Aptamer-Conjugated Liposome System for Targeting MUC1-Positive Cancer

doi:10.21203/rs.3.rs-5015227/v1

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Research Article

Aptamer-Conjugated Liposome System for Targeting MUC1-Positive Cancer

https://doi.org/10.21203/rs.3.rs-5015227/v1

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This study aims to overcome the adverse effects of conventional cytotoxic chemotherapy on healthy organs by developing a target-specific novel approach utilizing doxorubicin (DOX)-encapsulated liposomes conjugated with the S1.6 aptamer, known for its high binding affinity to the overexpressed Mucin-1 (MUC1) protein in various cancer types. The S1.6 aptamer is selected as a targeting ligand by comparing the thermodynamic stability, docking score, confidence score, and binding affinity with other MUC1 targeting aptamers in silico. Nano-drop, Gel electrophoresis, and Dynamic Light Scattering (DLS) confirm the size, zeta potential, DOX encapsulation rate, stability, and aptamer conjugation of liposomes. Flow cytometry results validate MUC1 expression in MCF7 cells while not in MDA-MB-231 cells. Confocal Microscopy further demonstrates the cellular uptake of the lipo-apt complex. Taken together, our results of this approach hold promise as a potential strategy to mitigate the side effects of conventional chemotherapy by enhancing the specificity of drug delivery to cancer cells through aptamer-mediated liposomal encapsulation of chemotherapeutic agents. Further studies are warranted to evaluate the therapeutic efficacy of this approach in vivo and its potential clinical applications in cancer treatment.

Chemotherapy

Liposome

Doxorubicin

Aptamer

MUC1 protein

Cancer cells

Nano-particle-based drug delivery systems have garnered significant attention in cancer research as a promising approach to enhance the effectiveness of cancer treatment [1]. This approach can minimize the adverse effects of traditional non-target-specific cytotoxic chemotherapeutic agents on normal organs. Among the available nanocarrier systems, liposomes have emerged as highly promising platforms for controlled and targeted drug delivery to cancer cells due to their customizable characteristics, such as size, surface charge, and lipid composition, allowing for tailoring and optimization to meet specific treatment requirements [2–5].

Liposomes are vesicles composed of phospholipids, featuring a bilayer membrane structure similar to biological membranes as well as it is easy to synthesize, time efficient, and cost-effective [2, 4]. They hold significant potential for improving the therapeutic effectiveness of anticancer agents by increasing drug concentrations within tumor cells while minimizing exposure to normal tissues [2, 6–8]. Liposomes are enabled with desired attributes for improved drug encapsulation, stability, and release kinetics. Additionally, liposomes exhibit biocompatibility and provide an ideal surface for attaching targeting ligands like antibodies, peptides, and aptamers [9]. Notably, liposomal formulations have already received clinical approval for various anticancer drugs [10].

However, in 2016, Petersen et al. provided evidence that conventional PEGylated liposomes carrying chemotherapeutic agents showed only limited improvement in clinical effectiveness compared to chemotherapeutics administered without a delivery vehicle [11]. Furthermore, the potential benefits of carrier-mediated chemotherapy, such as the enhanced permeability and retention (EPR) effect and the tumor microenvironment, have not translated into enhanced clinical efficacy. Challenges such as determining the optimal specificity for carrier-mediated agents and the lack of standardization in their implementation have hindered their success in clinical studies [11]. To address this limitation, researchers have developed a technique that involves attaching targeting ligands to the surface of liposomal vehicles. This approach, named targeted drug delivery system, aims to improve the specificity of drug delivery to cancer cells.

In addition, among all those targeting ligands, aptamers have recently emerged as a novel and promising tool for targeted drug delivery applications because of their desirable characteristics such as high target specificity, ease of synthesis, thermodynamic stability, and non-immunogenicity [12, 13]. The production process of aptamers occurs outside of biological systems, minimizing the risk of viral or bacterial contamination [14, 15]. Unlike antibodies, aptamers lack the Fc region, eliminating the potential for immune system stimulation upon systemic administration. Moreover, aptamers demonstrate superior tumor penetration, retention, and homogeneous distribution compared to antibodies and peptides [15, 16]. Their smaller size facilitates enhanced tumor penetration, overcoming a challenge faced by antibodies and peptides that often require size optimization [17]. Furthermore, attaching aptamers to nanoparticle surfaces is more amenable and reproducible compared to antibody attachment [7, 18]. Consequently, in the field of oncology, aptamers offer distinct advantages over antibodies and peptides.

As a targeting receptor, Mucin-1 (MUC1), a transmembrane glycoprotein, is frequently overexpressed in several epithelial adenocarcinomas, making it a prominent biomarker in cancer research [19–23]. The prevalence of overexpression is particularly noteworthy, with approximately 65% of newly diagnosed tumors in the United States displaying elevated levels of MUC1 [24]. It is highly expressed in lung, liver, colon, breast, pancreatic, and ovarian cancers [25]. Such a high occurrence makes it an attractive and viable target for tumor antigen-directed approaches in cancer diagnosis, treatment, and management [22]. As an essential oncogene, it plays a significant role in regulating numerous cancer-related processes, including cell growth, proliferation, metastasis, apoptosis, and developmental pathways [22, 23, 25]. The upregulation of MUC1 in cancerous tissues is a remarkable feature, as its expression levels can be 10–40 times higher compared to normal tissues [22]. Furthermore, MUC1 exhibits distinct glycosylation patterns, with neoplastic tissues showing under-glycosylation while healthy cells display high glycosylation [26, 27]. This disparity in glycosylation profiles provides an opportunity to differentiate cancerous cells from normal cells, presenting potential avenues for targeted drug delivery strategies [26, 27].

Numerous cancer-targeting aptamer-conjugated liposomes have already been developed by modifying them with aptamers that exhibit specific interactions with highly expressed MUC1 in cancer cells [28–30]. This “Aptabase: An aptamer database” provided us with a list of five aptamers, namely 5TR1, MA3, S1.6, S2.2, and STRG2, all of which have been previously reported to target the MUC1 protein. Moosavian et al. in 2019 investigated the therapeutic effectiveness of a 5TR1 aptamer-conjugated liposomal doxorubicin(DOX) delivery system targeted to MUC1, showing enhanced tumor accumulation and improved survival in mice [31]. Furthermore, Kim et al. developed dual-aptamer-conjugated liposomes for targeted delivery of DOX to breast cancer cells and cancer stem cells, resulting in increased cytotoxicity and inhibition of metastasis in 2019 [32]. Similarly, the MA3 aptamer has also been investigated to target MUC1 in previous works. These include the use of self-assembled DNA-protein hybrid nanospheres as biocompatible Nano-drug carriers [33] and the enhancement of thermal damage by iron nanoparticles [34]. The S2.2 aptamer, on the other hand, has been extensively studied and utilized for targeting MUC1. Mohapatra et al. developed S2.2 aptamer-conjugated liposomes, demonstrating synergistic anticancer effects and inhibition of tumor growth in 2017 [35]. It has been also used in various approaches, such as using silica nanoparticles to overcome Navitoclax resistance [36], targeted SPION siderophore conjugate loaded with DOX [37], and delivery of epirubicin using ferritin nanoparticles [38]. Additionally, in-silico studies conducted by Santini et al. and Rhinehardt et al. have shown promising results for the S2.2 aptamer [39, 40]. These studies highlight the versatility and potential applications of the MUC1-targeted aptamers in drug delivery and therapeutic interventions. The primary objective of our study was to select an aptamer through a comprehensive comparative analysis. Subsequently, we focused on developing liposomal systems conjugated with the selected aptamer and evaluating their cellular uptake. This approach allowed us to investigate the potential of the aptamer-conjugated liposomes for targeted drug delivery to MUC1-expressing cells, which is significant for enhancing the specificity and efficacy of cancer treatment.

Aptamer Modeling

To perform the comparative analysis among the MUC1 targeted aptamers, we utilized the “UNAFold” web server to generate the 2D of the aptamers which represents the Gibbs free energy change associated with the folding and hybridization of nucleic acid sequences [41]. During the 2D structure prediction, the targeted aptamers, namely S1.6, 5TR1, MA3, S2.2, and STRG2, provided the dG score of -2.27, -2.00, -8.62, -1.57, and − 2.67, respectively (Table 1). A lower dG score suggests a more stable structure or stronger hybridization between nucleic acid strands [41]. Figure 1A–E represent the 2D structures of the aptamers. In addition, Fig. 1F–J depict the first predicted 3D model for each aptamer obtained from the Xiao Lab web server [42, 43]. These 3D models were minimized and refined by Maestro 12.5 software for molecular docking.

Molecular Docking

In the molecular docking process to screen the aptamers against the MUC1 protein, we utilized the HDOCK SERVER, renowned for its proficiency in DNA-protein molecular docking. This approach provided the 3D model data visualized in Fig. 1K–O, illustrating the docking simulations between various aptamers and MUC1. Also, the HDOCK SERVER provided docking scores and confidence scores for the DNA-protein complexes [44]. As the lowest docking score means better interaction, examining Table 1, it is evident that the docking score of S1.6 is better than that of MA3, S2.2, and STRG2 while being slightly lower than 5TR1. These scores provide insight into the binding affinity and strength between the aptamers and the protein.

In addition to docking scores, confidence scores were also obtained to estimate the reliability of the predicted complexes. Higher confidence scores indicate a higher level of confidence in the predicted interactions. The maximum confidence score is 1.00, and in our case, 5TR1 showed the best score which is 0.9492 and the second best is S1.6 exhibited a confidence score of 0.9390.

Table 1

Aptamer sequences and scoring metrics
Aptamer Name	Aptamer Sequence 5’ to 3’	dG score	Docking Score	Confidence Score	Affinity (nM)
5TR1	GGGAGACAAGAATAAACGCTCAAG AAGTGAAAATGACAGAACACAACAT TCGACAGGAGGCTCACAACAGGC	-2.00	-296.36	0.9492	47.3
MA3	AACCGCCCAAATCCCTAAGAGTCG GACTGCAACCTATGCTATCGTTGAT GTCTGTCCAAGCAACACAGACACA CTACACACGCACA	-8.62	-279.99	0.9308	38.3
S 1.6	GGGAGACAAGAATAAACGCTCAAG CAACAGGGTATCCAAAGGATCAAAT TCGACAGGAGGCTCACAACAGGC	-2.27	-286.72	0.9390	0.2131
S 2.2	GCAGTTGATCCTTTGGATACCCTGG	-1.57	-251.94	0.8848	0.135
STRG2	GAGACAAGAATAAACGCTCAAGGCT ATAGCACATGGGTAAAACGACTTCG ACAGGAGGCTCACAACAGGC	-2.67	-279.06	0.9296	18.6

This table displays the following information: the names of the aptamers, their dG scores of 2D modeling, as well as the binding affinity, docking score, and confidence score of the lipo-aptamer and MUC1 interaction.

Aptamer Selection

A comprehensive analysis of various parameters was conducted including docking scores, confidence scores, binding affinity, and dG scores, to compare different aptamers and visualized through radar charts (Figure S1, 2). Despite our study showing only a slightly superior outcome of 5TR1 in terms of docking and confidence scores, S1.6 demonstrated significantly better binding affinity and dG score. Notably, while 5TR1 exhibited a binding affinity of 47.3 nM, S1.6 displayed a substantially lower value of 0.2131. This considerable disparity emphasizes the remarkable superiority of S1.6 over 5TR1.

Although MA3 exhibits a better dG score in our study, it falls short in other parameters such as docking score, confidence score, and binding affinity when compared to S1.6. As a result, S1.6 emerged as a more favorable choice over MA3.

During our study, S2.2 exhibited lower scores in all parameters except for binding affinity. Additionally, the difference between S2.2 and S1.6 in terms of binding affinity is only 0.078, which can be considered negligible. Therefore, once again, S1.6 proves to be the superior choice over S2.2.

Both S1.6 and STRG2 lack prior research, making them suitable candidates for further study (Figure S3). Furthermore, considering the binding affinity, docking, and confidence scores, S1.6 outperforms STRG2. Considering all the parameters assessed, it can be concluded that S1.6 is the most suitable aptamer among the options considered.

Liposome Preparation

Figure 2A illustrates the schematic method for preparing an aptamer-conjugated liposome encapsulated with doxorubicin(DOX). To synthesize the liposomes, we focused on three key factors: size, PDI (Poly Dispersity Index), and DOX encapsulation. We varied the molar ratios of DPPC (1,2-dipalmitoyl-sn-glycero-3-phosphocholine), cholesterol (Chol) and DSPE-PEG-Mal (N-[(3-Maleimide-1-oxopropyl)aminopropyl polyethyleneglycol-carbamyl] distearoylphosphatidyl-ethanolamine) to determine the optimal combination. In our approach, we added 0.5 mg of DOX to the solvent and measured its absorption, which yielded a value of 0.595.

Table 2 presents the absorption values of several DOX-encapsulated liposomes along with their encapsulation rates. After purification using a syringe filter and a PD-10 column, we identified the formulation with the highest absorption value of 0.455, corresponding to a DOX amount of 0.412 mg within the liposomes as shown in Fig. 2B. We measured the DOX concentration using a Nanodrop spectrophotometer at a wavelength of 480 nm. Importantly, we achieved a high encapsulation rate of 73.39% with this liposome formulation, making it suitable for further experiments.

The size analysis revealed that our DOX-encapsulated liposomes had an average size of approximately 84.86 d.nm, meeting our target size of less than 100 d.nm. We also confirmed the appropriate synthesis of the liposomes through transmission electron microscopy (TEM) imaging as shown in Fig. 2C.

In summary, based on the absorption, encapsulation rate, and size analysis, we synthesized liposomes that effectively encapsulate DOX for our further experiments.

Table 2

Characteristics of liposomes based on lipid molar ratio.
Molar Ratio	PBS (ml)	Abs (Before)	Abs (After)	Encapsulation rate	Size (d.nm)	PDI
15:9:1.0	2	0.612	0.060	9.80%	47.79	0.164
15:9:1.5	2	0.595	0.086	14.45%	50.73	0.147
12:8:1.0	2	0.623	0.046	7.38%	51.60	0.161
12:8:1.5	2	0.602	0.111	18.44%	51.98	0.163
10:6:1.0	2	0.586	0.208	35.49%	75.05	0.106
10:6:1.5	2	0.620	0.455	73.39%	84.86	0.166

The table provides information on the molar ratios utilized for liposome synthesis. It also includes the absorption scores before and after purification of doxorubicin (DOX) encapsulation by liposome. Additionally, the table presents the encapsulation rate of DOX, the size of the liposomes, and their PDI (Poly Dispersity Index) scores.

Aptamer Conjugation

The efficiency of aptamer conjugation was determined using Nano drop spectrophotometer, and the results are depicted in Fig. 2D. Before purification, the concentration of nucleic acid was measured as 152.5 ng/µl, while after purification, it reduced to 121.3 ng/µl, indicating a conjugation rate of 79.54%. To confirm aptamer conjugation, dynamic light scattering (DLS) measurements were performed to assess changes in size and charge. The graphical representation clearly illustrates the distinction between conjugated and non-conjugated samples. Figure 2E shows the alignment of aptamer-conjugated liposomes, while Fig. 2F displays random and scattered patterns for non-conjugated samples.

Gel electrophoresis was conducted to further validate the conjugation process. Figure 2G presents the results for our five samples, where liposome aptamer non-conjugated (LA-N) and liposome-doxorubicin (DOX) aptamer non-conjugated (LDA-N) showed bands similar to the aptamer alone (S1.6). In contrast, there were no bands observed for the conjugated liposome-aptamer and liposome-DOX-aptamer, indicating the absence of free aptamer molecules.

Zeta potential measurements were performed to assess the surface charge of the liposomes. The values obtained were − 0.26, -7.18, and − 5.39 for liposomes, liposome-aptamer, and liposome-DOX-aptamer, respectively. The negatively charged aptamer and positively charged doxorubicin contribute to the observed differences in zeta potential.

Regarding stability, the liposome-DOX-aptamer complex exhibited consistent size measurements at both 4℃ and 25℃, indicating its stability over time is detailed in Figure S4.

Cell line selection

The expression level of MUC1 was confirmed of using an anti-human MUC1 antibody through flow cytometry. While MCF7 cells are known to be MUC1-positive, previous literature suggested that MDA-MB-231 cells also exhibit MUC1 positivity. Surprisingly, our investigation revealed that MDA-MB-231 cells express MUC1 with a tandem repeat at extremely low levels as shown in Fig. 3F. The MUC1-targeting antibody used in our study, generated using a synthetic peptide representing a region near the beginning of the human MUC1 protein, potentially corresponding to the tandem repeat region, indicated negative MUC1 expression in MDA-MB-231 cells as determined by flow cytometry analysis. Consequently, we selected MDA-MB-231 cells as the negative cell line for our experiment, as our aptamer S1.6 specifically targets the tandem repeat region of the MUC1 extracellular domain.

In our study, we employed a primary antibody, rabbit anti-human MUC1, which can bind to human MUC1. The secondary antibody used was a fluorescence dye-conjugated antibody, specifically Goat anti-rabbit, enabling binding to the rabbit-derived primary antibody. This indirect staining method allowed us to investigate the specificity of the primary antibody for MUC1 to get better results at the binding of the anti-MUC1 antibody. The histogram data from Fig. 3 showcase three categories: "Non-treated" represents cells without any antibody treatment (Fig. 3D,H), "Control" denotes cells treated solely with the secondary antibody (Fig. 3C,G), and "Anti- MUC1 ab treated" signifies cells treated with both the primary and secondary antibodies (Fig. 3B,F). By examining the fluorescence intensity depicted on the X-axis of the graph, a shift towards the right indicates the binding of our fluorescence dye-conjugated antibody to the target antigen on the cells. Notably, the graph shifts in the "Anti- MUC1 ab treated" category is more pronounced compared to the "Control" and "Non-treated" groups, highlighting the specific binding of the primary antibody to MUC1 and the absence of nonspecific binding from the secondary antibody.

Cellular Association Study

The binding capacity of liposome was assessed using confocal microscopy imaging. In the case of cells alone, no doxorubicin (DOX) signal was detected (Fig. 4A4,B4,C4,D4) while only the nucleus was visible (Fig. 4A7,B7,C7,D7). With lipo-dox, a reduced presence of DOX was observed compared to lipo-dox-apt in both the positive and negative cell lines at both time points (Fig. 4A5,B5,C5,D5). Additionally, the presence of lipo-dox in both the positive and negative cell lines was similar at both time frames (Fig. 4A8,B8,C8,D8), indicating that cells uptake liposomes consistently over time.

However, a noticeable difference was observed between the positive and negative cell lines when treated with lipo-dox-apt at both time points (Fig. 4A6,B6,C6,D6). Furthermore, significant disparities were observed between lipo-dox and lipo-dox-apt (Fig. 4A8,A9,B8,B9). The absorption of liposomes increased over time, as indicated by the divergence in absorption rates between the 1-hour and 3-hour time points in the positive cell line for lipo-dox-apt. This emphasizes the enhanced binding affinity of liposomes mediated by the S1.6 aptamer compared to liposomes alone.

Based on these results, it can be concluded that the S1.6 aptamer is an effective ligand for specifically targeting MUC1, as demonstrated by the distinct binding patterns observed between the positive and negative cell lines.

In conclusion, our study presents a promising strategy for improving the effectiveness of drug delivery while reducing its detrimental side effects. By utilizing the S1.6 aptamer as a targeting ligand and encapsulating doxorubicin (DOX) within liposomes, we achieved specific and efficient targeting of cancer cells that overexpress the MUC1 protein. The characterization of the liposome confirmed its size, stability, and appropriate conjugation of the aptamer.

Through in vitro experiments using MCF7 and MDA-MB-453 cells, we demonstrated the ability of our approach to selectively target MUC1-expressing cancer cells, providing evidence for the potential clinical utility of this technique. By enhancing the specificity of drug delivery to cancer cells, our approach holds promise in mitigating the non-specific toxicity associated with traditional chemotherapy.

While our results are encouraging, further studies are warranted to evaluate the therapeutic efficacy of this approach in vivo. Animal models can provide valuable insights into the biodistribution, pharmacokinetics, and overall therapeutic response of the aptamer-mediated liposomal encapsulation of chemotherapeutic agents. Additionally, comprehensive investigations should be conducted to assess the long-term effects and potential side effects of this novel approach.

With continued research and refinement, it is possible to overcome the limitations of conventional chemotherapy and significantly improve patient outcomes and quality of life. The development of innovative strategies like aptamer-mediated liposomal encapsulation represents a crucial step toward personalized and more effective cancer treatment.

Materials

DPPC (1,2-dipalmitoyl-sn-glycero-3-phosphocholine) was obtained from Avanti Polar Lipids, located in Alabama, USA. Cholesterol (Chol), tris(2-carboxyethyl)phosphine hydrochloride (TCEP•HCl), Doxorubicin, and chloroform were purchased from Sigma-Aldrich, based in Seoul, Korea. DSPE-PEG-Mal (N-[(3-Maleimide-1-oxopropyl)aminopropyl polyethyleneglycol-carbamyl] distearoylphosphatidyl-ethanolamine) was obtained from Creative PEGWorks, situated in North Carolina, USA. The DNA aptamer (S1.6-SS) was purchased from Humanizing Genomics Macrogen, located in Seoul, Korea. MCF7 and MDA-MB-231 cell lines, along with the necessary supplies for the in vitro assay, were obtained from Korean Cell Line Bank located in Jong-no Gu, Seoul. The MUC1 (MUC1 (D9O8K) XP® Rabbit mAb #14161) antibody was purchased from Cell Signaling Technology, based in Massachusetts, USA. NanoDropTM 2000/2000c Spectrophotometer (Thermo ScientificTM; ND-2000), Zetasizer Nano ZS (Malvern), flow cytometry (Guava® easyCyte™ 5, Luminex), and Confocal Microscope Nikon ECLIPSE Ti2 (Tokyo, Japan) were used in our experiment.

Methodology

Three-dimensional (3D) structural modeling of Aptamers

To identify aptamers targeting the MUC1 protein and their binding affinity, we utilized the "Aptabase (www.iitg.ac.in/proj/aptabase)" as a resource Table-1. Binding affinity serves to evaluate and rank the strength of interactions between two molecules, which is expressed by the equilibrium dissociation constant (KD). A smaller KD value indicates a higher binding affinity, signifying a stronger attraction and interaction between the ligand and its target. Initially, we employed the UNAFold Web Server to predict the two-dimensional (2D) structure of the aptamers [41]. This web server utilizes thermodynamic calculations based on nearest-neighbor energy parameters to forecast the 2D structure of DNA. By analyzing the input DNA sequence, it determines stable secondary structures through considerations of base pairing interactions and the minimization of free energy within the system. We set the DNA sequence as linear and oligomer, the temperature at 37°C, and left other thresholds as default.

Subsequently, we utilized the Xiao Lab web server to design the three-dimensional (3D) models of our aptamers [42, 43]. This web server applies common approaches in DNA structure prediction, including molecular dynamics simulations and constraint-based modeling. These techniques leverage experimental data to guide the prediction process. Specifically, we selected DNA as the molecular type and chose "optimized" as the procedure option. For minimizing and refining the 3D structure of the aptamers, we employed the Maestro 12.5 software, developed by Schrödinger. Following this step, the models were prepared and ready for further analysis and investigation.

Screening the aptamers by Molecular Docking

For the molecular docking between the aptamers and MUC1 protein, we employed the HDOCK server, which is well-known for its capability in DNA-protein molecular docking [44]. To initiate the process, we retrieved the three-dimensional (3D) structure of the MUC1 protein from the RCSB Protein Data Bank website. The specific Protein Data Bank (PDB) ID we used, was 7V64, which represented the protein in complex with an antibody. To prepare the MUC1 protein for molecular docking, we utilized the Maestro 12.5 software, developed by Schrödinger. With the software, we isolated the protein from the complex and added hydrogen molecules to optimize its structure.

The HDOCK server is known for generating multiple docking poses based on shape complementarity and electrostatic interactions. The algorithm of the server produced numerous docking processes, which were then evaluated using a scoring function to determine their compatibility and quality. The scoring function considered various factors such as shape complementarity, protein-DNA interface interactions, and energy terms [44].

On the server webpage, we uploaded the 3D structure of the MUC1 protein as the receptor molecule and the aptamers as the ligand molecule for the docking. The docking performed was template-free, meaning it did not rely on a predefined template. The HDOCK server provided us with 100 predicted docking results, out of which we analyzed the top 10 models. Through comparison with other aptamers, we confirmed that our S1.6 aptamer emerged as a promising candidate for use as a target-specific ligand.

Liposome Preparation

The liposomes utilized in this study were prepared using the film hydration method. To create plain liposomes, a mixture consisting of DPPC (1,2-dipalmitoyl-sn-glycero-3-phosphocholine), cholesterol(Chol), and DSPE-PEG-Mal in a molar ratio of 10:6:1.5 was dissolved in a mixture of chloroform and methanol in a 2:1 volume-to-volume ratio [2, 45]. We employed several molar ratios to determine the optimal encapsulation of doxorubicin, as indicated in Table 2.

Subsequently, the solvent was evaporated using a stream of nitrogen gas, and the resulting lipid film was placed in a high vacuum overnight to remove any remaining organic solvent. To hydrate the lipid film, we used a phosphate-buffered saline (PBS) solution along with doxorubicin. Tip sonication was employed at room temperature to facilitate the hydration process [46]. Any non-encapsulated doxorubicin and unwanted residues were eliminated by passing the liposome suspension through a syringe filter with a pore size of 0.20 µm (DISMIC; 25 CS, ADVANTEC). Additionally, a PD-10 column (SephadexTM G-25 M, Cytiva) with a bed volume of approximately 2.5 mL was utilized for further purification [6].

To determine the encapsulation rate of doxorubicin, we employed a Nanodrop spectrophotometer (ScientificTM; ND-2000), which is a specialized instrument capable of measuring the absorbance and concentration of various biomolecules, including nucleic acids and proteins. It emits a broad-spectrum light beam that passes through the sample, and the transmitted light intensity is measured.

Following purification, the final concentration of doxorubicin was determined using the Nanodrop spectrophotometer. By dividing the final concentration by the initial concentration and multiplying by 100, we obtained the percentage of encapsulation for doxorubicin.

Conjugation of S1.6 to Liposomes

Aptamers are highly specific and exhibit a strong affinity for their molecular targets, making them valuable in active targeting and personalized medicine to minimize off-target effects. Additionally, disulfide bonds present in aptamers are prone to oxidative dimerization, forming disulfide bonds that impede their reaction with maleimides. To overcome this challenge, pre-reduction of disulfide bonds and exclusion of oxygen is necessary. In our study, we utilized TCEP (tris-carboxyethylphosphine) reagent for disulfide reduction [47, 48].

Initially, we performed the folding process of the aptamer to enable it to adopt a specific three-dimensional structure. It was mixed with a buffer solution containing 0.2 M NaHCO3 and 0.5 M NaCl at a 2X concentration, in a 1:1 molar ratio. The mixture was heated to 95°C for 10 minutes to denature the aptamer and then cooled down for 15 minutes at room temperature [48, 49].

For conjugating the S1.6-SS aptamer to the surface of liposomes, a method adapted from Li et al. was employed [50]. The liposomes, which had DSPE-PEG-Mal(N-[(3-Maleimide-1-oxopropyl)aminopropyl polyethyleneglycol-carbamyl] distearoylphosphatidyl-ethanolamine) on their surface, were incubated with S1.6-SS at a ratio of 5:1 for 1 hour. The reaction took place in the presence of TCEP•HCl (Tris(2-carboxyethyl)phosphine hydrochloride) at a pH range of 6–8, and the reaction was conducted in the dark at room temperature with moderate stirring. TCEP•HCl served as a reducing agent to facilitate the reductive reaction between the disulfide bonds of the aptamers and the maleimide groups on the surface of the liposomes [48, 50].

Characterization

The efficiency of conjugation between S1.6 and liposomes was determined by measuring the aptamer concentration before and after washing using the NanoDrop spectrophotometer. To wash the conjugated samples, a 30 kDa centrifuge filter tube was used, and the washing process was performed at 4000 rpm for four cycles. Additionally, dynamic light scattering (DLS) data was utilized to confirm the conjugation. DLS involves directing a laser beam at the sample and analyzing the scattered light to extract information about the Brownian motion of the particles, which is related to their size. By analyzing the Intensity Particle Size Distribution (PSD), Volume PSD, and Number PSD of the DNA-only, liposome-only, DNA-Liposome nonconjugated, and DNA-Liposome conjugated samples, the proper conjugation was confirmed.

Furthermore, electrophoretic mobility measurements were conducted using DLS to determine the velocity at which the particles move under an electric field. This information provided insights into the charge of the particles. The difference in zeta potential before and after conjugation also served as an indicator of conjugation.

To evaluate the stability of the aptamer-liposome complex, data was collected at various time intervals (0, 1, 3, 5, and 7 days) under storage conditions of 4°C and 25°C. This assessment aimed to understand the changes in the particle size, PDI (Poly Dispersity Index), and zeta potential over time, which are indicative of the stability and potential aggregation of the complex.

For examining the microstructure and morphology of the liposomal vesicles, transmission electron microscopy (TEM) was employed. TEM samples were prepared by placing a drop of diluted liposome dispersion onto a TEM grid and allowing it to air dry [51]. The images were captured using an accelerating voltage of 120 kV. This energy level was chosen to achieve sufficient electron penetration while minimizing sample damage, ensuring optimal visualization of the liposomes' characteristics.

Gel electrophoresis was conducted using a 3% TBE gel to confirm the lipo-aptamer conjugation [52]. The electrophoresis process was carried out for 80 minutes at 50 V in 1X TBE buffer. The gel's pore size was suitable for separating DNA fragments of different sizes, allowing the visualization and confirmation of the conjugated aptamer with the liposomes. TBE buffer was used to maintain the stability and integrity of the DNA throughout the electrophoresis process.

Analyzing MUC1 Expression

The assessment of MUC1 expression in cancer cell lines is crucial for targeting the cell line. We employed immunofluorescence staining to investigate the presence of MUC1 in MCF7 (MUC1 positive) and MDA-MB-231 (MUC1 negative) cell lines. The experimental procedure involved cell seeding, antibody treatment, washing steps, and fluorescence imaging. We utilized the rabbit anti-human MUC1 antibody as the primary antibody and a fluorescence dye-conjugated goat anti-rabbit antibody as the secondary antibody [22]. To ensure optimal conditions for staining, a 5% BSA (Bovine Serum Albumin) PBS buffer was prepared for cell washing and resuspension. The cells were first washed with DPBS and detached using Trypsin-EDTA, followed by cell counting and centrifugation. Subsequently, the cells were resuspended in 100 µL of 5% BSA PBS buffer. For MUC1 detection, the cells were treated with the primary antibody (0.25 µg/tube) and incubated in dark conditions at 4°C for 30 minutes. After three washes with ice-cold 5% BSA PBS solution, the cells were subjected to the secondary antibody treatment and incubated under the same conditions. Finally, the cells were washed again, and flow cytometry analysis was used to visualize the MUC1 expression pattern. This study provides valuable insights into the MUC1 status in MCF7 and MDA-MB-231 cell lines.

Receptor-Mediated Cellular Uptake

The binding specificity of lipo-dox-apt in the MCF7 MUC1 positive cell line and the MDA-MB-231 negative cell line was evaluated using a confocal microscope. Three samples were analyzed: Cells only, lipo-dox, and lipo-dox-apt, to identify any differences. Images were captured at 1-hour and 3-hour intervals.

MCF7 cells were cultured in the DMEM medium, while MDA-MB-231 cells were cultured in the RPMI medium. Both media were supplemented with 10% fetal bovine serum and 1% antibody. The cells were maintained in a humidified incubation chamber at 37°C and pH 7.4 with 5% CO2.

For the analysis of receptor-mediated cellular uptake of the liposomes, confocal imaging was employed using doxorubicin as a fluorescent compound. MCF7 and MDA-MB-231 cells were seeded onto confocal dishes at a density of 1 × 10⁵ cells and incubated at 37°C. After 24 hours, the cells were washed with PBS, and 2 mL of media was added. Subsequently, the cells were treated with lipo-dox and lipo-dox-apt (100 µL each) and further incubated at 37°C. Live cell images and fixed cell images were captured at 1-hour and 3-hour time intervals. To visualize the cell nuclei, Hoechst dye was used for nuclear staining. For fixed cells, the cells were washed with PBS and fixed with 4% paraformaldehyde (w/v) diluted in PBS at room temperature. In the case of live cells, after washing, each confocal dish was filled with 2 mL of media. Confocal laser scanning microscopy was utilized to observe the nuclear location and cellular uptake of doxorubicin [45].

ASSOCIATED CONTENT

DOX

Doxorubicin

MUC1

Mucin 1

DLS

Dynamic Light Scattering

PDI

Poly Dispersity Index

TEM

Transmission Electron Microscope

PDB

Protein Data Bank

PBS

Phosphate-buffered saline

DPPC

1,2-dipalmitoyl-sn-glycerol-3-phosphocholine

Chol

Cholesterol

TCEP.HCL

tris(2-carboxyethyl)phosphine hydrochloride

DSPE-PEG2k

1,2-Distearoyl-sn-glycerol-3-phosphoethanolamine (methoxy(polyethylene glycol)-2000)

DSPE-PEG-Mal

N-[(3-Maleimide-1-oxopropyl)aminopropyl polyethyleneglycol-carbamyl] distearoylphosphatidyl-ethanolamine

EPR

enhanced permeability and retention

Liposome-Doxorubicin

Liposome-Aptamer Conjugated

Lipo-apt

Liposome-Aptamer Conjugated

LA-N

Liposome-Aptamer Non-conjugated

LDA

Liposome-Doxorubicin-Aptamer Conjugated

Lipo-Dox-Apt

Liposome-Doxorubicin-Aptamer Conjugated

LDA-N

Liposome-Doxorubicin-Aptamer Non-conjugated

equilibrium dissociation constant

PSD

Particle Size Distribution

BSA

Bovine Serum Albumin

ASSOCIATED CONTENT

Supporting Information.

Comprehensive details, including information and charts on aptamer screening, a calibration curve for drug quantification, and particle characterization data. (PDF)

AUTHOR INFORMATION

Corresponding Author

* Hyung-Jun Im. E-mail: [email protected] / ORCHID: 0000-0002-4368-6685

Author Contributions

^‡MSAK, JP, and SL contributed equally to this work, proposing the conceptual idea, conducting all studies mentioned in the article, and collectively contributing to the writing of the paper. H.I. designed and directed the research, analyzed the data, and revised the paper. HJ, CO, MK, JS, SGL, JJ, BP, and HNJ participated in laboratory meetings and advised study designs.

Funding

This research was supported by grants funded by the National Research Foundation (NRF) of Korea (NRF-2019M2D2A1A01058210, NRF-2020R1C1C1009000, NRF-2021M2E8A1039564, NRF-2022R1A6A1A03063039 and BK21FOUR Program No.5120200513755), Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea government (MOTIE) (No. 20018522), and Korea Drug Development Fund funded by Ministry of Science and ICT, Ministry of Trade, Industry, and Energy, and Ministry of Health and Welfare (HN22C0632). Schematic illustrations were created with BioRender.

Notes

The authors declare no conflict of interest.

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No competing interests reported.

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You are reading this latest preprint version

Aptamer-Conjugated Liposome System for Targeting MUC1-Positive Cancer

Status:

Version 1

Abstract

Figures

Introduction

Results and Discussion

Aptamer Modeling

Molecular Docking

Aptamer Selection

Liposome Preparation

Aptamer Conjugation

Cell line selection

Cellular Association Study

Conclusion

Experiential Section

Materials

Methodology

Three-dimensional (3D) structural modeling of Aptamers

Screening the aptamers by Molecular Docking

Liposome Preparation

Conjugation of S1.6 to Liposomes

Characterization

Analyzing MUC1 Expression

Receptor-Mediated Cellular Uptake

ASSOCIATED CONTENT

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1