This study describes an “integrative Personal Omics Profile” (iPOP) by Prof. Mike Snyder, the corresponding author of the paper, as well as the Chair of Department of Genetics at Stanford University. This study is the first to perform extensive personal iPOP of an individual through healthy and diseased states. This paper was published in Cell, 2012.
At the genomic level, we are 99.9% identical to our neighbors and friends, but it is the subtle differences in the remaining 0.1% of our genome that define us as individuals. Some of these subtle genetic variants have important physiological consequences that are reflected in our overall health. Thus, both the course of disease and our response to treatments are intimately tied to our genome sequence. Beyond our genomes, person-to-person variation also manifests at the RNA, protein and metabolite levels.
iPOP data collection over a longitudinal period
iPOP was generated by combining genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from the blood components from Prof. Snyder over a 14 month period (Figure 1). The authors used various technologies (including whole-genome sequencing, RNA-seq, Human Cytokines assay and mass spectrometry) to generate this huge data set (it contains >3 billion measurements taken over 20 time points). Briefly, genomic profiling provides the genome sequence and germline variants of the subject. In addition, the transcriptomic, proteomic, metabolomic, and autoantibody profiles allow people to observe the dynamics of the gene expression trend for the subject during a period.
Figure 1. iPOP experimental framework and data analysis methodology. PBMC: peripheral blood mononuclear cell.
During this study, Prof. Snyder contracted two viral infections: a human rhinovirus (HRV) infection beginning on day 0, and a respiratory syncytial virus (RSV) infection starting on day 289. It provided the researchers an excellent opportunity to study the dynamics of gene expression during the response to viral infections.
iPOP predicts disease- and drug-associated variants
Decades of research have identified numerous genomic variation (i.e. biomarkers) for disease and drug usage. Thus, the authors first analyzed the genomic variation that have been reported to be associated with disease risk and drug response. They found that the genome sequence of Prof. Snyder contains various disease-associated rare variants, including type II diabetes, as well as some drug response-related variants (Figure 2).
Figure 2. Some important examples of the disease- and drug-associated variants.
iPOP monitors the traits of diabetes and helps treatment
Before this study, Prof. Snyder did not have the known factors associated with diabetes, and the glucose levels were normal for the first part of the study. As mentioned above, Snyder had a RSV infection during the study (starting on day 289). Not surprisingly, the activation of the body’s immune response was apparent. What was unexpected, however, was his body’s response to the virus coincided with down-regulation of insulin pathways and a concordant rise in blood glucose levels, which marked the onset of diabetes (Figure 3). The glucose levels elevated shortly after the RSV infection (after day 301) and was extended for several months.
Figure 3. Blood glucose trend during this study. There were two viral infections: a HRV infection beginning on day 0 (red arrow), and a RSV infection starting on day 289 (green arrow).
After a change in diet and exercise by Prof. Snyder, his glucose levels exhibited a gradual decrease. These results indicate that a genome sequence can be used to estimate disease risk in a healthy individual, and disease biomarkers (glucose herein) can be used to monitor and detect the treatment of that disease.
Integrated omics analysis provides more biomedical information
To further leverage the transcriptome and genome data, the authors performed an integrated analysis of transcriptome, proteomic and metabolomics data for each time point, observing how this corresponded to the varying physiological states (Figure 4). They especially aimed to systematically search for correlated patterns over time. To handle the heterogeneity in data quality and missing data points in the time-series, they applied a Fourier spectral analysis approach (Lomb-Scargle transformation) to construct the periodogram for each time-series curve. The Lomb-Scargle method has been successfully applied in astronomy for unevenly sampled time series data and implemented in various forms for biological problems.
Figure 4. The integrated analysis of the transcriptome, proteome, and metabolome data. The data points are clustered together to identify disease-related biological pathways.
The integrated analysis of the data sets confirmed previous discoveries. It revealed a clear systemic response to the RSV infection following its onset and post-infection response, including a pronounced response evident at day 18 post RSV infection. A variety of infection/stress response-related biological pathways were affected along with those associated to the high glucose levels in the later time points, including insulin response pathways.
iPOP provides a multidimensional view of medical states, including healthy states, response to viral infection, recovery, and diabetes onset. Overall, this proof-of-principle study revealed that applying an iPOP-based approach is helpful for realizing personalized medicine: disease risk can be identified from genomic sequence and disease state can be monitored through other molecular components.
Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. The wealth of data generated from this study will serve as a valuable resource to the community in the developing field of personalized medicine.
Finally, the authors constructed a website to facilitate the reuse of the iPOP resource (http://snyderome.stanford.edu).
<centering>Figure 5. The website providing iPOP data and results.</centering>
Chen R et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell, 2012.
Analyst: Genome Hunter | Editor: Hao Wang