BBAMCB-57782; No. of pages: 12; 4C: 9 Biochimica et Biophysica Acta xxx (2015) xxx–xxx

Contents lists available at ScienceDirect

Biochimica et Biophysica Acta journal homepage: www.elsevier.com/locate/bbalip

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Article history: Received 30 January 2015 Accepted 10 March 2015 Available online xxxx

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Keywords: Mitochondrial and peroxisomal protein profiles Fatty acids Cytokines Dyslipidemia NAFLD Gene expression

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Institute of Clinical Biochemistry and Pathobiochemistry, German Diabetes Center, Leibniz Center for Diabetes Research, Heinrich-Heine-University Duesseldorf, Aufm Hennekamp 65, Duesseldorf 40225, Germany b Institute for Diabetes Research, Department of General Internal Medicine, Asklepios Clinic St. Georg, Medical Faculty of Semmelweis University, Asklepios Campus Hamburg, Lohmuehlen Str 5, Hamburg 20099, Germany

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Major causes of lipid accumulation in liver are increased import or synthesis or decreased catabolism of fatty acids. The latter is caused by dysfunction of cellular organelles controlling energy homeostasis, i.e., mitochondria. Peroxisomes also appear to be an important organelle in lipid metabolism of hepatocytes, but little is known about their role in the development of non-alcoholic fatty liver disease (NAFLD). To investigate the role of peroxisomes alongside mitochondria in excessive hepatic lipid accumulation, we used leptin-resistant db/db mice on C57BLKS background, a mouse model that develops hyperphagia-induced diabetes with obesity and NAFLD. Proteome and gene expression analyses along with lipid analyses in the liver revealed differential expression of genes related to lipid metabolism and β-oxidation, whereas genes for peroxisomal proteins were predominantly regulated. Conclusion: Our investigations show that in fatty liver disease in combination with obesity and diabetes, the hepatocyte-protecting organelle peroxisome is altered. Hence, peroxisomes might indicate a stage of pre-NAFLD play a role in the early development of NAFLD and appear to be a potential target for treatment and prevention of NAFLD. © 2015 Published by Elsevier B.V.

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Birgit Knebel a, Sonja Hartwig a, Jutta Haas b, Stefan Lehr a, Simon Goeddeke a, Franciscus Susanto a, Lothar Bohne a, Sylvia Jacob a, Cornelia Koellmer a, Ulrike Nitzgen a, Dirk Müller-Wieland b, Jorg Kotzka a,⁎

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Peroxisomes compensate hepatic lipid overflow in mice with fatty liver

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1. Introduction

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Increased lipid accumulation in the liver is the clinical hallmark of non-alcoholic fatty liver disease (NAFLD) and is almost always found in patients with a combination of obesity and type 2 diabetes. The clinical spectrum of NAFLD ranges from fatty liver simply due to intracellular lipid accumulation, to fatty liver with inflammatory cell infiltration and signs of inflammation, e.g., steatohepatitis, which might progress to fibrosis and cirrhosis [1,2]. The liver is the key organ that regulates lipid metabolism, especially that of cholesterol and triglycerides. On a cellular level, lipid metabolism is a prominent example that compartmentation of cellular processes allows efficiency and tight control. The major subcellular compartments controlling intracellular lipid homeostasis are: cytosol for synthesis; lipid droplets for storage; and mitochondria for degradation, but peroxisomes also appear to be responsible. Although the exact mechanisms are unknown, hepatic lipid accumulation is the result of an imbalance

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Abbreviations: NAFLD, non-alcoholic fatty liver disease; FA, fatty acids; FFA, free fatty acids; TFA, total FFA; mtDNA, mitochondrial deoxyribonucleic acid; RT-PCR, real-time polymerase chain reaction; ANOVA, analysis of covariance; HOMA-IR, homeostatic model assessment of insulin resistance; PUFA, polyunsaturated FFAs; KEGG, Kyoto Encyclopedia of Genes and Genomes; NASH, non-alcoholic steatohepatitis ⁎ Corresponding author. Tel.: +49 211 3382 531. E-mail address: [email protected] (J. Kotzka).

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between syntheses, storage and catabolism of fatty acids (FAs) [3]. One cause of the disturbance is the increased availability of free fatty acids (FFAs) in the serum, which can accumulate in the liver. This overflow causes alterations in de novo lipogenesis, export of lipids or fatty oxidation rates. The decreased oxidative phosphorylation in mitochondria has been shown to be associated with reduced insulin sensitivity and increased intracellular lipid accumulation in non-diabetic insulinresistant individuals [3–5]. Together, any defect in these processes could result in an increased accumulation of lipids in hepatocytes, which can be either the cause or the result of fatty liver. β-Oxidation of fatty acids in eukaryotes occurs mainly in mitochondria and, to a lesser extent, in peroxisomes. Although there are enzymatic and functional overlaps, both organelles differ in respect to substrate specificities, FA import systems, the amount of reactive oxygen species and net energy (i.e., adenosine triphosphate [ATP] production) from βoxidation. Even though peroxisomes have specialized substrate specificity and an inefficient β-oxidation energy balance, in contrast to mitochondria, the lipid uptake is not restricted by a substrate-inhibited feedback mechanism. Therefore, it is tempting to speculate that peroxisomes appear to be designated to protect the liver from lipotoxicity. Recently, we have shown that the direct, parallel comparison of mitochondria and peroxisomes at a proteomic level allows the dissection of functional overlaps and specificities between both organelles [6]. Here, we wanted to test the hypothesis that in the leptin-resistant

http://dx.doi.org/10.1016/j.bbalip.2015.03.003 1388-1981/© 2015 Published by Elsevier B.V.

Please cite this article as: B. Knebel, et al., Peroxisomes compensate hepatic lipid overflow in mice with fatty liver, Biochim. Biophys. Acta (2015), http://dx.doi.org/10.1016/j.bbalip.2015.03.003

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2.1. Animals

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C57BL/KSlepr+/+ (BKS) and BKS.Cg-Leprdb (db/db) mice were bred and maintained in a regular 12 h light/dark cycle under constant temperature and humidity (22 ± 1 °C, 50 ± 5% humidity). Genotyping was performed according to Horvat and Bunger [9]. At 6 weeks of age, male littermates of each genotype (n = 20 of each) were kept under standardized conditions with free access to water and standard laboratory food (Ssniff, Soest, Germany). Weight gain and food intake of male mice were measured once a week and monitored for an observation period of 8 weeks. Food uptake per body weight and weight gain per food uptake were determined in each group of mice as the mean of the observation period. Mice were sacrificed by CO2 asphyxiation at 14 weeks of age. Blood samples were collected by left ventricular puncture, and organ samples were removed. The Animal Care Committee of the University Duesseldorf approved animal care and procedures (Approval#50.05-240-35/06).

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2.2. Animal characterization

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Phenotypical characterization, serum diagnostics of clinical parameters, lipid profiling in serum and liver tissue and determination of the cytokine profiles with low-density proteome arrays (Proteome Profiler™; R&D Systems, Abingdon, UK) were performed as previously described[10,11]. Triglycerides, cholesterol and liver enzymes (ALT, AST, GLDH) were determined on a Hitachie 912 laboratory automat (Roche Diagnostics, Mannheim, Germany). Leptin, insulin and PAI were determined by Multiplex immune assay (BioRad, Munich, Germany) Serum FFA and hepatic TFA content and specific fractional composition of FAs were determined by gas chromatography. FA data in the liver were further used to calculate the desaturase index (cC16:1/C16:0), DNL index (C16:0/cC18:2) and elongation index (C18:0/C16:0).

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2.3. Subcellular fractionation and marker enzyme activity

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Mitochondria and peroxisomes were prepared as formerly described from 1.5 g freshly isolated liver tissue, and the organelle quality of all preparation steps was monitored by assessing marker enzyme activity and electron microscopy [6].

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2.4. 2D-DIGE™ and protein identification by MALDI-MS

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2D-DIGE™ experiments of subcellular organelles and data analyses were performed as previously described [6]. Significantly altered protein spots had to be present in all replicate experiments. The analysis parameters were set to a standardized average spot volume ratio of 1.7fold, p b 0.01 and a coefficient of variation (CV) of 20%. All selected protein spots were excised from four separate 2D-DIGE™ gels and analyzed by MALDI-MS in a time-of-flight Ultraflex-Tof/Tof (BrukerDaltoniks, Bremen, Germany) as previously described8. Further analyses for protein identification against the mouse sub-set of Swiss-Prot (Sprot_2014) nonredundant database and mitochondrial or peroxisomal reference maps from our database (http://www.diabesityprot.de/) were performed as described [6].

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2.6. Gene expression analyses

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RNA extraction (Qiagen, Hilden, Germany) of biopsies and RT-PCR with gene-specific probes and 18S RNA as internal standard (Assay on Demand™, Applied Biosystems, Darmstadt, Germany) to determine relative RNA amounts of the specific target was performed as described [12]. RNA samples (n = 4 per genotype) for genome-wide gene expression analyses were processed according to the GeneChip One-Cycle eukaryotic Target Labeling Assay manufacturer’s recommendations. Synthesis steps were quality controlled and monitored with an RNA 6000 nano kit (Agilent, Taufkirchen, Germany). Complementary RNA (cRNA) samples were hybridized to Affymetrix GeneChip Mouse 430_2 (Affymetrix UK Ltd, Lahr, Germany). Detection of probe sets was performed using a GeneChip scanner 3000 7G (GDAS 1.4 package, Affymetrix). Data were analyzed with Genespring 12.0 (Agilent). Volcano

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Table 1 Physiological parameters of BKS and db/db mice at 14 weeks of age.

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136 137

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Quantification of mouse mtDNA copy number was performed by quantitative PCR (qPCR) with primers and double-fluorescent probes (Eurogentec, Liège, Belgium) on an ABI Prism 7000 Sequence Detection System (Life Technologies, Darmstadt, Germany) with NADH dehydrogenase subunit 1 gene (ND1) for quantification of mtDNA (forward: 5′-CTACAACCATTTGCAGACGC 3′, reverse: 5′ GGAACTCATAGACTTA ATGC 3′, probe: 5′ CCAATACGCCCTTTAACAACCTC 3′) and lipoprotein lipase (LPL)) as nuclear target (forward: 5′ GGTTTGGATCCAGCTGGG CC 3′, reverse: 5′ GATTCCAATACTTCGACCAGG 3′, probe 5′ CTTTGAGT ATGCAGAAGCCC 3′. Gene copy numbers were determined in comparison to log–linear standard curves determined from both PCR products subcloned into TOP-TA-cloning vectors (Life Technologies, Darmstadt, Germany) to distinct copy numbers for both plasmids.

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2. Materials and Methods

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2.5. Quantification of mtDNA

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diabetic (db/db) mouse model (BKS.Cg-Leprdb) with obesity and diabetes, fatty liver is associated with alterations in both mitochondria and peroxisomes. This mouse model reflects a status of dyslipidemia due to increased lipid intake by hyperphagia and decreased lipid catabolism [7,8]. We compared db/db mice and their C57BLKs (BKS) littermates in terms of cytokine patterns, ectopic lipid accumulation in hepatocytes, lipid profiles and mitochondrial and peroxisomal protein or gene expression profiles in the liver. The results indicate that peroxisomes might play a role in the development of fatty liver disease.

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Body weight (g) Food uptake/bodyweight (kJ/g) Weight gain/food uptake (mg/kJ) Liver weight (g) Blood glucose (mmol/l) Insulin (μU/ml) HOMA-IR (mg μU/ml) Leptin (ng/ml) ALT (U/l) AST (U/l) GLDH (U/l) PAI (ng/ml) Cholesterol (mg/dl) Triglyceride (mg/l) sICAM-1/CD54 (ng/ml)# CXCL1 (ng/ml)# CSF1 (ng/ml)# MCP-1 (ng/ml)# TIMP-1 (ng/ml)# TREM-1 (ng/ml)#

152 153 154 155 156 157 158 159 160 161 162 163

BKS (n = 20)

db/db (n = 20)

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26.7 ± 1.8 13.2 ± 0.5 2.0 ± 0.6 1.5 ± 0.2 7.0 ± 1.6 1.5 ± 1.1 0.5 ± 0.2 2.0 ± 1.8 45.6 ± 18.4 49.4 ± 18.3 14.2 ± 7.6 2.9 ± 0.6 98.1 ± 14.8 22.7 ± 3.3 101.5 ± 5.5 7.9 ± 0.4 66.6 ± 3.6 13.7 ± 0.8 19.0 ± 1.0 7.8 ± 0.5

46.2 ± 6.9⁎ 19.4 ± 1.8⁎ 3.1 ± 0.7⁎ 3.2 ± 0.8⁎ 43.5 ± 12.6⁎ 2.9 ± 1.8⁎ 5.7 ± 3.1⁎ 26.3 ± 8.1⁎ 175.4 ± 44.3⁎ 257.9 ± 87.5⁎ 128.1 ± 62.7⁎ 5.7 ± 1.7⁎ 127.0 ± 33.8 116.2 ± 27.7⁎ 23.7 ± 3.0⁎⁎⁎ 2.6 ± 0.3⁎⁎ 25.7 ± 3.6⁎⁎ 1.0 ± 0.1⁎⁎⁎ 7.6 ± 1.2⁎⁎ 1.4 ± 0.2⁎⁎⁎

t1:4 t1:5 t1:6 t1:7 t1:8 t1:9 t1:10 t1:11 t1:12 t1:13 t1:14 t1:15 t1:16 t1:17 t1:18 t1:19 t1:20 t1:21 t1:22 t1:23

Data are expressed as mean ± SD. Abundances of CXCL13, CSF-2, CSF-3, CCL-1, CCL-11, IFN-γ, IL1-α, IL1-β, IL-1ra, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-10, IL-12-p70, IL-13, IL-16, IL-17, IL-23, IL-27, CXCL-10, CXCL-11, MCP-5, CXCL-9, CCR-1a, CCL-4, CCL-2, CCL-5, CXCL-12, CCL-17 or TNF-α were below the detection limit in serum for BKS and db/db mice. ALT, alanine transaminase; AST, aspartate transaminase; CCL, chemokine (C-C motif) ligand; CSF, colony stimulating factor; CXCL, chemokine (C-X-C motif) ligand; GLDH, glutamate dehydrogenase; HOMA-IR, homeostatic model assessment of insulin resistance; IFN, interferon; IL, interleukin; MCP, monocyte chemo attractant protein; PAI, plasminogen activator inhibitor; sICAM, soluble intercellular adhesion molecule; TIMP, metalloproteinase; TNF, tumor necrosis factor; TREM, triggering receptor expressed on myeloid cells. ⁎ p b 0.01 by Student’s t test. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001 by 2-way ANOVA. # In six mice of each genotype.

Please cite this article as: B. Knebel, et al., Peroxisomes compensate hepatic lipid overflow in mice with fatty liver, Biochim. Biophys. Acta (2015), http://dx.doi.org/10.1016/j.bbalip.2015.03.003

t1:24 t1:25 t1:26 t1:27 t1:28 t1:29 t1:30 t1:31 t1:32 t1:33 t1:34 t1:35 t1:36 t1:37 t1:38

Table 2 KeGG pathways in differential abundant organelle proteins KeGG pathways in mitochondrial proteins.

t2:3 t2:4

KEGG pathway metabolic pathways 01100 C = 1184; O = 19; E = 0.55; R = 34.39; rawP = 1.34e-26; adjP = 2.41e-25

t2:5 t2:6 t2:7 t2:8 t2:9 t2:10 t2:11 t2:12 t2:13 t2:14 t2:15 t2:16 t2:17 t2:18 t2:19 t2:20 t2:21 t2:22 t2:23 t2:24 t2:25 t2:26

KEGG pathway, butanoate metabolism 00650 C = 30; O = 4; E = 0.01; R = 285.73; rawP = 1.02e-09; adjP = 9.18e-09 KEGG pathway, fatty acid metabolism 00071 C = 48; O = 4; E = 0.02; R = 178.58; rawP = 7.21e-09; adjP = 4.33e-08 KEGG pathway, arginine and proline metabolism 00330 C = 54; O = 4; E = 0.03; R = 158.74; rawP = 1.17e-08; adjP = 5.26e-08 KEGG pathway, primary bile acid biosynthesis 00120 C = 15; O = 3; E = 0.01; R = 428.60; rawP = 4.11e-08; adjP = 1.48e-07 KEGG pathway, peroxisome 04146 C = 80; O = 4; E = 0.04; R = 107.15; rawP = 5.80e-08; adjP = 1.49e-07 KEGG pathway, PPAR-signaling pathway 03320 C = 80; O = 4; E = 0.04; R = 107.15; rawP = 5.80e-08; adjP = 1.49e-07 KEGG pathway, lysine biosynthesis 00300 C = 3; O = 2; E = 0.00; R = 1428.67; rawP = 6.29e-07; adjP = 1.42e-06 KEGG pathway, valine, leucine and isoleucine degradation 00280 C = 50; O = 3; E = 0.02; R = 128.58; rawP = 1.75e-06; adjP = 3.50e-06 KEGG pathway, nitrogen metabolism 00910 C = 23; O = 2; E = 0.01; R = 186.35; rawP = 5.27e-05; adjP = 9.49e-05

t2:27 t2:28 t2:29 t2:30 t2:31 t2:32 t2:33 t2:34 t2:35 t2:36 t2:37 t2:38 t2:39 t2:40 t2:41 t2:42 t2:43 t2:44

KEGG pathway, citrate cycle (TCA cycle) 00020 C = 31; O = 6; E = 0.03; R = 238.27; rawP = 1.49e-13; adjP = 1.27e-12 KEGG pathway, valine, leucine and isoleucine degradation 00280 C = 50; O = 6; E = 0.04; R = 147.73; rawP = 3.19e-12; adjP = 1.81e-11 KEGG pathway, tryptophan metabolism 00380 C = 45; O = 5; E = 0.04; R = 136.79; rawP = 3.39e-10; adjP = 1.44e-09 KEGG pathway, peroxisome 04146 C = 80; O = 5; E = 0.06; R = 76.94; rawP = 6.52e-09; adjP = 2.22e-08 KEGG pathway, pyruvate metabolism 00620 C = 43; O = 4; E = 0.03; R = 114.52; rawP = 4.61e-08; adjP = 1.31e-07 KEGG pathway, arginine and proline metabolism 00330 C = 54; O = 4; E = 0.04; R = 91.19; rawP = 1.17e-07; adjP = 2.84e-07 KEGG pathway, butanoate metabolism 00650 C = 30; O = 3; E = 0.02; R = 123.11; rawP = 2.01e-06; adjP = 4.27e-06 KEGG pathway, glycine, serine and threonine metabolism 00260 C = 34; O = 3; E = 0.03; R = 108.63; rawP = 2.95e-06; adjP = 5.02e-06 KEGG pathway, propanoate metabolism 00640 C = 33; O = 3; E = 0.03; R = 111.92; rawP = 2.69e-06; adjP = 5.02e-06

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KeGG pathways in peroxisomal proteins KEGG pathway, metabolic pathways 01100 C = 1184; O = 25; E = 0.96; R = 25.99; rawP = 4.48e-30; adjP = 7.62e-29

P50544; P25688; Q921H8; P11725; Q07417; Q99K67; Q9QXD1; P35486; P26443; P29758; Q9DBG1; Q8C196; Q9WVM8; P54869; Q03265; P51660; Q8K2B3; Q91VA0; Q99LB7

Acadvl; Uox; Acaa1a; Otc; Acads; Aass; Acox2; Pdha1; Glud1; Oat; Cyp27a1; Cps1; Aadat; Hmgcs2; Atp5a1; Hsd17b4; Sdha; Acsm1; Sardh

Q07417; P35486; P54869; Q91VA0

Acads; Pdha1; Hmgcs2; Acsm1

Q07417; P50544; Q9WUR2; Q921H8

Acads; Acadvl; Eci2; Acaa1a

P11725; Q8C196; P26443; P29758

Otc; Cps1; Glud1; Oat

Q9DBG1; Q9QXD1; P51660

Cyp27a1; Acox2; Hsd17b4

Q9QXD1; Q9WUR2; Q921H8; P51660

Acox2; Eci2; Acaa1a; Hsd17b4

Q9DBG1; Q9QXD1; Q921H8; P54869

Cyp27a1; Acox2; Acaa1a; Hmgcs2

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Q99K67; Q9WVM8

Aass; Aadat

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Q07417; Q921H8; P54869

Acads; Acaa1a; Hmgcs2

Q8C196; P26443

Cps1; Glud1

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Tyrp1; Arg1; Sucla2; Dmgdh; Suclg2; Ces1d; Pah; Acads; Sord; Mat1a; Pdha1; Cat; Oat; Dld; Aldh2; Cps1; Ogdh; Hmgcs2; Prdx6; Ces3a; Acaa1b; Haao; Hibadh; Sardh; Sdha;

P07147; Q61176; Q9Z2I9; Q9DBT9; Q9Z2I8; Q8VCT4; P16331; Q07417; Q64442; Q91X83; P35486; P24270; P29758; O08749; P47738; Q8C196; Q60597; P54869; O08709; Q63880; Q8VCH0; Q78JT3; Q99L13; Q99LB7; Q8K2B3

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Q9Z2I9; P35486; Q9Z2I8; Q60597; O08749; Q8K2B3;

Sucla2; Pdha1; Suclg2; Ogdh; Dld; Sdha

Q07417; Q8VCH0; P47738; Q99L13; P54869; O08749;

Acads; Acaa1b; Aldh2; Hibadh; Hmgcs2; Dld

P47738; P40936; Q78JT3; Q60597; P24270; Q8VCH0; P35700; Q9DC50; P24270; P11930 P47738; P35486; Q9DBK0; O08749; P47738; Q61176; Q8C196; P29758;

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Aldh2; Inmt; Haao; Ogdh; Cat

B. Knebel et al. / Biochimica et Biophysica Acta xxx (2015) xxx–xxx

Please cite this article as: B. Knebel, et al., Peroxisomes compensate hepatic lipid overflow in mice with fatty liver, Biochim. Biophys. Acta (2015), http://dx.doi.org/10.1016/j.bbalip.2015.03.003

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Acaa1b; Prdx1; Crot; Cat; Nudt19

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Aldh2; Pdha1; Acot12; Dld

Aldh2; Arg1; Cps1; Oat

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Q07417; P35486; P54869;

Acads; Pdha1; Hmgcs2

Q9DBT9; O08749; Q99LB7;

Dmgdh; Dld; Sardh

P47738; Q9Z2I9; Q9Z2I8;

Aldh2; Sucla2; Suclg2;

User file and parameters: User file: organellkombi.txt, Organism: mmusculus, Id Type: uniprot_swissprot_accession, Ref Set: entrezgene, Significance Level: Top10, Statistics Test: Hypergeometric, MTC: BH, Minimum: 2. The results for each enriched KEGG pathway are listed in this table. For each KEGG pathway, the first row lists the KEGG pathway name and corresponding KEGG ID. The second row lists number of reference genes in the category (C), number of genes in the gene set and also in the category (O), expected number in the category (E), Ratio of enrichment (R), p value from hypergeometric test (rawP) and p value adjusted by the multiple test adjustment (adjP). Finally, genes in the pathway are listed. For each gene, the table lists the user uploaded ID and value (optional), Entrez ID, Ensembl Gene Stable ID, Gene symbol, and description. Ensembl Gene Stable ID and Entrez Gene ID are linked to the Ensembl and Entrez Gene databases, respectively.

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2.7. Web-based functional annotation of differentially expressed genes and identified proteins For functional annotation, web-based tools from public database sources were used: http://www.ncbi.nlm.nih.gov/, http:// www.informatics.jax.org/mgihome/, http://www.genome.jp/kegg/, http://geneontology.org/page/download-annotations, http://www. affymetrix.com, http://bioinfo.vanderbilt.edu/webgestalt/ [14], https:// toppcluster.cchmc.org/ [15], http://david.abcc.ncifcrf.gov/ [16] were used. Data sets were also analyzed using the categorical overrepresentation function of EASE.

2.8. Statistical analysis

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Values are presented as mean ± SD. Statistical analysis was performed with Student’s t test or by 2-way ANOVA calculated with Prism 5.01 (GraphPad Software Inc., San Diego, USA) as indicated.

t3:1 t3:2

Table 3 Pathway analyses of differential abundant genes in db/db compared to BKS mice.

t3:3 t3:4 t3:5

Drug metabolism—cytochrome P450 C = 87; O = 20; E = 1.76; R = 11.36; rawP = 6.43e-16; adjP = 7.52e-14

t3:6 t3:7 t3:8

Retinol metabolism C = 77; O = 17; E = 1.56; R = 10.91; rawP = 2.02e-13; adjP = 1.18e-11

t3:9 t3:10 t3:11

Metabolic pathways C = 1175; O = 64; E = 23.77; R = 2.69; rawP = 7.57e-13; adjP = 2.95e-11

t3:12 t3:13 t3:14 t3:15 t3:16 t3:17 t3:18 t3:19 t3:20 t3:21 t3:22 t3:23

PPAR-signaling pathway C = 80; O = 14; E = 1.62; R = 8.65; rawP = 7.29e-10; adjP = 2.13e-08 Metabolism of xenobiotics by cytochrome P450 C = 77; O = 13; E = 1.56; R = 8.35; rawP = 4.68e-09; adjP = 1.10e-07 Steroid hormone biosynthesis C = 55; O = 11; E = 1.11; R = 9.89; rawP = 1.13e-08; adjP = 2.20e-07 Protein processing in endoplasmic reticulum C = 169; O = 17; E = 3.42; R = 4.97; rawP = 6.75e-08; adjP = 1.13e-06

t3:24 t3:25 t3:26 t3:27 t3:28 t3:29 t3:30 t3:31 t3:32

Arachidonic acid metabolism C = 89; O = 12; E = 1.80; R = 6.66; rawP = 2.42e-07; adjP = 3.54e-06 Biosynthesis of unsaturated fatty acids C = 25; O = 7; E = 0.51; R = 13.84; rawP = 4.69e-07; adjP = 6.10e-06 Bile secretion C = 71; O = 10; E = 1.44; R = 6.96; rawP = 1.62e-06; adjP = 1.90e-05

3.1. Phenotype, laboratory parameters and cytokine profile

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At 14 weeks of age, BKS.Cg-Leprdb (db/db) mice had developed their full obese phenotype with overt diabetes compared to their C57BL/Kslepr+/+ (BKS) littermates. Hyperphagia in db/db mice was indicated by an increased food intake per body weight compared with BKS of around 50% (Table 1). Body and liver weights of db/db mice were approximately double those of BKS mice. Analyses of serum showed increased levels of blood glucose and insulin, indicating insulin resistance with increased homeostatic model assessment of insulin resistance (HOMA-IR). As expected for this model, circulating leptin was also significantly higher in db/db versus BKS mice. The surrogate parameters for liver function—alanine transaminase (ALT), aspartate transaminase (AST), glutamate dehydrogenase (GLDH) and the fibrinolysis inhibitor, plasminogen activator inhibitor (PAI)—were massively increased, indicating new or persisting liver dysfunction (Table 1). Total triglycerides were also elevated, whereas cholesterol was not significantly altered (Table 1). Serum analyses of 40 different cytokines and chemokines revealed significant reductions of soluble intercellular adhesion molecule (sICAM), chemokine (C-X-C motif) ligand 1 (CXCL1), colony stimulating factor 1 (CSF1), monocyte chemoattractant protein-1 (MCP-1), metalloproteinase (TIMP)-1 and triggering receptor expressed

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Plot analyses workflow was performed using default settings (paired t test, multiple testing correction: Benjamini-Hochberg) with a minimum two-fold difference among conditions as described [13]. Full data sets are available under accession number (www.ncbi.nlm.nih.gov/geo/).

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226564 13088 14858 14261 13089 13105 14870 72082 55990 14263 22238 71773 14864 226105 13097 13087 66447 13094 13098 14262

Cyp2b10; Cyp2b13; Lrat; Rdh16; Retsat; Cyp2c55; Cyp4a14; Cyp26a1; Ugt2b5; Ugt2b1; Cyp4a10; Cyp2c70; Cyp2c38; Cyp2a5; Cyp2b9; Cyp4a31; Cyp2c39

18534 13074 108682 19683 21915 14732 225913 11656 192156 13119 27053 328099 71773 20280 209558 242341 13117 13097 208665 15486 16891 70789 231510 14755 233799 246277 71519 23972 13088 72269 13089 12660 72082 13909 102247 15490 13122 23959 20454 18597 22238 28169 12408 14104 11647 226105 76952 55980 13087 54613 15493 18770 11671 13026 13094 15496 109674 15357 666168 18563 13098 619326 14377 14718

Pck1; Cyp17a1; Gpt2; Rdh16; Dtymk; Gpam; Dak; Alas2; Mvd; Cyp4a14; Asns; Prps1l3; Ugt2b1; Scp2; Enpp3; Atp6v0d2; Cyp4a10; Cyp2c38; Akr1d1; Hsd17b2; Lipg; Kynu; Agpat9; Pigq; Acsm2; Csad; Cyp2u1; Papss2; Cyp2b10; Cda; Cyp2b13; Chka; Cyp2c55; Ces3b; Agpat6; Hsd17b7; Cyp7a1; Nt5e; St3gal5; Pdha1; Ugt2b5; Agpat3; Cbr1; Fasn; Alpl; Cyp2c70; Nt5c2; Impa1; Cyp2a5; St3gal6; Hsd3b2; Pklr; Aldh3a2; Pcyt1a; Cyp2b9; Hsd3b5; Ampd2; Hmgcr; Cyp4a31; Pcx; Cyp2c39; 9130409I23Rik; G6pc; Got1

E

C

13088 13089 79235 19683 67442 72082 13119 13082 22238 71773 13117 226105 13097 13087 13094 666168 13098

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O

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t3:33 t3:34 t3:35 t3:36 t3:37

Fmo4; Cyp2b10; Gsta2; Fmo1; Cyp2b13; Cyp2d9; Gstp1; Cyp2c55; Fmo2; Fmo5; Ugt2b5; Ugt2b1; Gstm3; Cyp2c70; Cyp2c38; Cyp2a5; Mgst3; Cyp2b9; Cyp2c39; Fmo3

19016 13117 18534 16592 16956 20250 12491 19013 13119 13122 666168 11770 56473 20280

Pparg; Cyp4a10; Pck1; Fabp5; Lpl; Scd2; Cd36; Ppara; Cyp4a14; Cyp7a1; Cyp4a31; Fabp4; Fads2; Scp2

13088 14858 14864 13089 13097 226105 66447 14870 72082 13094 13098 22238 71773

Cyp2b10; Gsta2; Gstm3; Cyp2b13; Cyp2c38; Cyp2c70; Mgst3; Gstp1; Cyp2c55; Cyp2b9; Cyp2c39; Ugt2b5; Ugt2b1

20860 13074 208665 15493 15486 15496 15490 13122 22238 71773 13123

Sult1e1; Cyp17a1; Akr1d1; Hsd3b2; Hsd17b2; Hsd3b5; Hsd17b7; Cyp7a1; Ugt2b5; Ugt2b1; Cyp7b1

19358 11911 20338 69276 78943 18024 74126 70377 14828 15505 67475 15511 15519 94232 22433 67819 71853

Rad23a; Atf4; Sel1l; Sec62; Ern1; Nfe2l2; Syvn1; Derl3; Hspa5; Hsph1; Ero1lb; Hspa1b; Hsp90aa1; Ubqln4; Xbp1; Derl1; Pdia6

13088 13117 13089 13097 226105 72082 13094 13119 666168 13098 71519 12408

Cyp2b10; Cyp4a10; Cyp2b13; Cyp2c38; Cyp2c70; Cyp2c55; Cyp2b9; Cyp4a14; Cyp4a31; Cyp2c39; Cyp2u1; Cbr1

171282 26897 171281 56473 171210 20250 170439

Acot4; Acot1; Acot3; Fads2; Acot2; Scd2; Elovl6

11931 108114 76408 16835 18671 15357 13122 11829 28248 239273

Atp1b1; Slc22a7; Abcc3; Ldlr; Abcb1a; Hmgcr; Cyp7a1; Aqp4; Slco1a1; Abcc4

User file and parameters: Organism: mmusculus, Id Type: gene ID, Ref Set: entrezgene, Significance Level: Top10, Statistics Test: Hypergeometric, MTC: BH, Minimum: 2 The results for each enriched KEGG pathway are listed in this table. For each KEGG pathway, the first row lists the KEGG pathway name. The second row lists number of reference genes in the category (C), number of genes in the gene set and also in the category (O), expected number in the category (E), Ratio of enrichment (R), p value from hypergeometric test (rawP), and p value adjusted by the multiple test adjustment (adjP). Finally, genes in the pathway are listed. For each gene, the table lists the user uploaded ID and value (optional), Entrez ID, Ensembl Gene Stable ID, Gene symbol, and description. Ensembl Gene Stable ID and Entrez Gene ID are linked to the Ensembl and Entrez Gene databases, respectively.

Please cite this article as: B. Knebel, et al., Peroxisomes compensate hepatic lipid overflow in mice with fatty liver, Biochim. Biophys. Acta (2015), http://dx.doi.org/10.1016/j.bbalip.2015.03.003

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Changes in lipid composition are likely due to alterations in mitochondrial and peroxisomal function due to changed expression of relevant genes. Peroxisome proliferator-activated receptor (PPAR)-α and -γ and PPAR-γ coactivator 1-α (PCG-1a) genes are upregulated in the liver of db/db mice (Fig. 4a), which might indicate mitochondrial and peroxisomal biosynthesis and functional maintenance. This is further supported by increased expression of peroxisome-targeting signal type 1 (PTS-1)-containing PEX proteins involved in peroxisomal biogenesis and functionality (PEX5, PEX7), whereas PTS-2-containing PEX6 is reduced in db/db mice. The gene expression of organelle key

C

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3.3. Alterations in mitochondrial and peroxisomal protein patterns

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The db/db mice had enlarged fatty livers. To ascertain the origin of lipid accumulation in the liver, the expression levels of central genes in lipid metabolism were investigated (NAFLD 2). Key transcription factors in lipid metabolism are the sterol-regulatory element binding protein (SREBP) isoforms, SREBP-1a and SREBP-1c. In db/db mice, elevated expression was only observed for SREBP-1a, indicating general expression alterations of lipid-modifying genes. Increases in db/db mice were also seen for the lipid-converting enzymes, acyl-CoA carboxylase (ACC) and fatty acid synthase (FASN), which convert acetyl-CoA to palmitate (C16:0); and fatty acid elongase 5 and 6 (ELOVL5 and ELOVL6), which elongate C16 to higher C-bodies, or the fatty acid omegahydroxylase Cyp4A14. The db/db mice also had elevated expression of desaturases, such as Δ9 stearoyl-CoA desaturase 1 (SCD1), which introduces double bonds into saturated FAs as needed for conversion of C16:0 to cC16:1 in position C7; and Δ6 FA desaturase 1 or 2 (FADS1 or FADS2), which introduce double bonds into long-chain FAs, generating LC-PUFAs (Fig. 2). Furthermore, genes that code for proteins involved in lipid clearance, such as the low-density lipoprotein receptor (LDLR), hormone-sensitive lipase (HSL) or the FA binding protein 4 (FABP4)

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3.2. Alterations in lipid partitioning and composition

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were also elevated in db/db mice. Moreover, in db/db mice, the ratelimiting enzymes of cholesterol biosynthesis (3-hydroxy-3-methylglutaryl [HMG]-CoA reductase [HMG-CoAR]) and gluconeogenesis (phosphoenolpyruvate carboxykinase [PEPCK]) also showed increased expression (Fig. 2). FA compositions in the liver were determined in order to monitor the effect of altered gene expression and the impact of elevated serum FFAs. The db/db mice showed a nearly five-fold higher level of hepatic total fatty acids (TFAs) versus the BKS mice (Fig. 3a). The hepatic lipid pattern of db/db mice was similar to the serum pattern, with elevated cC16:1, cC18:1, cC18:2 and cC18:3, but the increase of cC18:1 was not as pronounced (Fig. 3b versus Fig. 1b). Unlike in serum, the longchained PUFA, cC20:4, was significantly increased in the liver (Fig. 3b). Interestingly, cC16:1 was increased in both serum and liver to similar levels. In the liver, the relative changes in lipid components for db/db compared to BKS mice clearly indicate reduction of saturated FFAs and increase of unsaturated FFAs (Fig. 3c). From these observations, increased FFA desaturation (Fig. 3d) with significant decreased hepatic de novo lipid (DNL) index (Fig. 3e) and unaltered FA elongation (Fig. 3f) were derived.

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on myeloid cells (TREM)-1 in db/db mice. As no parameters were elevated, no genotype-specific cytokine profile or systemic inflammation could be determined. (See Table 3.) Investigation of FFA content in serum revealed an elevation in db/db mice (Fig. 1a). Detailed analyses showed reduced levels of saturated FFAs (C16:0 and C18:0) in db/db mice, but monounsaturated FFAs (cC16:1 and cC18:1) and polyunsaturated FFAs (PUFAs) (cC18:2 and cC18:3) were increased (Fig. 1b). Therefore, the relative changes in lipid components in db/db compared with BKS mice clearly indicate a reduction in saturated FFAs and an increase of unsaturated FFAs except cC20:4, which was decreased (Fig. 1c).

E

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5

Fig. 1. Serum lipid compositions of BKS and db/db mice. (a) Free fatty acid content determined by GC (n = 20, each), (b) specific fractional composition of free fatty acid was determined by GC and (c) the %-change in db/db to BKS is given as means ± SD. Data are given as means ± SD (n = 20, each). Students’ t test was performed to determine significance (**p b 0.01).

Please cite this article as: B. Knebel, et al., Peroxisomes compensate hepatic lipid overflow in mice with fatty liver, Biochim. Biophys. Acta (2015), http://dx.doi.org/10.1016/j.bbalip.2015.03.003

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271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288

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enzymes such as carnitine palmitoyltransferases 1 and 2 (CPT1a and CPT2) and acyl-CoA thioesterase (ACOT)-2 were upregulated in db/db mice (Fig. 4a). Mitochondrial deoxyribonucleic acid (mtDNA) content—determined as the ratio of mtDNA to genomic DNA (gDNA)—did not, however, indicate differences between genotypes (Fig. 4b). Enzyme activities were measured in isolated organelles to assess organelle fitness, i.e., succinate dehydrogenase (SDH) for mitochondrial functionality and catalase for peroxisomal functionality. The investigations showed that the specific enzyme activity of mitochondrial SDH was not different (Fig. 4c). In contrast, the specific enzyme activity of catalase was significantly higher in db/db mice (Fig. 4d). These data indicate only alterations in organelle functionality of peroxisomes in db/db compared to BKS mice. As we have recently shown, the parallel comparison of both organelles is necessary to elucidate the basis and consequences of a metabolic shift [6]. To follow our concept of parallel investigation of mitochondria or peroxisomes functionality, we analyzed the organelle-specific proteome pattern in the liver of db/db versus BKS mice. Functional mitochondrial and peroxisomal protein fractions were subjected to comparative protein pattern analysis by two-dimensional difference gel electrophoresis (2D-DIGE™). In mitochondrial protein profiles of both mice, 971 protein spots were detected, of which 107 protein spots were altered. In peroxisomal protein profiles, 741 protein spots were detected and

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Fig. 2. Hepatic gene expression of key lipid metabolic genes in BKS and db/db mice. The hepatic expression level of genes were determined by RT-PCR (n = 20 each). The relative RNA amount shown in arbitrary units was calculated and plotted ± SD. BKS vs. db/db mice: **p b 0.01. *p b 0.01 by Student’s t test. ACC, acyl-CoA carboxylase; FABP, fatty acid binding protein; FADS, fatty acid desaturase; FASN, fatty acid synthase; HMG-CoAR, 3-hydroxy-3-methyl-glutaryl-CoA reductase; HSL, hormone-sensitive lipase; LDLR, low-density lipoprotein receptor; PEPCK, phosphoenolpyruvate carboxykinase; SCD, stearoyl-CoA desaturase; SREBP, sterol-regulatory element binding protein.

144 protein spots were significantly altered. Analysis of excised protein spots by mass spectrometry (MS) and searches of the Swiss-Prot nonredundant database with the acquired peptide mass information identified 28 mitochondrial and 48 peroxisomal non-redundant protein spots with at least 1.7-fold change in BKS or db/db mice (Table S1).

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Functional ontology by database-based annotation confirmed identified regulated proteins as having mitochondrial (EASE 1.70e–20; Benjamimi 10e–18) or peroxisomal (EASE 5.20e–07; Benjamini 6.90e–06) origins. Several identified proteins—e.g., catalase, ornithine aminotransferase, acyl-CoA dehydrogenase or HMG-CoA synthase—occurred in diverse spots, indicating their presence in different posttranslational modification states. According to the functional overlap of mitochondria and peroxisomes in both organelles, rate-limiting enzymes of lipid metabolism in both organelles—e.g., HMG-CoA synthase, acyl-coenzyme A synthetase (ACSM1), pyruvate dehydrogenase or acetyl-CoA dehydrogenase—were identified with different abundance in the mouse models. Proteins with oxido-reductase activity or involvement in redox reactions—e.g., acyl-CoA oxidase (ACOX2), isovaleryl-CoA dehydrogenase (ACAD2), glutamate dehydrogenase 1 or catalase—as well as proteins involved in cellular stress reactions, like various heat shock

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Please cite this article as: B. Knebel, et al., Peroxisomes compensate hepatic lipid overflow in mice with fatty liver, Biochim. Biophys. Acta (2015), http://dx.doi.org/10.1016/j.bbalip.2015.03.003

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proteins, were identified (Table S2). In mitochondria, a further 19 organelle-specific proteins were altered, including central enzymes of β-oxidation, e.g., ACOX2 or ACSM1, both being overrepresented in db/db mice. In peroxisomes, 40 of the 48 differentially abundant proteins were organelle specific. Key proteins involved in amino acid catabolism, pyruvate metabolism or the central metabolic citric acid cycle— e. g., aldehyde dehydrogenase 2, pyruvate carboxylase, acyl-CoA thioesterase 12, oxoglutarate dehydrogenase or succinate-Coenzyme A ligase—and inter-organelle shuttling of acyl-carnitines were regulated, revealing new molecular aspects of hepatic lipid accumulation (Table S2). According to the low number of regulated proteins identified, only associations to well-characterized pathways were considered for functional annotation of different abundant proteins (Table S3). In silico analyses of the mitochondrial and peroxisomal proteins confirmed functional overlap regarding catabolic function of lipids and amino acids indicating increased cellular degradation processes.

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Fig. 3. Liver lipid compositions of BKS and db/db mice. (a) Hepatic TFA content. (b) The specific fractional composition of TFA was determined by GC analyses in liver tissues as indicated in Table 2 (n = 20, each). (c) The % change of TFA in db/db to BKS is given is means ± SD. Fatty acid data determined in liver were further used to calculate (d) desaturase index (C16:0/C16:1), (e) de novo lipid synthesis index (DNL) (C16:0/C18:2) and (f) elongation index (C18:0/C16:0). Data are given as means ± SD (n = 20, of each phenotype). Students’ t test was performed to determine significance (**p b 0.01).

3.5. Alterations in hepatic gene expression

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In the db/db model of fatty liver, chronic metabolic alterations result in differential abundance of mitochondrial and peroxisomal proteins. To further assess the molecular mechanisms involved in the accumulation of excess lipids in lean tissues such as liver, we performed genome-wide gene expression analyses. For biological replicates, four db/db and four BKS littermates from independent breeding were used. A total of 721 transcripts corresponding to 597 unique genes (BKS N db/db: 304 genes; db/db N BKS: 293 genes) were identified to have at least a twofold difference in abundance (see Table S4 for details on identified genes, fold changes and significance; Table S5 for functional annotation). Differential gene regulation was confirmed by real-time polymerase chain reaction (RT-PCR) for exemplified genes with moderate expression differences or several magnitude differences in p values in order to confirm the array PCR results (data not shown). Overall, gene expression analyses were consistent with proteome analyses, indicating altered expression of central targets of lipid

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Please cite this article as: B. Knebel, et al., Peroxisomes compensate hepatic lipid overflow in mice with fatty liver, Biochim. Biophys. Acta (2015), http://dx.doi.org/10.1016/j.bbalip.2015.03.003

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Fig. 4. Alterations in mitochondrial and peroxisomal protein patterns. (a) The hepatic expression level of genes were determined by RT-PCR (n = 20 of each phenotype).The relative mRNA amount shown in arbitrary units was calculated and plotted as mean ± SD for BKS and db/db mice. (b) mtDNA content was determined in comparison to gDNA in BKS and db/db mice (n = 20). (c) Specific activities of mitochondrial SDH and (d) peroxisomal catalase were determined in liver homogenates of BKS and db/db mice (n = 20) (n.s. = not significant, *p b 0.05; **p b 0.01. ACOT, acyl-CoA thioesterase; CPT, carnitine palmitoyltransferase; PCG-1a, peroxisome proliferator-activated receptor-γ coactivator 1-α; PPAR, peroxisome proliferator-activated receptor; SDH, succinate dehydrogenase.

Please cite this article as: B. Knebel, et al., Peroxisomes compensate hepatic lipid overflow in mice with fatty liver, Biochim. Biophys. Acta (2015), http://dx.doi.org/10.1016/j.bbalip.2015.03.003

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degradation in both central organelles of energy control—mitochondria and peroxisomes.

metabolites or metabolic endpoints, such as bile acids, arachidonic 375 acid or xenobiotics (Table S6). 376

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3.7. Functional implication of organelle differences in db/db and BKS mice 377

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Grouping transcripts regulated in db/db mice according to their molecular function (GO annotation) showed that the annotations with the highest scores and values for significance were genes involved in lipid biosynthesis processes (EASE 2.3e–10, Benjamini 2.4e–07), including SCD, FADS, ELOVL, SAA or FABP gene family members. Furthermore, genes involved in the regulation of metabolic pathways of fatty acids (EASE-Score 9.9e–07; Benjamini 5.1e–04) and cholesterol (EASEScore 8.6e–06; Benjamini 3.0e–03) as key enzymes PEPCK, FASN, LDLR or HMG-CoA reductase, and lipoprotein particles (EASE-Score 6.2e–08; Benjamini 2.6e–06)—e.g., ApoM, VLDLR or LPL—were differentially abundant. Also, genes with electron carrier activity (EASE-Score 4.1e–08; Benjamini 2.8e–05)—e.g., various cytochromes—as well as nicotinamide adenine dinucleotide (NADH) or nicotinamide adenine dinucleotide phosphate-oxidase (NADPH) binders (EASE-Score 3.3e–04; Benjamini 1.8e–02), including various Fmo genes, oxido-reductase activity (EASEScore 4.2e–05; Benjamini 5.7e–03), especially gluthatione-transfer activity (EASE-Score 9.3e–04; Benjamini 4.7e–02), were observed. Interestingly, the cellular compartment annotation identified the peroxisomes with high significance (EASE-Score 7.8e–05; Benjamini 1.9e–03), including genes for PEX proteins (PEX5, PEX7, PEX11a), whereas mitochondrial components were only enriched with lower significance (EASE-Score 8.3e–02; Benjamini 5.2e–01), such as mitochondrial glycerol-3-phosphate acyl-transferase (Gpam), Slc25a isoforms or carnitine acyl-transferase (CrAT). Annotation to KEGG pathways revealed altered expression of genes involved in metabolic pathways, including biogenesis of unsaturated FFAs. Most pathways affected act on degradation and secretion of

The comparison of regulated transcripts and organelle proteins identified in db/db and BKS mice revealed an overlap, as central enzymes of mitochondria or peroxisomes showed alterations in the abundance of mRNA and protein levels, e.g., for aldehyde dehydrogenase, acyl-CoAoxidase or -thioesterase, pyruvate dehydrogenase and carboxylase, or cathepsine isoforms next to glycerol-3-phosphate dehydrogenase and HMG-CoA synthetase (Tables S1 and S4). Furthermore, transcripts involved in mitochondrial or peroxisomal function—e.g., PPARα, PPARγ, PGC-1A, Acot-2, PEX5, PEX6, PEX11a—and several proteins with clear organelle specificity—e.g., pyruvate dehydrogenase, acyl-CoA-oxidase, thioesterase, aldehyde dehydrogenase or gluthatione S-transferase— were differentially expressed on mRNA and protein level (Tables S1 and S4). To determine whether the NAFLD phenotype of the db/db model could be attributed to the alterations observed, the mitochondrial and peroxisomal proteins and differential abundant genes associated with either organelle were analyzed. Organelle-specific gene subsets from differentially regulated genes were selected according to gene function in organelle biosynthesis, function and maintenance according to analyses of gene annotation information from public database sources. These transcripts, in combination with the respective organelle proteins, were used for analyses of Mouse Genome Informatics (MGI) phenotype associations (https://toppcluster.cchmc.org/; default database parameters). For each phenotype MGI ID, computed –logP and genes are indicated in Table S7. These analyses identified pathologies related to the db/db phenotype specific for either mitochondria or peroxisomes, but also the broad overlap of both organelles in functionality, which

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Fig. 5. Functional summaries of genes and proteins identified in this study. Analyses of MGI phenotype associations of differential genes and proteins observed in db/db compared to BKS mice. A database search was performed with https://toppcluster.cchmc.org; default database parameters (Bonferroni p b 0.05). Details on corresponding gene lists and –logP values are given in Table S7.

Please cite this article as: B. Knebel, et al., Peroxisomes compensate hepatic lipid overflow in mice with fatty liver, Biochim. Biophys. Acta (2015), http://dx.doi.org/10.1016/j.bbalip.2015.03.003

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Mitochondria can, to a certain degree, adapt to metabolic surplus in increasing FA oxidation, thus preventing lipid accumulation [28], but long- and medium-chained FAs are converted to energy at a cost of generating reactive oxygen species (ROS) [29]. Once transported into the organelle, lipolysis oxidizes lipids and generates energy as ATP via mitochondria-specific β-oxidation pathways and respiratory chain components. Despite this, the net balance of ATP levels is lower in states of non-alcoholic steatohepatitis (NASH), which is a more severe form of NAFLD [30]. This might be due to uncoupling processes in mitochondria to prevent further ROS accumulation in stages of increased activity [31]. Our analyses revealed that energy and lipid metabolism, oxidative stress and enzymes necessary to cope with the surplus metabolites are the main target of regulation in the mitochondria of db/db animals. This adaptation is limited because mitochondria bear a lipid-level sensitive activated transport system of medium- and long-chain FAs. The intra-organelle carnitine shuttle was not sufficient in db/db mice, indicated by increased expression of carnitine acetyltransferase. This—in combination with altered abundance of rate-limiting enzymes of the citrate cycles and components of the respiratory chain and the urea cycle—will hamper mitochondrial efficiency in lipid degradation in the db/db model. One can also speculate that the findings represent a kind of substrate-restriction mechanism to prevent preliminary mitochondrial exhaustion, but it may also indicate alternative lipid degradation processes being favored in stages of hepatic lipid accumulation. One advantage of peroxisomal lipid degradation is the unrestricted lipid flux into the organelle because peroxisomal β-oxidation is not feedback inhibited by malonyl-CoA and the carnitine shuttle of a malonyl-CoA influx which seems to be reduce in db/db mitochondria. Peroxisomes masticate very long chain FAs (N C20) or branched medium- or long-chain FA, which then are mainly provided for further degradation in mitochondria [32]. If further degradation takes place by peroxisomal β-oxidation, the energy gain per molecule is reduced in comparison to mitochondria. This would be in line with the lower hepatic ATP homeostasis levels observed in NASH [30]. Furthermore, clearance of oxidative degradation products is nearly exclusively performed by peroxisomes [33]. During periods of increased influx of FAs into the liver, peroxisomal lipid metabolism might become increasingly important, because it is a non-restricted safeguard for cell toxicity by an excess of cellular lipids or cytotoxic oxidative stress products. Long-term regulation of increased lipid supply requires alterations in expression rates of genes encoding for components of FA metabolism. The gene expression alterations observed between db/db and BKS mice mainly reflect adaptation to increased lipid turnover in terms of modification and degradation. In addition, clearance of the surplus of metabolites is affected and arachidonic acid or glycerolipids may open a link to membrane composition and fluidity. Conversely, lipids can directly regulate gene expression of, for example, nuclear transcription factors such as PPARs [34,35], which are linked to organelle biosynthesis, differentiation or functional integrity [36–38]. PPAR-signaling pathways and the PPARα and PPARγ genes are upregulated in db/db mice, which might point towards adipocyte differentiation, but also to peroxisome biosynthesis. It is interesting to note that NASH patients show an increase in peroxisome proliferation in response to mitochondrial dysfunction [39]. Furthermore, in clinical studies, the PPARγ agonist—pioglitazone—has been shown to improve insulin sensitivity and fatty liver phenotype in NAFLD patients [40,41]. Along with the PPARs, several PEX genes are upregulated in the db/db mice, including PEX11a, probably pointing towards peroxisomal biosynthesis or maintenance. PEX11a knock-out mice have been shown to have elevated body weight and hepatic lipid accumulation [42].

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NAFLD is the result of increased ectopic accumulation of lipids in hepatic tissue due to a combination of imbalanced lipid import and synthesis and lipid degradation and export. However, human and animal studies of hepatic lipid accumulation have not yet provided a clear picture of what triggers accumulation of lipids in non-adipose or lean tissues, what keeps this process going and what the long-term effects are [17,18]. In this study, we provide direct evidence that not only mitochondria but also peroxisomes play a major role. The leptin resistance db/db mouse on BKS genetic background is a very well accepted mouse model of hyperphagia-induced obesity with overt diabetes and dyslipidemia with NAFLD [8,19,20]. In this study, db/db mice showed hyperphagia-related obesity, no inflammatory cytokine pattern, hyperlipidemia with fatty liver disease and alterations in liver transcriptome and proteome profiles. In general, obesity is accompanied by inflammation, and fatty liver can act as a cofactor to induce inflammation and altered cytokine levels [21,22]. In contrast, db/db animals show an overall decreased serum cytokine pattern, excluding increased inflammation as a key trigger in mechanisms related to increased ectopic lipid accumulation. Moreover, gene expression analyses of the liver gave no evidence of an increased expression of immune-active genes strengthening the results of the cytokine profiles detected. This phenomenon is in concordance with only weak inflammation observed in other mouse models of obesity [20]. In this respect, it is interesting to note that another mouse model with a similar degree of liver weight and hepatic steatosis but a lack of white adipose tissue, shows increased inflammatory reactions [23]. This might indicate that the existence of white adipose tissue acts as a buffer for lipotoxicity in terms of inflammation, and NAFLD per se is not enough for an inflammatory status which obviously requires a further trigger [24]. In hyperlipidemia, the two main pathways for systemic clearance are the increase in lipid storage and β-oxidation capacity. The lipid profiles determined in our experiments indicated reduced de novo lipid synthesis, unaltered elongation, but increased desaturase activity and consequently a shift of saturated FAs to monounsaturated and especially to polyunsaturated FAs. Therefore, it is tempting to speculate that an accumulation of non-neutral FAs occurs in the liver that needs to be metabolized to prevent lipotoxicity and cell damage. In general, an increased desaturase (SCD) index has been related to metabolic complications and obesity [25]. Interestingly, the hyperphagia-induced fatty liver model used in our analyses shows a different lipid profile than a high fat diet-induced model of NAFLD [26]. The SCD index in the latter model was decreased, in contrast to the db/db animals. This is probably due to the diabetic state of db/db mice with peripheral insulin resistance shown by high HOMA-IR and high FFA in serum generated by lipolysis of triglycerides in adipocytes. The shift to unsaturated FAs might be an indicator for mechanisms related to lipid storage, including in the liver itself. In this regard, it is interesting to note that an increase in PUFAs moves the focus towards peroxisomes, a backup organelle to compensate for increasing need in lipid degradation. Peroxisomes and their specialized role in lipid metabolism might be the key to neutralize pathophysiological alterations. Recently, we have reported that despite the predominant role of mitochondria, peroxisomes are also key players in lipid metabolism [6]. To follow this hypothesis, we isolated peroxisomes and mitochondria in parallel for proteomic profiling. The majority of differential organelle proteins in our study were consequently identified in peroxisomes and not in the classical lipiddegrading mitochondria. In general, the percentage of mitochondrial or peroxisomal proteins identified to be regulated here is relatively low compared with other investigations that identified up to 45%

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regulated mitochondrial proteins in diabetic states [27]. The majority 469 of proteins identified here confirm our previous observation of organ- 470 elle proteome in 24-week-old C57Bl6 mice [6]. 471

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might be summarized as the working share and interaction of both organelles in NAFLD (Fig. 5).

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B.K. and J.K. were responsible for experimental design, interpretation, writing and editing of the manuscript, and performed gene expression and in silico analyses. S.H., S.L. and U.N. researched the proteomic data. S.J., F.S. and L.B. researched data for metabolic characterization. J.H., C.K. and S.G. were responsible for animal handling and research for metabolic characterization. D.M.-W. contributed to experimental design, interpretation of data, review and editing of the manuscript. J.K. was the principal investigator of the study.

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The authors declare that they have no competing interests that might be perceived to influence the results and discussion reported in this paper. The authors declare no competing financial interests.

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Transparency Document

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The Transparency document associated with this article can be found, in the online version.

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In conclusion, our investigations clearly show that, in this mouse model of fatty liver disease in combination with obesity and diabetes, the hepatocyte-protecting organelle peroxisome is altered. This prompts us to state that increased peroxisomal activity might indicate a stage of pre-NAFLD. If the compensatory capacity of the peroxisomes is exhausted, accumulation of intracellular lipids will affect intracellular membrane composition and thus interfere with mitochondrial and peroxisomal function to progress from pre-NAFLD to NAFLD. Further elucidation of the role of this organelle in fatty liver disease might pave the way for new targets and strategies in the treatment and prevention of this most frequent liver disease and its associated risks of hepatitis, cirrhosis and cancer.

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Mechanistically, loss of PEX11a interfered with peroxisomal development and gene expression for peroxisomal FA oxidation [42]. Hepatic lipid overflow initiates an increase in the lipid degradation capacity of the peroxisomes, which is supported by the observation that, for example, in yeast the cellular peroxisome content increases if cells are grown in a lipid-rich environment [39]. This is accompanied by specific genes either being expressed due to the lipid-rich environment or being necessary for growth maintenance under these conditions [43]. Interestingly, some mouse ortholog of these candidates, like several ACOTs, PecI or Acsm2, were also identified in our investigation. Our investigations further support the hypothesis that the highly specialized and efficient role of mitochondria in cellular energy metabolism has to be paid for by a rather low flexibility in function. Especially in stages of metabolic alterations associated with affluent metabolites like lipids, mitochondrial adaptation is limited and has direct implications on glucose or amino acid metabolism, including triggering insulin resistance. In contrast, peroxisomes have a more specialized role in lipid metabolism in terms of substrate specificity. Moreover, the organelles prepare higher complex lipid components suitable for mitochondria for efficient β-oxidation. They also have the ability for lipid oxidation itself with non-feedback regulated substrate influx. This makes the peroxisome a kind of “safeguard” with respect to cellular survival. The peroxisome, therefore, can eliminate the metabolic overflow of lipids, but at the cost of a low energy balance to maintain cellular functionality and insulin sensitivity.

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