We are at war that does not involve firearms, but ventilators. The likes of the COVID-19 pandemic have not been seen since the decimations of the 1918 flu. With SomaLogic® advanced technology, we have a chance to prevent the same outcome. But we need comrades-in-arms to succeed.
As a reminder, SomaLogic measures proteins to deeply understand the biology health and disease. In a sense, the thousands of proteins in the blood together relay the details of the battle happening on the molecular level inside every COVID-19 victim. It is possible to use our technology to listen to the battle cries of bodily proteins in real-time, and we hope to translate that language to:
Predict who will experience severe illness and who will not. Proteins can provide the basis for developing a way to identify who will most likely develop life-threatening illness, and who won’t.
Identify the complication sub-types in those at risk for severe illness. Protein changes can help clinicians understand in real-time who will develop certain disease-related comorbidities, thus impacting disease treatment planning, the allocation of specific therapies, and resource utilization in constrained delivery situations.
Identify the biological drivers of COVID-19 disease, thus driving drug development. Proteins reveal “real-time” molecular biology, revealing both new drug targets and even unexpected targets for which there are already approved drugs or treatments that can be repurposed for COVID-19.
Accelerate vaccine development. Proteins can reveal the immune system response to a vaccine candidate, suggesting the likelihood of a longer-term clinical effect from that candidate.
We are committing ourselves to bringing this powerful weapon to the world’s war on COVID-19 war. But we need allies, specifically researchers with access to critical patient samples and collaborators who can help us defray the cost of this effort. Talk to us.
With pregnancy comes not just new life, but also the collection of huge amounts of data and numerous doctor visits to ensure the health of the mother and child. The pregnant body undergoes myriad biological changes that can give rise to problems that yield a bad result that all involved want to avoid.
A whole cottage industry of direct-to-consumer (DTC) products/services has sprung up, aimed at providing peace-of-mind health information between doctor visits. However, many of these products are likely having the opposite effect, creating more anxiety, more doctor visits and more unnecessary medical care (Thielking, 2019). Although the direction of prenatal care is toward greater empowerment of the mother and her supporters, the technology at hand (and the data it provides) is simply not yet good enough to have a positive rather than negative impact.
What data/technology should we be pursuing? A recent study in marsupials aimed at understanding how embryo implantation evolved provides a hint (Griffith et al., 2017). The research team noted that the implantation event appears to modify the normal inflammation response to a foreign body. The group suggests that this could explain the increased risk of miscarriage if a person is on anti-inflammatory medication during the implantation phase.
Griffith et al. further compared the gestation styles of marsupials and humans. Both humans and marsupials rely on inflammation for embryo implantation and for birth. Unlike marsupials, in which the newborn crawls into a pouch for further development to avoid the mother’s immune system, humans and other placental mammals do something different. After implantation, these creatures mount an anti-inflammation response until it is time to give birth. This gives the embryo time to mature and not be attacked by the mother’s immune system.
Monitoring the “on-off-on” cycling of the inflammatory response could be a great way to see how well the pregnancy is going and get early warning of problems from a mistimed immune response. (Also, this could be the sought-after answer to the question mentioned above.) One group is already on top of that and used the insightful SomaLogic® technology along with other information to monitor what happens to the immune system during the course of a pregnancy (Aghaeepour et al., 2017). Although the sample size was small, the group’s findings lay the groundwork for understanding what happens to the immune system during a healthy pregnancy in great detail. It could open the door to the possibility of new diagnostics to determine if a pregnant individual is at risk of problems with the pregnancy. It might be enough to put many worried minds at ease and reduce unnecessary doctor visits. Perhaps, it could even reduce the workload of practioners. Indeed, it might even revolutionize the field.
Aghaeepour, N., Ganio, E. A., McIlwain, D., Tsai, A. S., Tingle, M., Van Gassen, S., . . . Gaudilliere, B. (2017). An immune clock of human pregnancy. Sci Immunol, 2(15). doi:10.1126/sciimmunol.aan2946
Griffith, O. W., Chavan, A. R., Protopapas, S., Maziarz, J., Romero, R., & Wagner, G. P. (2017). Embryo implantation evolved from an ancestral inflammatory attachment reaction. Proc Natl Acad Sci U S A, 114(32), E6566-E6575. doi:10.1073/pnas.1701129114
Thielking, M. (2019, July 23) As pregnancy tech proliferates, women and their doctors wade through what’s helpful — and what’s a headache. STAT. Retrieved on August 5, 2019 from https://www.statnews.com/2019/07/23/pregnancy-tech-help-headache/.
Depending on your preferred authority, “middle age” begins at 40 or 45, and ends 20 years later at the beginning of “old age.” These years are a time of transition across the population, particularly in physical status. But what if an individual’s proteins offer a different take on the meaning of middle age (and even old age)?
Recently, an international team reported in Nature Medicine that how we view middle age is likely wrong (Lehallier et al., 2019). Using the SomaLogic technology, they looked at changes in thousands of circulating proteins among 4,263 healthy adults, spanning the ages of 18 to 95 years old. In digging through all those proteins, they uncovered a “proteomic clock” that marks the passage of biological time. Specifically, the team identified three events that happen in adulthood. The first aging event happens around 34 years of age. The second one occurs at 60 years of age, which was termed “middle age” by the authors. And the third one appears at 78 years of age, heralding the start of “old age.”
The researchers noted other fascinating phenomena. For example, their work confirms previous suggestions that people can be biologically younger than the age stated on their identification card. They also noted that people who performed well on cognitive and physical tests tended to age slower according to their protein profiles, and that women aged slower than men.
On the flip side, the researchers found that it is also possible to age faster. For example, the protein patterns in people with Alzheimer’s disease or Downs Syndrome resembled the patterns associated with the proteome of older people, which could help explain the rapid aging seen in these disorders.
So, perhaps 40 or 45 are not really middle age and 60 or 65 even are not old – at least from a biological perspective! A lovely thought one can have even if your birthday cake is highly illuminated.
Lehallier, B., Gate, D., Schaum, N., Nanasi, T., Lee, S. E., Yousef, H., . . . Wyss-Coray, T. (2019). Undulating changes in human plasma proteome profiles across the lifespan. Nat Med, 25(12), 1843-1850. doi:10.1038/s41591-019-0673-2
Do you know how many different hats you wear in a day? Think about all the different roles you may take on during the course of the day – parent, significant other, boss, employee, cook, cleaner, accountant, household zookeeper, negotiator, etc. The number of roles can seem endless, but they reflect just how “dynamic” you can be and have to be. Like you, life is, indeed, dynamic. Including our health, especially how it changes over time. The question is what data will help – static or dynamic data – in managing your health?
What would static data be? Well, your genetic code that you were born with could count as static for the most part. For those of us growing up in the post-Human-Genome-Project world, it has been drummed into our heads – hyped even – that our genes define us. By examining our DNA (the body’s equivalent to the 0’s and 1’s used to create software, such as genes), we can see what our health future has in store. But what if you are dealt a dreadful set of gene cards? Fortunately, we have learned enough now to know that you are not out of luck: We now know that genes do not dictate fate in the vast majority of cases.
Also, sequencing your genome (all the DNA stuff you inherit) may not accurately describe your complete genetic portrait. You may in fact have more than one genome residing in your body for a variety of reasons which, in addition to just the regular errors associated with DNA sequencing at scale, could compromise the accuracy of any conclusions drawn.
However, even if you had your complete and error-free genetic report in hand, interpreting what it means with regards to health is still uncertain. Turns out that a single mutation (i.e., variation) rarely means you will get “x” disease or condition. In fact, research has shown that variants found in 11,544 genes to be associated with at least one of 518 traits (Watanabe et al., 2019). So, thousands of genes may have influence on a single trait.
Now, let’s examine dynamic data. A wonderful example would be proteomic (all your proteins) data. Proteins are the products of your genes. Yet, how much or little a protein exists can be influenced by so many factors and change throughout the day, etc.
At SomaLogic, we have worked for 20 years to develop the technology to monitor the rise and fall of protein levels and understand how those reflect a tremendous amount of detail about your multi-tasking body and your health. In fact, these fluctuations have generated so much information that hundreds of papers have been written, with many more yet to appear. From the bounty, we see just how protein information might help with arthritis, alert patients about an impending early demise or a non-ideal surgery outcome (Fong et al., 2019), foretell possible failure of a clinical trial (Williams et al., 2018), provide a better alert than traditional “gold standards” regarding a cardiovascular event (Ganz et al., 2016), etc. Protein information could even tell how your body responds to a diet (Thrush et al., 2018) or how exercise is affecting it (Santos-Parker, Santos-Parker, McQueen, Martens, & Seals, 2018).
Let’s get back to our question. What data are as dynamic as you? The answer lies in your proteins. Knowing your protein changes and how to optimize them could help you more effectively meet all the demands of your many different roles.
Fong, T. G., Chan, N. Y., Dillon, S. T., Zhou, W., Tripp, B., Ngo, L. H., . . . Libermann, T. A. (2019). Identification of Plasma Proteome Signatures Associated With Surgery Using SOMAscan. Ann Surg. doi:10.1097/SLA.0000000000003283
Ganz, P., Heidecker, B., Hveem, K., Jonasson, C., Kato, S., Segal, M. R., . . . Williams, S. A. (2016). Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients With Stable Coronary Heart Disease. JAMA, 315(23), 2532-2541. doi:10.1001/jama.2016.5951
Santos-Parker, J. R., Santos-Parker, K. S., McQueen, M. B., Martens, C. R., & Seals, D. R. (2018). Habitual Aerobic Exercise and Circulating Proteomic Patterns in Healthy Adults: Relation to Indicators of Healthspan. J Appl Physiol (1985). doi:10.1152/japplphysiol.00458.2018
Thrush, A. B., Antoun, G., Nikpay, M., Patten, D. A., DeVlugt, C., Mauger, J. F., . . . Harper, M. E. (2018). Diet-resistant obesity is characterized by a distinct plasma proteomic signature and impaired muscle fiber metabolism. Int J Obes (Lond), 42(3), 353-362. doi:10.1038/ijo.2017.286
Watanabe, K., Stringer, S., Frei, O., Umicevic Mirkov, M., de Leeuw, C., Polderman, T. J. C., . . . Posthuma, D. (2019). A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet. doi:10.1038/s41588-019-0481-0
Williams, S. A., Murthy, A. C., DeLisle, R. K., Hyde, C., Malarstig, A., Ostroff, R., . . . Ganz, P. (2018). Improving Assessment of Drug Safety Through Proteomics: Early Detection and Mechanistic Characterization of the Unforeseen Harmful Effects of Torcetrapib. Circulation, 137(10), 999-1010. doi:10.1161/CIRCULATIONAHA.117.028213