Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes Chen, 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