see higher resolution image: https://imdevsoftware.files.wordpress.com/2014/06/asms-2014-j-fahrman.png

1. Unbiased metabolomics and lipidomics were combined with quantitative analysis of oxidized
fatty acids (oxylipins) to identify circulating markers of beta-cell loss and T1D progression.
Incorporation of these three platforms was used to measure > 1000 (397 annotated) lipids and
metabolites in n=70 NOD mice. Advanced statistical, multivariate, functional and network
analyses were used to identify key metabolic perturbations associated with T1D progression.
Univariate comparisons were used to identify ~500 (192, annotated; Figure 1) significantly altered
metabolites and lipids between diabetic and non-diabetic animals.
Global markers of T1D progression included 69%, 52% and 37% increase in circulating
carbohydrate, triglyceride and aromatic compounds and 150%, 140% and 140% decreases in
prostacyclin, lysophosphotidylcholine (LPC) and 20-carbon ketone species oxylpins. Many
changes in lipids were correlated with animal weight (Figure 4), which was significantly reduced
in diabetic compared to non-diabetic animals (Table 1).
Biochemical and structural similarity network analysis was used to integrate measurements from
the three analytical platforms (Figure 2). Metabolites were connected based precursor to product
relationships (KEGG) or structural similarities in molecular fingerprints (PubChem).
KEGG pathway enrichment analysis was used to identify significant changes in arachidonic acid
(Figure 3) and galactose metabolism in diabetic compared to non-diabetic animals.
increases in circulating carbohydrates and reduction in 1,5-anhydroglucitol, a marker of glycemic
control
Orthogonal partial least squares discriminant analysis (O-PLS-DA) coupled with analysis of partial
correlations was used to identify key biomarkers associated with type 1 diabetes progression in
NOD mice (Figure 5).
Acknowledgements
This research is supported in part by the NIH grant 1 U24 DK097154 and NIH West Coast Metabolomics
Center Pilot Program.
Multi-Platform Analysis of Metabolic Perturbations in Diabetic NOD Mice:
Evaluation of the Metabolome, Lipidome and Lipid Signaling Mediators
Johannes Fahrmann1, Dmitry Grapov1, Jun Yang1, Bruce Hammock1, Oliver Fiehn1, Manami Hara2
1NIHWest Coast Metabolomics Center, Davis, CA; University of California Davis, Davis, California
2Department of Medicine, The University of Chicago, Chicago, Illinois
Objective Identify plasma biomarkers predictive of Type 1 Diabetes Mellitus (T1D)
progression and beta-cell destruction.
Background and Significance Non-obese diabetic (NOD) mice are a widely-used model for
type 1 diabetes (T1D). However, not all animals develop overt diabetes, and are termed
non-progressors. We previously demonstrated marked heterogeneity in total residual
pancreatic beta-cell mass in T1D and non-progressor littermates (>8-wk), which was
independent of lymphocyte infiltration and endoplasmic reticulum stress. We identified a
threshold of ~70% of total beta-cell mass loss before, independent of age or sex, animals
developed chronic hyperglycemia and overt T1D.
Methods NOD Mice (n=71) were assessed as diabetic or non-diabetic based on their fasting
(4hr) blood glucose levels at sacrifice, which defined 30 hyperglycemic (glucose ≥ 250
mg/dL) and 41 normoglycemic animals. A multi-platform approach including: gas
chromatography time-of-flight mass spectrometry (GC-TOF), liquid chromatography
quadropole time-of-flight mass spectrometry (LC-Q-TOF) and liquid chromatography
tandem quadrople mass spectrometry (LC-MS/MS) was used to measure > 1000 plasma
primary metabolites, lipids and oxylipins. Statistical, multivariate, functional and network
analyses were used to integrate data from the three platforms and identify key metabolomic
and lipidomic perturbations associated with T1D progression.
Conclusions Analysis of the metabolome and lipidome revealed distinct T1D-dependent
metabolic perturbations extending to all measured biochemical domains.
Results
Table 1. Non-Obese Diabetic Mice Characteristics
Non-Diabetic Diabetic
Female 18 6
Male 23 24
Age (wks)† 36 (26,40) 38 (26,40)
Weight (g) 27.3 ± 4.8 19.5 ± 4.8‡
Glucose(mg/dL) 94 ± 34 513 ± 101‡
* values reported as mean ± standard deviation unless otherwise noted
† values reported median (minimum, maximum)
‡ unpaired two-sample t-test p-value ≤ 0.05
Figure 2. Biochemical and structural similarity network displaying metabolic differences between diabetic and non-diabetic NOD mice. Metabolites
are connected based precursor to product relationships (KEGG) or structural similarities in molecular fingerprints (PubChem, Tanimoto>0.07).
Figure 3. Significantly perturbed eicosanoids
(p<0.05) within the KEGG arachidonic acid
metabolism pathway. Pathway enrichment was
determined based on FDR adjusted
hypergeometric test p-values < 0.05 for KEGG
pathways for Mus musculus. Figure displays
relative fold changes in means between diabetic
and non-diabetic animals. Figure 5. Partial correlations between top predictors of animals’ diabetic status.
Relationships (FDR adjusted p-value<0.05) are displayed for statistically different (FDR
adjusted p-value<0.05 )and O-PLS-DA selected discriminants between diabetic and non-
diabetic animals. Node size shows the fold change in means relative to non-diabetics.
Figure 4. Heatmap displaying
hierarchically clustered
Spearman correlations
between animal characteristics
(weight and glucose) and major
classes of measured
metabolites and lipids.
Introduction
Methods Conclusion
Primary
Metabolites
Complex Lipids
Oxylipins
15µL Plasma
10µL Plasma
250µL Plasma
Extraction
Chilled 3:3:2
ACN/IPA/Water
Derivatization: MeOX
in pyridine +
MSTFA
Liquid-Liquid Extraction
Chilled 225µL MeOH
(+QC Mix ISTDs)
Chilled 750µL MTBE
(+ 22:1 CE ISTD)
188µL distilled water
Solid Phase Extraction
Waters Oasis HLB
Cartidges
(+ type 1 ISTDs)
Instrument
GC-TOFMS Analysis
Agilent 7890A Gas Chromatograph
coupled to a Leco Pegasus IV Time-
Of-Flight (TOF) Mass Spectrometer
UHPLC-QTOFMS Analysis
Infinity Ultra High Performance
Liquid Chromatograph coupled with
an Agilent 1290 Accurate Mass-
6530-QTOF
UPLC-MS/MS Analysis
Agilent 1200 SL UPLC coupled
to a 4000QTRAP Mass
Spectrometer
Column
30m long, 0.25mm i.d. Rtx5
Sil-MS column with 0.25µM
5% diphenyl film; +10m
integrated guard column
(Restek, Bellefonte PA)
Waters Acquity UPLC CSH
C18 (100mm length x 2.1mm
internal diamtere; 1.7µM
particles) + Water Acquity
Vanguard CSH C18 1.7µM
Pre-column
2.1 x 150mm Eclipse Plus
C18 column with a 1.8µM
particle size
Data Processing
Fiehnlab BinBase DB
MzMine 2.10
AB SCIEX Analyst Software
1.4.2
Primary Metabolites Complex Lipids Oxylipins
Figure 1. Number of significantly altered metabolites between diabetic and non-diabetic
animals. False discovery rate (FDR) adjusted Mann-Whitney U test p-value < 0.05.
Data Acquisition Data Analysis
Statistical
Analysis
Hierarchical
Clustering
Principal Components
(PCA) and Orthogonal
Partial Least Squares
(O-PLS)
DeviumWeb (https://github.com/dgrapov/DeviumWeb)
MetmapR (https://github.com/dgrapov/MetaMapR)
DeviumWeb
Software Method
MetMapR
Network Analysis and Visualization
Pathway Enrichment and Visualization