NEW YORK (GenomeWeb News) – A research team's work to track the genomic, transcriptomic, metabolomic, and proteomic profiles in one individual unexpectedly yielded an intimate look at the way these processes shift during the onset of type 2 diabetes.
As they reported online today in Cell, researchers from Michael Snyder's Stanford University genetics lab collaborated with investigators at Yale University and centers in Spain to do what they called an integrative personal omics profile, or iPOP, using Snyder himself as the study's subject. For samples collected over the course of more than a year, the team did analyses ranging from metabolite and protein profiling to whole-genome, transcriptome, and microRNA sequencing.
When they sifted through Snyder's genome sequence data, the researchers identified variants hinting that he was at a lower-than-average risk of certain conditions, including prostate cancer, obesity, and hypertension.
But other variants suggested his risk of basal cell carcinoma, high cholesterol, and heart disease might be higher than that found in the general population. Indeed, Snyder began taking cholesterol-lowering medication when samples taken near the start of the study pointed to elevated triglyceride levels found in his blood.
The team also saw that Snyder had type 2 diabetes-associated variants — a finding that caught Snyder off guard. "I was not aware of any type 2 diabetes in my family and had no significant risk factors," he said in a statement, "but we learned through genomic sequencing that I have a genetic predisposition to the condition."
While Snyder's blood glucose levels were in the normal range over most of the study, he was diagnosed with type 2 diabetes after his blood glucose levels spiked in samples collected shortly after he'd had a viral infection. The profiling prompted Snyder to not only visit his physician, but also to make dietary and lifestyle changes aimed at managing his blood glucose levels and preventing type 2 diabetes-related tissue damage.
"Normally, I go for a physical exam about once every two or three years," Snyder noted. "So under normal circumstances, my diabetes wouldn't have been diagnosed for one or two years."
"But with this real-time information," he added. "I was able to make diet and exercise changes that brought my blood sugar down and allowed me to avoid diabetes medication."
In addition to information relevant to Snyder's health decisions and disease management, the study provided some broader biological insights as well. For instance, the effort let researchers see unusual allele-specific transcript expression and RNA editing patterns — particularly in samples taken when Snyder's immune system was tackling a viral infection.
"Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism," the study's authors noted.
Most of the iPOP analyses were done using 20 blood samples collected from Snyder over 14 months, though additional sampling and blood glucose testing was done for up to two years.
Snyder, who was 54 years old when the study started, had his genome sequenced to 150 times coverage at Complete Genomics using that company's platform and to 120 times coverage at Illumina using the HiSeq 2000. HiSeq 2000 and Illumina GAIIx platforms were also used for exome, transcriptome, and small RNA sequencing stages of the study.
Meanwhile, the researchers used a combination of liquid chromatography and mass spectrometry to assess levels of thousands of blood proteins or metabolites in Snyder's blood samples over time.
Because he contracted two viral infections over the course of the study — a rhinovirus infection (or common cold) when the study began and a respiratory synctial virus several months later — the researchers were also able to look at how Snyder's body reacted to these pathogens.
For instance, enzyme-linked immunosorbent assays used to track the activity of cytokine and C-reactive protein activity showed a jump in pro-inflammatory cytokines and C-reactive protein during both infections, while autoantibody profiling with an Invitrogen microarray showed that an insulin receptor binding protein was among the proteins targeted by Snyder's autoantibodies during one of the infections.
Together, researchers explained, findings from the study highlight the biological information that can be gained from studying one individual over time as well as the potential of using 'omics information for personalized medicine.
"This study opens the door to better understanding this concerted regulation, how our bodies interact with the environment, and how we can best target treatment for many other complex diseases at a truly personal level," co-first author Jennifer Li-Pook-Than, a post-doctoral researcher in Snyder's Stanford lab, said in a statement.
"In the future, we may not need to follow 40,000 variables," Snyder added. "It's possible that only a subset of them will be truly predictive of future health. But studies like these are important to know which are important and which don't add much to our understanding."