Transfer RNAs (tRNAs) are molecular biology’s equivalent of the Rosetta Stone. They perform the critical task of translating genetic information encoded in DNA into amino acid sequences that ultimately become proteins. To keep up with the demands of protein production, human cells possess hundreds of different tRNAs. However, little is known about the composition, quantity and unique functions of these critical molecules due to technological limitations in sequencing them.
Now, researchers from the University of Chicago have developed an innovative method to sequence tRNA at high yield and throughput, allowing for accurate quantification of a complex sample of tRNAs. The new tRNA sequencing technology enables better understanding of the translation of protein sequences and the role of tRNA in diseases such as cancer and neurodegenerative disorders. Their results were published online in Nature Methods on July 27, 2015.
“We developed a method to quantitatively sequence tRNA, which has broad applications for diagnostic analysis and research,” said Tao Pan, PhD, professor of biochemistry and molecular biology at the University of Chicago and senior author on the study. “This now allows us to understand the dynamics of the most abundant RNA molecules in the cell, tRNA.”
Around 330 tRNA species have been identified in humans, each with its own unique sequence and function. On average, a single human cell maintains a pool of around one hundred million total tRNA molecules. Within this pool, individual tRNA species are not evenly represented, and different cell types – as well as diseased cells such as cancer – can vary highly in tRNA composition and quantity.
tRNAs are the last class of RNA molecule to remain unsequenced. Current high-throughput genetic sequencing technologies rely on enzymes, a class of proteins that carry out chemical reactions, to decipher genetic code. These enzymes read a string of DNA or RNA molecules and produce a specific signal for every nucleotide. However, the “enzymes used to carry out sequencing reactions cannot handle the chemical complexity of tRNAs,” said study lead author Guanqun Zheng, PhD, postdoctoral scholar at the University of Chicago.
tRNAs contain chemical modifications – specifically, methyl groups – that block enzymes from reading their sequence. In addition, tRNAs are folded into a unique structure that makes it difficult for enzymes to access them.
To address these issues, Pan and his team developed a method that utilizes two enzymes. The first, AlkB, was engineered to remove most methyl groups on tRNA. The second enzyme, TGIRT, was developed with the collaboratorating Lambowitz lab at the University of Texas. TGIRT reads through tRNA with much more efficiency than previously used enzymes and overcomes structural barriers.
“The use of AlkB and TGIRT for tRNA sequencing consistently increases sequencing readout by 80 percent or more,” said Zheng. “The implementation of these two enzymes is a game changer for those developing tRNA sequencing methodology.”
This technique enables efficient and high-throughput investigations into the composition and function of tRNAs in cells. “We know that tRNA abundance increases in cancer cell lines, for example,” Pan said. “We can now identify which of these tRNAs are more highly expressed and what roles they might play.”
The method also allows researchers to study the dynamics of tRNA modifications. By comparing the number of tRNA molecules before and after the removal of modifications, the relative quantity of modified tRNAs versus unmodified tRNAs can be measured. For example, studies by Chuan He, PhD, John T. Wilson Distinguished Service Professor in Chemistry, have found that RNA modifications such as messenger RNA methylation, can be heritable, dynamic, and are different in healthy and disease cells.
“We hope our method will enable broader research into tRNAs and help provide a better molecular picture of complex human diseases,” Pan said. “Our publication gives anyone the opportunity to utilize the methods for themselves.”
The study “Efficient and quantitative high-throughput tRNA sequencing” was funded by the NIH. Additional authors include Yidan Qin, Wesley C. Clark, Qing Dai, Chengqi Yi, Chuan He, and Alan M. Lambowitz.