Chickens, Proteins & Your Health

Chickens, Proteins & Your Health

What do you have in common with this chicken? Perplexed? So is the chicken. While the two of you may cross roads from time to time, the features that really bind the two of you together are proteins.

Yes, chicken is a source of healthy proteins, but that is not what I mean. The protein connection I want to talk about is the fact that you are made up of proteins, which can reveal quite a bit about your health (and probably the chicken’s, for that matter).

In fact, pretty much every biological process you can think of relies on proteins. You name it (seeing, hearing, tasting, etc.), proteins (tens, hundreds or even thousands) are involved. By studying changes in protein levels, it is possible to learn how to gain a better “health span” (how long you will enjoy good health) that will hopefully last longer than the chicken’s.

For example, protein levels reflect the molecular changes that happen in our blood as a result of the fat in our bodies. As many of us know all too well, our bodies can stuff fat pretty much everywhere; under the skin, in our livers and in between abdominal organs (visceral fat). Detecting and measuring that fat, especially in early storage stages, can be difficult for accuracy and cost reasons (e.g., the use of measuring tape and pincers or the need for high tech measurement machines). But we have found that we can accurately tell what you are packing (or not) just by looking at proteins in your blood.

Recently, SomaLogic scientists published a proof-of-concept study that illustrates the health-management potential of blood-based proteins for things like fat measurement. Using samples and data from a large health study, our researchers and their collaborators found that changes in 219 proteins could indicate a person’s body fat percentage, 115 proteins signifying a person’s lean body mass and 96 proteins specifying a person’s amount of visceral fat (For the statistic buffs, check out the paper (Williams et al., 2019)!).

We believe that this kind of information will empower people to live healthier lives. We have known for a long time that obesity can increase the chances of developing type two diabetes, metabolic syndrome and other chronic health problems. Even if a person is not classified as “obese,” they may be harboring quite a bit of visceral fat, negatively affecting their health without their knowledge (Shuster, Patlas, Pinthus, & Mourtzakis, 2012). We hope our approach can change not just how people can avoid metabolic disease, but a whole host of common diseases and conditions.

What does this mean for the chicken? It is still a protein source, but maybe not always a good one. A discussion for another time.

 

References

Shuster, A., Patlas, M., Pinthus, J. H., & Mourtzakis, M. (2012). The clinical importance of visceral adiposity: a critical review of methods for visceral adipose tissue analysis. Br J Radiol, 85(1009), 1-10. doi:10.1259/bjr/38447238

Williams, S. A., Kivimaki, M., Langenberg, C., Hingorani, A. D., Casas, J. P., Bouchard, C., . . . Wareham, N. J. (2019). Plasma protein patterns as comprehensive indicators of health. Nature Medicine. doi:10.1038/s41591-019-0665-2

 

Identical Twins Part 3: Chaos Rules

Identical Twins Part 3: Chaos Rules

Chaos (or randomness) is a fundamental law of life. Ever try to organize/clean something only to have a young one (two-legged or four legged) come through and undo all your efforts? That’s chaos exerting its authority! We experience chaos all the time.

However, when it comes to understanding our physical traits, we tend to forget about chaos and assume physical traits are pretty much determined through either genetics, personal decisions or environmental exposure. Our undying urge to understand ourselves tends to ignore (intentionally or not) the central component of randomness.

So how does chaos help determine physical traits? One great example involves the X-chromosome. In some animal species, such as humans or cats, females receive two X-chromosomes from their parents, but they only need to use one. To achieve this, one of the X-chromosomes will randomly “inactivate” early on in development, though not from the very beginning.

Calico/tortoiseshell female cats show just how random this inactivation can be. The gene controlling whether a cat will have gold or black fur happens to be on the X-chromosome. As an example, a female cat received a version of the gene encoding for black fur from one parent and one encoding for gold fur from the other parent. All those lovely patches of black and gold on the cat (such as the one in the picture) are from the inactivation of the X-chromosome that harbors either the black or gold coloring. As pictured, the cat does not possess equal parts of gold and black, but a beautiful, chaotic mix.

Another strange mammal, the nine-banded armadillo, also serves as an example of chaos disrupting genetic determinism (i.e., genes determine everything). Every nine-banded armadillo born is a quadruplet, i.e., having three “genetically identical” siblings. Even though the identical quadruplets share the same genes, researchers find that they can be quite different (behaviorally and physically) (Ballouz et al., 2019).

In a bioRxiv preprint, Ballouz et al. describe how they tried to control for any environmental variation in armadillo quadruplets to figure out where all the differences were coming from. In short, they found that chaos-driven X-chromosome inactivation happens soon after the four embryos physically separate, and that differences between the four can be tied directly to ~150 instances of X-chromosome inactivation.

Although this work has yet to be fully published, it reminds us that life is far more than nature and nurture, and that we have to account in some way for the seemingly random hand of chaos.

Reference

Ballouz S, Pena MT, Knight FM, Adams LB and Gillis JA. The transcriptional legacy of developmental stochasticity. bioRixv. December 12, 2019; doi: http://dx.doi.org/10.1101/2019.12.11.873265.

 

Foreseeing a silent killer: Early detection of NASH

Foreseeing a silent killer: Early detection of NASH

You may remember the classic Nash Rambler automobile. But you have probably never heard of the stealthy medical condition NASH (non-alcoholic steatohepatitis). NASH is a deadly form of non-alcoholic fatty liver disease (NAFLD), a disease marked by excessive buildup of fat in the liver, and which affects almost 25% of the US population. NASH goes beyond liver fat, however, to the active destruction of liver cells and thus, liver functions.

It may be surprising that such a destructive disease can actually be very hard to detect. The challenge, according to the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDKD), is that NASH often presents few symptoms (fatigue or some discomfort in the liver area) or even none, despite the onset of cirrhosis. The current gold standard for NASH diagnosis is a biopsy, a procedure that is both unpleasant and a bit dangerous for the unlucky patient. However, there are alternatives on the horizon, including proteomics.

To determine if measuring proteins circulating in blood could serve as a non-invasive alternative to liver biopsies, researchers used our technology to analyze serum samples from 577 participants in a study on NAFLD and obesity (Wood et al., 2017). They compared the protein data to results from genetic analysis and phenomic analysis (i.e., levels of glucose, insulin, high-density lipoproteins, triglyceride, alanine aminotransferase, ferratin, creatinine, chloride, zinc, drug/hormone usage, sleep apnea diagnosis) and concluded that the use of genomic data to assess liver health was a smidge better than flipping a coin (not a surprise). Phenomic data were better predictors than genomic data, but proteomic data really shined. Adding the phenomic and genomic findings to the proteomic data only marginally improved the results given by the proteomic data alone.

These findings were confirmed in a study published late last year, in which researchers used the SomaScan® technology to look at a range of health-related conditions (including liver fat), (Williams et al., 2019). These data, which are also providing new information about the biology of NAFLD/NASH and new avenues for treatment, strongly suggest that protein measurements alone can be an effective – and maybe even preferred – method of detecting NASH presence early enough to take corrective action.

 

References

Williams, S. A., Kivimaki, M., Langenberg, C., Hingorani, A. D., Casas, J. P., Bouchard, C., . . . Wareham, N. J. (2019). Plasma protein patterns as comprehensive indicators of health. Nature Medicine. doi:10.1038/s41591-019-0665-2

Wood, G. C., Chu, X., Argyropoulos, G., Benotti, P., Rolston, D., Mirshahi, T., . . . Gerhard, G. S. (2017). A multi-component classifier for nonalcoholic fatty liver disease (NAFLD) based on genomic, proteomic, and phenomic data domains. Sci Rep, 7, 43238. doi:10.1038/srep43238

 

A Battle Cry Against COVID-19

A Battle Cry Against COVID-19

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.

 

What Marsupials Tell Us About Human Pregnancy

What Marsupials Tell Us About Human Pregnancy

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.

 

References

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/.

 

Redefining Middle Age

Redefining Middle Age

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.

References

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