Two new methodologies could assist researchers with utilizing existing sequencing innovation to better-recognize RNA changes that influence how hereditary code is perused.
Kyoto University researchers are drawing nearer to tracking down ways of distinguishing changes to RNA arrangements that influence protein development and can cause illnesses. Their methodology, distributed in the diary Genomics, uses likelihood calculations along with a generally accessible, pocket-sized sequencing gadget.
“Modifications that are found in all types of biological RNA influence gene regulation, which ultimately decides how different cells function in our body,” explains Ganesh Pandian Namasivayam of Kyoto University’s Institute for Integrated Cell-Material Science (WPI-iCeMS). “Abnormalities in these modifications can lead to severe diseases, like diabetes, neurodevelopmental disorders and cancer. Knowing how and where these RNA modifications are is of prime importance from a clinical viewpoint,” adds Soundhar Ramaswamy, the first author of the study.
There are already ways to identify RNA modifications, but they are insufficient. Biophysical approaches such as chromatography and mass spectrometry can only process small amounts of RNA at a time. High-throughput sequencing methods, which can process large amounts of RNA, involve laborious sample preparation, can’t simultaneously map multiple modifications and are error-prone.
Namasivayam, Hiroshi Sugiyama and colleagues at Kyoto University tested and found two approaches that can relatively successfully distinguish a well-known and abundant RNA modification involving the replacement of the nucleotide base uracil with another called pseudouridine.
Similar to DNA, RNA is formed of a strand of varying combinations of four different nucleotide bases: uracil, cytosine, adenine and guanine. How these bases are arranged determines the code that signals what protein is meant to be made. When pseudouridine replaces uracil in the RNA backbone, it can lead to increased protein production or to changing the code from one that signals the interruption of information translation to one that signals amino acid formation.
The group’s methodology includes utilizing an all around accessible direct RNA sequencing stage created by Oxford Nanopore Technologies. In this stage, RNA strands go through little pores in a layer. Disturbances are caused in the ongoing traveling through the film contingent upon the request for the different RNA bases. This permits researchers to “read” the arrangement. Be that as it may, researchers utilizing this approach frequently find it challenging to recognize various sorts of changes from each other.
Shubham Mishra, a joint first creator of this review, created calculations to recognize a high likelihood of presence of a pseudouridine replacement contrasted with the likelihood that it was an alternate sort of base change.
One of their methodologies looks at short RNA runs of five nucleotide bases in which uracil, pseudouridine or cytosine are encircled on one or the other side by similar bases. The readings then, at that point, go through calculations that ascertain the likelihood of the center base being one of the three. They utilized their procedure, called Indo-Compare (Indo-C), on designed RNA arrangements and afterward on yeast and human RNA and found it was great at recognizing the pseudouridine replacements from the others.
They were also able to identify pseudouridine substitutions by mixing a chemical probe with RNA samples, which then selectively attaches to them. This changed the sequence readings in a way that identifies the modification.
“We believe our work will make nanopore sequencing-based methods less laborious for detecting RNA modifications and more capable of characterizing the impacts of these modifications on development and disease,” says Namasivayam.
The team next aims to optimize the use of both approaches together to more accurately identify RNA and DNA modifications. This will involve fabricating new chemical probes that correspond to specific changes. They also plan on further developing advanced machine learning algorithms that complement chemical probe-based direct RNA sequencing approaches.