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In vivo structure profiling reveals human tRNA structurome and interactions in response to stress

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NIAID Data Ecosystem2026-05-02 收录
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https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE262888
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Transfer RNAs (tRNAs) are the most abundant RNA family in copy numbers in a cell. It not only folds into defined structures, but also has complex interaction networks in cells involving aminoacyl-tRNA synthetases, translation factors, and ribosomes. The human tRNAome is comprised of chromosomal-encoded tRNAs with a large sequence diversity, and mitochondrial-encoded tRNAs that have A/U-rich sequences and many with non-canonical tertiary interactions. How tRNA folding and interaction in a eukaryotic cell respond to stress is poorly understood. Here, we develop DM-DMS-MaPseq, which utilizes in vivo dimethyl-sulfate (DMS) chemical probing and mutational profiling (MaP) coupled with demethylase (DM) treatment in transcriptome-wide tRNA sequencing to profile tRNA structures and their cellular interactions for human chromosomal and mitochondrial-encoded tRNAs. We found that tRNAs maintain stable structures in vivo, but the in vivo DMS profiles are vastly different from in vitro, which can be explained by their interactions with cellular proteins and the ribosome. We also identify tRNA structure and interaction changes upon arsenite treatment, an oxidative stress that induces translational reprogramming, that are consistent with enhancing reductions of translation. Our results reveal variations of tRNA structurome and dynamic interactome that have functional consequences in translational regulation. To investigate the structure and RNA-protein interactions of human cytosolic and mitochondrial tRNA in vivo. RNA from HEK293T cells were treated with DMS, either following deprotenation or on the live cells, and underwent multiplex small RNA-seq (MSRseq). Generated libraries were sequenced using Illumina. Following quality control processing and demultiplexing, libraries were aligned to hg38 tRNA genes. Mutation profile and abundance were calculated using custom Python and R scripts.
创建时间:
2025-06-24
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