Personal Omics Profiling Reveals Dynamic Molecular and Medical PhenotypesChen, et al (2012)

Robert Magie and Ronni Park

Personalized Medicine-Human Genome sequencing initially thought to be able to be used for personalized medicine-Before publication, met with limited success

iPOP-Integrative personal omics profile-Group decided to integrate multiple omics (Genomics, Transcriptomics, Proteomics, Metabolomics and Antibody Profiles)-First time on this scale

Methods

-iPOP profile generated using blood components (PBMCs), plasma and serum-Samples taken over a 14 month period

Genomics -Whole Genome Sequence via Illumina & Complete Genomics-Looked for single nucleotide variants (SNVs), small insertions and deletions (indels) and structural variants-Compared to 1000 Genome Project

Complete Genomics

Courtesy of Complete Genomics

Complete Genomics

Courtesy of Complete Genomics

Transcriptomics-RNA-Seq of 20 separate time points-RNA is isolated and then cDNA generated-cDNA then sequenced using Illumina -Allows for direct snapshot of transcripts currently in sample at one time

Proteomics-Proteins tagged and then analyzed using MS/MS-Looked at variance in proteins including from mutations, alternative splicing at different time points, during infection etc.

Metabolomics-Different classes of metabolites separated using Liquid Chromatography-Electrospray Ionization-Metabolites characterized using MS/MS

Data Processing-Have to filter out noise-Looked at overall trends using Cluster analysis of RNA transcription and protein expression

Infection-Subject was infected with HRV and RSV during the course of the study-Found groups of proteins/mRNA that rise and fall together during the course of an infection

Findings-Genome sequencing revealed risks for coronary artery disease, basal cell carcinoma, hypertriglyceridimia, and type 2 diabetes. Also indicated risk for Aplastic anemia

Diabetes-Glucose and HbA1c levels revealed onset of diabetes despite lacking many associated factors: nonsmoker, BMI ~23-Post-RSV infection, subject had a spike in blood glucose levels

Diabetes-Change in lifestyle lowered blood glucose levels

Aplastic Anemia-Condition where the bone marrow has been mostly replaced by fat-Deficiency in all types of blood cells -Five year survival rate of about 70% with treatment

Aplastic Anemia-Despite containing the TERT mutation associated with the disease subject showed no symptoms

Implications-Could be useful for predicting various risk factors-Integration of different -omics permits higher accuracy-Allows for comparison between healthy and diseased state

Critiques & Limitations-Multivariable diseases are hard to pinpoint, succeeded with diabetes but failed with Aplastic Anemia-Samples restricted to blood -Limited number of subjects

Future Directions-Increasing sample size could reveal more trends to better identify risk factors-Could lead to finding and treating diseases before onset possibly preventing them-Can investigate previously unknown protein/RNA trends in response to a disease