Novel Insights into the Adipokinome of Obese and Obese/Diabetic Mouse Models

Abstract

The group of adipokines comprises hundreds of biological active proteins and peptides released from adipose tissue. Alterations of those complex protein signatures are suggested to play a crucial role in the pathophysiology of multifactorial, metabolic diseases. We hypothesized that also the pathophysiology of type-2-diabetes is linked to the dysregulation of the adipocyte secretome. To test this, we investigated mouse models with monogenic defects in leptin signaling which are susceptible to adipositas (C57BL/6 Cg-Lep^ob (obob)) or adipositas with diabetes (C57BL/KS Cg-Lepr^db (dbdb)) according to their genetic background. At the age of 17 weeks, visceral fat was obtained and primary murine adipocytes were isolated to harvest secretomes. Quantitative proteome analyses (LC-ESI-MS/MS) identified more than 800 potential secreted proteins. The secretome patterns revealed significant differences connected to the pathophysiology of obese mice. Pathway analyses indicated that these differences focus on exosome modelling, but failed to provide more precise specifications. To investigate the relationship of secretome data to insulin sensitivity, we examined the content of diabetogenic lipids, i.e., diacylglycerols (DAGs), identified as key players in lipid-induced insulin resistance. In contrast to obob mice, fat tissue of dbdb mice showed elevated DAG content, especially of DAG species with saturated fatty acid C16:0 and C18:0, while unsaturated fatty acid C16:1 were only changed in obob. Furthermore, DAG signatures of the models specifically correlate to secreted regulated adipokines indicating specific pathways. In conclusion, our data further support the concept that the fat tissue is an endocrine organ that releases bioactive factors corresponding to adipose tissue health status.

1. Introduction

Obesity is a worldwide health burden caused by increased energy intake and sedentary lifestyle. It increases the overall risk for life threatening comorbidities including cardiovascular risk, hypertension, pulmonary obstructive syndrome, dyslipidemia, metabolic syndrome or diabetes, and cancer [1,2,3]. Adipose tissue comprises mature adipocytes, preadipocytes and various invasive immune cells which, in sum, act as secretory organ of bioactive proteins, designated as adipokines. The secreted adipokine patterns in a certain metabolic conditions or stage of obesity are thought to reflect the state of the adipose tissue condition and “health” or its “metabolic flexibility” [4].

A recent investigation described the secretome of visceral adipose tissue from two closely related, well-characterized and metabolically healthy mouse strains, i.e., C57BL/Ks (BKS) and C57BL/6 (C57) by combining state-of-the-art protein identification and quantification tools [5]. A reference map comprising about 600 adipokines was generated (http://www.diabesityprot.org). Both commonly used experimental “wild-type” mouse strains differ in their response to metabolic stress. In contrast to animals with C57 genetic background, mice with a BKS genetic background are prone to develop diabetes under such conditions [6,7]. Therefore, mouse models with genetic defects in leptin signaling are obese (obob; loss of function mutation in leptin) or obese and diabetic (dbdb; loss of function mutation in leptin receptor), depending on genetic background.

Obesity is accompanied by disturbed lipid metabolism, elevated levels of free fatty acids (FFA) and triglycerides (TG), either due to over-nutrition or increased hepatic de novo lipid synthesis [8]. Besides adipose tissue, multiple organs, e.g., liver, skeletal muscle, pancreas, or kidneys, are affected. These organs can be the target of ectopic lipid accumulation and obesity-associated insulin resistance. The systemic overflow with increased fluxes of plasma FFA and TGs towards these tissues leads to the ectopic accumulation of lipids and obesity associated insulin resistance, ultimately altering tissue glucose metabolism and affecting blood glucose clearance. Ectopic lipid accumulation is accompanied by the accumulation of bioactive metabolites, e.g., diacylglycerol (DAG), in the various tissues [9]. DAGs are a result of several metabolic fluxes, including triglyceride hydrolysis, triglyceride synthesis, or phosphoinositide hydrolysis. In liver, it has been shown that DAG content is significantly increased in lipid-induced hepatic insulin resistance. DAGs act as second messengers activating members of novel protein kinase C (nPKC) family [10] and the role of DAGs in Golgi/ER vesicular transport is conserved. This raised our hypothesis that an excess of these bioactive metabolites alters the intracellular signaling also in adipose tissue and in consequence, the inter-organ communication in form of the adipocyte secreted protein patterns.

We intended to investigate the specific differences in the secretome of adipocytes in states of obesity and obesity with diabetes. For this, we utilized obese and obese/diabetic mouse models to compare the adipocyte-derived, not fat tissue, secretion pattern of adipokines and return the information to the adipocyte-derived DAG patterns. Our results suggest that DAG-signaling in adipose tissue acts as intermediary between healthy or diabetic state.

2. Results and Discussion

C57BL/KS.Cg-Lepr^db (dbdb) mice on C57BL/KS (BKS) genetic background, a well-accepted mouse model of hyperphagia induced obesity with overt diabetes, and C57BL/6.Cg-Lep^ob (obob) mice on C57BL/6 (C57) background which are protected from diabetes [6,7,11,12,13,14,15] were selected for this study. The clinical characteristics of the investigated mouse models are summarized in Figure 1. The obese mouse model and the obese/diabetic mouse model showed increased body weight with more than 50% fat mass compared to the lean models. Overall, the models also showed specific differences in direct comparison to the genetic background model. Fasting glucose, triglycerides, insulin, and HOMA-IR were each significantly higher in dbdb compared to BKS mice, indicating the definitions of overt diabetes in the dbdb model compared to obob mice. In contrast, in obob mice, insulin and HOMA-IR were significantly higher compared to C57. HOMA-B were strongly elevated while glucose was normal indicating that the β cells are still capable to compensate required insulin levels. Leptin was significantly elevated only in dbdb, as expected from the genetic defect of this model. Similarly, glucagon and glucagon like peptide (GLP)-1 were significantly increased only in dbdb mice. This can be attributed to the diabetic state of these mice, which was confirmed by high HOMA-IR index and thus peripheral insulin resistance. Ghrelin was 4-fold reduced in the obese and 2-fold reduced in obese/diabetic mice, whereas glucose-dependent insulinotropic peptide (GIP), adiponectin or resistin levels did not differ significantly between models.

Figure 1.

Metabolic characterization of C57, BKS, obob and dbdb mice used in the study. Data are expressed as mean ± SD (n = 8 of each phenotype). *** p < 0.001 by Student’s t test.

To further analyze differences between adipose tissue of these mouse models, we determined the content of secreted proteins by mass spectrometry. The overall comparison is given in Supplementary Materials Table S1. According to our experimental design, identified proteins were located outside of intact primary adipocytes. Proteins traffic through the secretory pathway according to their N-terminal signaling sequence to reach their intracellular destination, e.g., an organelle, or to ultimately be secreted. Transport throughout the endomembrane system occurs via the endoplasmic reticulum and the golgi apparatus towards the plasma membrane. Here, the release can occur passive, active channel-mediated, or driven by formation of secretory granules and exosomes. Proteins are targeted according to classical (SP(+)) or non-classical (SP(−)) signal sequences. Proteins without any known signal sequence (NP) are thought to follow e.g., pore-mediated translocation across the plasma membrane, ABC transporter-based secretion or autophagosome/endosome-based secretion [16]. This classification can also help to determine transmembrane proteins [17]. Nevertheless, we cannot completely exclude that some of the proteins might be identified due to apoptosis or autophagy.

The comparisons identified 873 non-redundant proteins. Of these, 216 were assigned to contain a SP(+) signal peptide, 290 were SP(−), thus not carrying a classical signaling peptide and 367 were NP without a signaling domain. The NP proteins contain 164 proteins, which can be assigned to mouse adipocyte exosomes. Nevertheless, we cannot completely role out that some of the identified proteins were derived from autophagy or apoptosis in vitro cultured primary adipocytes after isolation from adipose tissue. The pairwise comparison of the four animal models showed significant alteration (Supplementary Materials Table S2). The comparisons of BKS and dbdb models identified 198 upregulated and 153 downregulated proteins in dbdb (94 SP(+), 118 SP(−), 139 NP). The comparisons of C57 and obob indicated 182 upregulated and 118 downregulated proteins in obob (88 SP(+), 98 SP(−), 114 NP). The individual comparisons indicated in the lean models 108 upregulated and 136 downregulated proteins in BKS (59 SP(+), 87 SP(−), 98 NP), and in the obese and obese/diabetic models 88 upregulated and 112 downregulated proteins in dbdb (61 SP(+), 75 SP(−), 64 NP). Table 1 (see Supplementary Materials Table S2 for complete analyses) summarizes the top 10 up- and down- regulated putative secreted (SP(+), SP(−)) proteins of the comparisons. Furthermore, there were proteins specific for either genotype in the comparisons (Table 2). Additionally, there where 38 solitaire proteins in BKS vs. C57 (8 SP(+), 13 SP(−), 17 NP), 22 in C57 vs. obob (10 SP(+), 6 SP(−), 6 NP), 60 in BKS vs. dbdb (17 SP(+), 17 SP(−), 26 NP) or 10 in dbdb vs. obob (5 SP(+), 2 SP(−), 3 NP).

Table 1.

Top putatively regulated proteins (SP(+), SP(−)) in the comparisons.

Protein Names	Protein ID	Gene Names	BKS_C57	BKS_C57
			Log2 Fold Change	p-Value (Welch Test)
Complement factor D	P03953	Cfd	−5.42	1.49 × 106
cAMP-dependent protein kinase type II-β	P31324	Prkar2b	−2.47	2.73 × 106
Apolipoprotein E	P08226	Apoe	−2.17	7.51 × 104
Receptor expression-enhancing protein 6	Q9JM62	Reep6	−2.14	9.00 × 104
Lipoprotein lipase	P11152	Lpl	−2.03	8.64 × 103
Prolargin	Q9JK53	Prelp	−1.90	8.59 × 104
26S protease regulatory subunit 10B	P62334	Psmc6	−1.89	6.65 × 105
Coiled-coil domain-containing protein 80	Q8R2G6	Ccdc80	−1.89	2.33 × 104
Pentraxin-related protein PTX3	P48759	Ptx3	−1.87	3.35 × 102
Tenascin	Q80YX1	Tnc	−1.86	6.09 × 105
Inositol polyphosphate 1-phosphatase	P49442	Inpp1	1.52	1.24 × 105
Carbonic anhydrase 2	P00920	Ca2	1.57	1.29 × 104
Dolichyl-diphosphooligosaccharide-protein glycosyltransferase 48 kDa subunit	O54734	Ddost	1.71	9.47 × 105
Dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 2	Q9DBG6	Rpn2	1.72	4.48 × 104
Nodal modulator 1	Q6GQT9	Nomo1	1.78	1.74 × 105
60S ribosomal protein L12	P35979	Rpl12	1.86	9.57 × 105
Ketohexokinase	P97328	Khk	1.94	9.40 × 105
Carbonyl reductase 3	Q8K354	Cbr3	1.98	1.06 × 104
Carbonic anhydrase 1	P13634	Ca1	2.38	3.45 × 107
Glutathione S-transferase θ-2	Q61133	Gstt2	3.25	4.80 × 109
Protein Names	Protein IDs	Gene Names	obob_C57	obob_C57
Complement factor D	P03953	Cfd	−9.46	5.52 × 1010
Collagen α-1(XII) chain	Q60847	Col12a1	−6.39	1.12 × 109
Collagen α-5(VI) chain	A6H584	Col6a5	−6.20	1.22 × 109
Angiotensinogen	P11859	Agt	−4.43	2.77 × 107
Fructose-1,6-bisphosphatase 1	Q9QXD6	Fbp1	−3.94	1.45 × 107
Carboxypeptidase Q	Q9WVJ3	Cpq	−3.64	1.15 × 106
α-Amylase 1	P00687	Amy1	−3.64	1.84 × 105
Coiled-coil domain-containing protein 80	Q8R2G6	Ccdc80	−3.47	2.67 × 107
Tissue α-l-fucosidase	Q99LJ1	Fuca1	−3.28	9.84 × 107
Ganglioside GM2 activator	Q60648	Gm2a	−3.26	2.29 × 106
Actin-related protein 2/3 complex subunit 3	Q9JM76	Arpc3	1.94	4.89 × 105
NADH-cytochrome b5 reductase 3	Q9DCN2	Cyb5r3	2.07	5.69 × 105
Epoxide hydrolase 1	Q9D379	Ephx1	2.12	3.21 × 108
Acyl-CoA dehydrogenase 9, mitochondrial	Q8JZN5	Acad9	2.18	5.98 × 108
Serum deprivation-response protein	Q63918	Sdpr	2.22	2.03 × 106
Serpin H1	P19324	Serpinh1	2.25	3.14 × 105
Galectin-3	P16110	Lgals3	2.25	6.50 × 107
GTP:AMP phosphotransferase AK3, mitochondrial	Q9WTP7	Ak3	2.28	2.06 × 107
Apolipoprotein A-IV	P06728	Apoa4	2.42	6.29 × 107
Interleukin-6	P08505	Il6	3.19	6.96 × 107
Protein Names	Protein IDs	Gene Names	dbdb_BKS	dbdb_BKS
Collagen α-5(VI) chain	A6H584	Col6a5	−7.87	3.53 × 1011
Fructose-1,6-bisphosphatase 1	Q9QXD6	Fbp1	−4.10	1.64 × 107
Tissue α-l-fucosidase	Q99LJ1	Fuca1	−3.92	4.32 × 108
Carboxypeptidase Q	Q9WVJ3	Cpq	−3.34	1.63 × 106
Complement factor D	P03953	Cfd	−3.33	2.71 × 104
Ganglioside GM2 activator	Q60648	Gm2a	−3.28	1.18 × 106
Carboxylesterase 1D	Q8VCT4	Ces1d	−2.97	1.49 × 1011
Dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 2	Q9DBG6	Rpn2	−2.87	2.22 × 104
Fructose-1,6-bisphosphatase isozyme 2	P70695	Fbp2	−2.80	5.61 × 105
Angiotensinogen	P11859	Agt	−2.44	2.67 × 104
Phospholipid transfer protein	P55065	Pltp	2.77	1.62 × 102
Serum deprivation-response protein	Q63918	Sdpr	2.86	5.17 × 107
Polymerase I and transcript release factor	O54724	Ptrf	2.93	1.87 × 108
Platelet-activating factor acetylhydrolase	Q60963	Pla2g7	2.97	4.03 × 103
Vimentin	P20152	Vim	3.12	8.70 × 1011
C-C motif chemokine 2	P10148	Ccl2	3.37	1.23 × 102
Prolargin	Q9JK53	Prelp	3.39	1.65 × 106
cAMP-dependent protein kinase type II-β	P31324	Prkar2b	3.70	4.52 × 109
Growth-regulated α protein	P12850	Cxcl1	4.36	1.13 × 106
Interleukin-6	P08505	Il6	5.35	2.09 × 1010
Protein Names	Protein IDs	Gene Names	dbdb_obob	dbdb_obob
Transthyretin	P07309	Ttr	−3.06	9.14 × 108
ATP-citrate synthase	Q91V92	Acly	−2.98	4.13 × 1011
Sarcosine dehydrogenase, mitochondrial	Q99LB7	Sardh	−2.34	3.51 × 106
GTP:AMP phosphotransferase AK3, mitochondrial	Q9WTP7	Ak3	−2.15	1.91 × 107
Serpin H1	P19324	Serpinh1	−2.01	2.23 × 104
Acyl-CoA dehydrogenase 9, mitochondrial	Q8JZN5	Acad9	−1.99	2.95 × 107
Vitamin D-binding protein	P21614	Gc	−1.90	9.29 × 107
3-Hydroxyisobutyrate dehydrogenase, mitochondrial	Q99L13	Hibadh	−1.86	3.24 × 104
Pyruvate dehydrogenase E1 mitochondrial	P35486	Pdha1	−1.81	1.05 × 103
Citrate synthase, mitochondrial	Q9CZU6	Cs	−1.70	1.07 × 105
Calmodulin	P62204	Calm1	1.51	3.03 × 105
Laminin subunit β-2	Q61292	Lamb2	1.55	7.46 × 107
C-X-C motif chemokine 5;GCP-2(1-78);GCP-2(9-78)	P50228	Cxcl5	1.77	4.79 × 104
Prolargin	Q9JK53	Prelp	1.82	3.83 × 103
Pentraxin-related protein PTX3	P48759	Ptx3	1.84	9.82 × 103
Desmin	P31001	Des	2.09	5.46 × 107
Lactotransferrin	P08071	Ltf	2.15	9.64 × 104
Collagen α-1(XII) chain	Q60847	Col12a1	4.45	1.31 × 107

p-Value: post hoc test (ANOVA).

Table 2.

Proteins detected in only one genotype in comparisons.

Protein Names	Protein ID	Gene Names	SP(+)	SP(−)	NP
BKS_C57
Tyrosine-protein phosphatase non-receptor type 6	P29351	Ptpn6			+
NADPH-cytochrome P450 reductase	P37040	Por		+
Vacuolar protein sorting-associated protein 13C	Q8BX70	Vps13c			+
Annexin A11	P97384	Anxa11		+
Epidermal growth factor receptor substrate 15	P42567	Eps15			+
Adenosine deaminase	P03958	Ada			+
Pyruvate dehydrogenase E1 subunit α, mitochondrial	P35486	Pdha1		+
Carnitine O-acetyltransferase	P47934	Crat		+
Histidine triad nucleotide-binding protein 1	P70349	Hint1		+
Heat shock protein 75 kDa, mitochondrial	Q9CQN1	Trap1		+
COP9 signalosome complex subunit 8	Q8VBV7	Cops8			+
Endothelial cell-selective adhesion molecule	Q925F2	Esam	+
Carbonic anhydrase 4	Q64444	Ca4	+
Arsenite methyltransferase	Q91WU5	As3mt			+
Deoxyguanosine kinase, mitochondrial	Q9QX60	Dguok		+
H-2 class I histocompatibility antigen, D-B α chain	P01899	H2-D1	+
Phosphoserine phosphatase	Q99LS3	Psph			+
Mannosyl-oligosaccharide 1,2-α-mannosidase IA	P45700	Man1a1			+
Band 4.1-like protein 2	O70318	Epb41l2			+
Glucosamine-6-phosphate isomerase 1	O88958	Gnpda1			+
β-Galactosidase	P23780	Glb1	+
Semaphorin-7A	Q9QUR8	Sema7a	+
Very long-chain acyl-CoA dehydrogenase, mitochondrial	P50544	Acadvl		+
Inter-α-trypsin inhibitor heavy chain H1	Q61702	Itih1	+
Glutathione S-transferase θ-1	Q64471	Gstt1		+
Catenin β-1	Q02248	Ctnnb1		+
BTB/POZ domain-containing protein KCTD12	Q6WVG3	Kctd12		+
Retinol-binding protein 2	Q08652	Rbp2			+
Small nuclear ribonucleoprotein Sm D3	P62320	Snrpd3		+
Coronin-7	Q9D2V7	Coro7			+
DNA topoisomerase 2-β	Q64511	Top2b			+
Mast cell protease 2	P15119	Mcpt2	+
Retinol-binding protein 1	Q00915	Rbp1			+
Phosphoglycolate phosphatase	Q8CHP8	Pgp			+
β-Hexosaminidase subunit β	P20060	Hexb	+
Putative hydroxypyruvate isomerase	Q8R1F5	Hyi			+
S-adenosylmethionine synthase isoform type-2	Q3THS6	Mat2a		+
Bisphosphoglycerate mutase	P15327	Bpgm			+
obob_C57
Semaphorin-7A	Q9QUR8	Sema7a	+
α-Methylacyl-CoA racemase	O09174	Amacr			+
Leukemia inhibitory factor	P09056	Lif	+
Fructose-1,6-bisphosphatase isozyme 2	P70695	Fbp2		+
Thiosulfate sulfurtransferase	P52196	Tst		+
Eukaryotic translation initiation factor 3 subunit C	Q8R1B4	Eif3c			+
Tyrosine-protein phosphatase non-receptor type 6	P29351	Ptpn6			+
Signal transducer and activator of transcription 1	P42225	Stat1			+
Fibromodulin	P50608	Fmod	+
Growth-regulated α protein	P12850	Cxcl1	+
Plasminogen activator inhibitor 2, macrophage	P12388	Serpinb2		+
1-Acyl-sn-glycerol-3-phosphate acyltransferase β	Q8K3K7	Agpat2	+
BTB/POZ domain-containing protein KCTD12	Q6WVG3	Kctd12		+
C-X-C motif chemokine 3	Q6W5C0	Cxcl3	+
Epidermal growth factor receptor substrate 15	P42567	Eps15			+
26S proteasome non-ATPase regulatory subunit 13	Q9WVJ2	Psmd13		+
Glutathione S-transferase Mu 7	Q80W21	Gstm7			+
Hereditary hemochromatosis protein homolog	P70387	Hfe	+
Very long-chain specific acyl-CoA dehydrogenase, mitochondrial	P50544	Acadvl		+
Cadherin-16	O88338	Cdh16	+
C-X-C motif chemokine 5	P50228	Cxcl5	+
α-1-Antitrypsin 1-5	Q00898	Serpina1e	+
dbdb_BKS
Tyrosine-protein phosphatase non-receptor type 6	P29351	Ptpn6			+
Hereditary hemochromatosis protein homolog	P70387	Hfe	+
Proteasome activator complex subunit 3	P61290	Psme3			+
C-C motif chemokine 9	P51670	Ccl9	+
Tripeptidyl-peptidase 2	Q64514	Tpp2			+
Vacuolar protein sorting-associated protein 13C	Q8BX70	Vps13c			+
Fibromodulin	P50608	Fmod	+
Eukaryotic translation initiation factor 2 subunit 1	Q6ZWX6	Eif2s1			+
Metalloproteinase inhibitor 2	P25785	Timp2	+
Mannosyl-oligosaccharide 1,2-α-mannosidase IA	P45700	Man1a1			+
Importin-9	Q91YE6	Ipo9		+
Carnitine O-acetyltransferase	P47934	Crat		+
Small glutamine-rich tetratricopeptide repeat-containing protein α	Q8BJU0	Sgta		+
T-complex protein 1 subunit ζ	P80317	Cct6a			+
Epidermal growth factor receptor substrate 15	P42567	Eps15			+
Arginase-1	Q61176	Arg1			+
Granulocyte colony-stimulating factor	P09920	Csf3			+
AP-2 complex subunit mu	P84091	Ap2m1			+
Phosphoserine phosphatase	Q99LS3	Psph			+
Histidine triad nucleotide-binding protein 1	P70349	Hint1		+
Plasminogen activator inhibitor 2, macrophage	P12388	Serpinb2		+
Basigin	P18572	Bsg	+
Dynactin subunit 2	Q99KJ8	Dctn2			+
COP9 signalosome complex subunit 8	Q8VBV7	Cops8			+
Coatomer subunit ζ-1	P61924	Copz1			+
6-Pyruvoyl tetrahydrobiopterin synthase	Q9R1Z7	Pts		+
Inter-α-trypsin inhibitor heavy chain H1	Q61702	Itih1	+
Annexin A11	P97384	Anxa11		+
Plastin-1	Q3V0K9	Pls1			+
Eosinophil cationic protein 1	P97426	Ear1	+
Isopentenyl-diphosphate δ-isomerase 1	P58044	Idi1			+
Cadherin-1	P09803	Cdh1	+
4-Hydroxy-2-oxoglutarate aldolase, mitochondrial	Q9DCU9	Hoga1		+
Nucleoside diphosphate-linked moiety X motif 19, mitochondrial	P11930	Nudt19		+
Dolichyl-diphosphooligosaccharide-protein glycosyltransferase 48 kDa subunit	O54734	Ddost	+
Thiosulfate sulfurtransferase	P52196	Tst		+
Cysteine and glycine-rich protein 1	P97315	Csrp1			+
N(G),N(G)-Dimethylarginine dimethylaminohydrolase 1	Q9CWS0	Ddah1		+
Small nuclear ribonucleoprotein Sm D3	P62320	Snrpd3		+
SUMO-conjugating enzyme UBC9	P63280	Ube2i		+
Band 4.1-like protein 2	O70318	Epb41l2			+
Semaphorin-7A	Q9QUR8	Sema7a	+
Adenosine deaminase	P03958	Ada			+
Coronin-7	Q9D2V7	Coro7			+
Nodal modulator 1	Q6GQT9	Nomo1	+
Phosphoglycolate phosphatase	Q8CHP8	Pgp			+
C-X-C motif chemokine 3	Q6W5C0	Cxcl3	+
β-Hexosaminidase subunit β	P20060	Hexb	+
Putative hydrolase RBBP9	O88851	Rbbp9			+
Ketohexokinase	P97328	Khk		+
Interleukin-1 receptor antagonist protein	P25085	Il1rn	+
α-Methylacyl-CoA racemase	O09174	Amacr			+
Protein kinase C δ-binding protein	Q91VJ2	Prkcdbp		+
Retinol-binding protein 1	Q00915	Rbp1			+
S-adenosylmethionine synthase isoform type-2	Q3THS6	Mat2a		+
Glutathione S-transferase θ-2	Q61133	Gstt2		+
Cadherin-16	O88338	Cdh16	+
Bisphosphoglycerate mutase	P15327	Bpgm			+
C-X-C motif chemokine 5	P50228	Cxcl5	+
α-1-Antitrypsin 1-5	Q00898	Serpina1e	+
dbdb_obob
Deoxyguanosine kinase, mitochondrial	Q9QX60	Dguok		+
Semaphorin-7A	Q9QUR8	Sema7a	+
Glutathione S-transferase Mu 7	Q80W21	Gstm7			+
Phosphoglucomutase-like protein 5	Q8BZF8	Pgm5			+
1-Acyl-sn-glycerol-3-phosphate acyltransferase β	Q8K3K7	Agpat2	+
Regulator of microtubule dynamics protein 3	Q3UJU9	Rmdn3		+
Eosinophil cationic protein 1	P97426	Ear1	+
H-2 class I histocompatibility antigen, D-B α chain	P01899	H2-D1	+
Interleukin-1 receptor antagonist protein	P25085	Il1rn	+
Putative hydroxypyruvate isomerase	Q8R1F5	Hyi			+

With regard to function, top regulated proteins or solitaire proteins were comprehensibly, among them proteins involved in lipid transport (e.g., ApoE, ApoA4), enzymes (e.g., Lpl, Aad9, Acadvl, Fbp1, Acyl, Ca4, Khk, Pgp), and signaling proteins (e.g., Il6, Sdpr, Gc, Esp15, Rbp-1, Cxcl-5, -3, -9) proteins. Overall, the total adipocyte secreted proteins were able to differentiate lean, obese and the obese/diabetic mouse models (Figure 2). Nevertheless, patterns do not only show overlap according to the lean or obese and obese/diabetic phenotype, but also according to genotype. So, we compared all differentially abundant proteins in the various groups (Figure 3, Supplementary Materials Table S3). With these analyses, we were able to account on any different abundance in conditions depending on genetic background. So, we identified 36 proteins that were solely differential abundant within lean and obese mice (C57 vs. obob), 67 proteins that differed in the comparison of lean to obese/diabetic mice (BKS vs. dbdb), and 42 proteins that differed in the lean background strains. These candidates might be of interest in regard to the phenotype, but still contain the genotype bias.

Figure 2.

Heatmap of all identified adipokines.

Figure 3.

Venn analyses of differential abundant proteins. Proteins with differential abundance in the comparisons C57 vs. BKS, C57 vs. obob, BKS vs. dbdb and dbdb vs. obob (>1.5 fold, one-way ANOVA posthoc p-value < 0.05) were analyzed for overlap to determine genotype specific and genotype independent alterations. Genotype independent differential abundant proteins for “obesity” (n = 106, turquoise), diabetes (n = 36, red) and diabetes despite obesity (n = 19, yellow) are highlighted. p-Value was determined by Welch test. Further information of proteins of all groups are detailed in Supplementary Materials Table S3.

Consistent with the experimental design of adipocyte secretome analyses, all comparisons in databases such as GO, KEGG or IPA annotated to keywords like “extracellular exosome” (FDR = 1.03 × 10¹⁰–7.13 × 10²⁷), or “membrane-bounded vesicle” (FDR = 1.03 × 10¹⁰–7.08 × 10²⁴) with the highest significance. Other keywords were rather unspecific e.g., “amide metabolism” (n = 7, FDR = 2.02 × 10³), or “regulation of protein metabolic process” (n = 13, FDR = 2.02 × 10³) for C57 based comparisons. BKS based comparisons also identified general metabolic pathways like “metabolic process” (n = 46, FDR = 2.86 × 10⁵), “regulation of protein transport” (n = 10, FDR = 7.30 × 10⁴), or “protein metabolic process” (n = 23, FDR = 8.94 × 10⁴).

The analyses further identified proteins, that differed in both obese and obese/diabetic models compared to the lean mice (n = 106, “obesity pattern”). These proteins were related to the obese phenotype independent of genotypes investigated. Another 19 proteins differed between obese and obese/diabetic regardless of genotype, and 36 proteins were specific for diabetes despite obesity as they differ among obese and obese/diabetic (Figure 3, Supplementary Materials Table S3). In pathway analyses of these protein sets, functional annotation only indicated direct secretion or vesicle secretion, as expected from experimental design (Supplementary Materials Table S3). Functional annotation identified key words like “extracellular exosome” (n = 70, FDR = 3.26 × 10⁴¹) or “membrane-bounded vesicle” (n = 75, FDR = 3.36 × 10⁴¹) for the obesity pattern, “extracellular region” (n = 13, FDR = 3.34 × 10⁵) for the diabetes pattern or “extracellular exosome” (n = 24, FDR = 5.89 × 10¹³), and “membrane-bounded vesicle” (n = 25, FDR = 3.96 × 10¹²) for the diabetes despite obesity pattern as best hits. Other key terms of potential interest to metabolic energy balance showed lower significance and limited numbers of assigned proteins e.g., mitochondria (BKS vs. dbdb, n = 17, FDR = 1.76 × 10³; diabetes despite obesity, n = 15, FRD = 6.44 × 10⁷), lipid metabolism (BKS genotype based differences, n = 11, FDR = 1.12 × 10³), lipid catabolic process, lipid- or phospholipid binding (obesity pattern, n = 5, FDR = 3.17 × 10³; n = 12, FDR = 4.68 × 10⁴; n = 9, FDR = 4.68 × 10⁴), or fatty acid degradation (diabetic despite obesity pattern, n = 3, FDR = 1.42 × 10⁴) (Supplementary Materials Table S3).

In general, enrichment analyses were used to facilitate the interpretation of numerous genes or proteins which are the usual outcome of hypothesis generating experimental designs. Thus, the accumulation of candidates with known biological function or interaction, either directly experiment proven or deduced from literature, were monitored in a dataset. Knowledge based pathway annotation or gene enrichment analyses can help to classify “Omics” data, but also bares some restrictions. Next to bioinformatics, the main issue being intrinsic to the experimental setting [18]. We use secreted proteins, so enrichment of secreted proteins or related pathways with highest significance confirmed our experimental approach. The other bias, for sure is the limited number of differential proteins in the specific regulations we focus on, which hampers annotations in a general way.

So, we decided to focus on our initial working hypothesis, i.e., to identify alterations in the adipocyte “communication” with regard to specific physiological states. Adipose tissue controls systemic energy storage and needs to expand in regard to metabolic needs. In healthy conditions, this can be due to hyperplasia, but in metabolically affected adipose tissue as in obesity or diabetes, increased ad libitum storage of fatty acids occurs, even in non-adipose tissues. Increased lipid load in these cells favors accumulation of fatty acids (FA)-derived metabolites such as fatty acyl-CoA or DAG which initiated cellular processes via PKC signaling [19]. As chronical process, the cells get insulin resistance with dysfunctional mitochondria resulting in the development of obesity and diabetes. With regard to adipocyte function, the combination of both should alter the DAG patterns in adipocytes, like observed in other insulin-sensitive tissues as liver, skeletal muscle or even pancreas [9,20,21,22].

Adipose tissue DAGs were determined by mass spectrometry (Figure 4). In contrary to the obese obob, there was an increase in total DAG content in obese/diabetic dbdb mice compared to their backgrounds. Nevertheless, the comparison of the distinct DAGs revealed that in both obese models the DAG species with the fatty acid C18:1 were equally regulated (C18:0_18:1; C18:1_18:1). DAG species with saturated fatty acid C16:0 and C18:0 were only changed in dbdb (C16:0_C18:0; C16:0_C18:1, C18:0_C20:4), whereas the DAG species with unsaturated fatty acid C16:1 were only changed in obob (C16:1_C16:1).

Figure 4.

Diacylglycerol pattern in adipose tissue of C57, BKS, obob and dbdb mice. Data are given in (μmol or nmol/g tissue) and expressed as mean ± SD (n = 5 of each phenotype). * p < 0.05, ** p < 0.01, *** p < 0.001 by Student’s t test.

According to our hypothesis, intensities of a vast amount of adipose secreted proteins correlated to the total adipose derived DAGs (n = 152; 23 SP(+), 53 SP(−), 76 NP). Of these 105 proteins (20 SP(+), 36 SP(−), 49 NP) also showed differential abundance in either comparison of mouse phenotypes. In addition, specific DAG species correlate to adipocyte-secreted proteins (Figure 5, Supplementary Materials Table S4). Here, all secreted correlated proteins can be assigned to metabolic active proteins with the highest prevalence. Of note, DAG species specific for obob shows poly(A) RNA binding proteins as highest annotation (poly(A)RNA binding, n = 30, FDR = 6.71 × 10⁹, RNA binding, n = 31, FDR = 4.10 × 10⁷). This is also observed, if not in highest position with DAG species specific for dbdb (n = 20, FDR = 5.96 × 10⁴) (Figure 5). This is of interest as it focused the differences in obesity and obesity/diabetes to the concept of moonlighting enzymes in metabolic control. Moonlighting proteins or gene sharing defines various functions of a certain gene and are independent to alternative splicing, posttranslational modification or multifunctionality. Especially ancestral and conserved proteins in central metabolic processes show moonlighting functions, e.g., glycolysis or tricarboxylic acid cycle enzymes [23]. This process of metabolic regulation can account for expression levels, differential localization, protein interactions and is mediated by binding of RNA species to a distinct, but not necessary active domain of an enzyme. Best known examples of metabolic enzymes regulated by binding of RNA species are GOT2, FASN, or GAPDH [24]. We identified Adk, Aldh6A1, Aldoa, Eno, Lta4h and Hsd17B10 to be secreted from adipocytes and to correlate to DAG species C16:1_C16:1 or DAG species C18:1_C20:4. All of these proteins were previously identified in RNA interaction studies and implicated to have moonlighting functions [24]. For example, the metabolic enzyme fructose-1,6-bisphosphate aldolase (Aldoa) which catalyzes the reversible cleavage of fructose-1,6-bisphosphate to glyceraldehyde 3-phosphate and dihydroxyacetone phosphate in glycolysis and gluconeogenesis pathways, has been shown to regulate insulin-dependent glucose transporter GLUT4 in mouse adipocyte cell lines 3T3-L1 [25]. Furthermore, enolase (Eno) catalyzes the dehydrolyzation of 2-phospho-d-glycerate to phosphoenolpyruvate in glycolysis, but has also been shown to bind plasminogen and to mediate its cell surface peptidase activity [26,27]. So, one could speculate that alterations in such regulatory processes might interfere with the subcellular localization and trafficking of proteins, depending on which functions is favored, and are also an essential target in the overall picture of metabolic regulation.

Figure 5.

Functional network of adipokines correlated to DAG species. Adipokines with significant correlation to total DAG content or indicated DAG species were used for over representation analyses. (A) Adipokines correlated to total DAGs, DAG C16:0_C18:0 DAG C16:0_C18:1 species are enriched in metabolic pathways (highlighted in red); (B) Adipokines correlating to DAG C16:1_C16:1 and DAG C18:0_C20:4 are enriched in metabolic pathways or poly(A) RNA binding (highlighted in red). Enrichment FDR is given. Graphs show results of interaction analyses (https://string-db.org).

3. Materials and Methods

3.1. Mouse Models

C57BL/6 (C57), C57BL/KS (BKS), C57BL/KS.Cg-^Leprdb (dbdb) and C57BL/KS.Cg-^Lepob (obob) mice were bred and maintained in a regular 12 h light/dark cycle under constant temperature, humidity (22 ± 1 °C, 50 ± 5% humidity), with free access to water and standard laboratory food (Ssniff, Soest, Germany). Mice were sacrificed by CO₂ asphyxiation at 17 weeks of age. Mice (n = 5 each genotype, males) were sacrificed at 7 a.m. after 6 h food restriction and visceral adipose tissue was removed [5,28]. Adipose tissue was either processed directly to isolate adipocytes or was snap frozen in liquid nitrogen and stored at −80 °C for lipid analyses. Serum was collected by left ventricular punctuation. The Animal Care Committee of the University Duesseldorf approved all animal care and procedures (Approval#50.05-240-35/06, August 2006).

3.2. Metabolic Characterization of the Mouse Models

Blood parameters were measured at 17 weeks of age (n = 8). Blood glucose was measured with Freestyle™ and leptin, insulin as well as glucagon levels were determines using quantitative Bio-Plex Pro Mouse Diabetes 8-Plex Assay (Bio-Rad, Munich, Germany) according to the manufacturer’s instructions. Data were collected and analyzed using a BioPlex 200 instrument equipped with BioManager analysis software (Bio-Rad). To determine insulin resistance and pancreatic β cell function the surrogate parameters HOMA-IR (homeostatic model assessment of insulin resistance) and HOMA-β (homeostatic model assessment of β cell function) were used. Body composition was measured using nuclear magnetic resonance (n = 9–23/per genotype, Whole Body Composition Analyzer; Echo MRI, Houston, TX, USA).

3.3. Secretome Profiling by Liquid Chromatography (LC)-Electrospray Ionization (ESI)-MS/MS and Data Analyses

Murine mature adipocytes from visceral fat isolated by collagenase digestion were cultured for 24 h (DMEM/F12 without FCS supplementation (Thermofisher Scientific, Darmstadt, Germany)), and secretomes were harvested as described [29]. Data of all mouse models were acquired in parallel as described in detail [5]. In brief, secretome samples were tryptically digested and analyzed using LC-ESI mass spectrometry using an Ultimate 3000 Rapid Separation liquid chromatography system (Dionex/Thermo Scientific, Idstein, Germany). Afterwards, mass spectrometry was carried out (Orbitrap Elite high resolution instrument, Thermo Scientific, Bremen, Germany). For the comparison of mouse strains, log2 PSM values were used. MaxQuant (version 1.4.1.2, Max Planck Institute for Biochemistry, Munich, Germany) was used for protein and peptide identification and quantification with default parameters if not otherwise stated. Searches were carried out using 16.671 mouse sequences from the Swiss-Prot part of UniProtKB (release 9.7.2014) applying the following parameters: mass tolerance precursor (Orbitrap): mass tolerance precursor: 20 ppm firt search and 4.5 ppm after recalibration (Orbitrap), mass tolerance fragment spectra: 0.4 Da (linear ion trap), trypsin specific cleavage (maximum of one missed cleavage site), fixed modification: carbamidomethyl, variable modifications: methionine oxidation and N-terminal acetylation. For peptide and protein acceptance, the false discovery rate (FDR) was set to 1%, only proteins with at least two identified peptides were used for protein assembly. Quantification was carried out using the label-free quantification algorithm implemented in MaxQuant using a minimal ratio count of 2 and the “match between runs” option enabled.

3.4. Lipid Analysis of Adipose Tissue

Extraction, purification and analysis of DAGs from frozen adipose tissue samples was conducted using an LC-MS/MS approach [21]. In brief, 20 mg of adipose tissue was homogenized in 20 mM Tris/HCL, 1 mM EDTA 0.25 mM EGTA, pH 7.4, using a tight-fitting glass douncer (Wheaton Lab Supplies, Birmingham, UK). Internal standard (d517:0-DAG; Avanti Polar Lipids, Alabaster, AL, USA) was added and lipids were extracted according to Folch et al., [30]. Diacylglycerols were separated from triglycerides using solid phase extraction (Sep Pak Diol Cartridegs; Waters, Milford, MA, USA). The resulting lipid phase was dried under a gentle flow of nitrogen and re-suspended in methanol. Diacylglycerols were separated using a Phenomenex Luna Omega column (1.6 µm 100 A; Phenomenex, Torrance, CA, USA) on an Infinity 1290 HPLC system (Agilent Technologies, Waldbronn, Germany) and analyzed by multiple reaction monitoring on a triplequadrupole mass spectrometer (Agilent 6495; Agilent Technologies), operated in positive ion mode.

3.5. Prediction and Annotation of Secretory Proteins

Secretory protein prediction and functional annotation was done using different independent methods. First, protein information of all identified proteins was extracted from the Swiss-Prot database (http://www.uniprot.org/). To assess secretory properties, protein sequences were analysed by SignalP 4.1 [17]; (http://www.cbs.dtu.dk/services/SignalP/), SecretomeP 2.0. [31]; (http://www.cbs.dtu.dk/services/SecretomeP/) and Exocarta (http://www.exocarta.org/) [32]. Literature screening was performed with NCBI/Pubmed (http://www.ncbi.nlm.nih.gov/pubmed) and protein-protein interaction analyses with https://string-db.org.

3.6. Web-Based Functional Annotation

The identification types were uniprot swissprot accession or gene ID, respectively. Information driven analyses including functional annotation was performed with String v10.5 (https://string-db.org) [33], David Bioinformatics Resources 6.8 (https://david.ncifcrf.gov) [34,35], and IPA (Ingenuity^TM, Qiagen, Hilden, Germany). For differential protein sets expression analyses, expression fold change (1.5×) and expression differences (p-value < 0.05) were analyzed following the core analyses modules. Differential abundant proteins (1.5× fold difference, p-value < 0.05 (one-way ANOVA, post hoc) were analyzed separately for C57 vs. BKS, C57 vs. obob, BKS vs. dbdb and dbdb vs. obob.

3.7. Statistical Methods

Statistical analyses were performed in GraphPad Prism 5.0 (GraphPad Software, Inc., San Diego, CA, USA) and SPSS 22 (IBM, Armonk, NY, USA). Data are given as mean ± standard deviation (SD) and data were directly compared with an unpaired Student’s t test. Figure legends indicate the statistical tests applied for each experiment in detail.

4. Conclusions

We showed that genetic mouse models, which are susceptible to obesity or obesity/diabetes according to their genetic background genotype show phenotype-specific differences in primary adipocyte adipokinome in quantitative proteome analyses. Knowledge based annotation of identified differentially regulated adipokinome did not add much further information. According to the predictive value of DAG-species for lipid metabolism and insulin resistance in liver and skeletal muscle [9], we determined DAG levels also as classifying parameter for lipid metabolism and insulin resistance in adipose tissue. Adipose tissue DAG patterns differ in obesity and obesity/diabetes especially of DAG species with saturated fatty acid C16:0 and C18:0 in diabetes and unsaturated fatty acid C16:1 in obesity or unsaturated fatty acid C20:4 in obesity/diabetes. Our study provides evidence that the analyses of one “Omics”-like secretome might not be sufficient to get insight in a complex phenotypical problem.

Here, the combination of specific DAG species and the holistic pattern of primary adipocyte-secreted proteins helped to get hints to an interacting mechanism and to unravel RNA-binding proteins involved in metabolic control differing in obesity and obesity/diabetes.

Supplementary Materials

Supplementary materials can be found at www.mdpi.com/1422-0067/18/9/1928/s1.

Author Contributions

Simon Goeddeke, Gereon Poschmann, Daniel F. Markgraf, Christina Preuss, Waltraud Passlack, Ulrike Nitzgen, Sylvia Jacob, Sonja Hartwig, researched the data. Birgit Knebel and Jorg Kotzka designed experiments, analyzed data and wrote the manuscript. Hans-Dieter Dicken curates the database (http://www.diabesityprot.org). Kai Stühler and Stefan Lehr contributed to design of the secretome analyses and supervised the adipokine identification. All authors have confirmed the final version of the manuscript. Jorg Kotzka is the guarantor of the work.

Conflicts of Interest

The authors declare no conflict of interest.