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Jul 16, 2013 - IN, USA, 3 Department of Computer Science, Purdue University, West Lafayette, IN, USA and 4 Department of Science, Faculty of Science, ...
Molecular Systems Biology 9; Article number 681; doi:10.1038/msb.2013.36 Citation: Molecular Systems Biology 9:681 www.molecularsystemsbiology.com

Targeted proteomics reveals strain-specific changes in the mouse insulin and central metabolic pathways after a sustained high-fat diet Eduard Sabido´1, Yibo Wu1, Lucia Bautista1, Thomas Porstmann1, Ching-Yun Chang2, Olga Vitek2,3, Markus Stoffel1,* and Ruedi Aebersold1,4,* 1 Department of Biology, Institute of Molecular Systems Biology, ETH Zu¨rich, Zu¨rich, Switzerland, 2 Department of Statistics, Purdue University, West Lafayette, IN, USA, 3 Department of Computer Science, Purdue University, West Lafayette, IN, USA and 4 Department of Science, Faculty of Science, University of Zu¨rich, Zu¨rich, Switzerland * Corresponding authors. M Stoffel or R Aebersold, Department of Biology, Institute of Molecular Systems Biology, ETH Zu¨rich, Wolfgang-Pauli-Strasse 16, 8093 Zu¨rich, Switzerland. Tel.: þ 41 44 633 4560; Fax: þ 41 44 633 1362; E-mail: [email protected] or Tel.: þ 41 44 633 1071; Fax: þ 41 44 633 1051; E-mail: [email protected]

Received 18.10.12; accepted 1.6.13

The metabolic syndrome is a collection of risk factors including obesity, insulin resistance and hepatic steatosis, which occur together and increase the risk of diseases such as diabetes, cardiovascular disease and cancer. In spite of intense research, the complex etiology of insulin resistance and its association with the accumulation of triacylglycerides in the liver and with hepatic steatosis remains not completely understood. Here, we performed quantitative measurements of 144 proteins involved in the insulin-signaling pathway and central metabolism in liver homogenates of two genetically well-defined mouse strains C57BL/6J and 129Sv that were subjected to a sustained high-fat diet. We used targeted mass spectrometry by selected reaction monitoring (SRM) to generate accurate and reproducible quantitation of the targeted proteins across 36 different samples (12 conditions and 3 biological replicates), generating one of the largest quantitative targeted proteomics data sets in mammalian tissues. Our results revealed rapid response to high-fat diet that diverged early in the feeding regimen, and evidenced a response to high-fat diet dominated by the activation of peroxisomal b-oxidation in C57BL/6J and by lipogenesis in 129Sv mice. Molecular Systems Biology 9: 681; published online 16 July 2013; doi:10.1038/msb.2013.36 Subject Categories: proteomics; molecular biology of disease Keywords: liver; metabolic syndrome; NAFLD; proteomics; SRM

Introduction The metabolic syndrome is a combination of medical disorders—including central obesity, insulin resistance, dyslipidemia, hypertension and hepatic steatosis—and predisposes to a range of diseases such as diabetes, cardiovascular disease and cancer (Eckel et al, 2010). Genetic and environmental components contribute to the initiation and establishment of this disorder, but it remains unclear how these factors interact to produce the metabolic syndrome and its related pathologies (Biddinger et al, 2005). A deregulation of the hepatic lipid metabolism has been proposed as one of the major features in the pathogenesis of the metabolic syndrome (Luyckx et al, 2000; Marchesini et al, 2001; Pagano et al, 2002). This disorder, which is also known as non-alcoholic fatty liver disease (NAFLD), leads to an increased storage of triacylglycerides in the liver and has been strongly associated with selective hepatic insulin resistance, i.e., the failure of insulin to suppress gluconeogenesis while sustaining the activation of lipolysis (Dixon et al, 2001, 2004). & 2013 EMBO and Macmillan Publishers Limited

In spite of intense research, the complex etiology of insulin resistance and its association with the accumulation of triacylglycerides in liver is not yet completely understood. Several studies have recently used mice of different genetic backgrounds as a model to study the link between insulinregulated networks and hepatic steatosis, and thus to identify potential genetic elements controlling the progression of the metabolic syndrome and its associated pathologies. In this regard, the effect of genetic heterogeneity on the development of diabetes was recently evaluated using genetically engineered variants of the mouse strains C57BL/6J (B6) and 129Sv (S9). Double heterozygous knockout mutations of the insulin receptor (IR) and insulin receptor substrate-1 (IRS-1) genes were generated on both backgrounds and the mice were tested for insulin resistance after a high-fat diet regimen (Kulkarni et al, 2003). The data showed that the phenotypes of these strains differed dramatically. The C57BL/6J double heterozygous mice showed marked hyperinsulinemia and islet hyperplasia and they developed early hyperglycemia and diabetes. In contrast, the 129Sv double heterozygous mice showed mild hyperinsulinemia, minimal islet hyperplasia and Molecular Systems Biology 2013 1

High-fat diet effect on insulin and metabolic pathways E Sabido´ et al

they hardly developed diabetes. Biddinger et al (2005) also reported acute phenotypic differences after several weeks of high-fat diet in wild-type C57BL/6J and 129Sv mice. Wild-type C57BL/6J and 129Sv mice were fed, in that case, with either a low-fat or high-fat diet to dissect the interaction of diet and genetic background under normal conditions. After several weeks of high-fat diet, the 129Sv mice developed typical features of the metabolic syndrome, such as obesity, hyperinsulinemia and glucose intolerance. In contrast, the C57BL/6J mice developed these features on both diets. In both strains, high-fat feeding led to increased hepatic steatosis and it decreased serum triglyceride levels and hypercholesterolemia, although the C57BL/6J mice developed a more serious steatosis and a larger increase in low-density lipoprotein (LDL) cholesterol than the 129Sv mice (Biddinger et al, 2005). Therefore, under constant conditions, these genetically different mouse strains show markedly different metabolic phenotypes. Several proteomics studies have analyzed different mouse strains in the past to relate observed phenotypes to protein changes in the context of the metabolic syndrome (Park et al, 2004; Schmid et al, 2004; Kirpich et al, 2011). However, most of these studies covered only a few experimental conditions and did not address the dynamics of the biological system at different time points, different treatments and in different mouse strains. The number of experimental conditions to be meaningfully compared in proteomics studies has traditionally been limited by the sampling effect of the frequently used discovery proteomic technique (Domon and Aebersold, 2010), which increases the number of missing values and thus, the sparseness of the data matrix (Bensimon et al, 2012) with increasing number of samples analyzed. Targeted proteomics by selected reaction monitoring (SRM) overcomes these technical limitations and constitutes the method of choice for the consistent quantification of a limited number of predefined proteins in multiple samples and conditions (Picotti et al, 2009; Costenoble et al, 2011; Sabido et al, 2012). In the present study, we used targeted mass spectrometry by SRM to generate accurate quantitative measurements in mouse liver homogenates, of proteins involved in the insulinsignaling pathway and central metabolism across 12 different conditions, with 3 biological replicates each. A total of 144 proteins—including metabolic enzymes, kinases, phosphatases, transcription factors and structural proteins—were reproducibly quantified in all samples representing different treatments, time points and mouse strains, thus generating a rich and near complete quantitative proteomics data set to evaluate the differential changes induced in the proteome after subjecting C57BL/6J and 129Sv mice to a sustained long-term high-fat diet. The data set revealed a rapid proteomic response to high-fat diet that diverged early in the feeding regimen in the obesity prone C57BL/6J mouse strain and the partially obesity and diabetes-resistant 129Sv strain.

Results SRM assay development for targeted proteomics SRM assays were initially designed to quantify 257 proteins covering the insulin-signaling pathway and the lipid and 2 Molecular Systems Biology 2013

carbohydrate metabolism pathways (Figure 1; Supplementary Table S1). SRM assays corresponding to 2–4 unique peptides per protein were developed, using synthetic peptides and following the general high-throughput strategy previously reported (Picotti et al, 2010). The peptides were selected based on the number of observations in previous MS experiments (Martens et al, 2005; Desiere et al, 2006) or, when no previous evidence was available, on the MS-suitability score predicted by PeptideSieve (Mallick et al, 2007). Out of the data generated from these peptides a spectral library containing SRM assays for 1418 peptides representing the 257 proteins was generated. These assays were used throughout this study to quantify the targeted proteins under different conditions and treatments to study the effect of high-fat diet on insulin-regulated signaling networks and hepatic steatosis, and they are all available in Supplementary information (Supplementary Table S1). Mouse strains C57BL/6J and 129Sv were selected for this study as they show distinctive phenotypic differences in response to a sustained high-fat diet. Indeed, after several weeks of high-fat diet the C57BL/6J mice showed higher lipid accumulation in the liver, higher weight increase, a pronounced hyperglycemia and higher hyperinsulinemia when compared with the 129Sv strain (Supplementary Table S2). Liver samples from mice fed with a high-fat rodent diet for 0, 6 and 12 weeks were homogenized, and the lysate of a SILAC (stable incorporation of labeled amino acids in cell culture) labeled mouse hepatocyte cell line was spiked in as an internal heavy-labeled reference. Whole cell homogenates were digested and subsequently fractionated by off-gel electrophoresis, and equal amounts of total protein were analyzed with targeted nLC-SRM. All experiments were done in triplicate. A total of 251 peptides representing 144 of the preselected proteins involved in the insulin-signaling pathway and central metabolism (Figure 1; Supplementary Table S3A) were consistently detected and quantified across different treatments (fasted overnight and fed ad libitum), strains and time points using targeted mass spectrometry. Proteins that could not be detected even after sample fractionation mostly corresponded to transcription factors and signaling proteins, which are normally present in the cell only at low abundance levels. Acquired data are summarized in Figure 2 and Supplementary Table S3. Unsupervised hierarchical clustering of all protein abundance changes showed two clearly distinct groups corresponding to the studied mouse strains, thus confirming the importance of the genetic background as the main determinant modulating the response to high caloric intake. The subset of identified proteins that more strongly contributed to the separation of the mouse strains were mainly metabolic enzymes belonging to the tricarboxylic acid cycle, glycolysis, b-oxidation, fatty acid biosynthesis and glycogen metabolism (Figure 2A; Supplementary Table S4A). Similar results were obtained when the data were subjected to a principal component (PC) analysis, although, in that case, also proteins distinguishing different time points and treatments could be identified (Figure 2B; Supplementary Table S4B). Three PCs were required to distinguish among strains, time points and treatments showing that there is enough variation in the abundance of the measured peptides and proteins to reflect the different conditions of the study (PC-1: 64.0%; PC-2: & 2013 EMBO and Macmillan Publishers Limited

High-fat diet effect on insulin and metabolic pathways E Sabido´ et al

INSULIN SIGNALING PATHWAY

INSULIN

INSR

SHC1

LEPR

+P

+P

FYN

IRS1

CBL

JAK1

IRS2

CRK

JAK2

PAK1

1433E SOS1 SOS2

PK3CA PK3C3

RASN RASK RASH

PTEN

PK3CD

P85A

PK3CG

P85B

PK3CB

P55G

RAF1 PIP3 RICTR

MYC

ELK4

MKNK2 SRF

DNA

+P FOS

+P

KS6A1

IF4EG1

+ PSTRAA +P

PRGC2

KS6B1 +P

DNA KAPCA

+P

PYGB

F16P1 PYGM LIPS

AL4A1 ARHG6 CAP1 CP2E1 DHB12 DHB7 EXOC7 FETA FX1R1 HPPD

GLYCOGENESIS FOXA2

PPARD

FOXA3 RS6

OTHERS

WBS14

F16P2

+P

CRTC2

KAPCB +P +P

PYGL

–P PP2AA

HIF1A

NUCL

FATTY ACID SYNTHESIS

PPARG

PROLIFERATION CELL GROWTH

LMNB1 MFN2 MGST1 MK14 MUTA

ANTILIPOLYSIS

PPARA

HNF4A

PROTEIN SYNTHESIS CRTC1

cAMP

AAKB1 GLYCOLYSIS

KS6B2 AAKG1

+P

GSK3B CTNB1

PP1G

PRGC1

2AAA

DNA

GTR4 GLUCOSE UPTAKE

AAPK1

IF4B

CBP

KPCI SRBP1

PP1B

STK11

IF4EG2 EIF3L CREB1

GROWTH CELL DIFFER.

RAB10

DNA LIPOGENESIS AND +P GLUCOLYSIS ENZYMES +P TSC1 ENERGY+ P +P +P STRESS RHEB TSC2 Q9WVH4 CAB39BAD FOXO1 PP1A PDE3B GSK3A LRP6

SODM SODC

NFKB2 DNA

KS6A3

CATA EIF3E

KPCD

AKT2

AKTS1

PROLIFERATION DIFFERENTIATIONFRAP RPTOR +P +P 4EBP1 +P

IF4E

DNA TRAF2

IKKA

KPCA AKT1

+P ELK1

STAT3 PTN11 SOCS3

IKBB

SHIP2

+P

MK03 +P

TRADD

+P

MTOR

MK01

MKNK1

+P

IKKB

JAK3

CIP4 EXOC7

PAK3 +P-P PTN1 MP2K4 +P +P MK08 JUN SHIP1

MP2K1 MP2K2

RHOQ

PAK2

1433B

ADR1 TNR1A TNR1B ADR2

SH2B2

+P

+P

GRB2

TNFa ADIPONECTIN

LEPTIN

OTC OXSR1 RUVB1 SC23A

SFRS2 SIRT2 SIRT3 TCEA2 VIME

DNA

CRTC2 LIPID METABOLISM ENZYMES

GLUCONEOGENESIS

ß-OXIDATION

GLYCOLYSIS AND GLUCONEOGENESIS

MCAT saturated ACOX1 acyl-CoA CPT1A acyl-CoA ACOX2 acyl-CoA (shorter) ECHB THIKA

HXK1-4

ACADS

THIM

glucose-6-phosphate

3-ketoacyl-CoA

2,3-enoyl-CoA

HCDH ECHP

ECHP ECHA

G6PC

G6PD1 G6PD2 6-phosphogluconolactone

glucose-6-phosphate

ACDSB

6PGL

G6PI

ACD1-11 ACADM

THIKB

PENTOSE PHOSPHATE PATHWAY

glucose

6-phosphogluconate

fructose-6-phosphate K6PL F16P1 F16P2

6PGD

fructose-1,6-biphosphate

ribulose-5-phosphate RPIA

ALDOA ALDOB ALDOC DHB4 ECHM dihydroxyacetone glyceraldehyde 3-hydroxyacyl-CoA HCD2 phosphate 3-phosphate TPIS G3P pyruvate CYCLE 1,3-biphosphoglycerate ODPA ODP2 +P PDK1 PGK1 PGK2 ODPB 3-phosphoglycerate acetyl-CoA PYC ACON PGAM1 PGAM2 isocitrate citrate 2-phosphoglycerate oxalacetate CISY Q9DAM4 IDHP ENOB ENOA MDHC MDHM IDHC ENOG ketoglutarate malate phosphoenolpyruvate ODO1 FUMH DLDH PCKGC ODO2 KPYR SUCA oxalacetate fumarate succinate succinyl-CoA ECHA DHB4

TCA

DHSA DHSB

SUCB1 SUCB2

RPE

ribose 5-phosphate

xylulose 5-phosphate

TKT TKTL1 TKTL2 glyceraldehyde + sedoheptulose 3-phosphate 7-phosphate TALDO fructose 6-phosphate

erythrose 4-phosphate

TKT TKTL1 TKTL2

xylulose 5-phosphate

pyruvate PYC

FATTY ACID BIOSYNTHESIS

KETOGENESIS acetyl-CoA

valine

2-methylpropanyl-CoA

leucine

3-methylbutynyl-CoA

isoleucine

2-methylbutynyl-CoA

BCAT1 ACSF2 MCCA ODBA BCAT2

MCCB ODBB IVD ODB2

acetyl-CoA Fatty acids

FAS malonyl-CoA ACACA ACACB acetyl-CoA

GLYCOGEN METABOLISM glucose

THIL

PYGL

hydroxymethylglutaryl-CoA HMGCL acetoacetate BDH

glucose-6-phosphate HXK1-4 PGM1 PGM2 glucose-1-phosphate

acetoacetyl-CoA HMCS1 HMCS2

acetone

UGPA

PYGB

UDP-glucose

PYGM

GYS1 GYS2 glycogen GLGB Q8CE68

hydroxybutirate

Figure 1 Schematic representation of the insulin-signaling and the central metabolic pathways. SRM assays were developed for the proteins colored in red and green. Gray-colored proteins indicate proteins not targeted in the study. Red colors indicate proteins that were consistently detected and quantified in murine liver. Yellow diamonds represent either DNA or small molecules. Protein codes refer to the Uniprot database identifiers without the suffix _MOUSE.

& 2013 EMBO and Macmillan Publishers Limited

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High-fat diet effect on insulin and metabolic pathways E Sabido´ et al

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S9_T1_ fed

S9_T2_ fed

S9_T0_ fed

S9_T1_ fasted

S9_T2_ fasted

B6_T1_ fed

S9_T0_ fasted

B6_T2_ fasted

B6_T2_ fed

B6_T1_ fasted

B6_T0_ fasted

Principal components analysis (Mice)



12



11 PC2

● ● ● 10 ● ● ● 9 ● ●● ● ● 8 ●

129Sv C57B6/J

● ● ● ●



● ● ●● ● ● ● ●

●● ● ● ● ● ●

7 5

10

15

20 PC1

25

30

Principal components analysis (Time) ●

12

● ● ● ● ● ● ●●

PC2

10 9 8

0 week 6 weeks 12 weeks



11







● ● 129Sv







C57B6/J

●● ● ● ● ● ●

5

● ● ●● ● ● ● ●



7 10

15

20 PC1

25

30

Principal components analysis (Treatment)

5

Fasted Fed



4 3 PC3

KS6A1_MOUSE PK3C3_MOUSE HMCS2_MOUSE TRADD_MOUSE ALDOC_MOUSE ODB2_MOUSE FOXA3_MOUSE ALDOB_MOUSE ODP2_MOUSE ECHP_MOUSE DHB12_MOUSE ACADS_MOUSE PGM1_MOUSE HMGCL_MOUSE HMCS1_MOUSE CATA_MOUSE PK3CB_MOUSE MCAT_MOUSE PCKGC_MOUSE RICTR_MOUSE KPYR_MOUSE Q8R5C9_MOUSE PDE3B_MOUSE THIKB_MOUSE IDHP_MOUSE PYGB_MOUSE CISY_MOUSE TKT_MOUSE G3P_MOUSE ENOA_MOUSE PYC_MOUSE ODPA_MOUSE MCCA_MOUSE ACOX2_MOUSE ECHB_MOUSE Q8CE68_MOUSE ACACA_MOUSE MCCB_MOUSE ODPB_MOUSE VIME_MOUSE K6PL_MOUSE GLGB_MOUSE FAS_MOUSE IVD_MOUSE ODBB_MOUSE MP2K4_MOUSE SRBP1_MOUSE CBP_MOUSE SIRT2_MOUSE TRAF2_MOUSE WBS14_MOUSE MFN2_MOUSE TKTL2_MOUSE NFKB2_MOUSE TCEA2_MOUSE AAKG1_MOUSE HPPD_MOUSE MGST1_MOUSE CAP1_MOUSE OTC_MOUSE SC23A_MOUSE FOXO1_MOUSE AKT1_MOUSE ACOX1_MOUSE RS6_MOUSE RASH_MOUSE CTNB1_MOUSE ODO1_MOUSE CRK_MOUSE AL4A1_MOUSE LMNB1_MOUSE DLDH_MOUSE SODM_MOUSE MK01_MOUSE MK08_MOUSE 1433B_MOUSE FXR1_MOUSE KS6B2_MOUSE MDHM_MOUSE AAPK1_MOUSE EXOC7_MOUSE 1433E_MOUSE NUCL_MOUSE MK14_MOUSE SRF_MOUSE FETA_MOUSE DHB7_MOUSE PGAM1_MOUSE PGK1_MOUSE IDHC_MOUSE SODC_MOUSE Q9DAM4_MOUSE MUTA_MOUSE MDHC_MOUSE PYGL_MOUSE F16P1_MOUSE CPT1A_MOUSE DHB4_MOUSE HCDH_MOUSE P85A_MOUSE SUCB1_MOUSE RUVB1_MOUSE FUMH_MOUSE PTN1_MOUSE MP2K2_MOUSE KAPCA_MOUSE PRGC2_MOUSE EIF3L_MOUSE TSC2_MOUSE PDK1_MOUSE EIF3E_MOUSE GRB2_MOUSE CRTC2_MOUSE MK03_MOUSE 2AAA_MOUSE ODO2_MOUSE TPIS_MOUSE PGM2_MOUSE ARHG6_MOUSE TALDO_MOUSE PP1A_MOUSE ALDOA_MOUSE IF4B_MOUSE KAPCB_MOUSE CAB39_MOUSE CP2E1_MOUSE ACADM_MOUSE ACSF2_MOUSE SIRT3_MOUSE AKT2_MOUSE PAK2_MOUSE CBL_MOUSE IRS1_MOUSE GSK3B_MOUSE IF4G2_MOUSE RPE_MOUSE PP2AA_MOUSE OXSR1_MOUSE IF4G1_MOUSE SFRS2_MOUSE MP2K1_MOUSE IF4E_MOUSE RAB10_MOUSE RASN_MOUSE





2 ●

●●● ●● ● ● ● ●

1

● ● ●





● ● ●

● ●

● ● ● C57B6/J





● ● ●● ● ●

129Sv

0 5

0

−1

0

10

15 PC1

20

25

30

1

Log2 fold-change

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High-fat diet effect on insulin and metabolic pathways E Sabido´ et al

10.4%; PC-3: 5.7% of the total variation). Each PC was orthogonal to the previous ones, and uncovered complementary variation. Together, the three components explained over 80% of the total variation (Figure 2B, dashed ellipses). These analyses showed that although the observed variation is mostly due to differences among mouse strains, time points and treatments still have a significant contribution to sample variability. These observations confirmed the rich information content of the acquired data and provided us with a first overview of the system under study. Further, detailed evaluation of the observed differences in the targeted proteome among mouse strains, time points and treatments is described in the next sections.

Comparison of mouse strains to elucidate the differential effect of the high-fat diet The quantitative data set acquired for the targeted proteins was initially used to evaluate the changes in protein abundance within each mouse strain after 6 and 12 weeks of high-fat diet under ad libitum condition. In both strains, several proteins exhibited significant changes in abundance after 6 (T1) and 12 weeks (T2) of high-fat diet. Most of the observed changes were found in proteins involved in the b-oxidation (DHB4 in C57BL/6J) and fatty acid biosynthetic pathways, as well as in proteins involved in glucose metabolism (Figure 3A and B; Supplementary Table S5). Moreover, some proteins involved in the insulin-signaling pathway, such as transcription factors SRBP1 and EIF3L and kinase MK01, showed significant abundance changes by mass spectrometry either in one (EIF3L in C57BL/6J) or both mouse strains (MK01 and SRBP1) (Figure 3B; Supplementary Figure S2). Overall, the fed C57BL/6J mice showed a more significantly altered targeted proteome after several weeks of high-fat diet than the fed 129Sv mice. The detected time-dependent changes in expression levels may, however, reflect diet-induced alterations as well as agedependent changes. Therefore, to elucidate the differential effect of a sustained high-fat diet in the proteome of C57BL/6J and 129Sv mice, we directly compared protein abundances in the two mouse strains at the same points of the regimen. This analysis revealed numerous proteins that showed different quantitative trajectories after several weeks of high-fat diet in the two strains. The data in Figure 4A, Supplementary Figure S3 and Supplementary Table S6 show that this differential response affected proteins in the main metabolic pathways, especially fatty acid synthesis and b-oxidation, and in the insulin-signaling pathway. The proteins involved in the insulin-signaling pathway that show strain-dependent responses to high-fat diet include several kinases such as KAPCA, KAPCB, GSK3B, PK3C3, PDK1, MP2K4, MK01, the oxidative stress enzymes CATA, SODM, and the transcription factor SRBP1 (Figure 4D).

Among proteins with different fold changes in the two mouse strains after either 6 or 12 weeks treatment, we observed numerous proteins that showed opposite quantitative trends in response to high-fat diet (Figure 4A and C). This was the case of several enzymes related to the TCA cycle (ODP2, ODPA, ODPB, CISY) and of some key proteins involved in the lipid biosynthetic pathway such as FAS and Q8R5C9 (ACACB), which showed increasing abundance levels in C57BL/6J whereas protein abundance was decreased in 129Sv mice. Other proteins that also exhibited opposite quantitative trends were some b-oxidation-related proteins (ACOX1 and ECHB), and pyruvate kinase (KPYR), a key enzyme in glycolysis (Figure 4A and D). Most of these proteins coincide with the proteins identified in the initial unsupervised hierarchical clustering as the subset of proteins most strongly differentiating the mouse strains. Among the proteins showing opposite quantitative patterns over time in the two mouse strains were not only proteins related to central metabolism but also other regulatory proteins such as the insulin-related cAMP-dependent protein kinase (KAPCA) (Figure 4D). The abundance of the former protein is significantly increased at the 12-week time point in C57BL/6J mice. This pattern is consistent with the inhibition of fatty acid synthesis and the promotion of the transcription of genes encoding lipid catabolic enzymes. Temporal behavior in protein abundance also differed between strains for SRBP1, a transcription factor that exhibits highly increased levels in 129Sv mice and that activates the transcription of genes related to hepatic lipogenesis (Figure 4A, golden squares; Supplementary Table S6). The observed differences in the abundance of these regulatory proteins between strains suggest that peroxisomal b-oxidation is actively promoted in fatty C57BL/6J mice after several weeks of high-fat diet, whereas lipogenesis activation dominates the response of 129Sv mice under the same conditions. Other proteins with significant differences in protein abundance between strains showed the same direction but different magnitude in response to high-fat diet. This is the case for proteins related to glycolysis and gluconeogenesis, for enzymes involved in ketogenesis and glycogen synthesis, and for several kinases and signaling proteins (Figure 4A, gray squares; Supplementary Table S6). Most proteins with a differential temporal response to highfat diet between the strains targeted in this study already showed this differential response within the first 6 weeks of high-fat diet (Figure 4B, red and blue dots), and in many cases differences persisted in the 6- to 12-week period (Figure 4B, red dots). Only few proteins showed a delayed response when comparing strains, i.e., they exhibited a significantly different abundance only in the 6- to 12-week period of high-fat diet. Among these are proteins related to oxidative stress (CATA and SODM)—a process that has recently been linked to the metabolic syndrome (Roberts and Sindhu, 2009; Whaley-Connell et al, 2011)—some b-oxidation and fatty acid

Figure 2 (A) Heatmap and unsupervised hierarchical clustering of protein abundance fold-changes of all samples with respect to the reference sample C57BL/6J (B6) mice fed ad libitum at 0 week (T0). T1 refers to animals after 6 weeks of high-fat diet and T2 corresponds to 12 weeks of high-fat diet. S9 is the abbreviation for 129Sv mice. (B) Principal component analysis of the relative protein abundance measured in different mouse strains, different time points after a sustained high-fat diet and different treatments. Three different principal components are required to distinguish between strains, time points and treatments. Orthogonality between the principal components is highlighted by dashed ellipses (PC-1: 64.0%; PC-2 10.4%; PC-3 5.7% of the total variation).

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High-fat diet effect on insulin and metabolic pathways E Sabido´ et al

β-OXIDATION

GLYCOLYSIS AND GLUCONEOGENESIS

FATTY ACID SYNTHESIS

B6

S9

B6

S9

B6

ACACA

ACADS

ACSF2

ACOX1

FAS

ALDOB

ACOX2

IVD

ALDOC

CPT1A

MCCA

ENOA

DHB4

MCCB

F16P1

ALDOA

ECHB

ODB2

G3P

ECHP

ODBB

K6PL

HCDH

Q8R5C9

PCKGC PGAM1 PGK1 PYC

GLYCOGEN

TPIS

S9 GLGB

KETOGENESIS B6

PGM1 S9

PGM2

HMCS1

PYGB

S9 T0

T0 –> T1 T1 –> T2 T0 –> T2

B6

T0 –> T1 T1 –> T2 T0 –> T2

Q8CE68 T0 –> T1 T1 –> T2 T0 –> T2

PYGL

HMGCL T0 –> T1 T1 –> T2 T0 –> T2

HMCS2

T2

T0 –> T1 T1 –> T2 T0 –> T2

B6

T0 –> T1 T1 –> T2 T0 –> T2

T0 –> T1 T1 –> T2 T0 –> T2

T0 –> T1 T1 –> T2 T0 –> T2

THIKB

KPYR T0 –> T1 T1 –> T2 T0 –> T2

T0 –> T1 T1 –> T2 T0 –> T2

MCAT

T0

S9

ACADM

B6 T2

T0

S9 T2

T0

T2

DHB4

SRBP1

Tubulin

Tubulin

Figure 3 (A) Changes in protein abundance after 6 (T1) and 12 weeks (T2) of high-fat diet in C57BL/6J (B6) and 129Sv (S9) mice fed ad libitum. Colors represent either increased (yellow) or decreased (blue) protein abundances, while the color intensity reflects the corresponding log2(fold change). Proteins were grouped according to metabolic pathways. Proteins with no significant fold-change are presented in black. All experiments were done in triplicate. Protein codes refer to the Uniprot database identifiers without the suffix _MOUSE. (B) Protein abundance of DHB4 and SRBP1 after 6 (T1) and 12 weeks (T2) of high-fat diet in C57BL/6J (B6) and 129Sv (S9) mice fed ad libitum validated by western blot.

metabolism enzymes (ACADM, HCDH and MCCA), as well as other proteins related to the insulin-signaling and other pathways (SRF, RICTR, EIF3L, PGM2, OTC, VIME and DHB12) (Figure 4B, green dots). Overall, most of the strainspecific changes therefore were apparent early in the regimen when phenotypic changes were already set, but not yet very pronounced.

Influence of high-fat diet in the fed–fasting transition We were also interested in establishing the effects of a persistent high-fat diet on the proteome changes observed in the fed–fasting transition. To address this question, we first analyzed those changes that take place in the normal proteome in the fed–fasting transition (i.e., in the initial time point: T0 or 0 weeks) and compared them between strains. Then, we asked whether the proteins initially showing a similar abundance profile between strains were perturbed after 12 weeks of high6 Molecular Systems Biology 2013

fat diet and whether the potential perturbations were strain specific. The analysis of the fed–fasted transition at the initial time point showed that several enzymes involved in b-oxidation and gluconeogenesis reacted similarly to fasting in both mouse strains (Figure 5A, both panels; Supplementary Tables S7 and S8), whereas numerous proteins involved in glycolysis, gluconeogenesis and fatty acid synthesis already showed a different pattern at the initial time point (Figure 5B; Supplementary Table S9). These differences, which were already present before any diet treatment, mostly reflect the different genetic backgrounds of both mouse strains. Several proteins that showed no abundance differences between strains in the initial response to the fed–fasting transition had a differential response to fasting after 12 weeks of high-fat diet. The identification of these proteins is of special interest to ascertain the differential effects of high-fat diet on the fed–fasting transition in each mouse strain and, thus, to identify potential alterations in response to food intake & 2013 EMBO and Macmillan Publishers Limited

High-fat diet effect on insulin and metabolic pathways E Sabido´ et al

TCA

GLYCOGEN

INSULIN SIGNALING

CISY FUMH

DLDH IDHC IDHP MDHC MDHM ODO1

GLGB PGM1

ODO2 ODP2

ODPA ODPB PDK1 Q9DAM4 SUCB1

PYGB PYGL Q8CE68

β-OXIDATION

1433B 1433E 2AAA AAKG1 AAPK1 AKT1 AKT2 CAB39 CATA

FATTY ACID SYNTHESIS

ACADM ACADS ACOX1 ACOX2 CPT1A DHB4

ECHB

PGM2

CRK CRTC2 CTNB1 EIF3E EIF3L FOXA3

CBL

FOXO1GRB2 GSK3B IF4B

IF4E IF4G1 IF4G2

IRS1

KAPCA KAPCB KS6A1 KS6B2 MK01 MK03 MK08 MP2K1 ACACA ACSF2

ECHP HCDH MCAT THIKB

FAS MP2K2 NFKB2 P85A PAK2 PK3C3PK3CB PP1A PP2AA

KETOGENESIS

PENTOSE PHOSPHATE RPE TALDO TKT

TKTL2

IVD

MCCA MCCB

ODB2

ODBB Q8R5C9

PRGC2 PTN1 RAB10 RASH RASN RS6 SODC SODM

HMCS1 HMCS2 HMGCL

SRBP1 SRF TRADD TSC2

OTHERS

GLYCOLYSIS AND GLUCONEOGENESIS ALDOA ALDOB ALDOC ENOA F16P1 PGAM1 PCKGC PGK1

PYC

G3P

K6PL

AL4A1 ARHG6 CAP1 CP2E1 DHB7 DHB12 EXOC7 FETA FXR1 HPPD LMNB1 MFN2

KPYR

MGST1 MK14 MUTA

TPIS

NUCL OTC OXSR1 RUVB1 SC23A SFRS2 SIRT3 TCEA2 VIME

WBS14

8

6

0

KAPCA SRBP1

SODM CATA CPT1A PCKGC

4

2

−1.0

ODPB EIF3L HCDH

0

2

DHB4 1433E

THIKB

PGAM1 GSK3B ACADM KAPCB PYC ECHB PDE3B

Q8R5C9 GLGB HMGCL

B6 T0

KPYR 10

T0

ODP2

ODPA T0 T1 T2 FAS

0.5 0.0 CISY

1.5

−1.0

T0 T1 T2

T0 T1 T2

KPYR

ACOX1

ECHB

T0 T1 T2

B6 T0

2.0 0.0 −1.0

0.0 −1.0 T0 T1 T2

T2

Q8R5C9

0.0 −1.0

S9 T2

S9 B6

T0 T1 T2

2.0

4 6 8 −Log10 P -value (T1−T0) fed

ODPB

0.5 0.0

FAS

MP2K4

B6 versus S9

Log2 relative abundance

−Log10 P -value (T2−T1) fed

10

S9 T2

T0

T2

FAS

KAPCA

Tubulin

Tubulin

Figure 4 (A) Quantified proteins grouped by metabolic or signaling pathway. Highlighted proteins (both in gray and gold) correspond to proteins showing a different profile when comparing the mouse strains fed ad libitum at different time points. Gray-colored features correspond to proteins with a significant change between strains but showing a similar trend, i.e., showing an increase (or decrease) in protein abundance over time in both strains. Gold-colored features indicate proteins showing opposite trends in the fold-change. (B) Scatter plot of P-values obtained when comparing protein abundance changes between mouse strains at the early stage of highfat diet—i.e., from 0 (T0) to 6 (T1) weeks (x axis)—and at its late stage—from 6 (T1) to 12 (T2) weeks (y axis). The plot is divided into three main parts: (i) proteins with a different profile between strains at the early stage (blue dots); (ii) proteins with a different profile between strains only at the late stage (green dots); and (iii) proteins with a different profile between strains at both the early and late stages (red dots). (C) Relative abundance profiles of selected proteins at 0 (T0), 6 (T1) and 12 (T2) weeks of high-fat diet for each mouse strain (B6, blue; S9, pink) fed ad libitum. Error bars correspond to standard errors. (D) Protein abundance of FAS and KAPCA after 6 (T1) and 12 weeks (T2) of high-fat diet in C57BL/6J (B6) and 129Sv (S9) mice fed ad libitum validated by western blot.

& 2013 EMBO and Macmillan Publishers Limited

Molecular Systems Biology 2013 7

High-fat diet effect on insulin and metabolic pathways E Sabido´ et al

A

B S9 (fasted−fed) T0

● ●● ● K6PL MK01 FAS CP2E1 ● PYGB PCKGC PDK1 MCAT ● ● GLGB ● DHB4● CPT1A IVD ● HCDH ● ● ●● MCCB ACADM

● ● CP2E1 ●

−1.0

ACADM T0 T2

KPYR PDK1

● Q8R5C9● ● ● ● ACOX2 ● ● ● ●

5 0

CPT1A PYC ECHP

ACACA

−2 −1 0 1 2 3 Log2 fold change (fasted−fed)

6

T2

THIKB T0 T2

1.0 0.0

DHB4

HMGCL KAPCA

4

PYC FETA EIF3E

2

T2

AKT1 CRK IDHC FXR1 ENOA

1433E

F16P1

MCCB T0

PGM1

AL4A1

G3P SUCB1 Q9DAM4

0

CPT1A THIKB Q8R5C9 ODB2 VIME

SODM IDHP MC AT

PYGB

K6PL PRGC2

ACACA

KAPCB ACADM ODPA ODO1 HCDH ALDOC

ODPB MK01 CAP1

2 4 6 8 −Log10 P-value at T0 (0 week)

Q8R5C9 T0 T2

10

ODB2 T0

T2

−1.5

Fa st ed Fe d

T0

GLGB KPYR

TKT

S9

PCKGC

SRBP1 AKT2 ECHB

B6

ECHB 1.0 0.0 −1.0

8

0

CPT1A T0 T2

1.0 0.0 −1.0

Fa st ed Fe d

Log2 relative abundance

C

2.0 0.0 1.0 Log2 fold change (fasted−fed)

PRGC2

PCKGC

DHB4

FAS RUVB1

Fa st ed Fe d

0

FAS

10

MCCB PDK1 CP2E1

Fa st ed Fe d

5

−Log10 P-value

10

Fa st ed Fe d

−Log10 P-value



(Fed−fasted) B6 versus S9

10 −Log10 P-value at T2 (12 weeks)

B6 (fasted−fed) T0

Figure 5 (A) Observed significant protein fold-changes during the fed–fasting transition in C57BL/6J (B6) and 129Sv (S9) mice fed with a normal diet (T0). Significance threshold was set at adjusted P-value of 0.05. (B) Scatter plot of P-values associated with the observed changes when comparing both mouse strains fed in normal diet—i.e., 0 weeks (T0, x axis); and after 12 weeks of high-fat diet (T2, y axis). The plot is divided into three main parts: (i) proteins with a different fasting-response pattern at normal diet (blue dots); (ii) proteins with a different fasting response among strains only after 12 weeks of high-fat diet (green dots); and (iii) proteins with a different fasting response both in normal diet and after 12 weeks of high-fat diet (red dots). (C) Protein abundance changes in the fed–fasting transition of selected proteins at 0 (T0) and 12 (T2) weeks of high-fat diet for each mouse strain (B6, blue; S9, pink). Error bars correspond to standard errors.

(Figure 5B, green dots; Supplementary Tables S9 and S10). Among these proteins was the SRBP1 transcription factor that is involved in the regulation of lipogenesis, numerous proteins involved in lipid metabolism (ACADM, ECHB, THIKB, CPT1A, MCCB, ODB2 and Q8R5C9), and several kinases that regulate the cellular response to insulin (AKT1, AKT2, KAPCA, KAPCB and PDK1). Together, these results indicate that a high-fat diet has a major impact on the proteome regulating lipid metabolism and, most probably, also on the phosphoproteome that mediates the signaling cascade in response to fasting or food intake (Figure 5C). Finally, some of the proteins that had a different initial (T0) response to fasting when comparing both strains also showed a different behavior after being subjected to a high-fat diet. This is the case of FAS, PCKGC, GLGB, KPYR, PYC and DHB4, among others (Figure 5B, red dots; Supplementary Figure S4).

Discussion The experimental design of quantitative proteomic studies to date has been limited to a low number of different experimental conditions (e.g., wild-type versus mutant strains). Three main reasons have precluded the systematic comparisons of larger numbers of sample proteomes. The first is the under sampling effects of the prevalent discovery proteomics 8 Molecular Systems Biology 2013

techniques that results in the irreproducible detection and quantification of proteins, particularly in complex samples. The effect is compounded by the observation that lower abundance proteins show a lower degree of reproducibility than more highly expressed proteins (Domon and Aebersold, 2006; Michalski et al, 2011). The second reason is the limited number of labeling plexes that are available in the available isotopic or isobaric labeling protocols (Dephoure and Gygi, 2012) and third, is the limited dynamic range of label-free LCMS pattern comparison methods which are inherently scalable but lack the analytical depth to quantify low abundance proteins. From a systems biology point of view, where a main objective is the quantitative measurement of a specific biological system under differentially perturbed states (Ideker et al, 2001; Bensimon et al, 2012) the comparative analysis of a larger number of samples and replicates is highly desirable. Targeted proteomics by SRM overcomes many of the aforementioned limitations and permits the consistent quantification of a pre-defined set of proteins in multiple samples and conditions. In the present study, we developed a set of SRM assays to systematically quantify murine proteins involved in the insulin-signaling pathway and central metabolism in an experimental design that intends to assess the changes of this system during the emergence of metabolic syndrome. The metabolic syndrome is a combination of medical disorders, & 2013 EMBO and Macmillan Publishers Limited

High-fat diet effect on insulin and metabolic pathways E Sabido´ et al

including central obesity, insulin resistance, dyslipidemia, hypertension and hepatic steatosis. These disorders predispose the patient to several diseases such as diabetes, cardiovascular disease and cancer, and in the last years this syndrome has become an important problem for the national health-care systems of most western countries. In spite of significant efforts by the scientific community and the extensive knowledge that has accumulated about individual enzymes and regulatory proteins, the complex etiology of insulin resistance and its association with the accumulation of lipids in liver is not yet completely understood. To obtain, for the first time, a comprehensive quantitative overview of the behavior of some of the main pathways involved in metabolic syndrome at the protein level, we designed a study in which the effect of three variables, genetic background, time and fed/ fast state on the system was systematically tested. Over 200 mouse proteins involved in the insulin-signaling pathway and the central metabolism were initially selected for quantitative targeted analysis. SRM assays were developed for each of the selected proteins and used to assess their detectability at endogenous levels. The developed SRM assays will be publicly available and provide a resource for others researchers to easily quantify the selected proteins in other studies. The targeted proteomic measurements were finally performed for 144 proteins across 36 samples, thus generating a protein abundance versus condition matrix of 5184 quantitative data points with a high degree of completion. The 36 conditions consisted of triplicates of liver samples from two genetically different mouse strains (C57BL/6J and 129Sv) each measured at three time points at fed and fasted state, thus becoming one of the biggest targeted proteomics studies performed in mammalian tissues. These two mouse strains were selected due to their different response to a sustained high-fat diet under normal conditions. Wild-type C57BL/6J mice showed altered physiological parameters such as higher body weight, increased fat accumulation in the liver, hyperglycemia and hyperinsulinemia, whereas 129Sv mice show an increased resistance to these phenotypic alterations. Despite the size of the data set and the consistency among measurements, our approach also had some limitations as not all the initially selected proteins could be finally quantified in the endogenous sample. Undetected targeted peptides mostly come from proteins present in low concentrations in the sample, peptides with low response to mass spectrometric analysis or peptides with high interfering signals. Nevertheless, detected proteins spread over all the different branches of the insulin-signaling pathway and covered most of the targeted central metabolism proteins, thus providing us with a complete overview of the current response of these pathways to a sustained high-fat diet. Initially, we evaluated proteins that showed different abundance profiles after several weeks of high-fat diet when directly comparing C57BL/6J and 129Sv mouse strains. Most of the observed changes were already evident within the first 6 weeks of high-fat diet, and they mainly targeted b-oxidation, fatty acid biosynthetic pathway and the insulin-signaling pathway. Significant protein abundance changes were detected in essential kinases such as KAPCA, KAPCB, GSK3B, PK3C3, PDK1, MP2K4 and MK01, which modulate & 2013 EMBO and Macmillan Publishers Limited

intracellular responses to glucose uptake and insulin stimulation and determine downstream phosphorylation patterns. These results point to potential changes in the liver protein phosphorylation status induced by long-term high-fat diet; and therefore, follow-up phosphoproteome studies might be relevant in this context. Overall, the observed differences suggest that peroxisomal b-oxidation is activated in fatty C57BL/6J mice after several weeks of high-fat diet whereas lipogenesis dominates the response of 129Sv mice under the same conditions. For instance, the different protein abundance profiles of insulinrelated cAMP-dependent protein kinase (KAPCA) and the sterol regulatory element-binding protein 1 (SRBP1) observed in 129Sv and C57BL/6J mice suggest an increased activity of peroxisomal b-oxidation in fatty C57BL/6J and a lipogenesis activation in 129Sv mice under the same conditions. This observation might be related to strain differences in AMP-activated protein kinase (AMPK) activation after a sustained high-fat diet. AMPK has previously been described as a molecular switch, associated with metabolic disorders, that modulates the activity of catabolic and anabolic pathways based on the energetic needs (Canto´ et al, 2009). Moreover, when evaluating the differential effect of a sustained high-fat diet in C57BL/6J and 129Sv mice, our results suggest that these strains exhibit a rapid and differential response to high-fat diet affecting most of the targeted proteins. These results indicate therefore that 6 weeks (T1) of treatment are sufficient to substantially change the landscape of the liver proteome related to insulin signaling and central metabolism. This observation is consistent with the previously reported physiological differences between these two mouse strains (Almind and Kahn, 2004). The changes detected in the proteome are, however, more rapid since they allow a clear discrimination of phenotypes at a much earlier stage than in other long-term experiments in which the highfat diet expanded over 18 weeks (Biddinger et al, 2005). Finally, we found that a persistent high-fat diet does not only differentially affect abundance of proteins participating in the main metabolic pathways and signaling pathways, but it also alters the transient changes that normally occur in C57BL/6J and 129Sv mice when individuals shift from a fed to a fasted state. In this regard, our results indicate that high-fat diet leads to major protein abundance changes in the cellular response to fasting or food intake even if acknowledging that some of the changes observed between strains can be ascribed to the different genetic backgrounds of the two mouse strains (Kulkarni et al, 2003). In conclusion, the study here reported constitutes one of the largest quantitative and dynamic targeted proteomics studies performed in mammalian tissues. The large data set generated has allowed us to study the effects of diet, genetic background and fasting in the context of the metabolic syndrome. Our analyses detected a rapid and different response in the two mouse strains to a sustained high-fat diet. Indeed, the response in the C57BL/6J mice is mainly characterized by the activation of peroxisomal b-oxidation, whereas lipogenesis likely dominates the response of the diabetes-resistant 129Sv mice. Our systemic proteomics approach to study the metabolic syndrome in mice can not only contribute to a better understanding of this syndrome in humans but it has proven Molecular Systems Biology 2013 9

High-fat diet effect on insulin and metabolic pathways E Sabido´ et al

that proteomics can generate the consistent protein versus condition data sets required to analyze the dynamics of biological systems.

Materials and methods Animals Three-week-old C57BL/6J and 129Sv mice were obtained from Charles River Laboratories International, Inc. The mice were maintained in chow diet for 1 additional week before starting the experiments with high-fat diet in a pathogen-free facility at the Institute of Molecular Systems Biology, ETH Zu¨rich, complying with official ETH Zu¨rich ethical guidelines. Mice from both strains were kept on a 12-h light/ dark cycle and fed with a high-fat rodent diet (60% fat content) once the experiments were started. C57BL/6J and 129Sv mice were fed ad libitum with a high-fat rodent diet for 0, 6 and 12 weeks according to the experimental design. In addition to the time and strain genotype, a third factor (treatment) was included in the experiment, whereby two groups were defined. The first group contained mice fasted overnight before they were killed. Mice of the second group were fed ad libitum. Fasted condition was controlled by serum biochemical parameters and the abundance of transcription factor SRBP1 known to be significantly decreased after a fasting period (Supplementary Table S2; Supplementary Figure S5). Therefore, the overall experimental design consisted of 12 different conditions (3 time points, 2 mouse strains and 2 treatments) with n ¼ 3 animals in each group.

Sample preparation PBS-perfused mouse livers were homogenized using RIPA-modified buffer (1% NP-40, 0.1% sodium deoxycholate, 150 mM NaCl, 1 mM EDTA, 50 mM Tris pH 7.5, protease inhibitors EDTA-free, 10 mM NaF, 10 mM sodium pyrophosphate, 5 mM 2-glycerophosphate) and a glassglass tight douncer homogenizer (Wheaton Science Products, USA). Homogenates were centrifuged (20 000 g, 41C, 15 min) and the supernatant was collected and kept at 41C. Pellets were resuspended with Urea-Tris buffer (50 mM Tris pH 8.1, 75 mM NaCl, 8 M urea, EDTA-free protease inhibitors, 10 mM NaF, 10 mM sodium pyrophosphate, 5 mM 2-glycerophosphate), and after sonication, they were centrifuged again (20 000 g, 41C, 15 min). The new supernatant was collected and mixed with the previous one. The resulting pellets were discarded. The total protein content was quantified with the BCA Protein Assay (Thermo Fisher Scientific, USA) using bovine serum albumin as a standard. Aliquots of the homogenates were immediately prepared and stored at  801C. Sample quality and concentration were also assessed with SDS–PAGE and silver staining (Supplementary Figure S1). In all, 1 mg of total protein was mixed with 1 mg of the heavy isotope-labeled reference proteome (see below for details) and the combined sample was precipitated overnight with six volumes of icecold acetone (16 h,  201C) for mass spectrometric analysis. The supernatant was discarded and the pellets were dried and resuspended in freshly prepared digestion buffer (8 M urea, 0.1 M NH4HCO3). Samples were reduced with 12 mM dithiothreitol (30 min, 371C) and alkylated with 40 mM iodoacetamide (45 min, 251C) in the dark. Samples were diluted with 0.1 M NH4HCO3 to a final concentration of 1.5 M urea and digested overnight at 371C with sequence grade trypsin (10 mg, Promega AG, Switzerland). After digestion, peptide mixtures were acidified to pH 2.8 with TFA and desalted with 500 mg Sep-Pak tC18 silica cartridges (Waters Inc., USA). Samples were dried under vacuum before off-gel fractionation (24 wells, 3–10 pI strips) in a 3100 OFFGEL Fractionator (Agilent Technologies). Peptide mixtures collected in each well were pooled in six different fractions (A: wells 1 and 2, B: wells 3 and 4, C: wells 5–8, D: wells 9–11, E: wells 12–18 and F: wells 19–24), acidified to pH 2.8 with trifluoroacetic acid and desalted with MacroSpin C18 silica columns (The Nest Group Inc., USA). Samples were dried under vacuum and re-solubilized to 1 mg ml  1 in 0.1% formic acid and 2% acetonitrile before mass spectrometric analysis. 10 Molecular Systems Biology 2013

Preparation of the heavy-labeled reference proteome A Hepa1-6 mouse cell line was obtained from the American Tissue Type Culture Collection (cat. CRL-1830), labeled with SILAC medium and used as a heavy-labeled reference proteome in all samples. Cells were grown at 371C and 5% CO2 in L-lysine- and L-arginine-depleted DMEM high glucose medium (Caisson Laboratories Inc.) supplemented with 10% dialyzed FCS (BioConcept), 1% penicillin/streptomycin 13 15 (Invitrogen) and L-13C15 6 N4-arginine and L- C6 N2-lysine (SigmaAldrich GmbH). Cells were cultured in 150-cm2 plates and passaged at 80% confluence. The incorporation of the heavy amino acids was regularly checked by mass spectrometry on cell aliquots and the culture was maintained until an incorporation of heavy amino acids 495% was achieved. Cells were then washed with ice-cold PBS, scraped from the plates and homogenized as described above.

Target protein selection In all, 257 proteins involved in the insulin-signaling pathway and in the lipid and carbohydrate metabolism pathways were selected for quantification using targeted mass spectrometric analysis. This set of proteins includes almost all the enzymes involved in b-oxidation, fatty acid synthesis, ketogenesis, glycogen synthesis and degradation, pentose phosphate pathway, glycolysis, gluconeogenesis and TCA cycle. Moreover, proteins of the different branches of the insulin-signaling pathway, as well as downstream effectors, and a few other proteins potentially linked to the metabolic syndrome, were also included in the final set of targeted proteins. Together, the selected set of proteins covered several protein classes (e.g., metabolic enzymes, kinases, phosphatases and transcription factors), and spanned a large dynamic range of cellular abundance, ranging from the few thousands of copies per cell (c.p.c.) of the nuclear factor NF-kappa-b (4000 c.p.c.) to the more than hundred thousand copies per cell in the case of fatty acid synthase (4140 000) (Schwanha¨usser et al, 2011). The proteins selected for targeting are listed in Supplementary Table ST1A.

SRM assay development SRM assays were developed following the general high-throughput strategy previously reported (Picotti et al, 2010). Initially, 4–6 unique peptides ranging from 6 to 20 amino acids in length, containing tryptic ends and no miscleavages, were chosen for each of the selected proteins. Unique peptides previously observed in discovery MS experiments (Martens et al, 2005; Desiere et al, 2006) were prioritized during the peptide selection process. For those proteins for which no peptides had been reported previously (i.e., for previously unidentified proteins), peptide selection was based on the MS-suitability score computed by PeptideSieve (Mallick et al, 2007). All peptides containing amino acids prone to undergo unspecific reactions (e.g., Met, Trp, Asn and Gln) were generally avoided and only selected when no other options were available (Lange et al, 2008). The selected peptides were chemically synthesized via SPOT synthesis (JPT Peptide Technologies) and used in unpurified form for the SRM assay development. Fragment ion spectra were collected for each peptide using SRM-triggered MS2 mode and two predicted high mass y-ions per peptide in a QTRAP 4000 instrument (AB/Sciex). The spectra were used to confirm identities, extract the optimal fragment ions for SRM analysis and to obtain peptide retention times. MS2 data were analyzed with the Mascot search engine (v. 2.1, MatrixScience) against a customized mouse Uniprot database (v. dated 07/2009) containing all the selected proteins and the corresponding decoy entries generated by inverting the amino-acid sequences of the tryptic peptides. Search parameters were set to 2.0 Da for the precursor mass tolerance, 0.8 Da for the fragment mass tolerance, fully tryptic peptides, no miscleavages and a false discovery rate (FDR) of 1% based on decoy assignments (Elias and Gygi, 2007). Cysteine carbamidomethylation was set to be a fixed modification, and methionine oxidation was used as a variable modification. A spectral library was generated from the validated fragment ion spectra using in-house written Perl scripts and for each

& 2013 EMBO and Macmillan Publishers Limited

High-fat diet effect on insulin and metabolic pathways E Sabido´ et al

spectrum, the four most intense y-ions were selected as optimal SRM transitions to be monitored. Mass spectrometry parameters, such as collision energy and declustering potential, and further details in the generation of the peptide library, development of SRM assays and spectral library creation can be found in previous reports (Picotti et al, 2009, 2010).

Measurements and data analysis SRM measurements were performed on a hybrid triple quadrupole/ion trap mass spectrometer (4000 Q-Trap, AB/Sciex) equipped with a nano-LC electrospray ionization source. Fused-silica microcapillary columns (75 mm) were pulled and packed with Magic C18 AQ 5 mm reverse-phase material (Michrom BioResources). Samples (2 mg) were loaded to a 2.5-cm pre-column (100 mm, New Objective) packed with C18 reverse-phase material and eluted with a linear gradient from 5% to 30% of buffer B in 30 min at a flow rate of 300 nl min  1 using a Tempo nanoLC system (Applied Biosystems, USA). Buffer A: 98% H2O, 2% ACN and 0.1% formic acid; Buffer B: 2% H2O, 98% ACN and 0.1% formic acid. Blank runs were performed between the SRM measurements of biological samples to avoid sample carryover. Measurements were done in scheduled SRM mode, using a retention time window of 5 min, ca. 400 transitions per method and a cycle time of 2.5 s, which ensured a dwell time over 10 ms per transition. Targeted peptides were only acquired in their corresponding OFFGEL fraction and three replicates for each time point were used in these measurements. Transition groups corresponding to the targeted peptides were evaluated with MultiQuant v. 1.1 Beta (Applied Biosystems) based on different parameters (in order of importance): (i) co-elution of the transition traces associated with a targeted peptide, both in its light and heavy form; (ii) presence of at least four co-eluting transition traces for a given peptide exceeding a signal-to-noise ratio of 3; (iii) rank correlation between the light SRM relative intensities and the heavy counterparts; (iv) rank correlation between the SRM relative intensities and the intensities obtained in the MS2 spectra during the SRM assay development; and (v) consistence among replicates. Initially, all peak intensities were transformed by the logarithm based 2 and transitions with completely missing intensities in more than two thirds of the samples were removed. A constant normalization was applied to all measurements to equalize the median peak intensities of reference transitions between runs (Zien et al, 2001). The remaining normalized SRM peak intensities were retained for quantitative analysis. In summary, a set of 144 targeted proteins were observed and represented by 1–4 peptides each, and each peptide was represented by 2–4 pairs of light–heavy transitions. Protein-level quantification and testing for differential abundance were performed using a linear mixed-effects model (McCulloch and Searle, 2001) as implemented in software package SRMstats (Chang et al, 2012). A list of P-values was calculated for each comparison, and was adjusted to control the FDR at a cutoff of 0.01 (Benjamini and Hochberg, 1995). Raw data were deposited in the PASSEL repository with the data set identifier PASS00244.

Supplementary information Supplementary information is available at the Molecular Systems Biology website (www.nature.com/msb).

Acknowledgements Support for this study was provided by the European Union via the ERC (European Research Council) advanced grant ‘Proteomics v3.0’ (grant# 233226) to RA, and the LiverX program of the Swiss Initiative for Systems Biology (SystemsX) to ES, YW and LB. Author contributions: RA and MS conceived the project; ES designed the experiments; ES, YW, LB and TP performed the experiments; C-YC and OV performed the statistical analyses; ES, LB, RA and MS interpreted the data; and ES, LB, OV, MS and RA wrote the manuscript.

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Conflict of interest The authors declare that they have no conflict of interest.

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