UK researchers are developing an online portal to show how biases in RNA sequences affect gene expression
LEXINGTON, Kentucky (June 29, 2022) — A recent publication from researchers at the University of Kentucky explains the importance of identifying and understanding how differences between tissues and cells alter gene expression without altering the underlying genetic code.
Introductory biology courses teach that DNA is transcribed into RNA, which is then translated into proteins. However, many cellular processes affect the speed of transcription and translation. Gene expression looks at differences in RNA concentrations in a cell and can help scientists know which genes are active in that tissue or cell.
“Changes in gene expression can significantly affect a variety of diseases and disease trajectories,” said Justin Miller, Ph.D., assistant professor at the UK College of Medicine. Department of Pathology and Laboratory Medicine.
Miller, who is also affiliated with the Sanders-Brown Center on Aging and Biomedical informatics, says he and his colleagues previously developed the first algorithm to identify ramp sequences from a single gene sequence. Through their recent work, Miller and fellow UK co-authors Mark Ebbert, Ph.D., and Matthew Hodgman created an online version of this algorithm and showed that ramp sequences change between tissues and cells without modify the RNA sequence.
A ramp sequence is part of the RNA sequence that slows down translation at the start of the gene using codons (sequences of three DNA or RNA nucleotides) that are not easily translated. Ramp sequences counterintuitively increase overall gene expression by evenly spacing the translation machinery and preventing collisions later in translation.
In their recent publication in NAR Genomics and Bioinformaticsresearchers present the first comprehensive analysis of tissue- and cell-type-specific ramp sequences and report over 3,000 genes with ramp sequences that change between tissues and cell types, consistent with increased gene expression in these tissues and cells.
“This research is the first time that variable ramp sequences have been described. Our comprehensive web interface allows other researchers to creatively explore ramp sequences and gene expression,” Miller said.
The research team says this work is important because while there are multiple ways for our RNA to encode the same proteins, the specific RNA sequence is important in regulating protein and RNA levels.
“Essentially, a ramp sequence works like an on-ramp to a highway so the ribosomes don’t crash into each other, but the length and speed limit of that on-ramp can change depending on of the cell and the resources available in that cell,” Miller explained.
He says he enjoyed working on this project not only with his colleagues in the UK, but also with his former colleagues from Brigham Young University and his brother, Kyle Miller, from Utah Valley University. Together, the group created a Web interface for people to see how ramp sequences correspond to human and COVID-19 gene expression in different tissues and cells.
Miller says he believes this work will eventually have an impact on patient care. “We created an online interface that allows researchers to query all human genes and see if a specific gene has a ramp sequence in a given tissue and how that gene is expressed in that tissue,” Miller said. “We also show that various human COVID-19 genes and entry factors for COVID-19 have ramp sequences that change between different tissues. Ramp sequences are much more likely to occur in tissues where the virus is known to grow. »
Thus, researchers believe that COVID-19 genes have genetic biases (ramp sequences) that allow them to use available cellular machinery to increase their expression. “Our research can help us better predict which tissues and cells the novel viruses will infect and also provides a potential therapeutic target to regulate tissue-specific gene expression without altering the translated protein,” Miller said.
The research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Numbers P30AG072946 and R01AG068331, and the National Institute of General Medical Sciences of the National Institutes of Health under award number R35GM138636. The content is the sole responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
This work was also funded by the BrightFocus Foundation, under awards A2020118F and A2020161S, and the Alzheimer’s Association, under award 2019-AARG-644082.