Problematic Repairs: Detecting Arthritis Sooner

Problematic Repairs: Detecting Arthritis Sooner

Our bodies are a lot like well-constructed houses built on solid foundations. It seems like they should last forever, but the fact is that even the best houses require constant maintenance and repairs, especially as they age. And, just like houses, vigilant maintenance and earlier repairs of our bodies can often stave off bigger problems.

But what happens when the maintenance and repairs fail? This can and does happen unfortunately. During an injury, our immune system engages to facilitate repairs, e.g., of a torn anterior cruciate ligament (ACL) in a weekend warrior. However, this repair process can also lay the foundations for osteoarthritis to set in for many of the afflicted (King et al., 2018).

Using the SOMAscan® Assay to study proteomics, King et al. set out to look at the joint fluid surrounding the area of the injury to better understand what is occurring at the repair site that leads to the unwanted effects of inflammation (King et al., 2018). In their findings, the team found the anticipated protein biomarkers, but also many markers typically associated with a different form of arthritis, i.e., rheumatoid arthritis. The researchers surmise that these findings could provide insights into the underlying mechanism of the immune “cannibalization” of joints and offer possibilities for new drug targets to help patients return to a state of normalcy.

While similar to osteoarthritis in terms of symptoms and suffering, rheumatoid arthritis differs in several ways, such patients also show an increase in immune cell production (B and T cells) in the synovium (membrane lining the joints). Medications are available to treat rheumatoid arthritis, but they may not always return the patient back to a pre-arthritis state. Tasaki et al. decided to use multiple approaches, including proteomics via SomaLogic® technology, to understand patients’ response to medication and determine if patients can return to a pre-arthritic state at the molecular level (Tasaki et al., 2018). For the different medications tested, a wide variety of responses were observed. One drug – tocilizumab – was found to fix the molecular imbalance more effectively and to restore the affected systems that remained untouched by other drugs tested. From their work, the researchers also identified the molecular signs that suggest a patient may be resistant to the treatment tested in the study.

Though osteoarthritis and rheumatoid arthritis are different conditions, they both are remarkable examples of what can go wrong when repairs have untoward effects. With a proteomic way of monitoring the body’s repair job, intervention can happen before a problem arises. Through proper intervention, joint cannibalization accompanied by detrimental pain can perhaps be avoided entirely.



King, J. D., Rowland, G., Villasante Tezanos, A. G., Warwick, J., Kraus, V. B., Lattermann, C., & Jacobs, C. A. (2018). Joint Fluid Proteome after Anterior Cruciate Ligament Rupture Reflects an Acute Posttraumatic Inflammatory and Chondrodegenerative State. Cartilage, 1947603518790009.doi:10.1177/1947603518790009

Tasaki, S., Suzuki, K., Kassai, Y., Takeshita, M., Murota, A., Kondo, Y., . . . Takeuchi, T. (2018). Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission. Nat Commun, 9(1), 2755. doi:10.1038/s41467-018-05044-4


From Cats to Kidneys: Sentinels of Disease

From Cats to Kidneys: Sentinels of Disease

A few years ago, when I was conducting thesis research, I happened upon an article authored by an EPA researcher that stated that cats could be considered “canaries” for environment-related thyroid problems arising in humans (Dye et al., 2007). Many years later, my beloved cat Noodle B. developed thyroid problems. While being medicated, the poor thing also went on to develop kidney problems.

While I made a mental note to keep tabs on my thyroid, I wondered, could cats also be considered canaries for kidney problems that we might face in the future? It’s possible—a clear link exists between the state of the thyroid and kidney health (Mariani & Berns, 2012)—but rather than wait for a cat model, a more direct path is now available. Our own proteomes are perhaps the best sentinel to tell us what might be happening with our kidneys, particularly when they suddenly begin to fail.

According to the Mayo Clinic, acute kidney injury (AKI) can happen suddenly and happens surprisingly often in hospitalized patients. The symptoms can be non-existent or non-obvious and if the injury is severe enough, dialysis or even kidney replacement may be needed.

With AKI requiring dialysis (AKI-D), the prognosis can be grim. Based on statistics from the Veteran’s Affairs/National Institutes of Health Acute Renal Failure Trial Network (ATN) study, the hyperacute phase, which lasts about the first eight days, has a 49% mortality (Yu et al., 2018). Past the 8-day mark or the acute phase, the survival chances improve to 77.6% during the 28-day time point used in the study.

In a recent article, a team of researchers looked to better understand the pathogenesis of AKI-D and discover a way to better predict survival early (Yu et al., 2018). Analyzing samples from the ATN study with the SOMAscan® Assay they found 33 proteins with elevated levels in patients who perished within eight days of the first blood sample being taken. These elevated proteins point to increased activity in processes involving inflammation, coagulation and endothelial cell injury. There were also several protein changes that hinted at a longer survival rate, but only by a few weeks.

This article is also the first one to implicate several proteins, such as tissue plasminogen activator, matrix metalloproteinase-8 and soluble urokinase plasminogen activator receptor in the increase risk of death in AKI-D. Is this a first step to a deeper understanding about the molecular underpinnings of AKI-D and to potential new therapies? It would be nice if changes in the proteome provide not only a dire warning of imminent danger but also a silver lining of effective treatment.



Dye, J. A., Venier, M., Zhu, L., Ward, C. R., Hites, R. A., & Birnbaum, L. S. (2007). Elevated PBDE levels in pet cats: sentinels for humans? Environ Sci Technol, 41(18), 6350-6356.

Mariani, L. H., & Berns, J. S. (2012). The renal manifestations of thyroid disease. J Am Soc Nephrol, 23(1), 22-26. doi:10.1681/ASN.2010070766

Yu, L. R., Sun, J., Daniels, J. R., Cao, Z., Schnackenberg, L., Choudhury, D., . . . Portilla, D. (2018). Aptamer-Based Proteomics Identifies Mortality-Associated Serum Biomarkers in Dialysis-Dependent AKI Patients. Kidney Int Rep, 3(5), 1202-1213. doi:10.1016/j.ekir.2018.04.012


I Don’t Want to, but the Body Says Better Pack It On

I Don’t Want to, but the Body Says Better Pack It On

Winter is coming. I just know it. I do not need the changing color of tree leaves to tell me this fact. I know it because my appetite is insatiable right now. I feel like one of those brown bears that stands in the river and lets salmon continuously leap into their open mouth (though I would prefer to receive lemon-curd filled donuts).

The downside of a constant donut binge would be the weight gain, and the health complications associated with it. I suppose if I hibernated for several months then the dreaded weight gain would actually be a good thing. But with food being available to me year-round, the need to store any extra bit of energy (fat) to cover me through the next famine is not needed (or desired). So, I will have to embark on a journey that so many others have taken to sever my ties with the fat that my body desperately wants to hoard.

The journey to break free of the tyranny of fat is not a trivial one. An international team of researchers used SomaLogic’s proteomic technology along with other techniques to better understand the variability seen in people trying to lose excess weight (Thrush et al., 2018). In their study, they separated a collection of individuals matched for gender, age and initial weight into two groups, obese-diet sensitive (ODS) and obese-diet resistant (ODR), and looked for differences in their blood proteins.

For those unable to lose weight, levels of the plasma proteins aryl hydrocarbon receptor protein complex, peptidylprolyl isomerase D and tyrosine-protein kinase Fgr were elevated after a high fat meal. For those able to lose weight, the researchers found higher levels of S-formyl glutathione hydratase, heat shock protein 70 kDa 1 A/B and eukaryotic translation initiation factor 5 regardless of whether the people had fasted or just eaten.

Then, the researchers conducted a really interesting experiment. The researchers made preparations from the plasma of ODS and ODR people and exposed them separately to muscle cells grown in a dish. The muscle cells exposed to ODS preparations showed an increase in fatty acid (the stuff that makes fat) metabolism, which is a good thing for those trying to lose weight. For the muscle cells exposed to ODR preparations, the researchers saw an increase of glycolysis (the breakdown of the sugar, glucose), which can lead to the creation of fat (good news for bears about to hibernate, not so good for beach body development).

The research suggests that we may be on the verge of having the insights necessary to determine if a person is at risk of diet failure, but a bit more work is still needed. When the day comes for the insights to become available, I would certainly like to know if I am risk of diet failure. It might help dissuade me from seeking out lemon curd donuts like a ravenous bear.



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


Braving the Perfect Storm: A Rare Disease Story

Braving the Perfect Storm: A Rare Disease Story

Born under atypical circumstances, a perfect storm wields unimaginable havoc. So, does a rare disease. What exactly constitutes a rare disease? In the U.S., the Orphan Drug Act of 1983 stipulated that “rarity” meant the disease affected fewer than 1 in 200,000 people, which applies to about 7,000 diseases (National Institutes of Health, 2017). With so many rare diseases, the NIH estimates that as many as 20 to 35 million Americans suffer from one of the ~7000 rare diseases, which is about 1 in 10. That means you likely know a person caught up in a not-so-rare perfect storm.

What makes a rare disease a perfect storm? Well, like a meteorological perfect storm, a unique combination of events transpires. Those afflicted with one of the roughly 7000 named biological storms hit the trifecta of difficulty in getting a correct diagnosis, finding a doctor knowledgeable of the disease and securing some type of treatment plan. Despite the good intent of the Orphan Drug Act of 1983, affordable treatments can be as rare as the disease itself. The rare disease not only challenge the actual patient, but also the doctors and researchers (Stoller, 2018).

Unlike the meteorological version, we have a shot at taming the biological perfect storm through new medical insights and improved communication. So, let us break out a new kind of powerful Doppler radar: proteomics. Yes, proteomics can reveal a lot about us and even potentially help people weather their particular storms.

Looking at Castleman disease, we can see proteomics in action (Pierson et al., 2018). This very rare and devastating disease involves enlarged lymph nodes and can be subdivided into one of three categories depending on symptoms and presence of a viral infection. One of these subgroups — Human Herpesvirus (HHV)-8-negative, idiopathic multicentric Castleman disease (iMCD) — wields extremely severe symptoms, can be fatal, arises from unknown origins and is difficult to treat. Dr. David Fajgenbaum and co-researchers used proteomics to tease out the details of iMCD, which can be further subdivided into two groups. They mentioned that initially it was thought that cytokines were the driving force behind iMCD, with interleukin-6 playing a significant role. However, only a third of patients responded to anti-interleukin-6 therapy. Through the proteomics, the researchers saw that the two subgroups of iMCD had two different proteomic profiles. This difference might explain why one group did not respond to anti-interleukin treatment and the other one did. Also, they found that although cytokines do play a part in the perfect storm, it is really a smaller group, chemokines (proteins that direct white blood cells to sites of infection) that are at the eye of the storm.

Just think, we maybe be witnessing the potential for calm after this storm! One day, we could have the means to determine quickly and accurately who may benefit from certain treatments. This work has also highlighted new targets for therapeutics or new potential treatment strategies. The proteomic Doppler radar could be our deliverance from the havoc wreaked by the perfect storm of rare disease.



National Institutes of Health/ National Center for Advancing Translational Sciences/ Genetic and Rare Diseases Information Center. (2017, November 30). FAQs About Rare Diseases. Retrieved on July 12, 2018 from

Pierson, S. K., Stonestrom, A. J., Shilling, D., Ruth, J., Nabel, C. S., Singh, A., . . . Fajgenbaum, D. C. (2018). Plasma proteomics identifies a ‘chemokine storm’ in idiopathic multicentric Castleman disease. Am J Hematol, 93(7), 902-912. doi:10.1002/ajh.25123

Stoller, J. K. (2018). The Challenge of Rare Diseases. Chest, 153(6), 1309-1314. doi:10.1016/j.chest.2017.12.018


Interpreting Genomic Art

Interpreting Genomic Art

What the …?? …. Standing back, I wonder what the artist envisioned while painting this piece. Did they just enjoy seeing the colors mix and build elaborate textures? Are they trying to communicate a new revelation about the human condition?

It turns out the artist is barely two years old. Far too young to really be grappling with existential questions. The artist most likely just enjoyed playing with the paint.

We have a normal tendency to over- or misinterpret things, whether it be art itself or the artist’s intentions. We can do the same with genomic data. With over 75,000 genetic tests available (Johnson, 2018), the deluge of information can wash over us and the medical experts, leaving us grappling with deeper questions of meaning and use.

Recently, a study came out about how primary care doctors, cardiologists or oncologists view genomic “art” (Pet et al., 2018). Many of the doctors surveyed showed concern over how to address the findings from consumer genomic tests that told healthy people they were at risk for serious diseases. They worried that the results could lead to unnecessary medical treatment, increased costs, potential complications and problems with insurance. Overall, they wanted the patients to be provided with clearly communicated, actionable results: “What does it mean and what should I do?”

How actionable or reliable can genomic test results be? If we scan the brush-strokes of the whole genome, many of us (including medical people) would be left scratching our heads and saying, “What the…?” As we previously noted, genomes are noisy to the point of being called “space vomit.” Yet, there is still a belief that genetic testing will be pivotal to healthcare in the future (Pet et al., 2018).

We are taking steps in the right direction for learning how to improve the interpretation of genomic art in a meaningful way, but do we even need to? Novartis scientists recently conducted a study to link genetic variants (think “mutations”) to disease by using proteomics (Emilsson et al., 2018). In their analysis of nearly 5,000 proteins found in serum, they learned that many proteins clustered into groups that were co-regulated. The study also revealed that the protein groupings could be used to glean health insights.

What happened when genomic data were layered onto the picture? Well, the study reiterated the difficulty in linking a genetic variant to a single protein, which highlights the complexity of our bodies. However, the study suggested that some variants could be connected to the identified protein groups.

Maybe just focusing on and maximizing the more immediate potential of proteins to reveal the inner working of our bodies is the most direct and interpretable route for realizing the artistic promise of precision medicine.



Johnson, C. (2018, May 7). Medicine’s Wild West: 10 new genetic tests enter the market each day. The Washington Post. Retrieved on August 1, 2018 from

Emilsson, V., Ilkov, M., Lamb, J. R., Finkel, N., Gudmundsson, E. F., Pitts, R., . . . Gudnason, V. (2018). Co-regulatory networks of human serum proteins link genetics to disease. Science. doi:10.1126/science.aaq1327

Pet, D. B., Holm, I. A., Williams, J. L., Myers, M. F., Novak, L. L., Brothers, K. B., . . . Clayton, E. W. (2018). Physicians’ perspectives on receiving unsolicited genomic results. Genet Med. doi:10.1038/s41436-018-0047-z


Our Proteomic Fingerprint

Our Proteomic Fingerprint

Your genome has been hacked! …literally. Recently, a direct-to-consumer genetic test company was hacked and  information about millions of people was compromised (Thielking, 2018). In this day and age, it is not surprising when a company gets hacked. What is surprising is one of the ways a genetics company can be hacked. The sneaky route involves the malware being programmed into a DNA sample. When sequenced, the malware grants the hackers access. Sounds like a Hollywood movie plot, but it happened (Thielking, 2018).

Just how private is our biological information, and are people concerned? In a recent survey, about 47% people had concerns about the confidentiality of their genetic information derived from genealogical genetic testing (Hensley, 2018). Sadly, genetic information is difficult to keep secret. Aside from companies or other holders of genetic information being hacked, you can also lose your genetic privacy if your blood relatives choose to give away their genetic info like beads at Mardi Gras. This should not be a surprise, given the growing forensic use of relatives’ DNA to crack cold cases.

Is this only a genes problem, or are an individual’s proteins also a privacy risk? Although proteins can change rapidly in response to many different environmental changes, we can be named by our proteins. Not too long ago, a group of researchers used a mass spectrometry approach to identify a person based on the proteins found in a hair sample (Parker et al., 2016). They demonstrated that if a person carries a genetic variation that alters the amino acid sequence of a protein, then that person could be identified based on the protein sequence in the sample. Although hair has a relatively small number of proteins, it is likely that more complex protein samples, such as blood, could also be used to single out a person.

Is there a way to make mass spectrometry less revealing? One proposal is to remove some of the potentially distinguishing  data, which would make it harder to link it back to a specific individual, but not impossible (Li, Bandeira, Wang, & Tang, 2016). Nevertheless, this is a step in the right direction, but it may already be too late.

A quick search on Google reveals numerous repositories where proteomic data have been shared. When it comes time to publish a scientific paper, some journals, such as PLOS journals), make sharing proteomic data mandatory ( It is worth noting that a few exceptions to making the data available do exist, but a chance of the data holder being hacked still remains. Although some journals merely recommend sharing the data (e.g., Cell, there are growing cries for more data transparency, including (Matheson, 2018). The government has made a similar proposition (Friedman, 2018). With such a demand for transparency and access to data, can we still hold onto our beloved privacy? And how does this affect people’s willingness to donate biological samples or partake in clinical studies? The only thing that is certain now is that once the data are out, there is no way to secure them again.



Curran, A. M., Fogarty Draper, C., Scott-Boyer, M. P., Valsesia, A., Roche, H. M., Ryan, M. F., . . . Kaput, J. (2017). Sexual Dimorphism, Age, and Fat Mass Are Key Phenotypic Drivers of Proteomic Signatures. J Proteome Res, 16(11), 4122-4133. doi:10.1021/acs.jproteome.7b00501

Friedman, L. (2018, March 26) The E.P.A. Says It Wants Research Transparency. Scientists See an Attack on Science. New York Times. (Retrieved on June 6, 2018 from

Hensley, S. (2018, June 1) POLL: Genealogical Curiosity Is A Top Reason For DNA Tests; Privacy A Concern. NPR. (Retrieved on June 3, 2018 from

Li, S., Bandeira, N., Wang, X., & Tang, H. (2016). On the privacy risks of sharing clinical proteomics data. AMIA Jt Summits Transl Sci Proc, 2016, 122-131.

Matheson S. (2018, May 30) Why you should deposit your raw data. Crosstalk [blog post]. Retrieved on June 6, 2018 from

Parker, G. J., Leppert, T., Anex, D. S., Hilmer, J. K., Matsunami, N., Baird, L., . . . Leppert, M. (2016). Demonstration of Protein-Based Human Identification Using the Hair Shaft Proteome. PLoS One, 11(9), e0160653. doi:10.1371/journal.pone.0160653

Thielking, M. (2018, June 5) Genealogy site MyHeritage says 92 million user accountscompromised. STAT. (Retrieved on June 6, 2018 from