Overview Our project aims to establish a real-time breast cancer surveillance system based on autologous adipocyte transplantation. By delivering sensor elements of RNA sensing using adenosine deaminases acting on RNA (RADAR) via rAAV infection, we constructed engineered adipocytes that respond to the simulations of cytokines secreted from breast cancer cells by generating visible output signals. In this section, we will introduce the overall design of the project, including chassis cell selection, biomarker screening, sensing mechanism, output reporter, and delivery system. Chassis cell selection According to published research, in the breast cancer microenvironment, adipocytes are frequently transformed into cancer-associated adipocytes (CAAs) under the influence of cytokines secreted by breast cancer cells. CAAs participate in tumor progression by up-regulating the expression of specific genes. Therefore, we came up with this idea of detection of cancer cells in microenvironment through sensing the changed gene expression in CAAs. Moreover, adipocytes have low proliferation capability and low frequency of carcinogenesis, ensuring them suitable for chassis cells for transplantation. Importantly, with the increasing progression in autologous adipocyte transplantation, more breast cancer patients are choosing autologous adipocyte transplantation to meet their needs of post-surgical body restoration. Based on all the features of adipocytes listed above, we have selected autologous adipocytes as chassis cells for surveillance of breast cancer. Biomarker screening Through extensive literature review, we found that cytokines such as PAI-1, CXCLs, TGF-β, and IL-1β play key roles in the breast cancer-induced generation of CAAs[1][2]. We generated all possible combinations of genes that were reported to be induced by these cytokines, including PLOD2, LIF, FAM3C, and IL-6, then analyzed and compared the expression of these genes between CAAs and normal adipocytes through self-constructed prediction model. We discovered that the combination of PLOD2 and LIF genes showed the best results to distinguish CAAs from normal adipocytes, which suggests that using both PLOD2 and LIF as biomarker genes can optimally balance the sensitivity and specificity of breast cancer surveillance. For further details, please refer to the MODEL page. Therefore, we selected the incresed expression of PLOD2 and LIF as biomarkers to target.
Schematic diagram of PLOD2 and LIF signaling pathways
Sensing mechanism RADAR is an RNA editing system, which consists of two core components: an artificially designed sensor RNA sequence and a double-stranded RNA adenine deaminase (ADAR). As shown in figure 2, the sensor RNA contains three modules: the mCherry sequence, the sensor sequence complementary to the biomarkers, and the output reporter sequence, each connected by an F2A sequence. The mCherry sequence serves as a marker gene, to validate the successful transfection system. The sensor sequence can pair with biomarker RNA in an reverse complementary manner with a C:A mismatch at the central stop codon UAG. The output sequence encodes the output reporter. After translation, the self-cleaving 2A sequence (F2A sequence) ensures the separation of adjacent peptide chains. ADAR is an enzyme capable of binding to double-stranded RNA in a sequence-independent manner and editing adenosine (A) nucleotides into guanine (G)-like inosine (I) nucleotides. The core mechanism of the RADAR is based on editing the central stop codon (UAG). The central stop codon in the sensor sequence prevents the translation of the output reporter gene. As shown in Figure 2, when the biomarker, named as trigger in RADAR system, combines with the sensor to form double-stranded RNA, the CCA sequence in the trigger creates an A-C mismatch at the UAG stop codon in the sensor. This structure recruits ADAR enzymes to mediate site-specific hydrolytic deamination of the adenosine (A) within the central stop codon (UAG), whereby a water molecule is utilized to generate a guanine (G)-like inosine (I) nucleotide with release of NH₃. This RNA editing reaction effectively suppresses translation termination by the UAG stop codon, thereby allowing protein translation of downstream reporter gene[3].
Schematic diagram of central termination codon editing
To achieve monitoring of two biomarkers simultaneously, we designed our own sensor with a tandem AND-gate structure—connecting the reverse complementary sequence modules targeting PLOD2 and LIF and introducing UAG stop codon at appropriate locations. Therefore, only when both PLOD2 and LIF are expressed, the translation termination of both UAG stop codons can be suppressed, allowing the translation of the downstream output reporter gene (Figure 3). For further details on the designing of our sensor sequences, please refer to the MODEL page.
Schematic diagram of AND-gate sensor to target PLOD2 and LIF
Output reporter gene To achieve the visualization of output signals, we selected Gaussia Luciferase as reporter gene. Gaussia Luciferase(Gluc) is a luciferase derived from marine copepods. It is a protein composed of 185 amino acids (19.9 kDa) with a short coding sequence (555 bp). The humanized form of Gaussia Luciferase could be expressed in mammalian cells, which is non-toxic, and secretable. After renal metabolism, it enters the urine[4]. The advantages of Gluc include high luminescence intensity, ATP independence, stability, and low immunogenicity, ensuring the sensitivity of detection results and the safety of in vivo synthesis[5]. The substrate for the Gluc is coelenterazine, which is oxidized to coelenteramide. This reaction could emit light at a peak of 480 nm. The expression of Gluc can be detected by collecting the conditioned medium of Gluc-transfected cells, adding coelenterazine and buffer solution, and measuring the chemiluminescence intensity[6]. Delivery systems To ensure the safety and long-term efficacy of our engineered adipocytes, we chose to deliver our designed RADAR system via rAAV infection. The reasons for choosing rAAV are as follows:
During the infection process, the genomic sequence packaged in rAAV does not integrate into the host genome, meeting the biological safety requirements[7].
In non-dividing cells, the rAAV genomes can persist as circular episomes and drive transgene expression for a long time, ensuring the long-term effectiveness of our designed breast cancer surveillance system[8].
Reference
Reference
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