Recruiting In Vitro Transcribed mRNA against Cancer Immunotherapy: A Contemporary Appraisal of the Current Landscape

Detection and Quantification of 5moU RNA Modification from Direct RNA Sequencing Data

Background

Chemically modified therapeutic mRNAs have gained momentum recently. In addition to commonly used modifications (e.g., pseudouridine), 5moU is considered a promising substitution for uridine in therapeutic mRNAs. Accurate identification of 5-methoxyuridine (5moU) would be crucial for the study and quality control of relevant in vitro-transcribed (IVT) mRNAs. However, current methods exhibit deficiencies in providing quantitative methodologies for detecting such modification. Utilizing the capabilities of Oxford nanopore direct RNA sequencing, in this study, we present NanoML-5moU, a machine-learning framework designed specifically for the read-level detection and quantification of 5moU modification for IVT data.

Materials and Methods

Nanopore direct RNA sequencing data from both 5moU-modified and unmodified control samples were collected. Subsequently, a comprehensive analysis and modeling of signal event characteristics (mean, median current intensities, standard deviations, and dwell times) were performed. Furthermore, classical machine learning algorithms, notably the Support Vector Machine (SVM), Random Forest (RF), and XGBoost were employed to discern 5moU modifications within NNUNN (where N represents A, C, U, or G) 5-mers.

Results

Notably, the signal event attributes pertaining to each constituent base of the NNUNN 5-mers, in conjunction with the utilization of the XGBoost algorithm, exhibited remarkable performance levels (with a maximum AUROC of 0.9567 in the "AGTTC" reference 5-mer dataset and a minimum AUROC of 0.8113 in the "TGTGC" reference 5-mer dataset). This accomplishment markedly exceeded the efficacy of the prevailing background error comparison model (ELIGOs AUC 0.751 for site-level prediction). The model's performance was further validated through a series of curated datasets, which featured customized modification ratios designed to emulate broader data patterns, demonstrating its general applicability in quality control of IVT mRNA vaccines. The NanoML-5moU framework is publicly available on GitHub (https://github.com/JiayiLi21/NanoML-5moU).

Conclusion

NanoML-5moU enables accurate read-level profiling of 5moU modification with nanopore direct RNA-sequencing, which is a powerful tool specialized in unveiling signal patterns in in vitro-transcribed (IVT) mRNAs.

Chitosan-Based Nanocarriers for Pulmonary and Intranasal Drug Delivery Systems: A Comprehensive Overview of their Applications

The optimization of respiratory health is important, and one avenue for achieving this is through the application of both Pulmonary Drug Delivery System (PDDS) and Intranasal Delivery (IND). PDDS offers immediate delivery of medication to the respiratory system, providing advantages, such as sustained regional drug concentration, tunable drug release, extended duration of action, and enhanced patient compliance. IND, renowned for its non-invasive nature and swift onset of action, presents a promising path for advancement. Modern PDDS and IND utilize various polymers, among which chitosan (CS) stands out. CS is a biocompatible and biodegradable polysaccharide with unique physicochemical properties, making it well-suited for medical and pharmaceutical applications. The multiple positively charged amino groups present in CS facilitate its interaction with negatively charged mucous membranes, allowing CS to adsorb easily onto the mucosal surface. In addition, CS-based nanocarriers have been an important topic of research. Polymeric Nanoparticles (NPs), liposomes, dendrimers, microspheres, nanoemulsions, Solid Lipid Nanoparticles (SLNs), carbon nanotubes, and modified effective targeting systems compete as important ways of increasing pulmonary drug delivery with chitosan. This review covers the latest findings on CS-based nanocarriers and their applications.