Researchers at Cancer Center Amsterdam have developed a new robust method for the high-throughput sequencing of transfer RNAs (tRNAs). The ALL-tRNAseq method incorporates enzymes that are able to bypass the numerous roadblocks that have long prevented the easy decoding of tRNAs. The researchers also show that the method is useful for the identification and classification of tumor-associated tRNA signatures in clinical tissue samples that may have comprised RNA quality.

Transfer RNAs (tRNAs) are small molecules in cells that carry amino acids to organelles called ribosomes. There, the amino acids are linked together according to instructions carried by messenger RNA (mRNA) molecules to create long chains called polypeptides or proteins. tRNAs are essential for translating the genes encoded within DNA into functional proteins that are essence of life.

There are several hundred thousand tRNA molecules in a cell. It is known that alterations in the cellular tRNA population can occur and directly affect protein synthesis. Recently, growing evidence has pointed to tRNAs as having a role in the pathogenic process of cancer. For example, the increase of specific tRNAs has been linked to the proliferation, metastasis, and invasiveness of cancer cells.

Overcoming roadblocks

Quantifying tRNA in cells or tissues has been limited due to many technical challenges. One such difficulty is the nature of tRNAs itself: they are tightly folded into stable tertiary structures. This stable structure and other common chemical modifications (i.e. methylation) act as roadblocks to the enzyme (reverse transcriptase, or RT) scientists use to read the underlying RNA sequence.

Researchers from the Neurosurgery Department at Amsterdam UMC have now developed a tRNA sequencing method that can overcome the many hurdles of decoding tRNAs in a collaborative effort with bioinformaticians from the University of Granada, and researchers, pathologists and clinicians within Amsterdam UMC, as well as from the HemoBase Population Registry Consortium in Leeuwarden.

In this ALL-tRNAseq approach, the biggest roadblocks are removed with the RNA demethylating enzyme AlkB. This blockade breaker is followed by the hyper-processive MarathonRT enzyme which is able to overcome any remaining obstacles for the decoding of mature full-length tRNA molecules.

“This tRNA profiling approach overcomes potential roadblocks for the recovery of full-length tRNAs,” says first author Chantal Scheepbouwer. “At the same time, all tRNA mapping reads are included in the analysis and this gives us an estimate of the quality of the sample.”

All tRNA sequencing data was then analysed with the recently updated computational pipeline sRNAbench.

“Using ALL-tRNAseq, we can find tumor-specific tRNA profiles. Even if the RNA sample is a little fragmented, our method improves classification of oncogenic signatures,” says Chantal. “This demonstrates that our tRNA sequencing approach can help improve our understanding of tRNA regulation in cancer tissues.”

For more information contact Chantal Scheepbouwer, or read the article:

http://genesdev.cshlp.org/content/early/2023/02/20/gad.350233.122

Funding

The work was supported by KWF Kankerbestrijding, Cancer Center Amsterdam Foundation, Stichting MRD Hodgkin Lymphoma, NWO Talent Programme Vidi grant, and EU Horizon 2020.

People involved from Amsterdam UMC:

Chantal Scheepbouwer

Cristina Gomez-Martin

Heleen Verschueren

Monique van Eijndhoven

Laurine E. Wedekind

Nathalie Hijmering

Lisa Gasparotto

Eleonora Aronica

Vivi M. Heine

Pieter Wesseling

David P. Noske

W. Peter Vandertop

Daphne de Jong

D. Michiel Pegtel

Tom Wurdinger

Alan Gerber

Danijela Koppers-Lalic