NEW YORK (GenomeWeb) – A team led by investigators at the University of Texas and Stanford University have published a proof-of-principle study that combines genomic, metabolomic, brain imaging, and health data over time to investigate brain function dynamics and related physical features.
As part of a phenome-wide study called the MyConnectome project, the researchers collected brain imaging data, mental and physical health information, gene expression patterns, and metabolic profiles from one healthy, genotyped individual — a male author on the study — over the course of a year and a half. The results, published online this week in Nature Communications, suggest healthy brain functions fluctuate over time in ways that might be partly reflected by transcripts and metabolic compounds in the blood.
"[W]e have demonstrated the presence of rich temporal dynamics in brain function related to both psychological and biological variability, suggesting that the purview of studies of human brain function can usefully be widened to encompass the study of temporal variability within individuals," senior author Jeanette Mumford, a psychology researcher at the University of Texas at Austin, and her colleagues wrote.
The team noted that long-term, detailed phenotypic information from healthy individuals will likely be required to get a sense of how psychiatric conditions arise and vary over time.
To begin generating such baseline information, the researchers set out to collect a broad range of phenotypic and genetic measurements over 18 months from a man who had been genotyped at almost a million SNPs using a commercially available 23andMe kit.
The 45-year-old individual had a history of anxiety disorder and plaque psoriasis, but was healthy overall and had a full physical before the study started.
Along with information on the participant's overall health, diet, weight, and sleep patterns, the team used brain imaging to gauge features such as connectivity over the course of the study.
All told, the team gleaned information from 84 magnetic resonance imaging runs, which included scans at rest and/or while the individual was doing tasks such as character or face recognition, or spatial memory testing.
The researcher also took blood samples from the individual every Tuesday morning, and used RNA sequencing on the Illumina HiSeq 2500 and gas chromatography time-of-flight spectrometry to profile gene expression and metabolomic patterns , respectively.
These data offered a peek at brain connection over time, during specific tasks, and even in the absence of caffeine, along with metabolite shifts related to the consumption of certain foods, and so on.
On the gene expression side, the team compared RNA sequence reads from four-dozen blood samples to sequence data reported in peripheral blood cells from the Snyderome study.
In particular, the study's authors focused on transcripts coinciding with 13,000 genes, defining dozens of co-expressed gene models related to processes such as immunity, metabolism, and signaling.
While they did not detect ties between the expression of immune-related genes and mood, for example, the gene expression results supported a previously reported association between severe psoriasis and the expression of certain immune receptor genes.
The team also saw hints that the participant's gene expression profiles might inform network analyses of gene expression data generated for larger, family studies such as the Genetics of Brain Structure and Function study.
Overall, the researchers noted, findings from the phenome-wide analyses are expected to "serve as a test bed to study the joint dynamics of human brain and metabolic function over time, an approach that is critical for the development of precision medicine strategies for brain disorders."