Jeffrey S. Buguliskis Ph.D. Technical Editor Genetic Engineering & Biotechnology News
The Use of RNA-Seq Has Enormous Applicability toward the Development of Clinical Diagnostics
DNA→RNA→Protein. This flow of genetic information has been the framework of modern biology since it was first enunciated by Francis Crick in 1958. However, in recent years researchers have begun to assemble a more comprehensive picture surrounding the “central dogma” of molecular biology, leading to the revelation that RNA is not just a simple genetic messenger—a middleman so to speak—but rather a complex signaling molecule that is present in an ever increasing number of structural forms. The various iterations in which RNA exists allow the molecule to act as the template for translating the genetic code into protein, as a gene silencer and post-transcriptional regulator of gene expression, and, as most recently discovered, a modulator of epigenetic elements.
In a search to understand human development better at the molecular level, Conrad H. Waddington, coined the term “epigenetics” in 1942, to describe the influence of genetics on developmental processes. Decades later scientists discovered that environmental factors caused heritable phenotypic changes in fruit flies that did not change the underlying DNA sequence—in essence, a change in phenotype without a change in genotype.
Epigenetic regulation represents an important driver of diversity within populations of various species and can be influenced by several factors, including age, disease state, and the environment in which a species lives. These various influences can lead to characteristic changes in organisms, such as guiding undifferentiated cells toward their final form. However, epigenetic mechanisms can also go awry and result in damaging effects, leading to the development of disease states like cancer.
Currently, investigators are pursuing three main pathways that are used by cells to bring about and maintain epigenetic changes: DNA methylation, modifications of core nucleosome proteins called histones, and gene silencing through noncoding RNA (ncRNA). In-depth analysis of these pathways is the research objective of many laboratories, with changes in DNA methylation currently representing the lion’s share of the knowledge base for epigenetics. However, many scientists are beginning to take notice of the potential influence ncRNAs have on epigenetic regulation, especially when it comes to the onset of diseases such as cancer.
“Non-coding RNAs, particularly Long, non-coding (lncRNAs) have had a long association with cancer development and progression,” states Charlie Nelson, associate director of product marketing at Illumina. “Because lncRNA expression profiles can be so very tissue specific, it allows researchers to not only establish biomarkers for particular types of cancer, it also provides insight into the mechanisms of the disease.”
Yet, RNA analysis methods have been historically cumbersome and often inefficient for providing global views of epigenetic changes. With the development of next-generation sequencing applied to RNA, commonly referred to as RNA-Seq, researchers have been able to create accurate, unbiased, and quantifiable data for ncRNA regulation mechanisms. Moreover, RNA-Seq is positioning itself as a truly translational research tool, seeking to improve current diagnostic techniques and even displace traditional gold-standard clinical methodologies.
From Bench to Bedside
In contrast to most traditional platforms for clinical RNA analysis, such as qRT-PCR and microarrays, RNA-Seq is an open-platform system that allows developers to create features and functions to meet researchers’ needs—for instance, having the capability to quantify known RNA transcripts as well as to detect and quantify rare and novel RNA variants within a sample. Additionally, in comparison to microarray technology, RNA-Seq has a greater dynamic range that expands the method’s potential to identify unique variants in clinical specimens.
“While many of the ncRNAs are known, RNA-Seq can provide a complete characterization of transcriptomics by not only identifying previously uncharacterized variants but also by providing an accurate quantitation of many of these lowly expressed molecules,” Nelson remarks.
“Using a targeted panel of gene fusions and copy number variants known to contribute to disease, an investigator can directly measure the active disease phenotype much more easily and economically,” states Herman Verrelst, vice president and general manager of the genomics solutions and clinical applications divisions at Agilent. “A targeted approach immediately makes analysis much easier and translates into the clinic.”
The ability to characterize unidentified RNA species is an essential feature of RNA-Seq platforms, particularly when used to analyze diverse types of ncRNA, such as microRNA (miRNA), PIWI-interacting RNA (piRNA), lncRNAs, and transfer RNAs (tRNAs). Recent evidence linking ncRNAs to misregulation of epigenetic mechanisms in various forms of cancer has opened the door toward considerable opportunities for RNA-Seq to move into the clinical arena.
“Perhaps the biggest clinical implication for RNA-Seq and epigenetics is that these high-resolution approaches offer diagnostic biomarkers of disease states that do not necessarily present themselves at the genetic level,” explains Ginger Zhou, Ph.D., associate director of genomics and molecular genetics at GENEWIZ. “RNA-Seq and epigenetics enable clinicians to capture many of the changes that occur at the functional and regulatory levels of the cell. By deploying RNA-Seq or epigenetic testing, clinicians can expand their arsenal of tools in identifying, managing, and hopefully curing disease states.”
Yet, even with state-of-the-art technology powering RNA-Seq methods, workflow logjams are still an impediment toward the method’s clinical transition. Determining the sequence of various genetic samples is only part of the process. Digitally reassembling what is essentially shotgun sequencing of the transcriptome represents at least half of the overall procedure—requiring the development of cutting-edge algorithms to make sense of the sequence “alphabet soup,” as well as employing an enormous amount of computing power. Software developers struggle to create solutions to keep up with scientists’ demands for robust and reliable bioinformatics.
“The rate at which RNA-Seq and sequencing technology has evolved has resulted in no shortage of bioinformatics packages with a dizzying array of clever acronyms,” Dr. Zhou states. “Each package has its strengths and weaknesses. This has resulted in no clear best-in-class tool that solves all end-user needs.”
Nelson agrees with Dr. Zhou, stating that “the key to successful RNA-Seq analysis is having the right tools to facilitate understanding and decision making, and the computational power to drive those tools.”
“As the field is maturing and making strides toward wider adoption in the clinic, the need to establish best practices, both experimentally and bioinformatically, has come to the forefront,” Dr. Zhou adds. “Overall, targeted RNA-Seq bioinformatics has come further along by asking focused biological questions compared with transcriptome sequencing. Though not the only determining factor, the ease of analysis for targeted RNA sequencing will likely continue to be a driver for faster adoption in the clinic compared with transcriptome sequencing.”
Future Flow of Genetic Information
While the central dogma is in no danger of being reworked or overturned, precisely how researchers view RNA within the context of streaming genetic information is rapidly changing. Moreover, with its ability to detect unbiasedly not only gene transcript levels but also a vast and diverse set of RNA species, RNA-Seq holds immense potential to transform clinical testing for a host of different diseases.
“There is no shortage of areas where RNA-Seq and epigenetics will have a great impact in the clinic over the years to come,” notes Dr. Zhou. “Certainly the first that comes to mind is cancer. Transcriptome sequencing has helped differentiate mRNA expression patterns associated with specific gene sets for various cancers.”
Although cancer seems the most logical choice for RNA-Seq to make its foray into clinical medicine, there are a host of genetic maladies that stand to benefit from the development of diagnostic tools rooted in this
“In the long run, health outcomes for such diseases as cancer, diabetes, Alzheimer’s, Parkinson’s, autism, and lupus will dramatically change,” Verrelst continued. “Doctors will be able to understand what stage the diseases are at and how to rescue the patient’s disorder using molecular therapeutics and other technologies.”
“We have only scratched the surface of understanding with respect to RNA-Seq and epigenetics,” Nelson added. “Gene expression patterns are often very dynamic, yet the regulatory elements that are involved in controlling them can be subtle
—this can make the use of these molecular interactions difficult in a clinical setting.”
RNA-Seq holds enormous promise for the future of translation science. As this platform technology begins to transition out of laboratory settings, studies will continue to validate the clinical utility of RNA-Seq and facilitate its adoption toward routine usage by clinicians.
This article was originally published in the May 2016 issue of Clinical OMICs. For more content like this and details on how to get a free subscription to this digital publication, go to www.clinicalomics.com.