Experimental and computational workflow for the analysis of tRNA pools from eukaryotic cells by mim-tRNAseq

Neurodevelopmental disorders associated variants in ADAT3 disrupt the activity of the ADAT2/ADAT3 tRNA deaminase complex and impair neuronal migration

The ADAT2/ADAT3 complex catalyzes the adenosine to inosine modification at the wobble position of eukaryotic tRNAs. Mutations in ADAT3 , the catalytically inactive subunit of the ADAT2/ADAT3 complex, have been identified in patients presenting with severe neurodevelopmental disorders (NDDs). Yet, the physiological function of ADAT2/ADAT3 complex during brain development remains totally unknown. Here we showed that maintaining a proper level of ADAT2/ADAT3 catalytic activity is required for correct radial migration of projection neurons in the developing mouse cortex. In addition, we not only reported 7 new NDD patients carrying biallelic variants in ADAT3 but also deeply characterize the impact of those variants on ADAT2/ADAT3 structure, biochemical properties, enzymatic activity and tRNAs editing and abundance. We demonstrated that all the identified variants alter both the expression and the activity of the complex leading to a significant decrease of I 34 with direct consequence on their steady-state. Using in vivo complementation assays, we correlated the severity of the migration phenotype with the degree of the loss of function caused by the variants. Altogether, our results indicate a critical role of ADAT2/ADAT3 during cortical development and provide cellular and molecular insights into the pathogenicity of ADAT3-related neurodevelopmental disorder.

Advances in methods for tRNA sequencing and quantification

In the past decade tRNA sequencing (tRNA-seq) has attracted considerable attention as an important tool for the development of novel approaches to quantify highly modified tRNA species and to propel tRNA research aimed at understanding the cellular physiology and disease and development of tRNA-based therapeutics. Many methods are available to quantify tRNA abundance while accounting for modifications and tRNA charging/acylation. Advances in both library preparation methods and bioinformatic workflows have enabled developments in next-generation sequencing (NGS) workflows. Other approaches forgo NGS applications in favor of hybridization-based approaches. In this review we provide a brief comparative overview of various tRNA quantification approaches, focusing on the advantages and disadvantages of these methods, which together facilitate reliable tRNA quantification.

Selective gene expression maintains human tRNA anticodon pools during differentiation

Transfer RNAs are essential for translating genetic information into proteins. The human genome contains hundreds of predicted tRNA genes, many in multiple copies. How their expression is regulated to control tRNA repertoires is unknown. Here we combined quantitative tRNA profiling and chromatin immunoprecipitation with sequencing to measure tRNA expression following the differentiation of human induced pluripotent stem cells into neuronal and cardiac cells. We find that tRNA transcript levels vary substantially, whereas tRNA anticodon pools, which govern decoding rates, are more stable among cell types. Mechanistically, RNA polymerase III transcribes a wide range of tRNA genes in human induced pluripotent stem cells but on differentiation becomes constrained to a subset we define as housekeeping tRNAs. This shift is mediated by decreased mTORC1 signalling, which activates the RNA polymerase III repressor MAF1. Our data explain how tRNA anticodon pools are buffered to maintain decoding speed across cell types and reveal that mTORC1 drives selective tRNA expression during differentiation.

Genome-Wide Profiling of tRNA Using an Unexplored Reverse Transcriptase with High Processivity

Monitoring the dynamic changes of cellular tRNA pools is challenging, due to the extensive post-transcriptional modifications of individual species. The most critical component in tRNAseq is a processive reverse transcriptase (RT) that can read through each modification with high efficiency. Here we show that the recently developed group-II intron RT Induro has the processivity and efficiency necessary to profile tRNA dynamics. Using our Induro-tRNAseq, simpler and more comprehensive than the best methods to date, we show that Induro progressively increases readthrough of tRNA over time and that the mechanism of increase is selective removal of RT stops, without altering the misincorporation frequency. We provide a parallel dataset of the misincorporation profile of Induro relative to the related TGIRT RT to facilitate the prediction of non-annotated modifications. We report an unexpected modification profile among human proline isoacceptors, absent from mouse and lower eukaryotes, that indicates new biology of decoding proline codons.

A ligation-independent sequencing method reveals tRNA-derived RNAs with blocked 3' termini

Despite the numerous sequencing methods available, the vast diversity in size and chemical modifications of RNA molecules makes the capture of the full spectrum of cellular RNAs a difficult task. By combining quasi-random hexamer priming with a custom template switching strategy, we developed a method to construct sequencing libraries from RNA molecules of any length and with any type of 3' terminal modification, allowing the sequencing and analysis of virtually all RNA species. Ligation-independent detection of all types of RNA (LIDAR) is a simple, effective tool to comprehensively characterize changes in small non-coding RNAs and mRNAs simultaneously, with performance comparable to separate dedicated methods. With LIDAR, we comprehensively characterized the coding and non-coding transcriptome of mouse embryonic stem cells, neural progenitor cells, and sperm. LIDAR detected a much larger variety of tRNA-derived RNAs (tDRs) compared to traditional ligation-dependent sequencing methods, and uncovered the presence of tDRs with blocked 3' ends that had previously escaped detection. Our findings highlight the potential of LIDAR to systematically detect all RNAs in a sample and uncover new RNA species with potential regulatory functions.