Transcript Dynamic Omics Analysis

• Personalized medicine
– Assess medical risks, monitor, diagnose and treat according to their
specific genetic composition and molecular phenotype.
• Many complex disease (such as diabetes, neurological disorder, cancer…)
Large number of
different genes
Large number of
different biological
pathways
Environmental
contributors
• Integrative personal omics profile (iPOP)
― Whole-genome sequence (WGS) + transcriptomic, proteomic,
metabolomic, and autoantibody profiles.
― Analyzed the iPOP of the individual over the course of healthy state and
two viral infection.
• Study patient
― 54-year-old male volunteer, over the course of 14 months.
― Performed iPOP on peripheral blood mononuclear cells (PBMCs).
Time course summary. The subject was monitored for a total of 726 days.
Number
RSV infection
HRV infection
(1) Increased exercise, (2) ingested 81mg of NSAID tablet each day,
(3) Substantially reduced sugar intake
Fasted time points
iPOP experimental design indicating the
tissues and analyses involved in this study.
Circos plot summarizing iPOP.
• Genomic DNA
– Deep WGS : complete genomics (CG, 35 nt paired end, 150-fold total
coverage), illumina (100 nt paired end, 120-fold total coverage).
– Exome sequensing : three different technologies to 80- to 100-fold
average coverage.
– Analysis using genotyping arrays and RNA sequencing.
• A large number of undocumented genetic can be identified by very deep
sequencing and de novo assembly.
– The vast majority of genomic sequences mapped to the hg19 (GRCh37)
reference genome : 91%.
– Sequences not present in the reference sequence : 9%.
SNVs : single nucleotide variants.
Indels : small insertion and deletions.
SVs : structural variants (large insertion, deletions, inversion).
• The list of high confidence SNVs and indels was analysis for…
– Rare alleles (<5% of the major allele frequency in Europeans).
– Changes in genes with Mendelian disease phenotypes.
• This list of genes was further examined for medical relevance.
RiskGraph of the top 20 diseases
with highest posttest probabilities
RiskOGraph of type 2 diabetes
Arrow : pretest probability according to the subject’s age, gender, and ethnicity.
Line : posttest probability after incorporating the subject’s genome sequence.
No. : number of independent disease-associated SNVs used to calculate the subject’s posttest probability.
HRV infection
(Day 0-21)
RSV infection
(Day 289-311)
C-reactive protein trend line
Serum cytokine profiles
I. Correlated patterns over time
• Autocorrelations were calculated to assess nonrandomness of the time-series.
• The unified framework approach was implemented on all the different data
sets both individually and in combination.
• A number of differential changes that occurred both during infectious states
and the varying glucose states.
RSV infection (Day 289-311)
• Upward trend : protein metabolism and influenza life cycle.
• Downward trend : TCR signaling in native CD4+ T cells, lysosome, B cell
signaling, androgen regulation, and insulin signaling/response pathways.
 accelerate after day 307, coincided with the beginning of the observed
elevated glucose levels in the subject.
• Spike maxima
― Common to the onset of both the RSV and HRV infection.
― Associated with immune processes and phagocytosis, major
histocompatibility genes.
• Spike minima
― Singular to day 307 (day 14 of RSV infection).
― Associated with TCR signaling, TGF receptors, and T cell and insulin
signaling pathways.
II. Single unusual events
• Integrated analysis of transcriptome, proteomic and metabolomics data for
each time point.
• Observing how this corresponded to the varying physiological states
monitored as described in the above sections.
• Examined in detail the onset of the RSV infection, during the times that our
subject began exhibiting high glucose levels.
• Phagosome, lysosome, protein processing in endoplasmic reticulum, and
insulin pathway emerged as significantly enriched.
 Showed a downward trend post-infection.
 Further accelerated after ~3 weeks following the initial onset of the RSV
infection.
• The elevated spike class showed a maxima cluster on day 18 post RSV
infection (one time point after the cytokine maximum).
• Enrichment in pathways such as the spliceosome, glucose regulation of insulin
secretion, various pathways related to a stress response.
• Inspection of metabolites reveals 23 that show the same exact trend.
• Another group showed down regulation in several pathways on day 18, such
as formation of platelet plug.
III. Extensive heteroallelic variation and RNA editing
• There were high-confidence coding-associated RNA edits, including A-to-G
and C-to-U deamination-like edits.
• A-to-G edits in PBMCs can be edited in other cell types.
• BLCAP exhibited two edited changes with edited/total ratios of 0.12-0.2 and
0.18-0.31, respectively (0.21 ratio previously observed in the brain).
• Two missense-causing edits, U-to-C in SCFD2 and G-to-A in FBXO25, indicating
an amination-like RNA-editing mechanism, previously not observed in human
cells.
• Expression of SNV-containing miRNAs was generally higher compared to SNVfree miRNA.
• Most SNV-containing miRNAs were not expressed.
miRNA : micro RNA, has very few nucleotides (an average of 22) compared with other RNAs.
SNPs : single nucleotide polymorphisms
• iPOP is an analysis that combines genomic, transcriptomic, proteomic,
metabolomic, and autorantibody profiles from a single individual.
• Longitudinal iPOP can be used to interpret healthy and diseased states by
connecting genomic information with additional dynamic omics activity.