Holding onto the Edge: A New Look at How a SOMAmer Binds

Holding onto the Edge: A New Look at How a SOMAmer Binds

A few fingers there, a couple more fingers over there and a couple well placed feet are all that separate rock climbers from the canyon floor hundreds of feet below. With the ease and grace of spiders, the experienced climbers maneuver quickly over the nearly smooth vertical rock face. What supernatural power do they have that keeps them from succumbing to gravity’s will? None. The climbers have instead developed the muscular strength, the know-how to maximize their grip and a few other handy tools to make the daunting feat easier.

SOMAmers are the elite rock climbers of the aptamer* world. They bear a set of “tools” that give them the advantage of gripping onto proteins in ways that other aptamers cannot. These tools include specialized chemical groups that provide the extra “sticky” factor necessary for SOMAmers to find new holds on their targeted proteins and latch on for an incredibly long period of time.

In recent years, a few papers have detailed how SOMAmers put these tools to work. A new discovery from Dr. Anna Marie Pyle’s lab at Yale University (in a collaboration with SomaLogic) has expanded our insight into how these super-aptamer rock climbers hold onto the rocky outcrops of proteins. They revealed the three-dimensional structure of a SOMAmer bound to interleukin 1α (IL-1α), a very difficult protein to bind with a traditional aptamer (Ren, Gelinas, von Carlowitz, Janjic, & Pyle, 2017). This detailed look at how the SOMAmer interacts with IL-1α revealed not only unique SOMAmer attributes, but also a view of IL-1α that had never been seen.

The structure of the IL-1α binder is truly unique, bearing little resemblance to anything that one might expect when told a SOMAmer is made from mostly DNA. The tiny SOMAmer looks like a ladder thrown off the side of a mountain and trampled by a herd of elephants. This contorted shape is thanks in part to the “tools” the SOMAmer possesses. Unlike other structures of SOMAmers in the literature, this one uses a fancy chemical attachment called “2Naphthyl” (Ren et al., 2017). In the structure, these 2Naphthyl tools form a building block (seen in other SOMAmer structures that use different “tools”) reminiscent of a miniature “zipper” that helps maintain the unusual bent shape (Ren et al., 2017). Aside from the little zipper, what’s really neat about this structure is its unexpectedness in this kind of molecule. It is a new take on “G-quadruplexes (Ren et al., 2017),” which are found throughout nature.

Given this unique and tortuous configuration, how does the SOMAmer hold onto its protein partner? Well, it turns out that the bent ladder structure created a “hand,” with the 2Naphthyl groups forming a sticky pocket in the palm of the hand that provided the bulk of the interaction’s strength (Ren et al., 2017). Additional contacts were made between negatively charged and positively charged atoms in the “fingers” (Ren et al., 2017).

As mentioned above, this unusual structure reveals a lot about the protein as well. Up until now, the research community was aware of the general structure of IL-1α, but knew none of the fine details (Ren et al., 2017). The inclusion of the SOMAmer hand in visualizing the structure helped pull the protein together to form an exquisite crystal that revealed the missing fine details. The research community now sees the elusive sidechains of IL-1α, which in turn illuminate the biology of inflammation and cancer development (Ren et al., 2017). As an extra bonus, the little SOMAmer could also inhibit the protein’s normal function; thus, making it a potential therapeutic for future development (Ren et al., 2017).

With a few tools and the ability to adopt contorted shapes, this tiny hand-like SOMAmer and others can tackle the most difficult of proteins and find great places to hold on. This sticky grip makes it possible to reach new vantage points not achievable by other types of technology. What can be seen from these lofty vantage points? Akin to the beautiful vistas bestowed to rock climbers, we will be able to gaze at never-before-seen vistas of our health.

*(a string of nucleic acids designed to bind to stuff)

References

 

Ren, X., Gelinas, A. D., von Carlowitz, I., Janjic, N., & Pyle, A. M. (2017). Structural basis for IL-1alpha recognition by a modified DNA aptamer that specifically inhibits IL-1alpha signaling. Nat Commun, 8(1), 810. doi:10.1038/s41467-017-00864-2

Take my Breath Away: Diagnosing Asthma’s Severity When Every Minute Counts

Take my Breath Away: Diagnosing Asthma’s Severity When Every Minute Counts

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.

 

References

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

 

A Beautiful Brain Besieged by Glioblastoma

The frog lies there, all splayed out and pinned. It will no longer ribbit or hop. Gone are its chances of being transformed into a prince with a simple kiss. Yet, this formaldehyde-soaked creature still fascinates. For it reveals to young eyes caught up in an anatomy lesson, just how intricate and miraculous biological bodies truly are.

Take the exposed brain peeking at the dissector through a hole in the frog’s cranium, for instance. Translucent lobes glisten in the fluorescent light. These lobes and other brain parts are the locus for everything that makes us (and even the frog) who we are. It’s the most terrifying of circumstances when a person is robbed of their very identity by brain cancer.

Glioblastoma, which has been in the news recently (both in politics and sports), is a particularly nasty form of brain cancer. It is highly aggressive, rapidly pillaging a person’s identity. It comes without any significant warning, perhaps via generic symptoms like a headache or nausea (Geez. This sounds like how a lot of things start.) (Young, Jamshidi, Davis, & Sherman, 2015). Victims can also experience a change in personality or memory loss pending on where in the brain the cancer is located (Young et al., 2015). Even with treatment, the average victim can fend off the pillager for only a little while (Davis, 2016; Young et al., 2015).

New and better treatments are obviously needed, which in turn require a better understanding of this plunderer. In an elegant assay, researchers demonstrated that the environment (not the genes) dictates the cancer’s pilfering path (Miller et al., 2017). Pending the testing (environmental) conditions, different proteins that regulate the making of RNA (a message for making protein) are activated. This in turn can affect how responsive the tumors are to certain chemotherapies (Miller et al., 2017). Given how these proteins (i.e., transcription factors) wielded such a huge impact on how the tumor responded to its environment, this work suggests that transcription factors are logical targets for new therapeutics.

It is still too soon to tell if this work will result in new therapeutics that safeguard our repository of uniqueness. We can be cautiously optimistic, but other research may burst that beautiful bubble (Mak, Evaniew, & Ghert, 2014). More and more evidence continues to show that what may save the life of lab animals will not work for us experimenters (Ugh).

So, what are we to do? Sit around, look pretty and twiddle our thumbs? No! We need to rise and emit some battle cries. We must guard that optimistic bubble. New procedures are needed to improve the translation of successes in the lab to the clinic. We also need to continue developing new defenses against glioblastoma. If we can develop better sentries (diagnostics), it is possible to spot the marauding tumors sooner. Even Dr. Philip E. Steig (founder and Chairman of Weill Cornell Brain and Spine Center) shares in this optimism that earlier detection, improved knowledge and new treatments could improve the odds of the cancer going into remission (Steig, 2016). The sooner we can tell if that headache is nothing versus cancer, the sooner we can battle and hold onto what constitutes our (and the frog’s) beautiful individuality.

References

Davis, M. E. (2016). Glioblastoma: Overview of Disease and Treatment. Clin J Oncol Nurs, 20(5), S2-8. doi:10.1188/16.CJON.S1.2-8

Mak, I. W., Evaniew, N., & Ghert, M. (2014). Lost in translation: animal models and clinical trials in cancer treatment. Am J Transl Res, 6(2), 114-118.

Miller, T. E., Liau, B. B., Wallace, L. C., Morton, A. R., Xie, Q., Dixit, D., . . . Rich, J. N. (2017). Transcription elongation factors represent in vivo cancer dependencies in glioblastoma. Nature, 547(7663), 355-359. doi:10.1038/nature23000

Young, R. M., Jamshidi, A., Davis, G., & Sherman, J. H. (2015). Current trends in the surgical management and treatment of adult glioblastoma. Ann Transl Med, 3(9), 121. doi:10.3978/j.issn.2305-5839.2015.05.10

Steig, P.E. (2016, August 8). Early Detection Can Be Key to Surviving a Brain Tumor. Retrieved from http://weillcornellbrainandspine.org/early-detection-can-be-key-surviving-brain-tumor.

 

A Mermaid’s Tale About Knowledge Sourced from Genomics

A Mermaid’s Tale About Knowledge Sourced from Genomics

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.

References

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

A Mad Hatter’s Question About Correlating Transcriptomics to Proteomics

A Mad Hatter’s Question About Correlating Transcriptomics to Proteomics

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?

Resources

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

Understanding the Ramifications of Mutations: Seeing Past the Trees to the Forest

Understanding the Ramifications of Mutations: Seeing Past the Trees to the Forest

Remember Jurassic Park?  I can. I still get shivers thinking about the dinosaurs crashing through the trees and devouring anything that moves. I also recall the adorable animation the park visitors watched to better understand how the dinosaurs were brought back from extinction via the DNA in their blood cells trapped in those ancient mosquitoes encased in amber. This introduction to genetic engineering via Hollywood initiated my scientific endeavor for learning more about nucleic acids. I was hooked.

Since then, I have joined the countless masses who have become enthralled with the double-helix. We believed that the message that dictated our uniqueness resided in that simple, repetitive molecule. Thanks to technological advances, reading these messages has become both easy and affordable. As a result, the scientific literature is inundated with genome wide association studies (GWAS), which search for genetic changes that are related to many different diseases and conditions (Manolio, 2017).

Boyle et al. raised the question as to whether or not they should limit their focus to just a small subset of genes identified from GWAS to better understand disease or inheritable traits (Boyle, Li, & Pritchard, 2017). In their recent Cell publication, the group of scientists concluded that while a “core” set of genes might be a cause for something, the overall phenotype (physical or observable characteristics) resulted from many small contributions from many more genes. The authors stated that while the initial impulse may be to do even more GWAS, a different approach may be needed. They recommended doing an omnigenics analysis of GWAS data.

What is “omnigenics” you may ask? For complex traits or diseases, any gene variant in the genome could be contributing to the manifestation of the disease or trait. Hence, to truly understand the genetic cause of a disease or trait, all DNA mutations need to be considered. Even if the mutation seems to affect a system seemingly unrelated to the disease or trait of interest, it should be included in the analysis.

Recently, a study was published that showed that individuals were carrying disease-causing gene variations, yet the carriers showed no physical manifestation of the disease (Chen et al., 2016). The reason could be what the Boyle et al. had suggested: The manifestation is dictated by the genomic forest and not by a few gene trees.

This omnigenics approach sounds lovely, but it comes with its own issues. How do we truly separate legitimate changes from the background to understand the causes of disease? If we look at the data, is it possible that we are just looking at a pile of sawdust trying to pretend to be the desired forest? With so much information embedded in DNA, it can be a daunting challenge to establish causality no matter how good the software that sifts through the info.

Instead of considering our DNA, maybe it would be worthwhile to change focus and look at the proteomic (all proteins that constitute our bodies) forest. Being the products of our genes, proteins offer a timelier view of what is happening in our bodies. Understanding how changes in the protein, such as concentration, correlate with disease or inheritable traits may shed more light onto how different biological systems interact with one another in the body. It could also help explain exactly how simple genetic change(s) in an unrelated pathway could affect a physical trait. This capability would certainly explain what happened in the Jurassic Park dinosaurs, which had frog DNA inserted into their genomes, to allow some of them to become male.

References

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

Manolio, T. A. (2017). In Retrospect: A decade of shared genomic associations. Nature, 546(7658), 360-361. doi:10.1038/546360a