Upload
others
View
3
Download
0
Embed Size (px)
Citation preview
TO DOWNLOAD A COPY OF THIS POSTER, VISIT WWW.WATERS.COM/POSTERS ©2018 Waters Corporation
INTRODUCTION
Obesity is a risk factor for manifestation of metabolic
syndrome, a combination of metabolic abnormalities
such as type 2 diabetes, hyperlipidemia, hyper-
cholesterolemia and hypertension. It has previously
been demonstrated that excessive glycosphingolipids
play a role insulin resistance, a major driver of the
metabolic syndrome. Reduction of glycosphingolipids in
obese mice with small compound iminosugars has been
found to negate symptoms1. The work presented here is
to provide an integrated multi-omic and system biology
analysis of the results from the relative quantitative LC-
MS analysis of protein and lipid liver extracts from
control and obese mouse models undergoing treatment
to either prevent or revert obesity, providing
mechanistic insights.
INTEGRATED MULTI-OMICS AND SYSTEMS BIOLOGY ANALYSIS OF THE TARGETED TREATMENT OF OBESITY WITHIN MOUSE MODELS
Martijn van der Lienden1, Gertjan Kramer2, Mina Mirzaian2, Lee A Gethings3, Jimmy Yuk1, Johannes PC Vissers3, Johannes MFG Aerts1, Carmen Argmann4, Marco van Eijk1,2 1 Department of Medical Biochemistry, Leiden Institute of Chemistry, The Netherlands, 2 Academic Medical Centre, University of Amsterdam, The Netherlands, 3 Waters Corporation, Wilmslow, United Kingdom, 4 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
Figure 1. Glycosphingolipid biosynthesis and pharmacological modula-
tion .
METHODS
Sample preparation
Proteins and lipids were extracted from liver tissue, which originated
from 3 control and 3 treated (MZ-21 inhibitor aka AMP-dnm) obese
mice. The protein extracts were prepared with 1% RapiGest SF prior to
reduction, alkylation and overnight digestion with trypsin.
Lipids were extracted by homogenizing liver tissue in chloroform-
methanol (2:1, v/v) and extracted according to the Bligh and Dyer
method. The extracts were centrifuged for phase separation and the
lower fraction collected for LC-MS analysis. An overview of the
experimental and analytical workflow is provided in Figure 1.
LC-MS conditions
Label-free LC-MS was used for qualitative and quantitative peptide
analyses. Experiments were conducted using a 90 min gradient from 5
to 40% acetonitrile (0.1% formic acid) at 300 nL/min using a nano-
ACQUITY system and a HSS 1.8 µm C18 reversed phase 75 µm x 15
cm nanoscale LC column. For lipid identification, the LC-MS
experiments consisted of a 20 min gradient from 3 to 40% isopropanol:
methanol (10 mM ammonium formate) at 500 µL/min using an
ACQUITY LC system. Here, a BEH 1.7 µm C8 reversed phase 2.1 x 10
cm LC column was used.
Proteomic data acquisition utilized data independent analysis (DIA) with
the LC system directly interfaced to a hybrid IMS-oaToF Synapt G2-Si
instrument. Ion mobility (IM) was used in conjunction with the proteomic
acquisition schema (HDMSE). Lipidomic measurements were conduc-
ted using a Xevo G2-S mass spectrometer, operating in broadband DIA
(MSE) mode.
References
1. Aerts et al. Glycosphingolipids and insulin resistance. Adv Exp Med Biol. 2011;721:99-119.
2. Aerts et al. Pharmacological inhibition of glucosylceramide synthase enhances insulin sensitivity.
Diabetes. 2007;56(5):1341-9.
3. Bijl et al. Reduction of Glycosphingolipid Biosynthesis Stimulates Biliary Lipid Secretion In Mice.
Hepatology. 2008;49(2):637-45
4. Lombardo et al. Correction of Liver Steatosis by a Hydrophobic Iminosugar Modulating
Glycosphingolipids Metabolism. PLoS One. 2012;7(10):e38520.
5. van Eijk et al. PLoS ONE. Reducing glycosphingolipid content in adipose tissue of obese mice restores
insulin sensitivity, adipogenesis and reduces inflammation. 2009;4(3):e4723.
6. Langeveld et al. Treatment of genetically obese mice with the iminosugar N-(5-adamantane-1-yl-
methoxy-pentyl)-deoxynojirimycin reduces body weight by decreasing food intake and increasing fat
oxidation.Metabolism. 2012;61(1):99-107
7. Gurzov et al. Hepatic oxidative stress promotes insulin-STAT-5 signaling and obesity by inactivating
protein tyrosine phosphatase N2. Cell Metab. 2014;20:85-102
Informatics
The LC-MS peptide data were processed and searched using
Progenesis QI for proteomics, whilst the lipid data was processed and
searched with Progenesis QI. In both cases normalized label-free
quantification was achieved from the Progenesis software. Additional
multivariate analysis was conducted with EZInfo. Pathway and network
analyses were performed with Ingenuity Pathway Analysis.
RESULTS
MZ21 inhibition has been studied previously2-6
and its efficacy
demonstrated as summarized in Figure 4. Here, multi-omics data and
results are presented, aiming to increase mechanistic insight in the
observed beneficial action of MZ-21.
CONCLUSION
A multi-omic study using DIA, label-free strategies has been applied to the study of obese mice which have been treated with an inhibitor of glucosylceramide metabolism
Over 1200 proteins were identified with 30% of the data showing differential expression
Phosphatidylcholines, sphingomyelins, triglycerides and lysophosphatidylcholines are identified as contributing towards the lipid variance
Carbohydrate and lipid metabolism were identified as pathways significantly influenced by MZ-21 treatment of mice.
A number of the noted metabolic changes induced by MZ-21 in the mice are consistent with literature on effects of stimulated STAT signaling
Reduced, alkylated& tryptic digestion overnight
Chloroform/water & centrifugation (lower phase analysed)
1D LC-DIA-IMS-MS
Label-freeQualitative/Quantitative Analysis
1D LC-DIA-MS
Proteins Lipids
Control(3 subjects)
MZ-21 treated(3 subjects)
Control(3 subjects)
MZ-21 treated(3 subjects)
Obese mice liver extracts(3 control and 3 treated)
Label-freeQualitative/Quantitative Analysis
Figure 2. Experimental design study for proteins and lipids extracted
from liver tissue of control and MZ-21 treated mice.
control
proteomics
MZ-21 treated
control
lipidomics
MZ-21 treated
Figure 6. Log-fold change significance (ANOVA (p)) vs. log2 protein fold
change distribution (top) allows highly probable, differentially expressed
proteins to be detected for control (grey) and treated (blue) subjects.
Discriminant analysis (OPLS-DA) results (bottom) illustrating the covari-
ance (x-axis) and correlation (y-axis) relationship between control and
MZ-21 treated lipid liver extract samples to identify, blue shaded, signifi-
cantly regulated lipids.
Figure 5. Unsupervised PCA scores plots for proteomic (upper) and lipi-
domic (lower). Inhibitor treated subjects are highlighted in red whilst
controls are black.
Figure 9. STAT5B and STAT5A transcription factors predicted by to be
upstream regulators of several proteins affected by MZ21 treatment.
Figure 3. Low energy precursor (light grey) and inverted elevated en-
ergy fragment (dark grey) ion m/z values as a function of retention time
(min) and drift time (bins) of liver control proteomic sample analyzed by
IM enabled DIA LC-MS.
Figure 8. Pathway analysis highlights ‘Lipid Metabolism’ as most the af-
fected function reflected in the differentially expressed proteins (bottom/
left) with the predicted metabolite changes, based on the proteomics re-
sults, confirmed by the lipidomics data (top/right).
Figure 4. MZ21 inhibition efficacy demonstrators/examples.
Figures 7. Qualitative results summary and identification examples.
The bubble plots are sized by log (1/p) and number of peptides for
the lipidomics and proteomics data sets, respectively (grey = control
and red = MZ-21 treated).
lipidomics
control
MZ-21
proteomics
MZ-21 control
MZ21 (AMP-dnm) is known to acutely increase inuslin sensitivity in
obese rodents2. Upon administration of the drug, as shown in Figure
10, insulin signaling in tissue such as liver is increased. The pathway
analysis of AMP-drug treated mouse liver points to a change in STAT5
signaling. This finding is consistent with the recent observation that ab-
normal insulin-STAT-5 signaling occurs in liver of obese mice7; appar-
ently the AMP-dnm treatment corrects this .
Small amounts of the purified liver extracts were analyzed to identify,
quantify and investigate the proteomic and lipidomic variance between
control and inhibitor treated subjects. PCA was initially used to identify
significant changes between control and inhibitor treated samples of
which an example is shown in Figure 5. Similar, complementary cluster-
ing patterns were observed for both the protein and lipid data sets.
Next, shown in Figure 6, MVA was conducted to detected differentially
regulated species. Following quantitative analysis, the proteomics and
lipidomics data were appended with identifications.
Searches were conducted within the data analysis software environ-
ments. In total, over 1200 protein groups , representing 2100 proteins,
were detected and quantified with ~ 30% of the proteins differentially
expressed. In the instance of the lipidomic LC-MS data, 73 regulated
lipid were tentatively identified of which ~ 20% were annotated using
rule-based logic. Shown in Figures 7 are typical examples of key regu-
lators.
The results of the integrated systems biology analysis of the LC-MS DIA
data are summarized in Figures 8 and 9. In summary, the combined
pathway analysis of the proteomic and lipidomic data identified ‘Lipid
Metabolism’ as the most affected function and transcription factors to be
predicted as upstream regulators of a signification portion of the de-
tected proteins demonstration chances in concentration/abundance as a
function of inhibitor treatment.
Figure 10. Improved insulin signaling in the liver of ob/ob mice treated
with MZ-21. Animals were fed for 2 weeks with 0, 5, or 25 mg AMP-
DNM/kg. To animals starved overnight, insulin (0.75 units/kg) was ad-
ministered via the vena porta. Livers were collected after 5 min. Insulin
receptor protein, and its autophosphorylation, and mTOR protein, and
its serine phosphorylation, were visualized and quantified.