Project description:N6-methyladenosine (m6A) and pseudouridine (Ψ) are the two most abundant modifications in mammalian mRNA, but the coordination of their biological functions remains poorly understood. We develop a machine learning-based nanopore direct RNA sequencing method (NanoSPA) that simultaneously analyzes m6A and Ψ in the human transcriptome. Applying NanoSPA to polysome profiling, we reveal opposing transcriptomic co-occurrence of m6A and Ψ and synergistic, hierarchical effects of m6A and Ψ on the polysome.
Project description:Pseudouridine (Ψ) is an abundant mRNA modification in the mammalian transcriptome, but its function has remained elusive due to the difficulty of transcriptome-wide mapping. We develop nanopore native RNA sequencing for quantitative Ψ analysis that utilizes native content training, machine learning model prediction, and single read coordination. We find interferon inducible Ψ modifications in the interferon stimulated gene transcripts, consistent with a role of Ψ in the efficacy of mRNA vaccines.
Project description:Pseudouridine (Ψ) is an abundant mRNA modification in the mammalian transcriptome, but its function has remained elusive due to the difficulty of transcriptome-wide mapping. We develop nanopore native RNA sequencing for quantitative Ψ analysis that utilizes native content training, machine learning model prediction, and single read coordination. We find interferon inducible Ψ modifications in the interferon stimulated gene transcripts, consistent with a role of Ψ in the efficacy of mRNA vaccines.
Project description:State-of-the-art algorithms for m6A detection and quantification via nanopore direct RNA sequencing have been continuously developed, little is known about their capacities and limitations, which makes a comprehensive assessment in urgent need. Therefore, we performed comprehensive benchmarking of 10 computational tools relying on current-based and base-calling “errors” strategies for m6A detection by nanopore sequencing.
Project description:Most proteins in the human proteome lack chemical probes, and several large-scale and generalizable small-molecule binding assays have been introduced to address this problem. How compounds discovered in such “binding-first” assays affect protein function, nonetheless, often remains unclear. Here, we describe a “function first” proteomic strategy that uses size exclusion chromatography (SEC) to assess the global impact of electrophilic compounds on protein complexes in human cells. Integrating the SEC data with cysteine-directed activity-based profiling identifies changes in protein-protein interactions that are caused by site-specific liganding events, including the stereoselective engagement of cysteines in PSME1 and SF3B1 that disassemble the PA28 proteasome regulatory complex and remodel the spliceosome, respectively. Our findings thus show how multidimensional proteomic analysis of focused libraries of electrophilic compounds can expedite the discovery of chemical probes with site-specific functional effects on protein complexes in human cells. In this experiment, we probed the RNA-binding profile of DDX42 by eCLIP via an endogenously introduced HA-tag in the context of various SF3B1 ligands.