Investigation the Binding Mechanism of Aptamers to Ochratoxin A and Development of Competitive Colorimetric Sensing Platforms

Investigation the Binding Mechanism of Aptamers to Ochratoxin A and Development of Competitive Colorimetric Sensing Platforms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigation the Binding Mechanism of Aptamers to Ochratoxin A and Development of Competitive Colorimetric Sensing Platforms Jingping Zhang, Zhenlin Fan, Yuzhu Song, Jinyang Zhang, Qinqin Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7902970/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Dec, 2025 Read the published version in Microchimica Acta → Version 1 posted 11 You are reading this latest preprint version Abstract Ochratoxin A (OTA) is widely present in various products such as grains, legumes and their products, coffee, wine, grape juice and dried fruits. It is highly carcinogenic and pathogenic and is classified as a Group 2B human carcinogen, posing a significant threat to human health. Therefore, the development of rapid, accurate and easy-to-operate new detection methods is particularly important. In this study, a long-chain aptamer (Apt) was truncated and optimized to obtain a short aptamer Apt-8 with significantly improved affinity and specificity. Further, microscale thermophoresis (MST), isothermal titration calorimetry (ITC), circular dichroism (CD), molecular dynamics simulation (MD) and molecular docking techniques were used to systematically analyze the binding affinity, heat changes during the binding process, conformational changes, binding mode, driving energy and key binding sites of Apt-8 and OTA, providing a solid structural basis for sensor design. In addition, a straightforward and efficient method for the detection of OTA has been developed by integrating terminal deoxynucleotidyl transferase (TdT) with aptamer-based colorimetry. The sensor showed a good linear relationship with the concentration of OTA, with a detection limit as low as 0.026 ng/mL and a spiked recovery rate of 98.33% to 106.3%, indicating high accuracy of the method. This detection method is simple to operate, rapid and efficient, with high sensitivity, strong stability and good repeatability, and is suitable for rapid visual detection of OTA, showing great potential in on-site point-of-care testing. OTA detection Nucleic acid aptamer Truncation optimization Molecular recognition Rapid detection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Ochratoxin (OT), a naturally occurring mycotoxin produced by the fungi Aspergillus and Penicillium , is widely distributed across the globe [ 1 – 3 ]. The ochratoxin family comprises OTA, OTB, OTC, and OTα, with OTA being the most prevalent due to its widespread occurrence and toxicity [ 4 – 7 ]. This substance was first identified by Van in 1965 [ 8 ]. OTA can be found in various products including grains, legumes, coffee, wine, dried fruits [ 9 – 11 ]. In the 1990s, the International Agency for Research on Cancer (IARC) classified OTA as a Group 2B human carcinogen based on its carcinogenic and pathogenic effects [ 12 – 14 ]. Toxicological studies have confirmed that OTA exhibits nephrotoxicity, hepatotoxicity, immunotoxicity, neurotoxicity, and cytotoxicity [ 15 ]. Jiang reported that OTA enhances intestinal pathogens such as Alistipes which leads to harmful metabolite production and inflammation while also causing kidney damage [ 16 ]. This renal impairment is associated with the accumulation of OTA resulting from reabsorption processes in both proximal and distal tubules. Furthermore, the liver serves as a primary target organ for biotransformation of OTA. Felizardo established that prolonged exposure to elevated levels of OTA significantly correlates with an increased risk of hepatocellular carcinoma (HCC) [ 17 ]. OTA-induced oxidative damage impacts lipids, proteins, and DNA. It alters antioxidant status while inhibiting protein synthesis and DNA replication. These consequences are considered potential mechanisms underlying genotoxicity and cytotoxicity induced by OTA [ 18 ]. Therefore, investigating the toxicological mechanisms associated with OTA is crucial for protecting human health. Bao developed a dual-improved enzyme-linked immunosorbent assay (DI-ELISA) for ultrasensitive detection of OTA in coffee samples [ 19 ], which rely on the specific binding reaction between antigens and antibodies, offering rapidity and suitability for large-scale sample screening (kit-based), with low cost and wide application range, but limited sensitivity, numerous influencing factors, and cumbersome procedures [ 20 ]. Dhanshetty developed and validated a multi-mycotoxin analysis method through the combination of ultra-performance liquid chromatography-fluorescence detection (UHPLC-FLD) and tandem mass spectrometry (LC-MS/MS), which enables simultaneous high-sensitivity, accuracy and precision determination of OTA in chili powder [ 21 ]. Based on the principle that different substances leave the column successively due to their structural similarities and produce distinct peak signals in the detector, providing high accuracy and good repeatability, and being the mainstream method in laboratories (especially for standard detection), but with expensive equipment, complex operation, and the need for specialized laboratories [ 22 ]. Rostami proposed a technical solution based on surface-enhanced Raman spectroscopy (SERS), achieving label-free detection of OTA on a porous SERS detection platform, which detect through changes in optical signals and can be rapid and sensitive (with the aid of nanomaterial amplification) [ 23 ], but have poor stability and are difficult to miniaturize. Sun developed a novel detection method on artificial antigen-modified silica photonic crystal microspheres (SPCMs), which can be integrated into a biochip array and enables multiplex mycotoxin detection by using antibody-functionalized gold nanoparticles (AuNPs) as SERS nanotags [ 24 ], which are high-throughput, rapid, and miniaturized, suitable for multi-toxin detection, but are prone to contamination and have limited sensitivity. These methods can also be used for the detection of OTA metabolites (mainly relying on chromatography), but related research is scarce. Traditional methods are difficult to meet the on-site rapid and simple detection requirements, and there is an urgent need to develop more sensitive, rapid, and easy-to-operate new technologies. In recent years, aptamer-based biosensors have developed rapidly and attracted extensive attention due to their high stability, convenient storage, low cost and easy synthesis. Common methods include colorimetric, fluorescence, electrochemical and electrochemiluminescence [ 25 ]. Among them, colorimetric methods are widely used due to their low cost and simple operation. Currently, various colorimetric sensors have been constructed based on OTA aptamers. However, these traditional colorimetric aptamer sensors are usually limited by low sensitivity or poor specificity. To improve the sensitivity, various nucleic acid signal amplification strategies have been developed, such as polymerase chain reaction (PCR). Jin developed a novel electrochemiluminescence biosensor for the detection of OTA based on the principle of PCR amplification, with a detection limit as low as 1.36×10 − 11 mg/mL [ 26 ]. Tang proposed a dual-modal (colorimetric and photothermal) OTA analysis system based on a G-quadruplex-hemin/iodide (G4-Hemin/I − ) mediated non-enzymatic redox cycling amplification (RCA) system, which is simple in structure and highly sensitive (1 pg/mL for colorimetric method and 0.8 pg/mL for photothermal method) [ 27 ]. A dual-proportional fluorescence aptamer sensor based on the hybridization chain reaction (HCR) principle was established for the simultaneous detection of OTA and aflatoxin B1 (AFB1) [ 28 ]. Under the optimal experimental conditions, the linear range of OTA and AFB1 was 0.05–200 ng/mL, with detection limits of 6.7 pg/mL and 8.6 pg/mL, respectively. However, the dependence on temperature control devices restricts the application of PCR in on-site detection, while RCA and HCR usually involve complex DNA template or probe design [ 29 ]. Fortunately, the template-free DNA chain extension reaction mediated by terminal deoxynucleotidyl transferase (TdT) does not require strict temperature control, providing a possible option for the development of new biosensors for food safety screening. To address the challenges present in current technologies, we have combined the inherent advantages of TdT amplification and aptamers. This approach is based on the competitive recognition dynamics among OTA, magnetic beads-OTA (MBs-OTA), and nucleic acid aptamers. TdT is introduced for signal amplification, leading to the development of a rapid and sensitive colorimetric biosensor. In the absence of OTA, OTA molecules that are pre-fixed on MBs recognize the aptamers present in solution. Consequently, TdT polymerizes directionally at the 3'-OH end of oligonucleotide aptamers, labeling long single-stranded DNA with biotin. Subsequently, with assistance from horseradish peroxidase-labeled streptavidin (SA-HRP), tetramethylbenzidine (TMB) is catalyzed to yield a blue product. Conversely, when OTA is present, these molecules competitively bind to the aptamers. As a result, the aptamers are removed from the supernatant through magnetic separation, which leads to a failure in both TdT amplification and subsequent signal enhancement. 2. Materials and Methods 2.1 Materials HAuCl 4 •3H 2 O was purchased from Beijing Kexin Biotechnology Co., Ltd. (Beijing, China), Na 3 C 6 H 5 O 7 •2H 2 O from Guangzhou Bolaisi Biotechnology Co., Ltd. (Guangzhou, China), Deoxynivalenol (DON) from Zhejiang Weina Technology Co., Ltd. (Ningbo, China), KCl and KH 2 PO 4 from Shanghai Beyotime Biotechnology Co., Ltd. (Shanghai, China), Na 2 HPO 4 •12H 2 O and MgCl 2 from Shanghai Sigma-Aldrich Co., Ltd (Shanghai, China), OTA and Fumonisin B1 (FB1) from Binzhi Biotechnology Co., Ltd. (Shanghai, China), Zearalenone (ZEN) from Millipore (USA), Streptavidin (SA) from Beckman (GER), N-Hydroxysuccinimide (NHS) and 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC) from Shanghai Maclin Biochemical Technology Co., Ltd. (Shanghai, China), soluble single-component TMB substrate solution from Agilent G9800A (USA), amine-modified magnetic beads (BioMag@Plus) from Sigma-Aldrich Chemical Co., Ltd. (USA), terminal deoxynucleotidyl transferase (TdT) from Polysciences, bovine serum albumin from Amresco (USA), and SA-HRP from Thermo Fisher Scientific Co., Ltd. (USA). AuNPs from Sigma-Aldrich (GER), The nucleic acid sequence information is listed in Table S1 . The circular dichroism spectrometer is from Apied Phophyics Ltd (UK). The Monolith molecular interaction instrument and the pure water instrument are from Millipore (USA). The isothermal titration calorimeter, ultraviolet spe ctrophotometer, standard instrument and pH meter are from Thermo (USA). The electronic analytical balance is from Sartorius (GER). The fluorescence spectrometer is from Agilent G9800A(USA). 2.2 Characterization of truncated aptamers First, an aptamer designated as Apt-0, characterized by moderate affinity and a relatively long sequence, was selected for further optimization. This aptamer was previously identified by Li [ 30 ]. The secondary structure prediction process was completed using the UNAFold web server. In this study, truncation was primarily performed based on the stem-loops predicted from the secondary structure—either by sequentially removing the included stem-loops or by deleting the fixed primers at both ends of the aptamer. The specificity and affinity of aptamers for mycotoxins and antibiotics were evaluated using the colloidal gold method. Aptamers Apt-0 to Apt-8 (sequences were listed in Table S1 ) were prepared at concentrations of 0, 50, 100, 200, 400, 800, 1000, and 1600 nM, respectively. The target OTA was prepared at a concentration of 1 µg/mL. 50 µL of different concentrations of Apt-0 to Apt-8 aptamers and 1 µg/mL OTA were added and mixed by pipetting. The mixture was incubated at room temperature for 30 min, then 4-fold concentrated 25 nM AuNPs solution was added, mixed by pipetting, and incubated at room temperature in the dark for 30 min. The optimized NaCl concentration was added, mixed by pipetting, and transferred to a 96-well plate. The absorbance at 520 nm was measured. The A520 nm value of 0 nM Apt was recorded as A 0 , and the A520 nm values of other different concentrations of Apt were recorded as A'. The value of (A' - A 0 )/A 0 was calculated. The aptamer concentration was plotted on the x-axis and (A' - A 0 )/A 0 on the y-axis for curve fitting to calculate Kd. Each measurement was repeated three times in parallel. The aptamers Apt-0 to Apt-8 was prepared at a concentration of 400 nM, and the target OTA at a concentration of 1 µg/mL. The non-specific targets OTB, DON, FB1, and α-amanitin was set at a concentration of 5 µg/mL. Mix is the mixture of all the standards. 50 µL of each aptamer Apt-0 to Apt-8 was mixed with the same volume of different standard solutions by pipetting. Incubated at room temperature for 30 min, then 25 nM AuNPs solution was added and concentrated 4 times, mixed by pipetting and incubated at room temperature in the dark for 30 min. NaCl was added at the optimized concentration, mixed and transferd to a 96-well plate. The absorbance was measured at 520 nm and 620 nm and the walue of A620 nm/A520 nm was calculated. The determination of absorbance value was the same as above. 2.3 Potential mechanisms of binding specificity changes in aptamer Apt-0 and Apt-8. Monolith Molecular Interaction. Using FAM-labeled aptamer as the target and OTA standard solution as the ligand, prepare solutions with a target concentration lower than the estimated Kd value and a ligand concentration 20–50 times that of Kd. Dilute OTA into 16 gradients of equal volume using PBST, then mix with FAM-Apt in equal volumes. Allow the mixture to stand at room temperature in the dark for 15 min before conducting on-machine detection. If adsorption occurs, increase Tween or other adsorbents appropriately in the buffer. Import the detection signal diagram into MO Affinity Analysis software for curve fitting to obtain the signal-to-noise ratio and affinity constant. Isothermal Titration Calorimetry (ITC), A PEAQ-ITC instrument (Malvern) was employed. All solutions were degassed prior to titration to avoid abnormal thermal changes induced by bubble rupture. Aptamer screening buffer was used as the titration buffer. Typically, 10 µM aptamer was loaded into the sample cell, 200 µM OTA into the titration syringe, and a 19-drop titration protocol was adopted. A control experiment (titration of OTA into buffer alone) was conducted to eliminate interference from dilution heat. Before formal experiments, water-to-water titration was performed to verify instrument stability, and OTA titration into aptamer-free buffer to confirm no extraneous heat generation. Both the sample cell and titration syringe were rinsed with experimental buffer. For mutant aptamer experiments, target B (350–400 µL) was placed in the sample cell, and target A (100 µL) in the titration syringe. For aptamers with known Kd values, sample concentrations were calculated using the formula, sample cell concentration = C*Kd*N (where C = 10–100). For those with unknown Kd values, the sample cell concentration was tentatively set at 10 µM–10 mM, with the syringe concentration 10–20 times higher (or an initial 200 µM-to-20 µM titration). Additionally, buffers for the two samples must be identical (pH difference < 0.05), organic solvents prohibited, and both samples subjected to degassing or centrifugation at 12,000 r/min for 10 min. Thermodynamic parameters including enthalpy change (ΔH), entropy change (ΔS), Gibbs free energy (ΔG), and stoichiometric ratio were derived from raw titration data analysis. Circular Dichroism (CD) measurements were performed on a Chirascan V100 spectropolarimeter at 25°C. The fixed parameters were set as follows, spectral range of 200–400 nm, data collection time of 0.5 s per point, and bandwidth of 1 nm. Samples were loaded into a 1 mm pathlength cuvette. Aptamers Apt-0 and Apt-8 were diluted to 20 µM with ultrapure water, heated at 95°C for 5 min, and then cooled to room temperature for 30 min. Solutions of K⁺, Na⁺, and Mg²⁺ were prepared at concentrations of 0, 25, and 75 mM, respectively. A 50 µL aliquot of the aptamer solution was mixed with 350 µL of the ion solution, followed by incubation for 30 min before scanning. Prior to measurement, the cuvette was rinsed with the sample solution of the current batch, and the blank scan was performed using the corresponding dilution buffer to establish the baseline. Molecular Docking. The chemical structure of OTA was drawn using ChemDraw 19.0, then converted to a three-dimensional (3D) model via Chem3D 19.0 and exported in the molⅡ format. The secondary structures of aptamers Apt-0 and Apt-8 were predicted using Mfold, with parameters set to match the aptamer screening conditions. The predicted structures were saved in the Vienna format, then imported into the online server ( http://biophy.hust.edu.cn/3dRNA ) for base substitution and deoxygenation, followed by conversion to the pdb format. The aforementioned files were imported into AutoDock 4 for energy minimization and docking result analysis, and the result with the lowest binding energy was selected. pymol was used for visualization to analyze hydrogen bonds, bond lengths, and docking residues. Molecular Dynamics (MD). Simulation referring to previous studies [ 31 ], the aptamer structure was optimized based on docking results to eliminate structural strain. After complex minimization, heating, and equilibration, the production run was initiated. The topology file was generated using Sobtop, and classical MD simulations were performed at 300 K for 100 ns under the Amber 99sb force field. Following trajectory deperiodization, the root mean square deviation (RMSD), root mean square fluctuation (RMSF), hydrogen bonds (H-bonds), and molecular mechanics Generalized Born surface area (MM/GBSA) of the complex were calculated, along with base-wise energy decomposition. 2.4 Construction of the sensor The TdT sensor immobilizes OTA on the surface of amino MBs through amidation reaction. In the absence of OTA, the OTA molecules fixed on MBs recognize the aptamer in the solution, and TdT catalyzes the continuous extension of the 3'-OH end without any template. With the introduction of SA-HRP, the substrate TMB is catalyzed to form a blue product. However, in the presence of OTA, the aptamer binds to the free OTA, and the aptamer is almost completely consumed and removed by magnetic separation. This means that there are basically no 3'-OH ends left for subsequent TdT amplification, thus resulting in no obvious colorimetric signal. As the concentration of free target OTA decreases, the number of aptamers recognizing the OTA fixed on MBs increases, and the colorimetric signal increases accordingly. The experimental procedure involves preparing OTA standard solutions with concentrations of 0, 0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.25, 0.5, 1, 5, 10, and 100 µg/mL for later use. 50 µL of the MBs-OTA solution was taken to perform magnetic separation to remove the supernatant, and 20 µL of 700 nm Apt was added along with an equal volume of various concentrations of OTA standard solutions to the precipitate. Incubated at room temperature for 30 min. The precipitate was washed three times with Tris-HCl buffer, then 20 µL of TdT mixed reaction solution was added. Mixed thoroughly by pipetting and incubated at 37°C for 40 min. Rinsed three times with buffer, blocked for 30 min, then SA-HRP was added and incubated at 25°C for an additional 20 min, followed by six washes with washing buffer. Finally, the substrate TMB was introduced and allow catalytic oxidation to occur at 37°C for 5 min before measuring absorbance intensity at a wavelength of 652 nm. Each set of measurement results is repeated in parallel three times. 2.5. Detection of OTA in real samples Due to the widespread distribution and numerous sources of contamination associated with OTA, its presence is prevalent in various aspects of daily life. Among food products, grains, fruits, and beverages exhibit the highest levels of contamination. To thoroughly investigate the matrix effect in actual samples, two solid and two liquid food items were selected for determination and analysis based on their respective forms. The specific products chosen were corn flour, wheat flour, red wine, and beer—all randomly purchased from local markets. The sample pretreatment steps were as follows, each type of flour (corn or wheat) was taken 4 g along with 0.4 g of sodium chloride in a 15 mL centrifuge tube. Then 13 mL of a mixture composed of acetonitrile and water (in a ratio of 9:1) was added and subjected to vigorous oscillation for 2 min. Subsequently, the mixture was filtered through a 0.22 µm filter membrane. Finally, it was centrifuged at 3000 rpm for 10 min. The supernatant was collected and stored at 4°C for future use. To mitigate any potential matrix effects caused by the coloration present in red wine and beer prior to experimentation, color adsorption treatment was performed. Specifically, 1 g of activated carbon was added to a volume of 10 mL from each wine sample to adsorb its color components until they became colorless. After that, the activated carbon was removed and the solution was filtered through a 0.22 µm filter membrane before centrifuging again at 3000 rpm for an additional 10 min. The resulting supernatant was then collected and stored at 4°C. To explore the accuracy of the detection system's measurement results, different concentrations of OTA standards were added to two groups of samples, and then the detection method was used for comparison. Prior to sample pretreatment, selected samples (wheat flour and red wine) were spiked with varying doses of OTA to achieve final concentrations of 0, 0.1, 2, and 10 µg/kg. According to the above-optimized experimental conditions, the absorbance intensity of the solution at 652 nm wavelength was monitored. The content of OTA in each sample was determined, and the average recovery rate was calculated. Each group of measurement results was repeated three times in parallel. Subsequently, the performance of the aptamer sensor was compared with the traditional ELISA method. To investigate the influence of complex matrix components on the sensor, a double-blind method was used for verification. Different concentrations of OTA standards were added to each processed sample (corn, wheat, wine, and beer) to simulate the real matrix environment. The OTA standard gradients were set at different levels. During the addition process involving these standards across four actual samples, neither the samples nor their corresponding OTA standards were labeled. Furthermore, standards were not added sequentially according to concentration gradients in order to maintain a double-blind effect. Each group had three parallel samples. Based on the positive and negative results of the detection, the practical applicability of the detection platform for real sample analysis was evaluated. 3. Results and discussion 3.1. Truncation optimization for altering the specificity of aptamer Apt-0 The secondary structure of the original aptamer for Apt-0 consists of four stem-loop structures. Based on the secondary structure of the aptamer and the results of molecular docking, a preliminary analysis was conducted, suggesting that the active site of the aptamer might be located on the stem-loop structures. Therefore, the 5' end of the original sequence was first truncated by cutting off the first 18 nt, and then the stem-loop structures were successively truncated and recombined in pairs or the fixed primers (12 nt) at both ends to obtain a new aptamer. Apt-0 represents the original sequence, while Apt-8 is the sequence obtained by combining stem-loops A and D. The predicted secondary structures of the truncated aptamers Apt-0 and Apt-8 are shown in Fig. 1 A and 1 B. The affinity of aptamers was determined by the colloidal gold spectrophotometry. The truncated aptamers Apt-0 and Apt-8 concentrations were taken as the abscissa, and (A’-A 0 )/A 0 as the ordinate. The curves fitted by GraphPad Prism 8.0.2 software (Fig. 1 C) were used to calculate the affinity constant (Kd). The Kd values of Apt-0 and Apt-8 were 184.4 ± 34.08 and 65.2 ± 1.87 nM, respectively. The Kd values of truncated aptamers were compared with those of the original chain aptamers to analyze the sequence optimization. The results showed that the Kd value of Apt-8 aptamer was 65.2 ± 1.87 nM, which was 2 times lower than that of Apt-0. Since the Kd value is inversely proportional to the affinity, it indicates that the affinity of Apt-8 aptamer was significantly improved after truncation. This preliminary result suggests that the designed truncation method of aptamers in this experiment is feasible, but its accuracy needs to be further verified by specificity identification and other affinity determination methods. Toxins that might coexist with OTA were selected and specifically identified by the colloidal gold method. As shown in Fig. 1 D, Apt-8 has improved specificity and anti-interference ability compared with the original chain aptamer, and its optimization result is more ideal. This might be because the sequence truncation retains the active region while reducing the steric hindrance effect of non-essential nucleotides. Therefore, Apt-8 was chosen for subsequent mechanism exploration and sensor establishment experiments. 3.2 Potential mechanisms of binding specificity changes in aptamer Apt-0 and Apt-8. 3.2.1 MST To verify the accuracy of affinity size determined via colloidal gold spectrophotometry, MST microscale thermophoresis was employed as an auxiliary verification method. The affinity fitting curves are shown in Fig. 2 A and 2 B. The affinity sizes of the original chain aptamer Apt-0 and the truncated aptamer Apt-8 determined by molecular interaction measurement are almost the same as those determined by the colloidal gold method, which are 184.4 ± 34.08 nM and 151.78 nM, 65.2 ± 1.87 and 78 nM respectively. Moreover, the signal-to-noise ratios of both are greater than 12. As shown in Table S2. A signal-to-noise ratio of 5–12 indicates binding force, a ratio greater than 12 indicates strong binding force or only proves the reliability and accuracy of the measurement results, and a ratio lower than 5 indicates no binding force or indicates an error in the measurement process, at which point the Kd value result is unreliable. This proves that both aptamers and OTA have strong binding forces, fully demonstrating the reliability of the affinity size results determined by the colloidal gold method. 3.2.2 ITC ITC titration was conducted on Apt-0 and Apt-8, with the MicroCal PEAQ-ITC Analysis Software used to determine the enthalpy change (ΔH) and fit Kd values of 177 nM and 69.7 nM, respectively. The titration curves are shown in Fig. 2 E and 2 F. During binding, ΔH reflects specific interactions like hydrogen bonds and van der Waals forces, while ΔS (entropy change) indicates conformational changes, steric hindrance, and hydrophobic effects. According to thermodynamic data in Table S3, when Apt-0 aptamer binds to OTA, ΔG, ΔH, and -TΔS are − 14.83, -11.94, and − 2.87 kJ/mol respectively. For binding between Apt-8 aptamer and OTA, ΔG is -25.78, ΔH is -17.83, and -TΔS is -7.95 kJ/mol. The negative values of -TΔS and ΔG indicate that both aptamers release heat upon binding to OTA while forming Apt-OTA complexes with low-energy. The differences in intensities of -TΔS and ΔH between Apt-0 and Apt-8 suggest that the increased affinity of Apt-8 over the original chain primarily results from reduced steric effects and enhanced van der Waals forces during recognition. 3.2.3 CD CD spectroscopy is a powerful technique for investigating the structures of aptamers. As illustrated in Figures S2A and S2B, we examined the effects of varying concentrations of K + , Na + , and Mg 2+ on the secondary conformations of the aptamers Apt-0 and Apt-8. The CD spectrum of Apt-0 exhibited minimal changes across different concentrations of K + , Na + , and Mg 2+ , indicating its structure remains relatively stable without significant alterations. In contrast, the CD effect peaks for Apt-8 were markedly enhanced in response to varying concentrations of Mg 2+ , suggesting substantial conformational changes occurred within this aptamer. The presence of Mg 2+ was found to facilitate stabilization of the secondary structure of Apt-8. The binding mode between Apt-8 and its target was further explored using CD spectroscopy, as depicted in Figure S2C. Both Apt-8 and the Apt-8/OTA complex displayed positive and negative characteristic peaks at 277 and 249 nm, respectively, confirming that Apt-8 exhibits typical B-type DNA characteristics. Previous research has established that base stacking results in positive Cotton effect peaks, while negative Cotton effect peaks arise from DNA's helical structure. Notably, the introduction of OTA resulted in a reduction in intensity for both characteristic Cotton effect peaks, implying that interaction between OTA and Apt-8 induces structural modifications within Apt-8, enhancing base pair stacking as well as helicity. This observation indicates that recognition by OTA leads to significant conformational adjustments in Apt-8, thus characterizing an induced-fit mechanism rather than adhering strictly to a lock-and-key model. 3.2.4 Molecular docking To further investigate the binding site between the truncated aptamer Apt-8 and OTA, we conducted molecular docking simulations involving both entities. The results are illustrated in Figures S2D and S2E. The active site is primarily characterized by hydrophobic grooves, which are situated on the surface of DNA. Small molecule ligands can either antagonize or activate the target through this active site, thereby influencing the signaling pathway. Analysis of 100 docking results reveals that the lowest energy conformation is -4.78 kcal/mol, with a docking score reaching − 8.761 kcal/mol, indicating excellent potential for targeted binding. The small molecule ligand predominantly accesses the DNA active site via hydrophobic interactions and van der Waals forces, engaging with bases such as G7, T8, G9, G10, C34, A35, and C36 within the major groove. Notably, an oxygen atom on the ligand forms a hydrogen bond with a hydrogen atom from the G9 base in DNA (bond length is 1.7 Å), while a hydroxyl hydrogen atom also establishes a hydrogen bond with a hydrogen atom from the G10 base (bond length is 2.0 Å). These hydrogen bonds facilitate targeting of DNA to either activate or inhibit its function. All predicted active sites identified by Apt-8 were subjected to mutation analysis. ITC was employed to validate the accuracy of our molecular docking findings as depicted in Fig. 2 C. During titration processes involving mutated Apt-8 and its target, no significant heat change was observed. This indicates that mutated Apt-8 exhibits negligible affinity for OTA. Further analysis demonstrates that there is no discernible trend in enthalpy change (ΔH) for control differences. Consequently, software fitting could not accommodate affinity data effectively. Based on these findings, we conclude that our molecular docking results are both reliable and accurate. The predicted base sites play an essential role in mediating aptamer binding to OTA. 3.2.5 MD MD is a computational technique employed to investigate the structure and interactions of biological systems, facilitating a deeper understanding of time-dependent conformational changes within these interactions. Based on the docking results, sobtop was selected to generate the topological file for the compound. The Amber 99sb force field was utilized to conduct classical molecular dynamics simulations at a temperature of 300 K over a duration of 100 ns. Following deperiodization of the trajectory, the RMSD, RMSF, H-bond interactions, and MMGBSA values were calculated for the complex respectively. As illustrated in Figure S3, the dynamic fluctuations of RMSD over time are initially visualized. This experiment exhibits significant variability, indicating that Apt-8 undergoes notable conformational changes in the presence of OTA, such as alterations in its ring structure or base stacking configurations. These findings align with previous CD results, further confirming that the interaction between Apt-8 and OTA is accompanied by substantial conformational motion. A residue decomposition analysis of RMSF was conducted, with results presented in Figure S4. Notably, the two nucleic acid segments—5–10 and 34–37—exhibit relatively high flexibility and may represent key regions responsible for biological functions. In contrast, other regions display comparatively low flexibility. However, further investigation is required to determine which specific areas are influenced by compound interactions. Additionally, it is noteworthy that the highly flexible fragments identified within this simulation overlap significantly with predicted sites of molecular docking activity based on prior results. The hydrogen bond interactions between nucleic acids and compounds were also analyzed. These interactions serve as an indicator of biological system stability—the lower the obtained value, the more stable the complex is. The outcomes are depicted in Figure S5. The combination of compounds and nucleic acids has reached a steady state, suggesting that Apt-8 maintains high thermal stability upon forming a complex with OTA—a factor that will be advantageous for sensor development in subsequent stages. The energy calculation and residue decomposition of the simulated trajectory are presented in Figure S6. The MMGBSA analysis shows an initial increase followed by a decrease, with the later downward trend gradually stabilizing, indicating that the complex model has stabilized after 60 ns of dynamics optimization. Subsequently, binding free energy was calculated using the MM/GBSA method. As shown in Figure S2H, the total binding free energy (∆Gtotal) for Apt-8 and OTA is -19.349 kcal/mol, with van der Waals energy (∆Gvdw) at -25.677 kcal/mol and electrostatic energy (∆Gele) at -12.457 kcal/mol. Notably, ∆Gvdw exceeds ∆Gele, suggesting that van der Waals forces are the primary driving force for the recognition of Apt-8 to OTA. This finding supports previous conclusions from isothermal titration experiments regarding the significance of van der Waals forces in binding processes. Additionally, molecular docking predictions indicate that Apt-8 and OTA bind non-covalently through hydrophobic interactions, van der Waals forces, and hydrogen bonds, further validating both techniques' accuracy. In addition, the contribution of each base was discussed by combining free energy calculation and free energy decomposition. As shown in Figure S2F, the total binding free energy of each base in Apt-8 was calculated and statistically analyzed. It was found that some sites, such as G7, T8, G9, C34, A35, and C36, had significant binding potential. Subsequently, energy decomposition was performed to discuss the contribution of each base, and the results are shown in Figure S2G. It can be seen that T8, G9, and A35 are important bases involved in binding and belong to the stem-loop region of Apt-8. This result is consistent with the flexibility study of the aptamer fragment in the RMSF curve. Although the total ∆G of G7, C34, and C36 is not high, according to their energy decomposition composition, it can be seen that their bases are not redundant and can play a role in anchoring the binding site during the recognition process. To verify the above result that T8, G9, and A35 are important bases involved in binding, they were simultaneously mutated to A8, C9, and T35, and ITC affinity determination was performed. The results are shown in Fig. 2 D. OTA was used to titrate the mutant aptamer. ∆H did not show a significant trend, and the software could not fit to obtain Kd data. The obtained heat curves did not show ITC binding, indicating that the results of molecular dynamics calculations are reasonable. 3.3 The principle of TdT-assisted aptamer sensor The principle is shown in Fig. 3 . The sensor immobilizes OTA on the surface of amino MBs through amidation reaction. In the absence of OTA, the OTA molecules fixed on MBs recognize the aptamer in the solution, and TdT catalyzes the continuous extension of the 3'-OH terminus without any template. With the introduction of horseradish peroxidase-labeled streptavidin (SA-HRP), the substrate TMB is catalyzed to form a blue product. However, in the presence of OTA, the aptamer binds to the free OTA, and the aptamer is almost completely consumed and removed by magnetic separation. This means that there are basically no 3'-OH termini left for subsequent TdT amplification, thus resulting in no obvious colorimetric signal. As the concentration of free target OTA decreases, the number of aptamers recognizing the OTA fixed on MBs increases, and the colorimetric signal also increases. Therefore, the concentration of free OTA is negatively correlated with the colorimetric signal. 3.4 Optimization of experimental conditions for TdT sensors To achieve the best experimental performance, the amount of MBs was optimized before investigating the detection capability of the sensor. If the amount of MBs was too low, it could not meet the requirement for complete binding of free Apt, resulting in a limited number of Apts initiating TdT amplification and thus a lower signal value. Conversely, an excessive amount of MBs not only increased the detection cost but also might reduce the sensitivity. As shown in Fig. 4 A, the signal difference increased rapidly at first and then leveled off as the amount of MBs-OTA increased. When the addition amount was 50 µL, the signal difference reached its maximum. Therefore, the optimal amount of MBs-OTA was 50 µL. The concentration of aptamers significantly affects the sensitivity of the colorimetric sensor. Both excessively high and low concentrations have negative effects. When the concentration is too low, the number of aptamers bound to the magnetic beads decreases, leading to a decline in the efficiency of TdT isothermal amplification and thus a lower colorimetric signal, which is not conducive to expanding the detection range. Conversely, when the aptamer concentration is too high, the excess free aptamers competitively bind to the target molecules, causing the remaining aptamers to preferentially bind to the MBs-OTA complex. As a result, some OTA cannot cause absorbance changes, thereby reducing the sensor's sensitivity. As shown in Fig. 4 B, the absorbance intensity change increases first and then decreases with the increase in aptamer concentration. When the aptamer concentration is 700 nM, the corresponding absorbance change is the most significant. Therefore, 700 nM is selected. Nucleic acid aptamers specifically bind to targets by forming stem-loop, hairpin or pseudoknot structures, thus requiring a certain incubation time. To ensure that Apt can firmly bind to OTA, the binding incubation time was optimized. As shown in Fig. 4 C, the ∆A value rapidly increased as the binding incubation time between the target and the aptamer increased. This might be because when the binding time was too short, the aptamer did not fully bind to the immobilized target, thereby reducing the isothermal amplification template and resulting in a lower colorimetric signal. When the binding incubation time reached 30 min, the ∆A value tended to stabilize and reached its maximum. This indicates that the incubation time has a significant impact on the binding of aptamers to targets, so 30 min was set as the optimal binding time. TdT amplification is the key to the success of this sensor, and the amplification time directly affects the detection efficiency. If the amplification time is too short, it will lead to insufficient embedding of biotin sites in the long single-stranded DNA, thereby reducing the number of HPRs that enter the ssDNA chain through biotin and streptavidin binding. Under other unchanged conditions, this will reduce the catalytic oxidation efficiency of the substrate TMB, which is not conducive to signal amplification. However, if the amplification time is too long, it will prolong the detection cycle, reduce efficiency and increase costs. Therefore, the TdT amplification time was optimized. As shown in Fig. 4 D, when the amplification time is within the range of 10–40 min, the absorbance difference significantly changes with the increase of TdT amplification time, and the overall trend is to rise first and then stabilize. When the amplification time reaches 40 min, the absorbance difference is the largest, so 40 min is selected as the optimal amplification time. 3.5 Evaluation of the sensor's sensitivity, specificity, stability, and reproducibility Under the optimal experimental conditions, the sensitivity of the aptamer sensor was verified by detecting and analyzing a series of OTA standard solutions. As shown in Fig. 5 A, as the concentration of OTA increased from 0 µg/mL to 10 µg/mL, different shades of blue products became observable under natural light—demonstrating the visual detection capability inherent in our sensor system. Meanwhile, as shown in Fig. 5 B, the response value of the maximum absorption peak at 652 nm gradually decreased as the OTA concentration increased, indicating a negative correlation with the absorbance intensity. As shown in Fig. 5 D, the ∆A value increased synchronously with the increase of OTA concentration, showing a clear correspondence between them. As shown in Fig. 5 C, a standard curve was plotted with the logarithm of OTA concentration as the abscissa and ∆A652 as the ordinate. The results indicated a good linear relationship within the range of 0–10 µg/mL, with the formula Y = 0.4488x + 1.461 and R² = 0.9922. According to the limit of detection (LOD) calculation formula, the LOD of this TdT-based colorimetric aptamer sensor was 0.026 ng/mL, which can be converted to 0.026 µg/kg, which was significantly higher in sensitivity than the acceptable limits for OTA detection set by the European Union and China. In the European Union, the maximum content of OTA has been stipulated, with the lowest limit set for infant products at 0.5 µg/kg [ 32 ]. In China, the minimum detection limit and quantification limit for the determination of OTA in food as per the national food safety standards are 0.1 µg/kg and 0.3 µg/kg respectively [ 33 ]. The specificity of the aptamer sensor based on TdT isothermal amplification is essentially an investigation of the specific binding of nucleic acid aptamers to OTA. Considering various chemical properties, six targets were selected as negative controls for specificity identification, namely OTB, ZEN, AFB1, AFM1, DON, and α-amanitin. Among them, OTB is a structural analog of OTA, while ZEN, AFB1, and DON share the same contamination sources as OTA and may coexist in food. ZEN, OTB, and AFB1 have the same carboxyl functional groups as OTA, α-amanitin is a classic peptide toxin. As shown in Fig. 5 E, although the concentration of each non-specific target was 10 times than that of OTA, the signal fluctuations caused by them were negligible. None of the compounds could cause significant ∆A652 signal values and color changes like the OTA experimental group. This fully demonstrates that the detection system will not produce false positives due to similar structures, groups, and contamination sources to OTA. Simultaneously, based on sensitivity analysis regarding OTA's response signal strength, it becomes evident that this constructed detection platform exhibits exceptional selectivity and specificity towards detection applications. To study the repeatability of the sensor, a sensor was constructed at different times within three days. The concentrations of the three target measurements were 0.96, 0.97, and 1.01 µg/kg respectively. The RSD was calculated to be 2.6%, demonstrating that the developed sensor has good reproducibility. To study the storage stability of the sensor and the stability of the MBs-OTA conjugated material during transportation, the prepared sensor was stored at 4°C in a refrigerator and at room temperature for a long time. The related materials were placed in the trunk of an electric vehicle, and the visualization detection of 1 µg/mL OTA was conducted at different days. The results are shown in Fig. 5 F. After being stored at 4°C for one month, the detection performance of the sensor remained basically unchanged. At room temperature, the signal value slightly decreased on the 14th d. The related materials in the trunk of the electric vehicle were also negatively affected after two weeks, and the results were similar to those at room temperature, indicating that long-term exposure to room temperature would affect the material activity, while the impact of jolting during transportation on the stability of the MBs-OTA conjugated material was relatively small. Therefore, the detection system should not be stored at room temperature for more than two weeks, and it is best to choose low-temperature transportation for long-distance transportation. It can be stored in a 4°C refrigerator for a long time in the dark. 3.6 Detection of OTA in real sample In the actual sample detection, the ability to resist the interference of complex matrices and accurately determine is crucial for the success of the sensor. To further evaluate the practical applicability and accuracy of this detection platform for real sample analysis, selected samples (wheat flour and red wine) were added with different OTA doses (0, 0.1, 2, and 10 µg/kg), analyzed and calculated the recovery rate using two sensors. Subsequently, the performance of the aptamer sensor was compared with the traditional ELISA method. The results are shown in Table 1 . Using the constructed aptamer sensor, the peak recovery rate of wheat flour was between 99.77% and 106.3%, and the peak recovery rate of red wine was between 98.3% and 101.4%, indicating good tolerance to sample matrices. The maximum relative standard deviation (RSD%, n = 3) of all samples was 3.68%. The ELISA kit results confirmed that the results of the two detections were highly consistent. In addition, the ELISA method did not detect the peak concentration of OTA below 2 µg/kg, which may have exceeded its detection limit. These results indicate that the developed sensor has better sensitivity and a wider detection range than ELISA, with the minimum detectable concentration being several orders of magnitude lower. Table 1 Results of OTA analysis in maize and wheat samples with the FP sensor and the ELISA kit. Sample Addition amount (µg/kg) Detection value (TdT) (µg/kg) Recycling rate (%) RSD(%) n = 3 Detection value (ELISA) (µg/kg) Recycling rate (%) RSD(%) n = 3 Maize 0 4.18 - 2. 15% - - - 0.1 4.21 106.3 3.26% - - - 2 6.17 101.5 2.91% 1.94 99.75 2.51% 10 13.27 99.77 1.83% 9.82 99.65 1.47% 0 2.24 - 3. 14% - - - 0.1 2.29 101.4 1.65% - - - Wheat 2 3.88 98.33 2.86% 1.98 98.72 3.68% 10 12.16 100.1 6 1.57% 9.96 99.82 2.42% The actual sample detection ability of the prepared biosensor and its matrix effect on real samples were further verified by a double-blind method. Four different actual samples (corn, wheat, red wine, and beer) were added with OTA standards of different concentrations to simulate the performance of OTA in products. The samples were not labeled during the addition process, and the standards were not added in the order of concentration gradients to achieve a double-blind effect. The results are shown in Table 2 (-: indicates undetectable, +: indicates detectable). Only three samples were undetectable, and the sensitivity was higher than that of the ELISA kits. Compared with the ELISA method, the detection results of the two methods were consistent, indicating that the matrix effect of this method in the detection of actual samples was low, and it had satisfactory feasibility and reliability. For the detection of OTA in grain products, this method has the advantages of high sensitivity, strong specificity, and visualization, which is helpful to fully verify its practical significance and value. Table 2 Double-blind detection of actual samples. Sample Method Concentration of sample (ng/mL) 0 0.01 0.05 0. 1 0.5 1 2 5 8 10 20 50 80 100 1000 Maize This work + + + + + + + + + + + + + + + Elisa - - - - + + + + + + + + + + + Wheat This work + + + + + + + + + + + + + + + Elisa - - - - + + + + + + + + + + + Beer This work - + + + + + + + + + + + + + + Elisa - - - - - - + + + + + + + + + Red Wine This work - + + + + + + + + + + + + + + Elisa - - - - - + + + + + + + + + + 4. Conclusion OTA is widely present in various products such as grains, beans, coffee, and wine, posing an increasing risk to human health [ 34 , 35 ]. Therefore, there is an urgent need to develop new detection methods that are highly sensitive, selective, and easy to operate. Based on the long-chain aptamer of OTA, its secondary structure was predicted by Mfold, and a short aptamer Apt-8 with 44 nt was screened out by gradually truncating the stem-loop. The affinity of Apt-8 for OTA was twice as high as that of the long-chain aptamer. The molecular mechanism study revealed the key binding sites and explored the induced fit and conformational changes during the recognition process of OTA and Apt-8. Meanwhile, an innovative competitive colorimetric biosensing detection method for OTA was established. To improve the efficiency and sensitivity of the sensor, four key parameters, including the amount of MBs, aptamer concentration, binding incubation time, and TdT amplification time, were optimized. Under the optimal conditions, this platform demonstrated excellent analytical performance, with a good linear relationship between the colorimetric signal and the OTA concentration. The detection limit was 0.026 ng/mL, which was significantly lower than the national standard value and several orders of magnitude higher. The TdT amplification does not require strict temperature control or complex experimental design, making this method easy to perform and cost-effective, while achieving high sensitivity detection. In addition, the combination of TdT-assisted DNA extension and colorimetric strategy can be applied in non-laboratory environments, facilitating the development of on-site detection. Through comparison with commercial ELISA kits, this method was verified to have high specificity. OTA was covalently assembled on the surface of MBs, improving structural stability and laying the foundation for commercial application. Declarations Declaration of competing interest No potential conflicts of interest were reported by the authors. Funding This study was supported by the National Natural Science Foundation of China (NSFC Grant No. 32460249). Author Contribution Conceptualization, Q.H.; methodology, J.Z. and Z.F.; validation, J.Z.; investigation, J.Z. and Z.F.; data curation, J.Z.; Software, Z.F.; writing—original draft preparation, J.Z.; writing—review and editing, Q.H.; vis-ualization, Z.F.; supervision, Q.H., Y.S. and J.Z.; project administration, Q.H.; funding acquisition, Q.H. 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Foods. https://doi.org/10(10).10.3390/foods10102429 Additional Declarations No competing interests reported. Supplementary Files SupportingInformation.docx Cite Share Download PDF Status: Published Journal Publication published 18 Dec, 2025 Read the published version in Microchimica Acta → Version 1 posted Editorial decision: Revision requested 11 Nov, 2025 Reviews received at journal 06 Nov, 2025 Reviewers agreed at journal 02 Nov, 2025 Reviewers agreed at journal 01 Nov, 2025 Reviews received at journal 30 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers invited by journal 21 Oct, 2025 Editor assigned by journal 21 Oct, 2025 Submission checks completed at journal 21 Oct, 2025 First submitted to journal 20 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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17:33:54","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":53352,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7902970/v1/cbd3a91e0e18da74117fef49.png"},{"id":95048435,"identity":"456c1381-4fd4-43a0-860f-db200b0b768c","added_by":"auto","created_at":"2025-11-03 17:33:55","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130372,"visible":true,"origin":"","legend":"","description":"","filename":"2062106a14bc47ab8b1b1a4ce8812de21structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7902970/v1/067d68304e42a2b7560d7aa1.xml"},{"id":95223131,"identity":"7162491c-e39f-4857-a2ff-521f33221fde","added_by":"auto","created_at":"2025-11-05 16:21:42","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":135512,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7902970/v1/7a7e15d31a13aa9952bef176.html"},{"id":95048415,"identity":"766f0c42-da5e-4e04-8d80-8cf638923c90","added_by":"auto","created_at":"2025-11-03 17:33:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79059,"visible":true,"origin":"","legend":"\u003cp\u003eTruncation and characterization of aptamers. (A) Secondary structure prediction from Apt-0. (B) Secondary structure prediction from Apt-8. (3) Affinity curves of Apt-0 and Apt-8. (D) Specificity Identification of Apt-0 and Apt-8.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7902970/v1/4ca338b28404a062e97966a5.jpg"},{"id":95048416,"identity":"c7dcd074-1349-4634-b0a0-bfd6aed34b39","added_by":"auto","created_at":"2025-11-03 17:33:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80373,"visible":true,"origin":"","legend":"\u003cp\u003eITC titration and MST curves for Apt-0 and Apt-8 VS OTA. (A) MST curves for Apt-0. (B) MST curves for Apt-8. (C) ITC determination of Apt-8 mutant aptamer. (D) The ITC curve of the mutant nucleic acid aptamer Apt-8. (E) ITC titration curves for Apt-0 VS OTA. (F) Comparison of ITC titration curves for Apt-8 VS OTA.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7902970/v1/48a1a6a82357707f8fc57a14.jpg"},{"id":95222361,"identity":"283d65f5-1ce3-4387-86bb-9243fe9dcfa9","added_by":"auto","created_at":"2025-11-05 16:20:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125469,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of sensor conditions\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7902970/v1/099a32b9edea25832deaa5c6.jpg"},{"id":95222602,"identity":"63644315-63e2-4626-ab47-410fd50dcf1a","added_by":"auto","created_at":"2025-11-05 16:20:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":93620,"visible":true,"origin":"","legend":"\u003cp\u003eOptimization of sensor conditions. (A) Optimization of the addition amount of Mbs-OTA. (B) Optimization of aptamer concentration. (C) Optimization of the target-binding incubation time. (D) Optimization of TdT isothermal amplification time.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7902970/v1/edec3028a1166100fed42bf8.jpg"},{"id":95048420,"identity":"143e3fa7-eab5-41e9-a336-f4fd97b9f782","added_by":"auto","created_at":"2025-11-03 17:33:54","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":82960,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of the Sensor's Sensitivity, Specificity, Stability, and Reproducibility. (A) Visual detection of the OTA sensor. (B) Relationship between OTA concentration and absorbance. (C) Standard curve determination of the OTA. (D) Range of detection sensor. (E) Sensor specificity study. (F) The stability of the sensor.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7902970/v1/9bb8a32431a534cded09e7e2.jpg"},{"id":98814073,"identity":"6b8c77d5-4a42-4884-b7e3-9bd2245d7ce7","added_by":"auto","created_at":"2025-12-22 16:10:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1432272,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7902970/v1/65ee6dff-8c42-49bd-af8d-0d42eb5491c4.pdf"},{"id":95048436,"identity":"cf20e380-3493-45d9-b6a8-1a5325fcaed3","added_by":"auto","created_at":"2025-11-03 17:33:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":8481929,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7902970/v1/1371327d184cff4d99e14a49.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigation the Binding Mechanism of Aptamers to Ochratoxin A and Development of Competitive Colorimetric Sensing Platforms","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOchratoxin (OT), a naturally occurring mycotoxin produced by the fungi \u003cem\u003eAspergillus\u003c/em\u003e and \u003cem\u003ePenicillium\u003c/em\u003e, is widely distributed across the globe [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The ochratoxin family comprises OTA, OTB, OTC, and OTα, with OTA being the most prevalent due to its widespread occurrence and toxicity [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This substance was first identified by Van in 1965 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. OTA can be found in various products including grains, legumes, coffee, wine, dried fruits [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In the 1990s, the International Agency for Research on Cancer (IARC) classified OTA as a Group 2B human carcinogen based on its carcinogenic and pathogenic effects [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Toxicological studies have confirmed that OTA exhibits nephrotoxicity, hepatotoxicity, immunotoxicity, neurotoxicity, and cytotoxicity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Jiang reported that OTA enhances intestinal pathogens such as \u003cem\u003eAlistipes\u003c/em\u003e which leads to harmful metabolite production and inflammation while also causing kidney damage [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This renal impairment is associated with the accumulation of OTA resulting from reabsorption processes in both proximal and distal tubules. Furthermore, the liver serves as a primary target organ for biotransformation of OTA. Felizardo established that prolonged exposure to elevated levels of OTA significantly correlates with an increased risk of hepatocellular carcinoma (HCC) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. OTA-induced oxidative damage impacts lipids, proteins, and DNA. It alters antioxidant status while inhibiting protein synthesis and DNA replication. These consequences are considered potential mechanisms underlying genotoxicity and cytotoxicity induced by OTA [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, investigating the toxicological mechanisms associated with OTA is crucial for protecting human health.\u003c/p\u003e\u003cp\u003eBao developed a dual-improved enzyme-linked immunosorbent assay (DI-ELISA) for ultrasensitive detection of OTA in coffee samples [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which rely on the specific binding reaction between antigens and antibodies, offering rapidity and suitability for large-scale sample screening (kit-based), with low cost and wide application range, but limited sensitivity, numerous influencing factors, and cumbersome procedures [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Dhanshetty developed and validated a multi-mycotoxin analysis method through the combination of ultra-performance liquid chromatography-fluorescence detection (UHPLC-FLD) and tandem mass spectrometry (LC-MS/MS), which enables simultaneous high-sensitivity, accuracy and precision determination of OTA in chili powder [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Based on the principle that different substances leave the column successively due to their structural similarities and produce distinct peak signals in the detector, providing high accuracy and good repeatability, and being the mainstream method in laboratories (especially for standard detection), but with expensive equipment, complex operation, and the need for specialized laboratories [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Rostami proposed a technical solution based on surface-enhanced Raman spectroscopy (SERS), achieving label-free detection of OTA on a porous SERS detection platform, which detect through changes in optical signals and can be rapid and sensitive (with the aid of nanomaterial amplification) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], but have poor stability and are difficult to miniaturize. Sun developed a novel detection method on artificial antigen-modified silica photonic crystal microspheres (SPCMs), which can be integrated into a biochip array and enables multiplex mycotoxin detection by using antibody-functionalized gold nanoparticles (AuNPs) as SERS nanotags [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], which are high-throughput, rapid, and miniaturized, suitable for multi-toxin detection, but are prone to contamination and have limited sensitivity. These methods can also be used for the detection of OTA metabolites (mainly relying on chromatography), but related research is scarce. Traditional methods are difficult to meet the on-site rapid and simple detection requirements, and there is an urgent need to develop more sensitive, rapid, and easy-to-operate new technologies.\u003c/p\u003e\u003cp\u003eIn recent years, aptamer-based biosensors have developed rapidly and attracted extensive attention due to their high stability, convenient storage, low cost and easy synthesis. Common methods include colorimetric, fluorescence, electrochemical and electrochemiluminescence [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Among them, colorimetric methods are widely used due to their low cost and simple operation. Currently, various colorimetric sensors have been constructed based on OTA aptamers. However, these traditional colorimetric aptamer sensors are usually limited by low sensitivity or poor specificity. To improve the sensitivity, various nucleic acid signal amplification strategies have been developed, such as polymerase chain reaction (PCR). Jin developed a novel electrochemiluminescence biosensor for the detection of OTA based on the principle of PCR amplification, with a detection limit as low as 1.36\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e mg/mL [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Tang proposed a dual-modal (colorimetric and photothermal) OTA analysis system based on a G-quadruplex-hemin/iodide (G4-Hemin/I\u003csup\u003e\u0026minus;\u003c/sup\u003e) mediated non-enzymatic redox cycling amplification (RCA) system, which is simple in structure and highly sensitive (1 pg/mL for colorimetric method and 0.8 pg/mL for photothermal method) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A dual-proportional fluorescence aptamer sensor based on the hybridization chain reaction (HCR) principle was established for the simultaneous detection of OTA and aflatoxin B1 (AFB1) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Under the optimal experimental conditions, the linear range of OTA and AFB1 was 0.05\u0026ndash;200 ng/mL, with detection limits of 6.7 pg/mL and 8.6 pg/mL, respectively. However, the dependence on temperature control devices restricts the application of PCR in on-site detection, while RCA and HCR usually involve complex DNA template or probe design [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Fortunately, the template-free DNA chain extension reaction mediated by terminal deoxynucleotidyl transferase (TdT) does not require strict temperature control, providing a possible option for the development of new biosensors for food safety screening.\u003c/p\u003e\u003cp\u003eTo address the challenges present in current technologies, we have combined the inherent advantages of TdT amplification and aptamers. This approach is based on the competitive recognition dynamics among OTA, magnetic beads-OTA (MBs-OTA), and nucleic acid aptamers. TdT is introduced for signal amplification, leading to the development of a rapid and sensitive colorimetric biosensor. In the absence of OTA, OTA molecules that are pre-fixed on MBs recognize the aptamers present in solution. Consequently, TdT polymerizes directionally at the 3'-OH end of oligonucleotide aptamers, labeling long single-stranded DNA with biotin. Subsequently, with assistance from horseradish peroxidase-labeled streptavidin (SA-HRP), tetramethylbenzidine (TMB) is catalyzed to yield a blue product. Conversely, when OTA is present, these molecules competitively bind to the aptamers. As a result, the aptamers are removed from the supernatant through magnetic separation, which leads to a failure in both TdT amplification and subsequent signal enhancement.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Materials\u003c/h2\u003e\u003cp\u003eHAuCl\u003csub\u003e4\u003c/sub\u003e\u0026bull;3H\u003csub\u003e2\u003c/sub\u003eO was purchased from Beijing Kexin Biotechnology Co., Ltd. (Beijing, China), Na\u003csub\u003e3\u003c/sub\u003eC\u003csub\u003e6\u003c/sub\u003eH\u003csub\u003e5\u003c/sub\u003eO\u003csub\u003e7\u003c/sub\u003e\u0026bull;2H\u003csub\u003e2\u003c/sub\u003eO from Guangzhou Bolaisi Biotechnology Co., Ltd. (Guangzhou, China), Deoxynivalenol (DON) from Zhejiang Weina Technology Co., Ltd. (Ningbo, China), KCl and KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e from Shanghai Beyotime Biotechnology Co., Ltd. (Shanghai, China), Na\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e\u0026bull;12H\u003csub\u003e2\u003c/sub\u003eO and MgCl\u003csub\u003e2\u003c/sub\u003e from Shanghai Sigma-Aldrich Co., Ltd (Shanghai, China), OTA and Fumonisin B1 (FB1) from Binzhi Biotechnology Co., Ltd. (Shanghai, China), Zearalenone (ZEN) from Millipore (USA), Streptavidin (SA) from Beckman (GER), N-Hydroxysuccinimide (NHS) and 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC) from Shanghai Maclin Biochemical Technology Co., Ltd. (Shanghai, China), soluble single-component TMB substrate solution from Agilent G9800A (USA), amine-modified magnetic beads (BioMag@Plus) from Sigma-Aldrich Chemical Co., Ltd. (USA), terminal deoxynucleotidyl transferase (TdT) from Polysciences, bovine serum albumin from Amresco (USA), and SA-HRP from Thermo Fisher Scientific Co., Ltd. (USA). AuNPs from Sigma-Aldrich (GER), The nucleic acid sequence information is listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe circular dichroism spectrometer is from Apied Phophyics Ltd (UK). The Monolith molecular interaction instrument and the pure water instrument are from Millipore (USA). The isothermal titration calorimeter, ultraviolet spe ctrophotometer, standard instrument and pH meter are from Thermo (USA). The electronic analytical balance is from Sartorius (GER). The fluorescence spectrometer is from Agilent G9800A(USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Characterization of truncated aptamers\u003c/h2\u003e\u003cp\u003eFirst, an aptamer designated as Apt-0, characterized by moderate affinity and a relatively long sequence, was selected for further optimization. This aptamer was previously identified by Li [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The secondary structure prediction process was completed using the UNAFold web server. In this study, truncation was primarily performed based on the stem-loops predicted from the secondary structure\u0026mdash;either by sequentially removing the included stem-loops or by deleting the fixed primers at both ends of the aptamer.\u003c/p\u003e\u003cp\u003eThe specificity and affinity of aptamers for mycotoxins and antibiotics were evaluated using the colloidal gold method. Aptamers Apt-0 to Apt-8 (sequences were listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) were prepared at concentrations of 0, 50, 100, 200, 400, 800, 1000, and 1600 nM, respectively. The target OTA was prepared at a concentration of 1 \u0026micro;g/mL. 50 \u0026micro;L of different concentrations of Apt-0 to Apt-8 aptamers and 1 \u0026micro;g/mL OTA were added and mixed by pipetting. The mixture was incubated at room temperature for 30 min, then 4-fold concentrated 25 nM AuNPs solution was added, mixed by pipetting, and incubated at room temperature in the dark for 30 min. The optimized NaCl concentration was added, mixed by pipetting, and transferred to a 96-well plate. The absorbance at 520 nm was measured. The A520 nm value of 0 nM Apt was recorded as A\u003csub\u003e0\u003c/sub\u003e, and the A520 nm values of other different concentrations of Apt were recorded as A'. The value of (A' - A\u003csub\u003e0\u003c/sub\u003e)/A\u003csub\u003e0\u003c/sub\u003e was calculated. The aptamer concentration was plotted on the x-axis and (A' - A\u003csub\u003e0\u003c/sub\u003e)/A\u003csub\u003e0\u003c/sub\u003e on the y-axis for curve fitting to calculate Kd. Each measurement was repeated three times in parallel.\u003c/p\u003e\u003cp\u003eThe aptamers Apt-0 to Apt-8 was prepared at a concentration of 400 nM, and the target OTA at a concentration of 1 \u0026micro;g/mL. The non-specific targets OTB, DON, FB1, and α-amanitin was set at a concentration of 5 \u0026micro;g/mL. Mix is the mixture of all the standards. 50 \u0026micro;L of each aptamer Apt-0 to Apt-8 was mixed with the same volume of different standard solutions by pipetting. Incubated at room temperature for 30 min, then 25 nM AuNPs solution was added and concentrated 4 times, mixed by pipetting and incubated at room temperature in the dark for 30 min. NaCl was added at the optimized concentration, mixed and transferd to a 96-well plate. The absorbance was measured at 520 nm and 620 nm and the walue of A620 nm/A520 nm was calculated. The determination of absorbance value was the same as above.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Potential mechanisms of binding specificity changes in aptamer Apt-0 and Apt-8.\u003c/h2\u003e\u003cp\u003eMonolith Molecular Interaction. Using FAM-labeled aptamer as the target and OTA standard solution as the ligand, prepare solutions with a target concentration lower than the estimated Kd value and a ligand concentration 20\u0026ndash;50 times that of Kd. Dilute OTA into 16 gradients of equal volume using PBST, then mix with FAM-Apt in equal volumes. Allow the mixture to stand at room temperature in the dark for 15 min before conducting on-machine detection. If adsorption occurs, increase Tween or other adsorbents appropriately in the buffer. Import the detection signal diagram into MO Affinity Analysis software for curve fitting to obtain the signal-to-noise ratio and affinity constant.\u003c/p\u003e\u003cp\u003eIsothermal Titration Calorimetry (ITC), A PEAQ-ITC instrument (Malvern) was employed. All solutions were degassed prior to titration to avoid abnormal thermal changes induced by bubble rupture. Aptamer screening buffer was used as the titration buffer. Typically, 10 \u0026micro;M aptamer was loaded into the sample cell, 200 \u0026micro;M OTA into the titration syringe, and a 19-drop titration protocol was adopted. A control experiment (titration of OTA into buffer alone) was conducted to eliminate interference from dilution heat. Before formal experiments, water-to-water titration was performed to verify instrument stability, and OTA titration into aptamer-free buffer to confirm no extraneous heat generation. Both the sample cell and titration syringe were rinsed with experimental buffer. For mutant aptamer experiments, target B (350\u0026ndash;400 \u0026micro;L) was placed in the sample cell, and target A (100 \u0026micro;L) in the titration syringe. For aptamers with known Kd values, sample concentrations were calculated using the formula, sample cell concentration\u0026thinsp;=\u0026thinsp;C*Kd*N (where C\u0026thinsp;=\u0026thinsp;10\u0026ndash;100). For those with unknown Kd values, the sample cell concentration was tentatively set at 10 \u0026micro;M\u0026ndash;10 mM, with the syringe concentration 10\u0026ndash;20 times higher (or an initial 200 \u0026micro;M-to-20 \u0026micro;M titration). Additionally, buffers for the two samples must be identical (pH difference\u0026thinsp;\u0026lt;\u0026thinsp;0.05), organic solvents prohibited, and both samples subjected to degassing or centrifugation at 12,000 r/min for 10 min.\u003c/p\u003e\u003cp\u003eThermodynamic parameters including enthalpy change (ΔH), entropy change (ΔS), Gibbs free energy (ΔG), and stoichiometric ratio were derived from raw titration data analysis.\u003c/p\u003e\u003cp\u003eCircular Dichroism (CD) measurements were performed on a Chirascan V100 spectropolarimeter at 25\u0026deg;C. The fixed parameters were set as follows, spectral range of 200\u0026ndash;400 nm, data collection time of 0.5 s per point, and bandwidth of 1 nm. Samples were loaded into a 1 mm pathlength cuvette. Aptamers Apt-0 and Apt-8 were diluted to 20 \u0026micro;M with ultrapure water, heated at 95\u0026deg;C for 5 min, and then cooled to room temperature for 30 min. Solutions of K⁺, Na⁺, and Mg\u0026sup2;⁺ were prepared at concentrations of 0, 25, and 75 mM, respectively. A 50 \u0026micro;L aliquot of the aptamer solution was mixed with 350 \u0026micro;L of the ion solution, followed by incubation for 30 min before scanning. Prior to measurement, the cuvette was rinsed with the sample solution of the current batch, and the blank scan was performed using the corresponding dilution buffer to establish the baseline.\u003c/p\u003e\u003cp\u003eMolecular Docking. The chemical structure of OTA was drawn using ChemDraw 19.0, then converted to a three-dimensional (3D) model via Chem3D 19.0 and exported in the molⅡ format. The secondary structures of aptamers Apt-0 and Apt-8 were predicted using Mfold, with parameters set to match the aptamer screening conditions. The predicted structures were saved in the Vienna format, then imported into the online server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biophy.hust.edu.cn/3dRNA\u003c/span\u003e\u003cspan address=\"http://biophy.hust.edu.cn/3dRNA\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for base substitution and deoxygenation, followed by conversion to the pdb format. The aforementioned files were imported into AutoDock 4 for energy minimization and docking result analysis, and the result with the lowest binding energy was selected. pymol was used for visualization to analyze hydrogen bonds, bond lengths, and docking residues.\u003c/p\u003e\u003cp\u003eMolecular Dynamics (MD). Simulation referring to previous studies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], the aptamer structure was optimized based on docking results to eliminate structural strain. After complex minimization, heating, and equilibration, the production run was initiated. The topology file was generated using Sobtop, and classical MD simulations were performed at 300 K for 100 ns under the Amber 99sb force field. Following trajectory deperiodization, the root mean square deviation (RMSD), root mean square fluctuation (RMSF), hydrogen bonds (H-bonds), and molecular mechanics Generalized Born surface area (MM/GBSA) of the complex were calculated, along with base-wise energy decomposition.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Construction of the sensor\u003c/h2\u003e\u003cp\u003eThe TdT sensor immobilizes OTA on the surface of amino MBs through amidation reaction. In the absence of OTA, the OTA molecules fixed on MBs recognize the aptamer in the solution, and TdT catalyzes the continuous extension of the 3'-OH end without any template. With the introduction of SA-HRP, the substrate TMB is catalyzed to form a blue product. However, in the presence of OTA, the aptamer binds to the free OTA, and the aptamer is almost completely consumed and removed by magnetic separation. This means that there are basically no 3'-OH ends left for subsequent TdT amplification, thus resulting in no obvious colorimetric signal. As the concentration of free target OTA decreases, the number of aptamers recognizing the OTA fixed on MBs increases, and the colorimetric signal increases accordingly.\u003c/p\u003e\u003cp\u003eThe experimental procedure involves preparing OTA standard solutions with concentrations of 0, 0.0001, 0.001, 0.005, 0.01, 0.05, 0.1, 0.25, 0.5, 1, 5, 10, and 100 \u0026micro;g/mL for later use. 50 \u0026micro;L of the MBs-OTA solution was taken to perform magnetic separation to remove the supernatant, and 20 \u0026micro;L of 700 nm Apt was added along with an equal volume of various concentrations of OTA standard solutions to the precipitate. Incubated at room temperature for 30 min. The precipitate was washed three times with Tris-HCl buffer, then 20 \u0026micro;L of TdT mixed reaction solution was added. Mixed thoroughly by pipetting and incubated at 37\u0026deg;C for 40 min. Rinsed three times with buffer, blocked for 30 min, then SA-HRP was added and incubated at 25\u0026deg;C for an additional 20 min, followed by six washes with washing buffer. Finally, the substrate TMB was introduced and allow catalytic oxidation to occur at 37\u0026deg;C for 5 min before measuring absorbance intensity at a wavelength of 652 nm. Each set of measurement results is repeated in parallel three times.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Detection of OTA in real samples\u003c/h2\u003e\u003cp\u003eDue to the widespread distribution and numerous sources of contamination associated with OTA, its presence is prevalent in various aspects of daily life. Among food products, grains, fruits, and beverages exhibit the highest levels of contamination. To thoroughly investigate the matrix effect in actual samples, two solid and two liquid food items were selected for determination and analysis based on their respective forms. The specific products chosen were corn flour, wheat flour, red wine, and beer\u0026mdash;all randomly purchased from local markets. The sample pretreatment steps were as follows, each type of flour (corn or wheat) was taken 4 g along with 0.4 g of sodium chloride in a 15 mL centrifuge tube. Then 13 mL of a mixture composed of acetonitrile and water (in a ratio of 9:1) was added and subjected to vigorous oscillation for 2 min. Subsequently, the mixture was filtered through a 0.22 \u0026micro;m filter membrane. Finally, it was centrifuged at 3000 rpm for 10 min. The supernatant was collected and stored at 4\u0026deg;C for future use. To mitigate any potential matrix effects caused by the coloration present in red wine and beer prior to experimentation, color adsorption treatment was performed. Specifically, 1 g of activated carbon was added to a volume of 10 mL from each wine sample to adsorb its color components until they became colorless. After that, the activated carbon was removed and the solution was filtered through a 0.22 \u0026micro;m filter membrane before centrifuging again at 3000 rpm for an additional 10 min. The resulting supernatant was then collected and stored at 4\u0026deg;C.\u003c/p\u003e\u003cp\u003eTo explore the accuracy of the detection system's measurement results, different concentrations of OTA standards were added to two groups of samples, and then the detection method was used for comparison. Prior to sample pretreatment, selected samples (wheat flour and red wine) were spiked with varying doses of OTA to achieve final concentrations of 0, 0.1, 2, and 10 \u0026micro;g/kg. According to the above-optimized experimental conditions, the absorbance intensity of the solution at 652 nm wavelength was monitored. The content of OTA in each sample was determined, and the average recovery rate was calculated. Each group of measurement results was repeated three times in parallel. Subsequently, the performance of the aptamer sensor was compared with the traditional ELISA method.\u003c/p\u003e\u003cp\u003eTo investigate the influence of complex matrix components on the sensor, a double-blind method was used for verification. Different concentrations of OTA standards were added to each processed sample (corn, wheat, wine, and beer) to simulate the real matrix environment. The OTA standard gradients were set at different levels. During the addition process involving these standards across four actual samples, neither the samples nor their corresponding OTA standards were labeled. Furthermore, standards were not added sequentially according to concentration gradients in order to maintain a double-blind effect. Each group had three parallel samples. Based on the positive and negative results of the detection, the practical applicability of the detection platform for real sample analysis was evaluated.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Truncation optimization for altering the specificity of aptamer Apt-0\u003c/h2\u003e\u003cp\u003eThe secondary structure of the original aptamer for Apt-0 consists of four stem-loop structures. Based on the secondary structure of the aptamer and the results of molecular docking, a preliminary analysis was conducted, suggesting that the active site of the aptamer might be located on the stem-loop structures. Therefore, the 5' end of the original sequence was first truncated by cutting off the first 18 nt, and then the stem-loop structures were successively truncated and recombined in pairs or the fixed primers (12 nt) at both ends to obtain a new aptamer. Apt-0 represents the original sequence, while Apt-8 is the sequence obtained by combining stem-loops A and D. The predicted secondary structures of the truncated aptamers Apt-0 and Apt-8 are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe affinity of aptamers was determined by the colloidal gold spectrophotometry. The truncated aptamers Apt-0 and Apt-8 concentrations were taken as the abscissa, and (A\u0026rsquo;-A\u003csub\u003e0\u003c/sub\u003e)/A\u003csub\u003e0\u003c/sub\u003e as the ordinate. The curves fitted by GraphPad Prism 8.0.2 software (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) were used to calculate the affinity constant (Kd). The Kd values of Apt-0 and Apt-8 were 184.4\u0026thinsp;\u0026plusmn;\u0026thinsp;34.08 and 65.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87 nM, respectively. The Kd values of truncated aptamers were compared with those of the original chain aptamers to analyze the sequence optimization. The results showed that the Kd value of Apt-8 aptamer was 65.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87 nM, which was 2 times lower than that of Apt-0. Since the Kd value is inversely proportional to the affinity, it indicates that the affinity of Apt-8 aptamer was significantly improved after truncation. This preliminary result suggests that the designed truncation method of aptamers in this experiment is feasible, but its accuracy needs to be further verified by specificity identification and other affinity determination methods.\u003c/p\u003e\u003cp\u003eToxins that might coexist with OTA were selected and specifically identified by the colloidal gold method. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, Apt-8 has improved specificity and anti-interference ability compared with the original chain aptamer, and its optimization result is more ideal. This might be because the sequence truncation retains the active region while reducing the steric hindrance effect of non-essential nucleotides. Therefore, Apt-8 was chosen for subsequent mechanism exploration and sensor establishment experiments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Potential mechanisms of binding specificity changes in aptamer Apt-0 and Apt-8.\u003c/h2\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 MST\u003c/h2\u003e\u003cp\u003eTo verify the accuracy of affinity size determined via colloidal gold spectrophotometry, MST microscale thermophoresis was employed as an auxiliary verification method. The affinity fitting curves are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. The affinity sizes of the original chain aptamer Apt-0 and the truncated aptamer Apt-8 determined by molecular interaction measurement are almost the same as those determined by the colloidal gold method, which are 184.4\u0026thinsp;\u0026plusmn;\u0026thinsp;34.08 nM and 151.78 nM, 65.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87 and 78 nM respectively. Moreover, the signal-to-noise ratios of both are greater than 12. As shown in Table S2. A signal-to-noise ratio of 5\u0026ndash;12 indicates binding force, a ratio greater than 12 indicates strong binding force or only proves the reliability and accuracy of the measurement results, and a ratio lower than 5 indicates no binding force or indicates an error in the measurement process, at which point the Kd value result is unreliable. This proves that both aptamers and OTA have strong binding forces, fully demonstrating the reliability of the affinity size results determined by the colloidal gold method.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 ITC\u003c/h2\u003e\u003cp\u003eITC titration was conducted on Apt-0 and Apt-8, with the MicroCal PEAQ-ITC Analysis Software used to determine the enthalpy change (ΔH) and fit Kd values of 177 nM and 69.7 nM, respectively. The titration curves are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF. During binding, ΔH reflects specific interactions like hydrogen bonds and van der Waals forces, while ΔS (entropy change) indicates conformational changes, steric hindrance, and hydrophobic effects. According to thermodynamic data in Table S3, when Apt-0 aptamer binds to OTA, ΔG, ΔH, and -TΔS are \u0026minus;\u0026thinsp;14.83, -11.94, and \u0026minus;\u0026thinsp;2.87 kJ/mol respectively. For binding between Apt-8 aptamer and OTA, ΔG is -25.78, ΔH is -17.83, and -TΔS is -7.95 kJ/mol.\u003c/p\u003e\u003cp\u003eThe negative values of -TΔS and ΔG indicate that both aptamers release heat upon binding to OTA while forming Apt-OTA complexes with low-energy. The differences in intensities of -TΔS and ΔH between Apt-0 and Apt-8 suggest that the increased affinity of Apt-8 over the original chain primarily results from reduced steric effects and enhanced van der Waals forces during recognition.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 CD\u003c/h2\u003e\u003cp\u003eCD spectroscopy is a powerful technique for investigating the structures of aptamers. As illustrated in Figures S2A and S2B, we examined the effects of varying concentrations of K\u003csup\u003e+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, and Mg\u003csup\u003e2+\u003c/sup\u003e on the secondary conformations of the aptamers Apt-0 and Apt-8. The CD spectrum of Apt-0 exhibited minimal changes across different concentrations of K\u003csup\u003e+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, and Mg\u003csup\u003e2+\u003c/sup\u003e, indicating its structure remains relatively stable without significant alterations. In contrast, the CD effect peaks for Apt-8 were markedly enhanced in response to varying concentrations of Mg\u003csup\u003e2+\u003c/sup\u003e, suggesting substantial conformational changes occurred within this aptamer. The presence of Mg\u003csup\u003e2+\u003c/sup\u003e was found to facilitate stabilization of the secondary structure of Apt-8.\u003c/p\u003e\u003cp\u003eThe binding mode between Apt-8 and its target was further explored using CD spectroscopy, as depicted in Figure S2C. Both Apt-8 and the Apt-8/OTA complex displayed positive and negative characteristic peaks at 277 and 249 nm, respectively, confirming that Apt-8 exhibits typical B-type DNA characteristics. Previous research has established that base stacking results in positive Cotton effect peaks, while negative Cotton effect peaks arise from DNA's helical structure. Notably, the introduction of OTA resulted in a reduction in intensity for both characteristic Cotton effect peaks, implying that interaction between OTA and Apt-8 induces structural modifications within Apt-8, enhancing base pair stacking as well as helicity. This observation indicates that recognition by OTA leads to significant conformational adjustments in Apt-8, thus characterizing an induced-fit mechanism rather than adhering strictly to a lock-and-key model.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.4 Molecular docking\u003c/h2\u003e\u003cp\u003eTo further investigate the binding site between the truncated aptamer Apt-8 and OTA, we conducted molecular docking simulations involving both entities. The results are illustrated in Figures S2D and S2E. The active site is primarily characterized by hydrophobic grooves, which are situated on the surface of DNA. Small molecule ligands can either antagonize or activate the target through this active site, thereby influencing the signaling pathway. Analysis of 100 docking results reveals that the lowest energy conformation is -4.78 kcal/mol, with a docking score reaching \u0026minus;\u0026thinsp;8.761 kcal/mol, indicating excellent potential for targeted binding. The small molecule ligand predominantly accesses the DNA active site via hydrophobic interactions and van der Waals forces, engaging with bases such as G7, T8, G9, G10, C34, A35, and C36 within the major groove. Notably, an oxygen atom on the ligand forms a hydrogen bond with a hydrogen atom from the G9 base in DNA (bond length is 1.7 \u0026Aring;), while a hydroxyl hydrogen atom also establishes a hydrogen bond with a hydrogen atom from the G10 base (bond length is 2.0 \u0026Aring;). These hydrogen bonds facilitate targeting of DNA to either activate or inhibit its function.\u003c/p\u003e\u003cp\u003eAll predicted active sites identified by Apt-8 were subjected to mutation analysis. ITC was employed to validate the accuracy of our molecular docking findings as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC. During titration processes involving mutated Apt-8 and its target, no significant heat change was observed. This indicates that mutated Apt-8 exhibits negligible affinity for OTA. Further analysis demonstrates that there is no discernible trend in enthalpy change (ΔH) for control differences. Consequently, software fitting could not accommodate affinity data effectively. Based on these findings, we conclude that our molecular docking results are both reliable and accurate. The predicted base sites play an essential role in mediating aptamer binding to OTA.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.2.5 MD\u003c/h2\u003e\u003cp\u003eMD is a computational technique employed to investigate the structure and interactions of biological systems, facilitating a deeper understanding of time-dependent conformational changes within these interactions. Based on the docking results, sobtop was selected to generate the topological file for the compound. The Amber 99sb force field was utilized to conduct classical molecular dynamics simulations at a temperature of 300 K over a duration of 100 ns. Following deperiodization of the trajectory, the RMSD, RMSF, H-bond interactions, and MMGBSA values were calculated for the complex respectively.\u003c/p\u003e\u003cp\u003eAs illustrated in Figure S3, the dynamic fluctuations of RMSD over time are initially visualized. This experiment exhibits significant variability, indicating that Apt-8 undergoes notable conformational changes in the presence of OTA, such as alterations in its ring structure or base stacking configurations. These findings align with previous CD results, further confirming that the interaction between Apt-8 and OTA is accompanied by substantial conformational motion.\u003c/p\u003e\u003cp\u003eA residue decomposition analysis of RMSF was conducted, with results presented in Figure S4. Notably, the two nucleic acid segments\u0026mdash;5\u0026ndash;10 and 34\u0026ndash;37\u0026mdash;exhibit relatively high flexibility and may represent key regions responsible for biological functions. In contrast, other regions display comparatively low flexibility. However, further investigation is required to determine which specific areas are influenced by compound interactions. Additionally, it is noteworthy that the highly flexible fragments identified within this simulation overlap significantly with predicted sites of molecular docking activity based on prior results.\u003c/p\u003e\u003cp\u003eThe hydrogen bond interactions between nucleic acids and compounds were also analyzed. These interactions serve as an indicator of biological system stability\u0026mdash;the lower the obtained value, the more stable the complex is. The outcomes are depicted in Figure S5. The combination of compounds and nucleic acids has reached a steady state, suggesting that Apt-8 maintains high thermal stability upon forming a complex with OTA\u0026mdash;a factor that will be advantageous for sensor development in subsequent stages.\u003c/p\u003e\u003cp\u003eThe energy calculation and residue decomposition of the simulated trajectory are presented in Figure S6. The MMGBSA analysis shows an initial increase followed by a decrease, with the later downward trend gradually stabilizing, indicating that the complex model has stabilized after 60 ns of dynamics optimization. Subsequently, binding free energy was calculated using the MM/GBSA method. As shown in Figure S2H, the total binding free energy (∆Gtotal) for Apt-8 and OTA is -19.349 kcal/mol, with van der Waals energy (∆Gvdw) at -25.677 kcal/mol and electrostatic energy (∆Gele) at -12.457 kcal/mol. Notably, ∆Gvdw exceeds ∆Gele, suggesting that van der Waals forces are the primary driving force for the recognition of Apt-8 to OTA. This finding supports previous conclusions from isothermal titration experiments regarding the significance of van der Waals forces in binding processes. Additionally, molecular docking predictions indicate that Apt-8 and OTA bind non-covalently through hydrophobic interactions, van der Waals forces, and hydrogen bonds, further validating both techniques' accuracy.\u003c/p\u003e\u003cp\u003eIn addition, the contribution of each base was discussed by combining free energy calculation and free energy decomposition. As shown in Figure S2F, the total binding free energy of each base in Apt-8 was calculated and statistically analyzed. It was found that some sites, such as G7, T8, G9, C34, A35, and C36, had significant binding potential. Subsequently, energy decomposition was performed to discuss the contribution of each base, and the results are shown in Figure S2G. It can be seen that T8, G9, and A35 are important bases involved in binding and belong to the stem-loop region of Apt-8. This result is consistent with the flexibility study of the aptamer fragment in the RMSF curve. Although the total ∆G of G7, C34, and C36 is not high, according to their energy decomposition composition, it can be seen that their bases are not redundant and can play a role in anchoring the binding site during the recognition process.\u003c/p\u003e\u003cp\u003eTo verify the above result that T8, G9, and A35 are important bases involved in binding, they were simultaneously mutated to A8, C9, and T35, and ITC affinity determination was performed. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD. OTA was used to titrate the mutant aptamer. ∆H did not show a significant trend, and the software could not fit to obtain Kd data. The obtained heat curves did not show ITC binding, indicating that the results of molecular dynamics calculations are reasonable.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3 The principle of TdT-assisted aptamer sensor\u003c/h2\u003e\u003cp\u003eThe principle is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The sensor immobilizes OTA on the surface of amino MBs through amidation reaction. In the absence of OTA, the OTA molecules fixed on MBs recognize the aptamer in the solution, and TdT catalyzes the continuous extension of the 3'-OH terminus without any template. With the introduction of horseradish peroxidase-labeled streptavidin (SA-HRP), the substrate TMB is catalyzed to form a blue product. However, in the presence of OTA, the aptamer binds to the free OTA, and the aptamer is almost completely consumed and removed by magnetic separation. This means that there are basically no 3'-OH termini left for subsequent TdT amplification, thus resulting in no obvious colorimetric signal. As the concentration of free target OTA decreases, the number of aptamers recognizing the OTA fixed on MBs increases, and the colorimetric signal also increases. Therefore, the concentration of free OTA is negatively correlated with the colorimetric signal.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Optimization of experimental conditions for TdT sensors\u003c/h2\u003e\u003cp\u003eTo achieve the best experimental performance, the amount of MBs was optimized before investigating the detection capability of the sensor. If the amount of MBs was too low, it could not meet the requirement for complete binding of free Apt, resulting in a limited number of Apts initiating TdT amplification and thus a lower signal value. Conversely, an excessive amount of MBs not only increased the detection cost but also might reduce the sensitivity. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, the signal difference increased rapidly at first and then leveled off as the amount of MBs-OTA increased. When the addition amount was 50 \u0026micro;L, the signal difference reached its maximum. Therefore, the optimal amount of MBs-OTA was 50 \u0026micro;L.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe concentration of aptamers significantly affects the sensitivity of the colorimetric sensor. Both excessively high and low concentrations have negative effects. When the concentration is too low, the number of aptamers bound to the magnetic beads decreases, leading to a decline in the efficiency of TdT isothermal amplification and thus a lower colorimetric signal, which is not conducive to expanding the detection range. Conversely, when the aptamer concentration is too high, the excess free aptamers competitively bind to the target molecules, causing the remaining aptamers to preferentially bind to the MBs-OTA complex. As a result, some OTA cannot cause absorbance changes, thereby reducing the sensor's sensitivity. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, the absorbance intensity change increases first and then decreases with the increase in aptamer concentration. When the aptamer concentration is 700 nM, the corresponding absorbance change is the most significant. Therefore, 700 nM is selected.\u003c/p\u003e\u003cp\u003eNucleic acid aptamers specifically bind to targets by forming stem-loop, hairpin or pseudoknot structures, thus requiring a certain incubation time. To ensure that Apt can firmly bind to OTA, the binding incubation time was optimized. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, the ∆A value rapidly increased as the binding incubation time between the target and the aptamer increased. This might be because when the binding time was too short, the aptamer did not fully bind to the immobilized target, thereby reducing the isothermal amplification template and resulting in a lower colorimetric signal. When the binding incubation time reached 30 min, the ∆A value tended to stabilize and reached its maximum. This indicates that the incubation time has a significant impact on the binding of aptamers to targets, so 30 min was set as the optimal binding time.\u003c/p\u003e\u003cp\u003eTdT amplification is the key to the success of this sensor, and the amplification time directly affects the detection efficiency. If the amplification time is too short, it will lead to insufficient embedding of biotin sites in the long single-stranded DNA, thereby reducing the number of HPRs that enter the ssDNA chain through biotin and streptavidin binding. Under other unchanged conditions, this will reduce the catalytic oxidation efficiency of the substrate TMB, which is not conducive to signal amplification. However, if the amplification time is too long, it will prolong the detection cycle, reduce efficiency and increase costs. Therefore, the TdT amplification time was optimized. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, when the amplification time is within the range of 10\u0026ndash;40 min, the absorbance difference significantly changes with the increase of TdT amplification time, and the overall trend is to rise first and then stabilize. When the amplification time reaches 40 min, the absorbance difference is the largest, so 40 min is selected as the optimal amplification time.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Evaluation of the sensor's sensitivity, specificity, stability, and reproducibility\u003c/h2\u003e\u003cp\u003eUnder the optimal experimental conditions, the sensitivity of the aptamer sensor was verified by detecting and analyzing a series of OTA standard solutions. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, as the concentration of OTA increased from 0 \u0026micro;g/mL to 10 \u0026micro;g/mL, different shades of blue products became observable under natural light\u0026mdash;demonstrating the visual detection capability inherent in our sensor system. Meanwhile, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, the response value of the maximum absorption peak at 652 nm gradually decreased as the OTA concentration increased, indicating a negative correlation with the absorbance intensity. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, the ∆A value increased synchronously with the increase of OTA concentration, showing a clear correspondence between them. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, a standard curve was plotted with the logarithm of OTA concentration as the abscissa and ∆A652 as the ordinate. The results indicated a good linear relationship within the range of 0\u0026ndash;10 \u0026micro;g/mL, with the formula Y\u0026thinsp;=\u0026thinsp;0.4488x\u0026thinsp;+\u0026thinsp;1.461 and R\u0026sup2; = 0.9922. According to the limit of detection (LOD) calculation formula, the LOD of this TdT-based colorimetric aptamer sensor was 0.026 ng/mL, which can be converted to 0.026 \u0026micro;g/kg, which was significantly higher in sensitivity than the acceptable limits for OTA detection set by the European Union and China. In the European Union, the maximum content of OTA has been stipulated, with the lowest limit set for infant products at 0.5 \u0026micro;g/kg [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In China, the minimum detection limit and quantification limit for the determination of OTA in food as per the national food safety standards are 0.1 \u0026micro;g/kg and 0.3 \u0026micro;g/kg respectively [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe specificity of the aptamer sensor based on TdT isothermal amplification is essentially an investigation of the specific binding of nucleic acid aptamers to OTA. Considering various chemical properties, six targets were selected as negative controls for specificity identification, namely OTB, ZEN, AFB1, AFM1, DON, and α-amanitin. Among them, OTB is a structural analog of OTA, while ZEN, AFB1, and DON share the same contamination sources as OTA and may coexist in food. ZEN, OTB, and AFB1 have the same carboxyl functional groups as OTA, α-amanitin is a classic peptide toxin. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, although the concentration of each non-specific target was 10 times than that of OTA, the signal fluctuations caused by them were negligible. None of the compounds could cause significant ∆A652 signal values and color changes like the OTA experimental group. This fully demonstrates that the detection system will not produce false positives due to similar structures, groups, and contamination sources to OTA. Simultaneously, based on sensitivity analysis regarding OTA's response signal strength, it becomes evident that this constructed detection platform exhibits exceptional selectivity and specificity towards detection applications.\u003c/p\u003e\u003cp\u003eTo study the repeatability of the sensor, a sensor was constructed at different times within three days. The concentrations of the three target measurements were 0.96, 0.97, and 1.01 \u0026micro;g/kg respectively. The RSD was calculated to be 2.6%, demonstrating that the developed sensor has good reproducibility.\u003c/p\u003e\u003cp\u003eTo study the storage stability of the sensor and the stability of the MBs-OTA conjugated material during transportation, the prepared sensor was stored at 4\u0026deg;C in a refrigerator and at room temperature for a long time. The related materials were placed in the trunk of an electric vehicle, and the visualization detection of 1 \u0026micro;g/mL OTA was conducted at different days. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF. After being stored at 4\u0026deg;C for one month, the detection performance of the sensor remained basically unchanged. At room temperature, the signal value slightly decreased on the 14th d. The related materials in the trunk of the electric vehicle were also negatively affected after two weeks, and the results were similar to those at room temperature, indicating that long-term exposure to room temperature would affect the material activity, while the impact of jolting during transportation on the stability of the MBs-OTA conjugated material was relatively small. Therefore, the detection system should not be stored at room temperature for more than two weeks, and it is best to choose low-temperature transportation for long-distance transportation. It can be stored in a 4\u0026deg;C refrigerator for a long time in the dark.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Detection of OTA in real sample\u003c/h2\u003e\u003cp\u003eIn the actual sample detection, the ability to resist the interference of complex matrices and accurately determine is crucial for the success of the sensor. To further evaluate the practical applicability and accuracy of this detection platform for real sample analysis, selected samples (wheat flour and red wine) were added with different OTA doses (0, 0.1, 2, and 10 \u0026micro;g/kg), analyzed and calculated the recovery rate using two sensors. Subsequently, the performance of the aptamer sensor was compared with the traditional ELISA method. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Using the constructed aptamer sensor, the peak recovery rate of wheat flour was between 99.77% and 106.3%, and the peak recovery rate of red wine was between 98.3% and 101.4%, indicating good tolerance to sample matrices. The maximum relative standard deviation (RSD%, n\u0026thinsp;=\u0026thinsp;3) of all samples was 3.68%. The ELISA kit results confirmed that the results of the two detections were highly consistent. In addition, the ELISA method did not detect the peak concentration of OTA below 2 \u0026micro;g/kg, which may have exceeded its detection limit. These results indicate that the developed sensor has better sensitivity and a wider detection range than ELISA, with the minimum detectable concentration being several orders of magnitude lower.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of OTA analysis in maize and wheat samples with the FP sensor and the ELISA kit.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAddition amount (\u0026micro;g/kg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDetection value (TdT) (\u0026micro;g/kg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRecycling rate (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRSD(%) n\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDetection value (ELISA) (\u0026micro;g/kg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRecycling rate (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRSD(%) n\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eMaize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2. 15%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.26%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.91%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e99.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.51%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.83%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e99.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.47%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3. 14%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.65%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWheat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.86%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e98.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.68%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.1 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.57%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e99.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.42%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe actual sample detection ability of the prepared biosensor and its matrix effect on real samples were further verified by a double-blind method. Four different actual samples (corn, wheat, red wine, and beer) were added with OTA standards of different concentrations to simulate the performance of OTA in products. The samples were not labeled during the addition process, and the standards were not added in the order of concentration gradients to achieve a double-blind effect. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (-: indicates undetectable, +: indicates detectable). Only three samples were undetectable, and the sensitivity was higher than that of the ELISA kits. Compared with the ELISA method, the detection results of the two methods were consistent, indicating that the matrix effect of this method in the detection of actual samples was low, and it had satisfactory feasibility and reliability. For the detection of OTA in grain products, this method has the advantages of high sensitivity, strong specificity, and visualization, which is helpful to fully verify its practical significance and value.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDouble-blind detection of actual samples.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"17\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMethod\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"15\" nameend=\"c17\" namest=\"c3\"\u003e\u003cp\u003eConcentration of sample (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0. 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMaize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThis work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElisa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWheat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThis work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElisa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBeer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThis work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElisa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRed Wine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThis work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElisa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eOTA is widely present in various products such as grains, beans, coffee, and wine, posing an increasing risk to human health [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Therefore, there is an urgent need to develop new detection methods that are highly sensitive, selective, and easy to operate. Based on the long-chain aptamer of OTA, its secondary structure was predicted by Mfold, and a short aptamer Apt-8 with 44 nt was screened out by gradually truncating the stem-loop. The affinity of Apt-8 for OTA was twice as high as that of the long-chain aptamer. The molecular mechanism study revealed the key binding sites and explored the induced fit and conformational changes during the recognition process of OTA and Apt-8. Meanwhile, an innovative competitive colorimetric biosensing detection method for OTA was established. To improve the efficiency and sensitivity of the sensor, four key parameters, including the amount of MBs, aptamer concentration, binding incubation time, and TdT amplification time, were optimized. Under the optimal conditions, this platform demonstrated excellent analytical performance, with a good linear relationship between the colorimetric signal and the OTA concentration. The detection limit was 0.026 ng/mL, which was significantly lower than the national standard value and several orders of magnitude higher. The TdT amplification does not require strict temperature control or complex experimental design, making this method easy to perform and cost-effective, while achieving high sensitivity detection. In addition, the combination of TdT-assisted DNA extension and colorimetric strategy can be applied in non-laboratory environments, facilitating the development of on-site detection. Through comparison with commercial ELISA kits, this method was verified to have high specificity. OTA was covalently assembled on the surface of MBs, improving structural stability and laying the foundation for commercial application.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e\u003cp\u003eNo potential conflicts of interest were reported by the authors.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (NSFC Grant No. 32460249).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Q.H.; methodology, J.Z. and Z.F.; validation, J.Z.; investigation, J.Z. and Z.F.; data curation, J.Z.; Software, Z.F.; writing\u0026mdash;original draft preparation, J.Z.; writing\u0026mdash;review and editing, Q.H.; vis-ualization, Z.F.; supervision, Q.H., Y.S. and J.Z.; project administration, Q.H.; funding acquisition, Q.H. All authors have read and agreed to this version of the manuscript as final.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe data related to this study can be obtained from the authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLinzhi L, Xiaofeng W, Jian C et al (2023) A novel electrochemiluminescence immunosensor based on resonance energy transfer between g-CN and NU-1000(Zr) for ultrasensitive detection of ochratoxin a in coffee. 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Foods. https://doi.org/10(10).10.3390/foods10102429\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microchimica-acta","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"miac","sideBox":"Learn more about [Microchimica Acta](https://link.springer.com/journal/604)","snPcode":"604","submissionUrl":"https://submission.springernature.com/new-submission/604/3","title":"Microchimica Acta","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"OTA detection, Nucleic acid aptamer, Truncation optimization, Molecular recognition, Rapid detection","lastPublishedDoi":"10.21203/rs.3.rs-7902970/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7902970/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOchratoxin A (OTA) is widely present in various products such as grains, legumes and their products, coffee, wine, grape juice and dried fruits. It is highly carcinogenic and pathogenic and is classified as a Group 2B human carcinogen, posing a significant threat to human health. Therefore, the development of rapid, accurate and easy-to-operate new detection methods is particularly important. In this study, a long-chain aptamer (Apt) was truncated and optimized to obtain a short aptamer Apt-8 with significantly improved affinity and specificity. Further, microscale thermophoresis (MST), isothermal titration calorimetry (ITC), circular dichroism (CD), molecular dynamics simulation (MD) and molecular docking techniques were used to systematically analyze the binding affinity, heat changes during the binding process, conformational changes, binding mode, driving energy and key binding sites of Apt-8 and OTA, providing a solid structural basis for sensor design. In addition, a straightforward and efficient method for the detection of OTA has been developed by integrating terminal deoxynucleotidyl transferase (TdT) with aptamer-based colorimetry. The sensor showed a good linear relationship with the concentration of OTA, with a detection limit as low as 0.026 ng/mL and a spiked recovery rate of 98.33% to 106.3%, indicating high accuracy of the method. This detection method is simple to operate, rapid and efficient, with high sensitivity, strong stability and good repeatability, and is suitable for rapid visual detection of OTA, showing great potential in on-site point-of-care testing.\u003c/p\u003e","manuscriptTitle":"Investigation the Binding Mechanism of Aptamers to Ochratoxin A and Development of Competitive Colorimetric Sensing Platforms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 17:33:50","doi":"10.21203/rs.3.rs-7902970/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-12T00:58:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T03:29:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311659707413807736112968813548365502394","date":"2025-11-03T01:39:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43518010579514423258636665926544808116","date":"2025-11-02T02:44:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-30T07:41:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121677058045592649258467730655456906693","date":"2025-10-24T00:42:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9133739756321178148599847305184568201","date":"2025-10-23T07:49:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-22T00:32:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-21T11:58:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-21T09:48:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microchimica Acta","date":"2025-10-20T07:06:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microchimica-acta","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"miac","sideBox":"Learn more about [Microchimica Acta](https://link.springer.com/journal/604)","snPcode":"604","submissionUrl":"https://submission.springernature.com/new-submission/604/3","title":"Microchimica Acta","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"30f00d1d-da1c-4b83-9da1-52d57d05ad23","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T16:05:04+00:00","versionOfRecord":{"articleIdentity":"rs-7902970","link":"https://doi.org/10.1007/s00604-025-07775-w","journal":{"identity":"microchimica-acta","isVorOnly":false,"title":"Microchimica Acta"},"publishedOn":"2025-12-18 15:57:55","publishedOnDateReadable":"December 18th, 2025"},"versionCreatedAt":"2025-11-03 17:33:50","video":"","vorDoi":"10.1007/s00604-025-07775-w","vorDoiUrl":"https://doi.org/10.1007/s00604-025-07775-w","workflowStages":[]},"version":"v1","identity":"rs-7902970","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7902970","identity":"rs-7902970","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}