Nanopore sequencing of intact aminoacylated tRNAs using aa-tRNA-seq

The intricate landscape of tRNA modification poses strong analytical challenges, and to date no single approach has enabled simultaneous measurement of the sequence, modification, and aminoacylation state of individual tRNAs. To address these challenges, we introduce “aa-tRNA-seq”, an integrated method that uses chemical ligation to sandwich the amino acid of a charged tRNA in between the body of the tRNA and an adaptor oligonucleotide, followed by high throughput nanopore sequencing. Our approach reveals the identity of the amino acids attached to all tRNAs in a cellular sample, at the single molecule level. We describe machine learning models that enable the accurate identification of amino acid identities based on the unique signal distortions generated by the interactions between the amino acid in the RNA backbone and the nanopore motor protein and reader head. We have applied aa-tRNA-seq to characterize the impact of the loss of specific tRNA modification enzymes, confirming the hypomodification-associated instability of specific tRNAs, and identifying putative novel targets of modification. Our studies lay the groundwork for understanding the efficiency and fidelity of tRNA aminoacylation as a function of tRNA sequence, modification, and environmental conditions.

转运RNA（transfer RNA，tRNA）修饰的复杂机制带来了严峻的分析挑战，迄今为止尚无单一方法能够同时测定单条转运RNA的序列、修饰状态与氨酰化状态。为解决这些难题，我们提出了“aa-tRNA-seq”这一整合式方法：该方法利用化学连接将带电转运RNA所携带的氨基酸夹在转运RNA本体与衔接子寡核苷酸之间，随后进行高通量纳米孔测序。我们的方法可在单分子水平上解析细胞样本中所有转运RNA所结合的氨基酸种类。我们构建了机器学习模型，可基于RNA骨架上的氨基酸与纳米孔运动蛋白及阅读头相互作用所产生的独特信号畸变，精准识别氨基酸种类。我们已将aa-tRNA-seq应用于表征特定转运RNA修饰酶缺失所带来的影响，证实了特定转运RNA存在与低修饰相关的不稳定性，并鉴定出潜在的新型修饰靶点。本研究为理解转运RNA序列、修饰状态与环境条件共同影响下的转运RNA氨酰化效率与保真性奠定了基础。