This document summarizes a study that used multi-omic profiling to identify metabolic perturbations in type 1 diabetic (T1D) mice compared to non-diabetic mice. The study found: 1) Increased markers of oxidative stress and reduced levels of anti-inflammatory lipids in T1D mice; 2) Elevated triglycerides and reductions in major structural lipids in T1D mice, indicating hypertriglyceridemia; 3) Over 1000 plasma metabolites were measured and biochemical network analysis identified differences between T1D and non-T1D mice related to oxidative stress, inflammation, and lipid metabolism.

1. Heatmap displaying
hierarchically clustered
Spearman correlations
between animal
characteristics (weight
and glucose) and major
classes of measured
metabolites and lipids.
CLUSTER ANALYSIS
Comprehensive measurements of > 1000 plasma lipids and
metabolites were used to identify age- and sex-independent
metabolic perturbations associated with T1D, which include:
1) increased carbohydrate oxidation products, glucono delta
lactone and galactonic acid, and reduced cysteine,
methionine and threonic acid; supporting increased oxidative
stress 2) reductions in circulating polyunsaturated fatty acids
and lipid signaling mediators, most notably arachidonic acid
(AA) and AA-derived eicosanoids, implying impaired states of
systemic inflammation 3) elevations in circulating
triacylglyercides reflective of hypertriglyceremia and 4)
reductions in major structural lipids, most notably
lysophosphatidylcholines and phosphatidylcholines.
Multi-omic profiling of type 1 diabetes progression in NOD mice reveals increased markers
oxidative stress and hypertriglyceremia
Dmitry Grapov1,2, Johannes Fahrmann2, Jun Yang2, Bruce Hammock2, Oliver Fiehn2, Manami Hara3
1Genome Analytics, Monsanto, Chesterfield, MO
2NIH West Coast Metabolomics Center, Davis, CA; University of California Davis, Davis, California
3Department of Medicine, The University of Chicago, Chicago, Illinois
METHODS
Reference:
Systemic Alterations in the Metabolome of Diabetic NOD Mice Delineate Increased
Oxidative Stress Accompanied by Reduced Inflammation and Hypertriglyceridemia
American Journal of Physiology - Endocrinology and
Metabolism 2015 Vol. no. , DOI: 10.1152/ajpendo.00019.2015
Non-obese diabetic (NOD) mice are a commonly-
used model of type 1 diabetes (T1D). However, not
all animals develop hyperglycemia despite
undergoing similar pancreatic autoimmune insult,
which presents a unique opportunity to identify
metabolic markers of T1D progression. A multi-
omic approach comprised of gas chromatography
time-of-flight (GC-TOF), ultra high performance
liquid chromatography accurate mass quadruple
time-of-flight (UHPLC-qTOF) and UHPLC-tandem
mass spectrometry platforms, was used to identify
circulating metabolic alterations in NOD mice
progressing or not progressing to T1D.
ABSTRACT
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).
BIOCHEMICAL and STRUCTURAL SIMILARITY NETWORK
Primary
Metabolites
Complex
Lipids
Signaling
Lipids
Number of significantly altered metabolites between diabetic and non-diabetic animals. False
discovery rate (FDR) adjusted Mann-Whitney U test p-value < 0.05.
STATISTICAL ANALYSIS
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.
BIOCHEMICAL PATHWAY ENRICHMENT
CONCLUSIONS
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.
EMPIRICAL NETWORK
Acknowledgements
This research is supported in part by the NIH grant 1 U24 DK097154 and NIH West Coast Metabolomics Center Pilot Program.