable lipid nanoassemblies (C-MNAzymes). This micelle structure generates a natural spatial confinement effect, thereby increasing the local concentration of probes and accelerating surface catalytic reactions. Furthermore, the densely packed micelle structure effectively protects the DNA probe ends from nuclease degradation, resulting in significantly enhanced nuclease resistance and enabling stable operation in complex physiological environments. Figure 3 As shown, the complete DNAzyme was transformed into a multicomponent nucleic acid enzyme (MNAzyme) system by splitting the catalytic core into two inactive fragments, each linked to a target recognition arm. The KRAS G12D mutation site was strategically designed at the first nucleotide adjacent to the linker region of the catalytic core recombination (the designed core). When the perfectly matched KRAS G12D target binds to the two recognition arms via classical base pairing, the split catalytic fragments are brought closer together and reassembled into the native active conformation, thereby restoring Mg. 2+ The catalytic cleavage activity is dependent on the G–T single-base mismatch. Conversely, when a wild-type KRAS sequence containing a G–T single-base mismatch binds to the recognition arm, the mismatched base is forced to embed into the catalytic center region during catalytic core recombination, resulting in significant local structural distortion and thus significantly inhibiting cleavage efficiency. Therefore, single-base differences can be translated into significant differences in catalytic activity, achieving high-fidelity single-base resolution.

[0023] Example 3: Assembly and Characterization of Materials (I) Experimental Methods like Figure 2 As shown in Figure A, the preparation of C-MNAzyme (1) Assembly of MNAzyme Equimolar amounts of nucleic acid chains (Sub, Arm1, and Arm2) were mixed in TM buffer (50 mM Tris, 100 mM NaCl, 50 mM KCl, 20 mM MgCl2, pH 7.4) to prepare a final concentration of 2 μM MNAzyme. The mixture was heated at 95 °C for 5 min and then slowly cooled to room temperature to promote MNAzyme assembly.

[0024] (2) Formation of cholesterol micelles Cholesterol-modified DNA (Chol-DNA, 10 μM) was diluted to 5 μM with 1× Tris buffer (20 mM Tris, 150 mM NaCl, 5 mM MgCl2, pH 7.4). The mixture was then heated at 95 °C for 5 min and slowly cooled to room temperature to induce micelle formation.

[0025] (3) Assembly of C-MNAzyme Mix 10 μL of MNAzyme (2 μM), 3 μL of cholesterol micelles (5 μM), and 1 μL of 10×™ buffer, then add deionized water (ddH2O) to a total volume of 20 μL. Incubate the mixture at 37 °C for 1 h to complete assembly, then store at 4 °C for later use.

[0026] Dynamic light scattering (DLS) and zeta potential were used to detect the particle size and potential of the system material.

[0027] Transmission electron microscopy was used to verify the size of the Chol-micelld and C-MNAzyne systems.

[0028] The successful synthesis of C-MNAzyme was verified using polyacrylamide gel electrophoresis (PAGE). Specifically: (a) Non-denaturing PAGE (n-PAGE): 8 μL of different reaction mixtures were mixed with 4 μL of loading working solution (DNA loading buffer containing SYBR Green) and then loaded onto the gel. Electrophoresis was performed on a 12% non-denaturing polyacrylamide gel in 0.5× TBE buffer (4.5 mM Tris, 4.5 mM boric acid, 0.1 mM EDTA, pH 7.9) at 90 V. After electrophoresis, imaging analysis was performed using the Bio-Rad GelDoc Go imaging system.

[0029] (b) Zeta potential and dynamic light scattering (DLS) determination: C-MNAzyme and Chol-micelles were prepared according to the method described in the "Preparation of C-MNAzyme" section. The reaction solution was diluted to 100 μM with ddH2O, and 1 mL of sample was added to the detection cell. The hydrodynamic particle size and zeta potential of the DNA nanostructures were determined using a Litesizer DLS 500 (Anton Paar, Austria).

[0030] (c) Transmission electron microscopy (TEM) imaging C-MNAzyme and cholesterol micelles were prepared according to the method described in the "Preparation of C-MNAzyme" section. Subsequently, 10 μL of the reaction solution was added to a TEM copper microgrid, incubated for 30 min, and then allowed to air dry. The samples were then negatively stained with 3% (w / v) phosphotungstic acid solution and rinsed with deionized water (ddH2O) to remove excess stain. Finally, images were acquired using a transmission electron microscope (TEM, 200 kV, accelerating voltage, JEM-F200).

[0031] (II) Experimental Results like Figure 2 B and Figure 2 As shown in Figure C, dynamic light scattering (DLS) and zeta potential analysis confirmed the successful assembly of the C-MNAzyme nanostructure. After loading with MNAzyme, the hydration kinetic diameter increased from 186 ± 2.3 nm to 213 ± 1.7 nm, while the zeta potential significantly shifted negatively from −13.46 ± 3.8 mV to −36.26 ± 4.1 mV, consistent with the introduction of negatively charged DNA strands. Figure 2 D and Figure 2 As shown in E, transmission electron microscopy further confirmed this result, showing a significant increase in micelle size after modification with MNAzyme. Figure 2As shown in F, n-PAGE further confirms the assembly process. After MNAzyme formation, an upward-shifting band appears in lane 6, while cholesterol micelles exist alone in lane 7. The MNAzyme band disappears after assembly (lane 8), indicating that it is effectively loaded onto the micelle surface. After the addition of the target (lane 9), a lower molecular weight cleavage product band appears, proving that the assembled C-MNAzyme system still maintains catalytic activity. Furthermore, as... Figure 2 As shown in G, the system exhibits typical Mg behavior. 2+ Catalytic behavior dependent on Mg 2+ As the concentration increases, the cleavage product gradually becomes stronger.

[0032] Example 4: Feasibility Analysis of System for KRAS G12D Single Base Mutation Detection (I) Experimental Methods Different system compositions (af) were subjected to 12% n-PAGE, and 20 μL of the reaction mixture was diluted with 180 μL of 1×PBS buffer. Fluorescence spectra were recorded using a fluorescence spectrophotometer with an excitation wavelength of 492 nm and an emission wavelength acquisition range of 500–600 nm. The excitation and emission slit widths were both set to 5 nm, the integration time to 0.05 s, and the emission scan step size to 1 nm for fluorescence measurement. Simultaneously, 20 μL of the reaction mixture was added to an EP tube and imaged in real-time using a Bio-Rad GelDoc Go imaging system. The initial fluorescence intensity and the fluorescence intensity after the reaction were calculated. The fluorescence change value was obtained by subtracting the initial fluorescence intensity from the fluorescence intensity after the reaction.

[0033] (II) Experimental Results like Figure 3 As shown in Figure A, n-PAGE analysis revealed a cleavage product band after target recognition, while KRAS WT still failed to produce a detectable signal. Figure 3 Further fluorescence experiments on B, C, D, and E in the sample showed that only KRAS G12D and Mg... 2+ A strong fluorescence response is only generated when both are present, with a signal-to-noise ratio of 18.7. Especially... Figure 3 D and Figure 3 The comparison between samples d and f in E shows that the C-MNAzyme platform has excellent single nucleotide discrimination ability.

[0034] Example 5: System sensitivity detection of targets at different concentrations (I) Experimental Methods Different concentrations of KRAS G12D targets were added to the C-MNAzyme system, and fluorescence measurements were performed after 3 hours of reaction.

[0035] (II) Experimental Results like Figure 4 , 5 As shown, the target KRAS G12D concentrations were set to 0 fM, 100 fM, 500 fM, 1 pM, 10 pM, 100 pM, 500 pM, 1 nM, 5 nM, 2 nM, 4 nM, 6 nM, 8 nM, 10 nM, 25 nM, and 50 nM. Fluorescence measurements were performed according to the method. Under optimized conditions, the C-MNAzyme biosensor exhibited excellent analytical performance. The fluorescence signal showed good linearity in the KRAS G12D concentration range of 100 fM to 50 nM, with a detection limit of 100 fM.

[0036] Example 6: The system has a high specificity and low abundance detection rate for KRAS gene mutations. (I) Experimental Methods Different types of KRAS mutant genes at the same concentration were added to the C-MNAzyme system, and fluorescence was measured after 3 hours of reaction. Different proportions (0.01%-100%) of KRAS G12D and KRAS WT mixtures were added to the C-MNAzyme system, incubated for 3 hours, and fluorescence changes were measured.

[0037] (II) Experimental Results Distinguishing KRASG12D from wild-type and other homologs remains a significant challenge due to the high sequence similarity among different KRAS mutants. To assess its specificity, a series of KRAS homologs were challenged, including single-base mismatches (KRAS WT, KRAS G12A, KRAS G12V), double-base mismatches (KRAS G13C, KRAS G13S, KRAS G13R), and triple-base mismatches (KRAS G13V). The mutation site is located at... Figure 5 The A in the text is marked in red, such as Figure 6 Of the B, C, and D variants, only the perfectly matched KRAS G12D could activate the C-MNAzyme system and produce a strong fluorescence signal, while all other variants produced only a weak signal close to the background. Notably, when KRAS G12D was present in a mixed system containing multiple homologous sequences, a significant fluorescence signal could still be detected, indicating that the system was unaffected by interfering nucleotide polymorphisms. Figure 5 D in the image represents fluorescence imaging. Bright fluorescence was observed only in the KRAS G12D group and the mixture group, further demonstrating the high specificity of the system.

[0038] The challenge in detecting single nucleotide polymorphisms (SNPs) lies not only in the low absolute target concentration but also in the low frequency of mutant alleles. Most clinically relevant SNPs occur at frequencies below 1%, typically against a background of abundant wild-type sequences. To evaluate the practical applicability of the system in detecting low allele frequency mutations, such as... Figure 7 In section A, mixtures containing different proportions of KRAS G12D and KRAS WT (ranging from 100% to 0.01% mutants) were prepared and subjected to C-MNAzyme-based fluorescence analysis. Figure 6 As shown in Figure B, the fluorescence intensity gradually decreases with the decrease in the proportion of KRAS G12D. Notably, even with a mutant allele frequency as low as 0.1%, the fluorescence signal still showed a significant statistical difference from that of pure KRAS WT, indicating that this system can detect low-abundance mutations in a vast wild-type background.

[0039] Example 7: Kinetics of the system detection and stability of nucleotide polymorphism determination under physiological conditions (I) Experimental Methods Kinetics: The fluorescence changes of C-MNAzyme system and MNAzyme system with and without KRAS G12D target were detected after incubation for different times.

[0040] Stability: The C-MNAzyme system and the MNAzyme system were incubated with 10% fetal bovine serum, and fluorescence measurements and n-PAGE were performed after 0-24 hours. The C-MNAzyme system and the MNAzyme system were incubated with fetal bovine serum of different concentrations, with or without the addition of KRAS G12D target, and fluorescence changes were measured after 3 hours.

[0041] (II) Experimental Results Dynamics: such as Figure 8 As shown, time-resolved fluorescence experiments demonstrate that the C-MNAzyme system exhibits significantly accelerated reaction kinetics, reaching reaction equilibrium within approximately 3 hours, and demonstrates a higher signal-to-noise ratio than free MNAzyme.

[0042] Stability: such as Figure 9 A and Figure 9 As shown in C, when assessing the system stability under simulated physiological conditions (10% fetal bovine serum, FBS), n-PAGE gel analysis revealed that MNAzymes anchored to cholesterol micelles remained undegraded for 24 hours, while free MNAzymes were rapidly degraded. Figure 9 B and Figure 9The C-MNAzyme probe remained stable and had a very low background signal after long-term incubation, while the free MNAzyme probe rapidly degraded and produced obvious background fluorescence.

[0043] like Figure 10 As shown, the C-MNAzyme system maintains high specificity and low interference under different FBS concentrations (0–10%).

[0044] Example 8: System detection of clinical samples (I) Experimental Methods Blood samples were centrifuged at 1800 × g for 15 min, and serum was collected for subsequent analysis. C-MNAzyme was prepared according to the method described in the "Preparation of C-MNAzyme" section. Subsequently, 20 μL of the C-MNAzyme system was mixed with 5 μL of serum and incubated at 37 °C for 4 h. After the reaction, 175 μL of 1× PBS was added and thoroughly mixed. Finally, the fluorescence change was measured.

[0045] Calculation of relevant parameters of confusion matrix The relevant evaluation metrics (including accuracy, precision, sensitivity, and specificity) in the confusion matrix are calculated using the following formula: Precision = TP ×100 % / (TP + FP); Accuracy = (TP + TN) ×100 % / (P + N); Specificity = TN×100 % / (TN + FP); Sensitivity = TP ×100 % / (TP + FN); Wherein, TP, TN, FP, and FN represent the number of true positive, true negative, false positive, and false negative samples obtained by the C-MNAzyme system, respectively; P represents the total number of positive samples, and N represents the total number of standard negative samples.

[0046] Based on previous experimental results, the C-MNAzyme system exhibits high specificity and stability in detecting single-base mutations. The next step is to validate its detection performance in real-world, complex biological environments. Given the high incidence of KRAS G12D mutations in colorectal and pancreatic cancer patients, serum samples from these patients will be analyzed.

[0047] The experimental procedure is as follows Figure 11 As shown in A: Blood samples were collected, serum was separated by centrifugation at 1800×g for 15 minutes, and incubated with the C-MNAzyme system for 3 hours before fluorescence detection. Figure 11 As shown in B in Figure 10 and C in Figure 10, among the 29 included patients, samples 1–9 had fluorescence signals below the cutoff value (28.29) and were considered negative; while samples 10–29 had signals above the cutoff value and were considered positive. Notably, patients 1–8 carried other KRAS variants, and patients 9–29 were confirmed to be KRAS G12D positive by sequencing. The C-MNAzyme system showed a significant statistical difference between the KRAS G12D positive and negative groups (p<0.001, unpaired t-test). Comparison of C-MNAzyme detection results with the gold standard sequencing results… Figure 11 As shown in D, only sample number 9 showed a false negative, while the remaining samples were all accurate. Receiver Operating Characteristic (ROC) curve analysis. Figure 11 As shown in E, the area under the curve is 0.9940, indicating excellent diagnostic accuracy. Furthermore, as... Figure 11 The F-value in the image shows a confusion matrix analysis, indicating that the KRAS G12D detection had a sensitivity of 93%, a specificity of 100%, and an overall accuracy of 96%. These results collectively confirm that the C-MNAzyme platform can robustly and accurately identify KRAS G12D mutations in clinical serum samples, highlighting its potential for guiding personalized cancer treatment.

[0048] Example 9: The universality of the system for detecting single-base mutations (I) Experimental Methods By designing the system, the target was replaced with KRAS G12V, and the recognition arms of the C-MNAzyme system were replaced with KRAS G12VA1 and KRAS G12V A2. The newly designed C-MNAzyme system for recognizing KRAS G12V was incubated with different KRAS mutations at the same concentration for 3 hours, and the fluorescence changes were measured.

[0049] (II) Experimental Results To assess the broad applicability of the design concept, other single nucleotide variants were detected using the C-MNAzyme system. For example... Figure 12 As shown in A, the recognition arm was redesigned to target KRAS G12V, and the system was challenged with a series of homologous KRAS variants. Figure 12 B and Figure 11 As shown in C, fluorescence measurements revealed that the KRASG12V target elicited a significantly enhanced signal compared to other variants.

[0050] Example 10: System for detecting two single-base mutations using multiple logic gates (I) Experimental Methods The recognition arms of the C-MNAzyme system were replaced with Two-arm 1 and Two-arm 2 to form a new detection system for recognizing two single-base mutations. Then, F-1 and different mutation target systems were added and incubated for 3 hours, followed by denaturing PAGE: 5 μL of the reaction mixture was mixed with 5 μL of DNA / RNA loading buffer (2×, for denaturing PAGE) and loaded onto the gel. The reaction products were separated in a 20% denaturing polyacrylamide gel containing 420 g / L urea. The gel system contained 10× TBE buffer (Tris 108 g / L, boric acid 55 g / L, EDTA 0.5 M, 20 mL / L) and 40% (w / w) acrylamide / bisacrylamide (29:1). Electrophoresis was performed in 0.5× TBE buffer at room temperature and run at 300 V for 30 min.

[0051] (II) Experimental Results Therefore, in addition to single-target detection, the system's programmability inspired DNA-based logic gates for the simultaneous detection of multiple single nucleotide mutations. Figure 13 A and Figure 13 Figure B illustrates the design of a two-input "AND" logic gate. In this system, the C-MNAzyme is activated (on) only when both perfectly matched target strands M-1 and M-2, corresponding to two different mutations, are present simultaneously. If either input is replaced by a mismatched mutant strand (W-1 or W-2), the mismatched base pair forces a nucleotide to insert into the catalytic core—two nucleotides when one mismatch occurs, and four nucleotides when both inputs are mismatched—thus inactivating the DNAzyme and keeping the fluorescence off. This strict AND logic was validated by d-PAGE analysis. Figure 13 As shown in C, a unique cleavage product band (lane 2) appears only when both M-1 and M-2 are present, while no product is detected in the control group (lanes 1, 3, 4, 5) where any mismatched strands are present. This design ensures that a positive readout is only generated when both target mutations are present, thus achieving highly specific dual detection of SNVs.

[0052] In summary, a DNAzyme-based single nucleotide variant detection platform, C-MNAzyme, was developed. By anchoring multi-component DNAzyme probes within cholesterol micelle structures, this system exhibits higher stability and faster catalytic kinetics in serum environments, enabling stable operation in complex physiological conditions without requiring complex procedures or expensive protease systems; a single-pot mixing method suffices. The platform achieves a detection limit as low as 100 fM and reliably identifies KRAS G12D mutations with allele frequencies as low as 0.1% against a background of abundant wild-type sequences. Validation experiments in serum samples from cancer patients demonstrated excellent diagnostic performance, with an area under the ROC curve of 0.994, showing high consistency with sequencing results. Thanks to its modular structure, the system can be adapted to different mutation targets through simple recognition sequence redesign and can be further constructed with DNA logic gates to achieve multiplex mutation detection. These advantages make the C-MNAzyme platform a promising candidate for precision molecular diagnostics and mutation-guided targeted therapy.

Claims

1. A multi-component nuclease system based on the self-assembly of cholesterol nanomicelles, characterized in that, It includes multi-component nuclease probes and cholesterol micelles. The multi-component nuclease probes are densely arranged on the surface of cholesterol micelles through base pairing to form stable lipid nanoassemblies C-MNAzyme.

2. The multi-component nuclease system based on cholesterol nanomicelle self-assembly according to claim 1, characterized in that, The multicomponent nuclease probe is derived from a DNAzyme. The DNAzyme splits into two inactive fragments via a catalytic core, each attaching to a target recognition arm. The KRAS G12D mutation site is designed at the first nucleotide adjacent to the recombination linker region of the catalytic core on the target sequence. When the perfectly matched KRAS G12D target binds to the two target recognition arms via base pairing, the two inactive catalytic fragments are pulled closer and reassembled into an active conformation, restoring Mg2+. 2+ Dependent catalytic cleavage activity; when a wild-type KRAS sequence containing a G–T single base mismatch binds to the target recognition arm, the mismatched base is inserted into the catalytic core recombination region, causing local structural distortion and inhibiting catalytic cleavage activity.

3. The multi-component nuclease system based on cholesterol nanomicelle self-assembly according to claim 1 or 2, characterized in that, The multi-component nuclease probe includes nucleic acid chains Partzyme1, Partzyme2, and Substrate. The sequence of Partzyme1 is CTACGTGCATCTCTTCTCCGAGCCAGCTCCAACT, the sequence of Partzyme2 is TACGCCATCGGTCGAAATAGTGAGTCGCTCT, and the sequence of Substrate is AGTTAGTTTCCCTATAGAGCGACTCACTA / iBHQ1dT / / rA / GGAAGAGA / i6FAMdT / GCACGTAG.

4. The multi-component nuclease system based on cholesterol nanomicelle self-assembly according to claim 3, characterized in that, The Partzyme1, Partzyme2, and Substrate are in an equimolar ratio.

5. The multi-component nuclease system based on cholesterol nanomicelle self-assembly according to claim 1 or 2, characterized in that, The cholesterol micelles are formed by the spontaneous formation of micelle nanostructures from cholesterol-modified DNA through hydrophobic-driven self-assembly.

6. The multi-component nuclease system based on cholesterol nanomicelle self-assembly according to claim 5, characterized in that, The sequence of the cholesterol-modified DNA is ATAGGGAAACTAACTGGGGGG-TEG-Cholesterol.

7. The application of the multi-component nuclease system based on cholesterol nanomicelle self-assembly according to any one of claims 1-6 in SNP detection.

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