Post on 14-Jul-2015
PMID:24204828
2009
~10% variance explained
Many diseases, including aging, have dominant metabolic components (e.g. metabolic syndrome)
Genotype + metabolome >40% variance explained
Type 2 Diabetes
Integromics
Nature Genetics 46, 543–550 (2014) doi:10.1038/ng.2982
Variance in SNPs mapped to variance in metabolite concentrations
Empirical metabolic network displaying gene-metabolite associations
Utilize network manifold to uncover latent relationships
Applications of Metabolomics: DiabetesType 2 Diabetes x genotype
Grapov et. al., PLoS ONE (2012) doi:10.1371/journal.pone.0048852
•mitochondrial function is a determinant of T2D severity
•signaling lipids are stored in adipose triglycerides
Type 1 Diabetes non-progressors
Grapov et. al., Metabolomics (2014) doi:10.1007/s11306-014-0706-2
•genetically and environmentally identical animals avoid T1D onset and display significant metabolic differences
TEDDY: The Environmental Determinants of Type 1 Diabetes in the Young
Time•multi-Omic longitudinal study involving > 15,000 samples acquired over 3 yrs
http://teddy.epi.usf.edu/TEDDY/Time
Applications of Metabolomics: Early Life
J Matern Fetal Neonatal Med. (2014) PMID 2528417
Markers of Autism in Twins Birth Weight
J Matern Fetal Neonatal Med. (2014) PMID 25284173
•Metabolomics can offer non-genetic insight into into pathpphysiological states with complex heritability patterns
Milk Glycans and Immune Markers
J Nutr (2013) PMID: 24047700
•Maternal phenotype has a large impact on milk protein expression, modification (e.g. glycosylation) and function
Milk Proteins
Grapov et. al.,Journal of Proteome Research (2014, in Press)
•Changes in milk protein composition can lead to lasting perturbations in infant gut microbiota and energy metabolism
Biochemical
Applications of Metabolomics: CancerLung Cancer
Grapov et. al., Cancer Prevention Research (2014, under review )
•Multifactorial diseases such as cancer require unique of combinations of algorithms and analyses to identify important drivers of biochemical changes associated with these complex states
Empirical
Applications of Metabolomics: Interventions
Grapov et. al., Circ. Cardiovasc. Genet. (2014, in press).doi:10.1161/CIRCGENETICS.114.000606
Drug Response
Lifestyle (diet and exercise)
Grapov et. al.,PLoS ONE (2014) doi:10.1371/journal.pone.0084260
Journal of Proteome Research (2014, revision)
Drug effects
•Metabolomics can offer real-time insight into treatment efficacy and drive personalized medicine decisions
Analysis at the Metabol-OMIC ScaleDynamic a priori or a posteriori network construction, visualization and analysis
MappingsNetwork Mapped Network
Grapov D.,American Society of Mass Spectrometry Conference (2013, 2014)
Network Mapping
+ =
Network Mapping
Ranked statistically significant differences within a a biochemical
context
Statistics
Multivariate
Context
+
+
=
Statistical and Multivariate AnalysisGroup 1
Group 2
What analytes are different between the
two groups of samples?
Statistical
significant differenceslacking rank and
context
t-Test
Multivariate
ranked differences lacking significance
and context
O-PLS-DA
Network Mapping
Ranked statistically significant differences within a a biochemical
context
Statistics
Multivariate
Context
+
+
=
Statistical and Multivariate AnalysisGroup 1
Group 2
What analytes are different between the
two groups of samples?
Statistical
significant differenceslacking rank and
context
t-Test
Multivariate
ranked differences lacking significance
and context
O-PLS-DA
To see the big picture it is necessary to view the data from many different angles
Data Visualization
http://uncyclopedia.wikia.com/wiki/Pac-Man_(walkthrough)
Seems like a legitimate solution, but how can we confirm?Hint: Visualize!
Data Visualization
TROLL LVL 99
http://uncyclopedia.wikia.com/wiki/Pac-Man_(walkthrough)
Can not be the solution because it does not conform to square boundaries. (Level 8)
Data Quality Assessment• accuracy, precision, etc.
Statistical• hypotheses testing, FDR• power analysis, design of experiments
Multivariate• exploratory, non- or semi-supervised
• clustering, dimensional reduction, feature selection
• predictive modeling, classification, machine learning
Functional• biochemical enrichment or overrepresentation
Network• relationships, graph analyses
Network Mapping• data integration, visual data mining• pattern recognition
Data Analysis and Visualization
DeviumDynamic MultivariatE Data Analysis and VIsUalization PlatforM
https://github.com/dgrapov/DeviumWeb
• Interactive visualizations• Statistics• Clustering • Multivariate• Predictive modeling• Machine Learning• Pathway analysis• Etc.
DeviumDynamic MultivariatE Data Analysis and VIsUalization PlatforM
https://github.com/dgrapov/DeviumWeb
Metabolomic Networks
Biochemical (substrate/product)•Local Database •Web services
Chemical (structural or spectral similarity )•fingerprint generation•similarity calculation
Empirical (dependency)•correlation•partial-correlation
BMC Bioinformatics 2012, 13:99 doi:10.1186/1471-2105-13-99
Pathway Independent Omic-Integration
Modified from Barend Mons, 2012
Concept:
Use metabolic networks as a foundation to form the core of large-scale small molecule, protein and gene ‘interaction’ networks
Challenges:
•Database optimization
•Visualization
Domain independent network generation
Topological Data Analysis (TDA): mapping multivariate properties of data (nodes) to a network like manifold
Test hypotheses on the manifold representation of the data