RNAs (tRNAs) transport specific amino acids into ribosomes while translating mRNA into proteins. The abundance of tRNAs can have a profound effect on cell physiology, but measuring the amount of all tRNA in cells has been limited due to technical challenges. Researchers at the Max Planck Institute for Biochemistry have now overcome these limitations with mim-tRNAseq, a method that can be used to measure tRNA in any organism and will help improve our understanding of RNA regulation in health and disease.
The cell contains hundreds of thousands of tRNA molecules, each consisting of just 70 to 90 nucleotides folded into a pattern similar to a clover leaf. At one end, the RNA carries one of the twenty amino acids that serve as the building blocks of the protein, while the opposite terminal pairs with a codon fix this amino acid in the mRNA during translation. Although there are only 61 codons for twenty amino acids, cells from different organisms can contain hundreds of unique tRNA molecules, which differ from each other by only one nucleotide. Many nucleotides in RNA are also decorated with chemical modifications, which assist the RNA in folding or binding the correct codon.
TRNA levels are dynamically regulated in different tissues and during development, and tRNA defects are associated with neurological diseases and cancer. The molecular origins of these bonds remain unclear, because quantifying the abundance and modifications of RNA in cells has long been a challenge. Danny Nedialkova’s team at the MPI Institute for Biochemistry developed mim-tRNAseq, a method that accurately measures the abundance and modification status of various RNA in cells.
Adjusting barriers and decisions
To measure levels of multiple RNAs simultaneously, scientists use an enzyme called reverse transcriptase to first rewrite the RNA into DNA. Millions of these DNA copies can then be measured in parallel via high-throughput sequencing. Rewriting the RNA into DNA was very difficult because many RNA modifications block reverse transcription, causing it to stop DNA synthesis.
“Numerous studies have proposed elegant solutions to this problem, but all of them only highlight a fraction of the modification barriers in RNA,” explains Danny Nidalkova, head of the Max Planck research group at the Max Planck Institute for Biochemistry. “We have noticed that one particular reverse transcript appears to be much better at reading through modified tRNA sites. By improving the reaction conditions, we can greatly improve the efficiency of the enzyme, enabling it to read nearly all of the RNA modification barriers,” adds Nidalkova. This made it possible to generate DNA libraries from full-length tRNA transcripts and use them in high-throughput sequencing.
Mim-tRNAseq Computational Toolkit
Analysis of the resulting sequence data also presented significant challenges. “We identified two main issues: The first is the overall similarity in the sequence between different copies of the RNA,” explains Andrew Behrens, PhD student at Nedialkova Group and first author of the paper. “The second one comes from the fact that an incorrect nucleotide (misconfiguration) is inserted into many modified sites during reverse transcription. Both make it extremely difficult to assign all of the read DNA to the tRNA molecule from which it originated,” adds Birns.
The team addressed these problems with new computational methods, including the use of annotation modification to guide accurate reading alignment. The resulting comprehensive toolkit is assembled in a freely available pipeline for alignment, analysis, and visualization of tRNA-derived sequence data. Researchers can use mim-tRNAseq not only to measure RNA abundance, but also to map and measure the RNA modifications that induce nucleotide incorporation by reverse transcription. “Mim-tRNAseq opens up countless possibilities for the way forward,” says Nidalkova. “We expect it will help us and others address many of the outstanding questions about the biology of tRNA in health and disease.”
A. Behrens, G. Rodschinka, DD Nedialkova
High-resolution quantitative profiling of RNA abundance and modification state in eukaryotes by mim-tRNAseq.
Molecular cell February 2021
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