The process by which hereditary information passes from one generation to the next remains one of science’s greatest and most complex mysteries. Our desire to understand this process is the motivating force behind some of the most important research now underway in the life sciences.
Hereditary information is expressed through a complex biological process first simplified by Francis Crick in 1958, in what is referred to as the central dogma of biology. Crick’s original position that DNA makes RNA and RNA makes protein, while simplistic, became the basis of laboratory investigations that are at the heart of molecular biology.
We now appreciate that DNA is the blueprint of life. It creates a “messenger” in the form of RNA that directs vital processes within cells and tissues of the body. A portion of this RNA message goes on to create proteins that are essential for life’s function. The remaining RNA does not code for a functional protein. Rather, noncoding RNAs play important roles in the regulation of genes that give rise to proteins, and they can even regulate each other. Researchers have powerful tools that are capable of deciphering the complex “messages” that direct what happens inside our body’s cells, including cells of the immune system found in blood.
IQuity has used the latest RNA technologies to create a testing platform, IQIsolate, that assists providers in identifying and treating autoimmune and related illnesses. RNA testing captures the molecular conversations taking place inside cells and provides a real-time snapshot of what is happening at any given moment in a patient blood sample. By measuring the dynamic patterns of RNA in blood, IQuity can find evidence of distinct RNA patterns that are consistent with a specific disease. One such disease is multiple sclerosis (MS), a chronic demyelinating disease of the central nervous system.
The diagnosis of MS relies not only on clinical signs and symptoms, but also on imaging and diagnostic laboratory investigations. RNA-based testing is now available that complements this existing diagnostic approach and can lead to a quicker diagnosis. Early diagnosis and treatment reduce the chance of long-term disability.
Genetics and the Emergence of RNA
When Gregor Mendel first described the origins of a gene in 1865, his description of this foundational unit of inheritance was an abstraction. A gene, he said, was merely a discrete determinant or “particulate” transmitted intact across generations, giving rise to a phenotype that was visible to the naked eye. Thomas Morgan and Hermann Muller advanced our spatial understanding of genes, showing that genes are material structures located on chromosomes, and they contain the material responsible for inheritance.
This work was preceded by George Beadle and Edward Tatum, who discovered that DNA works by directing the structure of proteins. Each protein is controlled by a single gene. Until this point, it was theorized that highly abundant and diverse molecules made up of chains of amino acids called proteins contained the information responsible for inheritance.
In 1944, Oswald Avery found that DNA is the material that constitutes genes and chromosomes. However, it wasn’t until 1952 that Alfred Hershey and Martha Chase confirmed that DNA, not protein, contains genetic information, and DNA is responsible for heredity. A year later, in 1953, work by Francis Crick, James Watson, Maurice Wilkins, and Rosalind Franklin culminated in the first structure of DNA: a twisted ladder of two complementary strands.
Even with this knowledge, critical questions remained: How does a DNA molecule create a protein, and what signal is transmitted to activate this process? The answers came through the discovery of an intermediate process by Sydney Brenner and Francois Jacob in 1961. Brenner and Jacob determined that a “messenger” in the nucleus of a cell is required to translate the genetic information stored in the DNA. This messenger journeys from the nucleus to the cytoplasm to create a protein. Brenner and Jacob found that the messenger involved is a close cousin of DNA called RNA.
In the early 1960s, Jacob and Jacques Monod demonstrated that the process of creating an RNA message is dynamic, and genes can be turned on or off by manipulating the RNA that is produced. Individual RNAs can often have more than one function, leading to the creation of large, interwoven biological pathways consisting of multiple genes and RNA intermediates.
The Genomics Era and Human Disease
In the late 1980s, humanity embarked upon one of its greatest technological journeys to date: to sequence the entire DNA blueprint necessary to create a human being. The project, in the end, took just over a decade. With the completion of the Human Genome Project and the follow-on ENCODE (ENCyclopedia Of DNA Elements) Project, the vast majority of our genome (more than 75 percent) is transcribed in some cell type over the course of a lifetime. Annotation of the human genome now documents dozens of distinct classes of RNAs that have a variety of functions.
Over the past half century, our understanding of the variety and importance of these RNA messages has found that genes do not always give rise to proteins and can be used to produce RNA alone. “Noncoding” varieties of RNA often regulate expression and function of proteins or other RNAs. Noncoding regions of the genome—initially described as “junk DNA” or genomic “dark matter” because they do not produce proteins—play a pivotal role in human development and survival of the species. This genomic “junk” holds remarkable biological power.
One particular class of noncoding RNA, the long noncoding RNAs (lncRNAs), are defined as RNA “messages” or transcripts that are at least 200 nucleotides in length and do not code for protein. lncRNAs are the largest class of noncoding RNA genes in mammalian genomes. A single lncRNA can regulate hundreds, if not thousands, of protein-coding genes and play an important role in developmental processes, ranging from chromosome inactivation to neuronal development and regulation of the immune system. In vertebrates, the number of lncRNA genes is thought to greatly exceed the number of protein-coding genes. lncRNAs are also thought to drive biologic complexity observed in vertebrates compared to invertebrates.
Evidence of this complexity is seen in many cellular compartments of vertebrate organisms such as the T-lymphocyte compartment of the human adaptive immune system. For example, cells as similar as the double negative stages of thymocyte development, DN1, DN2, DN3, and DN4, express many more unique lncRNAs than unique protein-coding genes. A general view is that lncRNAs may define the many different cell lineages observed in a vertebrate organism.
Problems arise with increasing organismal complexity, and humans develop many more complex diseases than other organisms. This may be due, in part, to the fact that our genomes contain many more genes that encode lncRNAs. Association studies have been performed that document the role of discrete lncRNAs in diseases ranging from cancer to genetic disorders, including diabetes as well as neurological and neurodegenerative disorders.
Given the extent of the genomic activity present in the human genome, our work has sought to better elucidate if lncRNAs could hold promise for understanding the origins of complex human diseases such as autoimmunity. We investigated the following: If lncRNAs exhibit tissue type specificity, could they also be strong indicators of individual diseases or disease categories compared with other genetic markers? From a peripheral whole blood draw easily obtained from patients, we discovered that lncRNAs are harbingers of disease at the earliest clinical stages. We also examined the potential for these RNAs to mark disease progression and response to therapy over time. The result is an untapped source of information that is clinically actionable across many diseases.
lncRNA: A Case Study in Multiple Sclerosis Diagnosis
The diagnosis of MS rests on clinical symptoms and examinations as outlined in the revised McDonald criteria, supported by appropriate magnetic resonance imaging findings or other laboratory tests, including detection of oligoclonal bands in cerebrospinal fluid and evoked potential testing.1 The need for early diagnosis is clearly emphasized in a 2015 position paper from the MS Brain Health initiative, titled “Brain health: Time matters in multiple sclerosis.”2 This position has been endorsed by major organizations and foundations that advocate for MS research, providers, as well as patients.
The authors note that, “a therapeutic strategy that offers the best chance of preserving brain and spinal cord tissue early in the disease course needs to be widely accepted—and urgently adopted.” They further observe that, “significant delays often occur before a person with symptoms suggestive of MS sees a neurologist for diagnosis and treatment.” Thus, the investigators conclude that early intervention is vital.2
According to Andrew J. Solomon, MD, Assistant Professor at the University of Vermont College of Medicine, obtaining accurate, early information can present a diagnostic challenge for providers in this field. “It is generally accepted that early diagnosis and early treatment leads to the best long-term outcomes in MS,” Dr. Solomon notes. “However, in the presence of an abnormal MRI finding or unexplained neurological symptoms, there is a significant potential for misdiagnosis in patients if they aren’t adequately monitored for clinical and imaging changes over time that are consistent with MS. The consequence of a rushed MS diagnosis can involve unnecessary exposure of patients to the known side effects and risks associated with MS disease-modifying therapies and create unnecessary costs for the US health care system.”
Identification of biomarkers could enhance our existing ability to distinguish MS from other diseases. Our recent investigations in MS have focused on examining differences in gene expression in lncRNAs and protein-coding genes between healthy patients and those with other inflammatory and non-inflammatory neurologic diseases. We have found that differences in both classes of RNA molecules can be detected in a whole blood sample at the earliest stages of MS, including at the diagnosis of a clinically isolated syndrome (CIS),3-4 which is the first neurologic episode lasting at least 24 hours, possibly caused by focal inflammation or demyelination.
Although this episode is characteristic of MS, not all who experience a CIS will go on to develop MS. Clinically isolated syndrome can be accompanied with the detection of lesions in the central nervous system found by MRI. However, in certain cases, MRI findings have yet to fulfill the criteria for clinically definite MS, requiring evidence of dissemination of these lesions in time. From both the provider and patient perspective, this can lead to a period of diagnostic uncertainty.
Retrospective studies of patients with CIS who later transitioned to clinically definite MS found that both protein-coding and lncRNA signatures specific for MS can be identified and tracked at each stage of clinical evaluation. At the time of a CIS, our work has shown the ability to distinguish MS from non-MS with greater than 90 percent accuracy.3 The highest measureable differences across all RNAs we examined were found at the time of MS diagnosis before the initiating therapy. We would argue that the origins of this RNA profile are present for a period of time before conventional diagnosis, and this information could be integrated into the clinical decision-making process for MS, particularly at moments in which it is difficult to definitively say “yes” or “no” based upon clinical and radiographic findings.
At IQuity, we have launched the first RNA-based tool that providers can use to distinguish MS from healthy controls, as well as inflammatory and non-inflammatory neurologic diseases, with a high degree of accuracy. Work performed in our CLIA-approved lab suggests that analysis of RNA profiles leads to an overall test accuracy of 94 percent using samples obtained from major MS centers in the United States through our scientific collaborations and two academic collaborations in Europe.
MS will remain a clinical diagnosis that neurologists are tasked to determine. Although any provider can order an IQuity test, final diagnosis and treatment decisions for MS should be referred to a neurologist or MS specialist. One benefit of the test is that it can help speed up the decision to refer a patient suspected of having MS to a specialty clinic. As documented in “Brain Health: Time Matters in Multiple Sclerosis,” significant delays are often experienced between symptom onset and the first visit to a healthcare provider, commonly a primary care provider or non-MS specialist.2
RNA-based tools can signal to primary care doctors that a particular patient presenting with signs and symptoms of MS should be referred to an MS specialist. This could improve and expedite access to care for patients who may have MS. The goal of IQuity’s RNA-based testing approach is to offer a complementary tool for providers that reduces diagnostic uncertainty and speeds up time to diagnosis for patients with signs and symptoms of disease.
This innovative RNA-based test is available for provider order on our web portal: https://iquity.com/lab-login.
Charles “Chase” F. Spurlock, III, PhD is Founder and CEO of IQuity. He also serves on the faculty at Vanderbilt University in Nashville, Tennessee.
Thomas M. Aune, PhD is Co-Founder of IQuity and a Professor of Medicine at Vanderbilt University.
1. Brownlee WJ, Hardy TA, Fazekas F, Miller DH. Diagnosis of multiple sclerosis: progress and challenges, The Lancet, 2017;389:10076: 1336-1346.
2. Giovannoni G, Butzkueven H, Dhib-Jaibut S, et al. Brain health: time matters in multiple sclerosis, Multiple Sclerosis and Related Disorders. 2016; 9:S5-S48.
3. Spurlock CF, et al. Long noncoding RNA gene expression signatures to classify multiple sclerosis. Multiple Sclerosis Journal. 2017;23:1.
4. Aune TM, Crooke PS, Patrick AE, et al. Expression of long noncoding RNAs in autoimmunity and linkage to enhancer function and autoimmune disease risk genetic variants, Journal of Autoimmunity. 2017;81:99-109.