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?
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