Presented by Jean-Louis Bru and Brittany Gentry Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes Chen et al., 2012 Presented by Jean-Louis Bru and Brittany Gentry

Terminology iPOP: Integrative personal omics profile Analysis of a person’s molecular data and activity PBMCs: peripheral blood mononuclear cells Lymphocytes (T cells, B cells, NK cells) and monocytes ASE: allele specific expression SNVs: Single nucleotide variants Single nucleotide variant (SNV) is a variation in a single nucleotide without any limitations of frequency and may arise in somatic cells. Single-nucleotide polymorphism (SNP) is a variation in a single nucleotide that occurs at a specific position in the genome, where each variation is present to some appreciable degree within a population. Structural variation, variation in structure of an organism's chromosome, consisting of deletions, duplications, copy-number variants, insertions, inversions and translocations. Typically a structure variation affects a sequence length about 1Kb to 3Mb, which is larger than SNPs and smaller than chromosome abnormality.

Background One size fits all medicine → Personalized treatment of diseases & assessment of healthy individuals Integrative omics profiles: limited and not applied to analysis of healthy individuals Techniques used in the iPOP Whole Genome Sequencing → Genomic Profile RNA-Seq → Transcriptomic profile Microarray → Autoantibody profile Whole Genome Sequencing includes Illumina, DNA sequencing, Sanger sequencing, exome-sequencing, paired-end mapping, read depth, split reads, junction mapping Strand-specific RNA-seq libraries were prepared and analyzed using Illumina

Main Goals Generation of an iPOP and the examination of as many biological components as possible Compare and contrast how -omics change during healthy and diseased states Combining information determined from different states and genome to estimate disease risk and gain new insights into diseased states

Figure 1 From blood samples, separate into: PBMCs Serum Then performed integrative personal omics profile (iPOP) with various techniques. Took blood samples from a 54-year-old male volunteer over the course of 14 months. Samples used taken every 401 days. With peripheral blood mononuclear cells [PBMCs], performed whole genome sequencing, whole transcriptome sequencing (mRNA and miRNA), and proteome profiling With serum, performed untargeted and targeted proteome profiling, metabolome profiling, autoantibodyome profiling, complete medical exam and laboratory tests to measure blood glucose. Serum, blood without write or red blood cells and clotting factors, but still have all other kinds of proteins. PBMCs: peripheral blood mononuclear cells [Lymphocytes (T cells, B cells, NK cells) and monocytes], pretty much white blood cells.

Single nucleotide variants (SNVs) Small insertions and deletions (indels) Structural variants (SV) Circos Plot: Outer to inner rings- chromosome ideogram; genomic data (pale blue ring), structural variants > 50 bp (deletions [blue tiles], duplications [red tiles]), indels (green triangles); transcriptomic data (yellow ring), expression ratio of HRV infection to healthy states; proteomic data (light purple ring), ratio of protein levels during HRV infection to healthy states; transcriptomic data (yellow ring), differential heteroallelic expression ratio of alternative allele to reference allele for missense and synonymous variants (purple dots) and candidate RNA missense and synonymous edits (red triangles, purple dots, orange triangles and green dots, respectively).

Figure 2C Posttest probability of T2 Diabetes Left: associated genes, SNV, subject’s genotypes Middle: graph of posttest probability Right: likelihood ratio (LR), number of studies, cohort sizes, posttest probability High prevalence % = higher chance to have T2D Subject’s posttest probability of type 2 diabetes calculated using 28 independant single nucleotide variants (SNV) Middle graph: posttest probability Left: associated genes, SNV, subject’s genotypes Right: likelihood ratio (LR), number of studies, cohort sizes, posttest probability Order: based on number of studies For this subject, prevalence is 27%, meaning he has 27% chance of developing type 2 diabetes in the future. Cohort is a group of subjects who share a defining characteristic (typically subjects who experienced a common event in a selected time period, such as birth or graduation). The TCF7L2 rs7903146 (C/T) polymorphism is associated with risk to type 2 diabetes mellitus in Southern-Brazil.

Figure 2D Possibility of RSV triggering aberrant glucose metabolism through the activation of a viral inflammation response Subject had a predisposition to T2D, possible connection? Hemoglobin A1c levels between 5.7% and 6.4% mean you have a higher chance of getting of diabetes. Levels of 6.5% or higher mean you have diabetes T1D had been associated with viral infections Possible combination of virus and predisposition for T2D

Figure 2E CRP : measures general levels of inflammation in your body Spikes correlate with HRV and RSV infections A complete medical exam plus laboratory and additional tests were performed before the study officially launched (day 123) C-reactive protein (CRP) is a blood test marker for inflammation in the body. Infection means inflammation and production of cytokine (figure 2F, with serum cytokine profiles) since cytokine role is important in host response to infection.

Figure 2F Inflammation means increase of cytokine level Cytokine plays an important role in host response to infection Infection means inflammation and production of cytokine (figure 2F, with serum cytokine profiles) since cytokine role is important in host response to infection.

Figure 3B Following RSV infection, upward trend (top, in red) of 2,023 genes downward trend (bottom, in green) of 2,207 genes Upward auto correlated trend enriched pathways: Influenza life cycle & protein metabolism Downward autocorrelation cluster showing enriched pathways: TCR signaling in naive CD4+ T cells & many more Significance: many genes are involved in these pathways showing the same trend Red: upregulation, green: downregulation of genes. Bottom: days after infection Pathways enriched including protein metabolism and influenza life Upward autocorrelated trend revealed a number of pathways as enriched, including protein metabolism and influenza life cycle. Downward autocorrelation cluster showed a multitude of enriched pathways, such as TCR signaling in naive CD4+ T cells, lysosome, B cell signaling, androgen regulation, and of particular interest, insulin signaling/response pathways.

Figure 3B We observed that the downward trend, that began with the onset of the RSV infection and appeared to accelerate after day 307, coincided with the beginning of the observed elevated glucose levels in the subject (figure 2D). Significance: showed genes that were unknown to be involved in these pathways but displaying same trend Following RSV infection, we observe an upward trend (top, in red) (2,023 genes) and a similar coincidental downward trend (down, in green) (2,207 genes). Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay. Informally, it is the similarity between observations as a function of the time lag between them.

Figure 5C Differential ASE is distinct during healthy and infected states 100 of the ASE sites were specific to HRV ASE = allele specific expression SVIL: The gene product is tightly associated with both actin filaments and plasma membranes TRIM5: It appears to function as a E3 ubiquitin-ligase and ubiqutinates itself to regulate its subcellular localization. It may play a role in retroviral restriction. 100 sites specific to HRV Differential ASE genes in the HRV compared to healthy phase were enriched for those encoding SNARE vesicular transport proteins

Figure 6D Used to reevaluate the extent of RNA editing Possible amination-like RNA-editing mechanism not previously known The missense edits were validated by using Sanger and PCR indicating an amination-like RNA-editing mechanism, previously not observed in human cells. identification of their peptide counter- parts by mass spectrometry : in order to see if the edits effected proteins These results indicate that a large fraction of personal variants are expressed as transcripts and a number of these are also translated as proteins.

Conclusion Genome sequencing can be used to direct the monitoring of specific diseases Analyzing large numbers of molecules allows for a more comprehensive view of disease states and their relation to physiological states With iPOP, we can potentially improve disease risk assessment, accuracy of diagnosis, disease monitoring, targeted treatments Specific Diseases: Aplastic anemia and diabetes

Additional Readings Li-Pook-Than, Jennifer, and Michael Snyder. "iPOP goes the world: integrated personalized Omics profiling and the road toward improved health care." Chemistry & biology 20.5 (2013): 660-666. Rosenblum, Daniel, and Dan Peer. "Omics-based nanomedicine: the future of personalized oncology." Cancer letters 352.1 (2014): 126- 136.