When I first came to the Colorado, the mountains captivated me. They looked so imposing, yet enchantingly beautiful at the same time. A few months later, some of my mountaineering friends convinced me to climb one of these enchantresses (known locally as “14’ers”). They picked an “easy” one because I had spent all my life at a few hundred feet above sea level near the Mississippi River.
We started out on a beautiful crisp fall morning. The sun had yet to rise and illuminate the aspen trees that were already turning a golden yellow. As we climbed higher, the aspen grew smaller and it got colder. As the air grew colder and thinner, I found myself having a difficult time breathing. I kept soldiering on, but the fight for breath was getting harder. According to one friend’s watch, I had made it about 13,700 feet before the struggle for breath grew too much. I had to retreat back to a more oxygen-rich environment. The mountain won this round.
There are individuals who experience the fight for breath every day. The cause of this fight is a condition known as asthma, which can vary greatly in its severity. Diagnosing the severity and determining the correct course of treatment is not always straightforward, quick or cheap (Israel & Reddel, 2017). If new diagnostic tests became available, could they speed up the process of determining asthma severity and thus identifying the best treatment?
An international team of researchers united to answer that very question (Rossios et al., 2017). They queried sputum (another name for phlegm) samples from patients with different degrees of asthma to look for changes in the patients’ transcriptomic (looking at all RNA levels) and proteomic (looking at all protein levels) profiles. The researchers successfully found changes in those profiles that provide new insights about the underpinnings of asthma severity and may even help expedite the diagnosis (Rossios et al., 2017).
These researchers took not just one small step, but one giant leap towards summiting Mt. Improved Diagnostics. Instead of focusing on just one biomarker and looking for its presence in samples provided by patients with different degrees of asthma severity, the researchers utilized technologies that could cast a broad net (Rossios et al., 2017). Using the SOMAscan assay, they could scan various molecular pathways simultaneously, see the differences and achieve a better understanding (Rossios et al., 2017).
The asthma researchers certainly share a great vantage point with others who use proteomics. Proteins, which are the end product of our genes, are responsible for how our bodies respond to the environment, disease, etc. Aside from responding to cues, rogue proteins can also be the cause of disease. By looking at how proteins interact with one another and the downstream effects of those interactions, the scientific community can better discern the onset of disease (Fessenden, 2017). By thinking deeply about the data, it will feasible to scale the enchantress, Mt. Improved Diagnostics, with greater ease and surer breath.
Fessenden, M. (2017). Protein maps chart the causes of disease. Nature, 549(7671), 293-295. doi:10.1038/549293a
Israel, E. & Reddel, H. K. (2017). Severe and difficult-to-treat asthma in adults. N Engl J Med, 377(10), 965-976. doi:10.1056/NEJMra1608969
Rossios, C., Pavlidis, S., Hoda, U., Kuo, C. H., Wiegman, C., Russell, K., . . . Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes Consortia Project Team. (2017). Sputum transcriptomics reveal upregulation of IL-1 receptor family members in patients with severe asthma. J Allergy Clin Immunol. doi:10.1016/j.jaci.2017.02.045
In Disney’s The Little Mermaid (1989), a vivacious, inquisitive mermaid becomes infatuated with humans. She eagerly learns everything she can about these mysterious creatures. In her scholarly pursuits, she talks extensively with a seabird who satiates her curiosity with extremely inaccurate knowledge, such as using a fork to comb hair. While we might chuckle at the misuse of the fork, we too often apply knowledge that only in hindsight causes chuckling or a quiet reflection of “OMG! What the *@#% were we thinking?”
For instance, consider genomics. Upon the near completion of the Human Genome Project, shell trumpets and fish choruses heralded the genetic revolution that was going transform medicine. In 2001, Francis Collins (current Director of the National Institutes of Health) and Victor McKusick co-wrote, “Genomic medicine holds the ultimate promise of revolutionizing the diagnosis and treatment of many illnesses (Collins & McKusick, 2001).” Since the completion of the project, it seems the floodgates have opened, releasing waves of genetic tests.
Nearly two decades later, the wave of enthusiasm is crashing. In a recent survey, most oncologists acknowledged that genomic testing does not meet expectations and has been overhyped (Genomeweb, 2017). They and other medical professionals also admitted that they did not have the expertise to adequately decipher the large amount of genetic data or to communicate them effectively to patients (Genomeweb, 2017; (Mikat-Stevens, Larson, & Tarini, 2015). Doctors often relied on the test manufacturer’s interpretations (Graber, 2015).
Consequently, knowledge acquired from genetics is not applied correctly. “Poor unfortunate souls” are getting over diagnosed and/or receiving unnecessary treatment based on the results. Recently, a young teenager received an implantable defibrillator based on genetic tests that said he was at risk for a fatal heart condition (Ackerman et al., 2016). A second opinion revealed that he did not have the fatal syndrome and that the implantable defibrillator was not necessary (Ackerman et al., 2016). Another study highlighted that half of women who undergo double mastectomies in the hopes of avoiding breast cancer had mutations, but not the kind known to increase cancer risk (Conger, 2017). Needless to say, a significant percentage of the doctors said that they would treat these women with mutations of unknown significance the same as those with the mutations known to increase risk (Conger, 2017).
It would have been ideal to have a clinical geneticist help guide both doctor and the patient through the murky data. However, there are too few of them for the huge demand placed upon them (Graber, 2015). Even some admit that they are having a hard time keeping up with the literature and new findings (Graber, 2015), which can totally change how one evaluates risk of disease based on genetics (Boyle, Li, & Pritchard, 2017; Chen et al., 2016).
Our understanding of genetics is really a drop in the ocean. As mentioned earlier, new findings are highlighting how little the scientific and medical communities know about a “simple” code. Healthy people are walking about with what might be considered “bad” genetics. Through lifestyle choices or some other undiscovered biological reason, these people are just fine (Chen et al., 2016; Khera et al., 2016). Callouts are being made to change how genetic contributions to diseases are determined (Boyle et al., 2017). Let’s face it, genomes are more complex than anyone could have foreseen.
Have we come to a point where we start quietly reflecting about the use of genomics in medicine? Yes. Already, the National Cancer Institute and the US Food and Drug Administration are considering proteogenomics as the new frontier for clinical diagnostics (Bonislawski, 2017). This approach, which couples genetic information with protein information to improve understanding of biological processes, could yield a more favorable outcome warranting celebratory singing by a Caribbean crab with a thick accent. As to not repeat the past, however, extreme care should be made to ensure that results and their limitations are clearly communicated and recipients know how to properly use the knowledge.
Ackerman, J. P., Bartos, D. C., Kapplinger, J. D., Tester, D. J., Delisle, B. P., & Ackerman, M. J. (2016). The Promise and Peril of Precision Medicine: Phenotyping Still Matters Most. Mayo Clin Proc. doi:10.1016/j.mayocp.2016.08.008
Bonislawski, A. (2017, July 21). FDA and NCI Memorandum Indicates Growing Interest in Proteogenomics as
Clinical Approach. Retrieved from https://www.genomeweb.com/proteomics-protein-research/fda-nci-memorandum-indicates-growing-interest-proteogenomics-clinical.
Boyle, E. A., Li, Y. I., & Pritchard, J. K. (2017). An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell, 169(7), 1177-1186. doi:10.1016/j.cell.2017.05.038
Chen, R., Shi, L., Hakenberg, J., Naughton, B., Sklar, P., Zhang, J., . . . Friend, S. H. (2016). Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases. Nat Biotechnol, 34(5), 531-538. doi:10.1038/nbt.3514
Collins, F. S., & McKusick, V. A. (2001). Implications of the Human Genome Project for medical science. JAMA, 285(5), 540-544.
Conger, K. (2017, April 12). Physicians’ misunderstanding of genetic test results may hamper mastectomy decisions for breast cancer patients. Retrieved from https://med.stanford.edu/news/all-news/2017/04/misunderstanding-genetic-test-results-may-increase-double-mastectomies.html
Genomeweb (2017, May 2). In Survey, Oncologists See Genomic Testing as Important Advance, But Value
‘Below Expectations.’ Retrieved from https://www.genomeweb.com/molecular-diagnostics/survey-oncologists-see-genomic-testing-important-advance-value-below.
Graber, C. (2015, February 5, 2015). The Problem with Precision Medicine. The New Yorker.
Khera, A. V., Emdin, C. A., Drake, I., Natarajan, P., Bick, A. G., Cook, N. R., . . . Kathiresan, S. (2016). Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease. N Engl J Med, 375(24), 2349-2358. doi:10.1056/NEJMoa1605086
Mikat-Stevens, N. A., Larson, I. A., & Tarini, B. A. (2015). Primary-care providers’ perceived barriers to integration of genetics services: a systematic review of the literature. Genet Med, 17(3), 169-176. doi:10.1038/gim.2014.101
Why is a raven like a writing desk? Lewis Carroll penned this head-scratcher over a century ago in his book, Alice’s Adventures in Wonderland. Since then, people keep trying to draw parallels between these two unrelated items to answer the Mad Hatter’s baffling riddle. In Omicsland, a similar riddle could be uttered at a social gathering, “Why is transcriptomics (looking at the RNA levels in an individual) like proteomics (looking at the protein levels in an individual)?”
If logic based on biology’s central dogma (DNA begets RNA which begets protein) is applied, the initial response might be because they are equivalent with respect to tracking protein levels. If the level of mRNA (messenger RNA that codes for the protein) rises, then the amount of the corresponding protein would rise too. This logic is not entirely sound. It was shown that mRNA levels do not always correlate with protein levels (Vogel & Marcotte, 2012). Recently, additional research has poked holes into research saying that the two omics correlate well (Fortelny, Overall, Pavlidis, & Freue, 2017). I’ll briefly elaborate about this lack of correlation, but a more thorough explanation (Liu, Beyer, & Aebersold, 2016) bears a tag saying, “Read me.”
One mRNA does not beget just one protein. Many proteins can be created from the same mRNA. The efficiency of this process can be affected by several factors. One of the major influences can be found within some, but not all mRNAs themselves. The mRNA can possess chemical modifications that can affect the process of making protein (Zhao, Roundtree, & He, 2017), possess internal elements that serve as video game-like cheat codes to fast track the process (Walters & Thompson, 2016) or contain binding sites for proteins that help regulate when the mRNA should be used (Nelson, Leidal, & Smibert, 2004). Another major influence can be found in the regulation of proteins (besides the ribosome) that are involved in converting the mRNA code into a protein (Nho & Peterson, 2011).
Aside from biological reasons, technology issues can sometimes explain why mRNA levels do not correlate with protein levels. Variations in how a technique is executed and how data are analyzed abound, and can affect the results. Also, technical approaches have their limits and may not be the best ones to use for certain tasks (e.g., using flamingos as croquet mallets). Best practices and new approaches are being proposed to help address the limits and reduce the variation that can arise (Conesa et al., 2016; Hu, Noble, & Wolf-Yadlin, 2016).
As noted earlier, correlation between proteomics and transcriptomics is low. However, a small percentage of protein levels do correlate with mRNA levels. This correlation, however, may only happen in certain instances or biochemical pathways (Liu et al., 2016; Zhang et al., 2016).
Let’s revisit the original question, why is transcriptomics like proteomics? The answer could simply be that the two are alike because they are both complicated. Correlating the two is feasible, but not without peril. With a low correlation, is it worth jumping down the rabbit hole to draw parallels between the two?
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., . . . Mortazavi, A. (2016). A survey of best practices for RNA-seq data analysis. Genome Biol, 17, 13. doi:10.1186/s13059-016-0881-8
Fortelny, N., Overall, C. M., Pavlidis, P., & Freue, G. V. C. (2017). Can we predict protein from mRNA levels? Nature, 547(7664), E19-E20. doi:10.1038/nature22293
Hu, A., Noble, W. S., & Wolf-Yadlin, A. (2016). Technical advances in proteomics: new developments in data-independent acquisition. F1000Res, 5. doi:10.12688/f1000research.7042.1
Liu, Y., Beyer, A., & Aebersold, R. (2016). On the Dependency of Cellular Protein Levels on mRNA Abundance. Cell, 165(3), 535-550. doi:10.1016/j.cell.2016.03.014
Nelson, M. R., Leidal, A. M., & Smibert, C. A. (2004). Drosophila Cup is an eIF4E-binding protein that functions in Smaug-mediated translational repression. EMBO J, 23(1), 150-159. doi:10.1038/sj.emboj.7600026
Nho, R. S., & Peterson, M. (2011). Eukaryotic translation initiation factor 4E binding protein 1 (4EBP-1) function is suppressed by Src and protein phosphatase 2A (PP2A) on extracellular matrix. J Biol Chem, 286(37), 31953-31965. doi:10.1074/jbc.M111.222299
Vogel, C., & Marcotte, E. M. (2012). Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet, 13(4), 227-232. doi:10.1038/nrg3185
Walters, B., & Thompson, S. R. (2016). Cap-Independent Translational Control of Carcinogenesis. Front Oncol, 6, 128. doi:10.3389/fonc.2016.00128
Zhang, H., Liu, T., Zhang, Z., Payne, S. H., Zhang, B., McDermott, J. E., . . . Investigators, C. (2016). Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer. Cell, 166(3), 755-765. doi:10.1016/j.cell.2016.05.069
Zhao, B. S., Roundtree, I. A., & He, C. (2017). Post-transcriptional gene regulation by mRNA modifications. Nat Rev Mol Cell Biol, 18(1), 31-42. doi:10.1038/nrm.2016.132