SUCNR1-mediated chemotaxis of macrophages aggravates obesity-induced inflammation and diabetes

Aims/hypothesis Obesity induces macrophages to drive inflammation in adipose tissue, a crucial step towards the development of type 2 diabetes. The tricarboxylic acid (TCA) cycle intermediate succinate is released from cells under metabolic stress and has recently emerged as a metabolic signal induced by proinflammatory stimuli. We therefore investigated whether succinate receptor 1 (SUCNR1) could play a role in the development of adipose tissue inflammation and type 2 diabetes. Methods Succinate levels were determined in human plasma samples from individuals with type 2 diabetes and non-diabetic participants. Succinate release from adipose tissue explants was studied. Sucnr1 −/− and wild-type (WT) littermate mice were fed a high-fat diet (HFD) or low-fat diet (LFD) for 16 weeks. Serum metabolic variables, adipose tissue inflammation, macrophage migration and glucose tolerance were determined. Results We show that hypoxia and hyperglycaemia independently drive the release of succinate from mouse adipose tissue (17-fold and up to 18-fold, respectively) and that plasma levels of succinate were higher in participants with type 2 diabetes compared with non-diabetic individuals (+53%; p < 0.01). Sucnr1 −/− mice had significantly reduced numbers of macrophages (0.56 ± 0.07 vs 0.92 ± 0.15 F4/80 cells/adipocytes, p < 0.05) and crown-like structures (0.06 ± 0.02 vs 0.14 ± 0.02, CLS/adipocytes p < 0.01) in adipose tissue and significantly improved glucose tolerance (p < 0.001) compared with WT mice fed an HFD, despite similarly increased body weights. Consistently, macrophages from Sucnr1 −/− mice showed reduced chemotaxis towards medium collected from apoptotic and hypoxic adipocytes (−59%; p < 0.05). Conclusions/interpretation Our results reveal that activation of SUCNR1 in macrophages is important for both infiltration and inflammation of adipose tissue in obesity, and suggest that SUCNR1 is a promising therapeutic target in obesity-induced type 2 diabetes. Data availability The dataset generated and analysed during the current study is available in GEO with the accession number GSE64104, www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64104. Electronic supplementary material The online version of this article (doi:10.1007/s00125-017-4261-z) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

age-matched chow fed mice. 0.2 g of tissue was directly brought into culture in 1 ml DMEM containing 1 mmol/l glucose for 24 hours.

Macrophage and adipose tissue co-culture
Bone marrow-derived macrophages (BMDMs) were obtained from C57Bl/6 mice and differentiated for 3 days in DMEM with 10% serum, supplemented with 5% L929 conditioned medium. BMDMs were subsequently plated in 24-wells plates and exposed to a transwell chamber (0.4 µm Corning) containing 50 mg adipose tissue explants (BMDMs with adipose tissue) or an empty transwell chamber (BMDMs without adipose tissue) for another 3 days.
BMDMs were subsequently scraped for isolation of RNA and RT-qPCR analysis.

Morphologic analysis of adipose tissue and quantification of macrophage number
Hematoxylin and eosin (H&E) staining of sections followed standard protocols on 5 µm-thick sections of white adipose tissue. Morphometric analysis of individual fat cells was done using digital image analysis software. For this, microscopic images were digitized in 24 bit RGB (specimen level pixel size 1.28 × 1.28 um 2 ) and recognition of fat cells was performed by applying a region-growing algorithm on manually indicated seed points. To quantify macrophage numbers in epididymal white adipose tissue, sections were immunohistochemically stained using a rat anti-mouse F4/80 antibody (Serotec, Düsseldorf, Germany) followed by a biotinylated rabbit anti-rat antibody and an avidin-biotin-complex (ABC) coupled to peroxidase (Vector Labs, Brunschwig Chemie, Amsterdam, the Netherlands). Visualization of the complex was done using 3,3'-diaminobenzidene for 5 min.
With negative controls, primary antibodies were omitted. Macrophages and crown-like structures were counted with a microscope at a magnification of 200x, 10 images per tissue per mouse and expressed per number of adipocytes that were counted in the same image (N=3).

Succinate measurements
Reagent solutions were dissolved according to the kit's protocol. Standard curve and 1:5 diluted plasma samples (100 μl) were measured in duplicate in 96 wells plates. A standard curve was generated with the following molarities of succinate: 0-10-20-40-80-120-160-200 μM. Samples were added to a non-enzyme control plate and an enzymatic assay plate. A reaction mix of 33.6 μl per well (10 μl of solution 1, 2 and 3, 1 μl of solution 4, and 2.6 μl of 20 mmol/l Tris pH 7.4) was prepared and added to the sample. Next, 20 μl of a mixture of 5 μl solution 1, 14 μl mQ water and either 1 μl solution 5 or 1 μl mQ was added per well. Abs340 was read every 5 minutes in a plate reader (Biorad Benchmark Plus) until values stabilized.
Data was processed by subtracting the abs340 of the enzyme-treated samples from the abs340 of the negative control plate and succinate concentrations were calculated by applying the Δabs340 value in the standard curve.

1H NMR spectroscopy
One-dimensional 1H NMR spectroscopy was performed to investigate the concentration of succinate in the medium from samples. For this, the medium was filtered through a 10 kDa filter, the volume adjusted to 700 μl with water, and pH adjusted to 2.5 using 3M HCl, after which 20 μl of 20.2 mmol/l sodium 3-trimethylsilyl-2,2,3,3-tetradeuteropropionate (TSP; Aldrich) in D2O (Catalogue No. 435 767; Aldrich) was added. The samples were then placed in 5-mm NMR tubes and 1H NMR spectra were obtained using a Bruker 500 MHz spectrometer (pulse angle, 90°; delay time, 4 s; number of scans, 256). Water resonance was suppressed by gated irradiation centered on the water frequency. The spectral width in the F1 and F2 domains were 5500 Hz. A total of 2K data points were collected in t2, 256 t1 increments with 32 transient per increment were used. The relaxation delay was set to 2 seconds. Before the Fourier transformation, a sine-bell function was applied in both time domains. During the relaxation delay, the water resonance was presaturated.
The free-induction decays measured for these samples were processed using Topspin software (Bruker, Billerica, Massachusetts, USA). Fourier transformation was applied on the free-induction decay of the samples and the resulting spectra were phase and baseline corrected. The chemical shifts in the spectra were referenced to the internal standard, TSP.
Assignment of peak positions for compound identification was performed by comparing the peak positions in the spectra of the metabolites with the reference spectral database of model compounds at pH, 2.5 using Amix version 3.9.14 (Bruker Bio-spin). Quantification of identified compounds was performed by manual integration of chosen peak(s) for a specific metabolite.

In vitro cytokine production
Peritoneal macrophages were isolated from mice by injecting 5 ml of ice-cold sterile PBS (pH 7.4) into the peritoneal cavity. After centrifugation and washing, cells were resuspended in RPMI 1640 culture medium containing 1 mmol/l pyruvate, 2 mmol/l L-glutamine, and 50 mg/liter gentamicin. Cells were counted using a Z1 Coulter particle counter (Beckman Coulter; Woerden, The Netherlands) and cultured in 96-well round-bottom microtiter plates (Costar, Corning, The Netherlands) at 1 × 10 5 cells/well in a final volume of 200 μl. After 24 h of incubation with LPS (10 ng/mL) at 37°C and 5% CO 2 , the plates were centrifuged at 1,400 × g for 8 min, and the supernatants were collected and stored at −80°C until cytokine assays were performed.
The concentrations of mouse tumor necrosis factor alpha (TNF-α) and IL-1β were determined by specific radioimmunoassay (RIA). Interleukin-6 (IL-6) and keratinocyt-derived chemokine (KC) were measured using mouse IL-6 and KC ELISA kits (R&D Systems, MN, USA) according to the instructions of the manufacturer.

Transwell chemotaxis assay
(BMDMs were obtained from 3-4 month old mice and differentiated for 7 days in DMEM with 10% (vol/vol) serum, supplemented with 30% (vol/vol) L929 conditioned medium. BMDM migration assays were performed using 8.0 µm pore-size 24-well Transwell chambers (BD Biosciences). BMDMs (2 x 10^5 cells/well) were placed in the upper chamber and medium containing the chemoattractant was added in the lower chamber, all diluted in DMEM supplemented with 0.1% (wt/vol) BSA. Chemoattractants used were 1) various concentrations of succinate 2) various concentrations of medium derived from hypoxic and apoptotic 3T3-L1 adipocytes and 3) 10% zymosan activated serum (ZAS) (wt/vol) as a positive control. For the hypoxic/apoptotic 3T3-L1 cell medium, mouse 3T3-L1 cells were cultured and differentiated towards adipocytes as described [1] and subsequently incubated for 24h at 1% O 2 or exposure to 200 mJ UV radiation to induce hypoxia or apoptosis, respectively. For the chemoattractant solution, supernatant of the hypoxic and apoptotic adipocytes was mixed in a 1:3 vol/vol ratio. To generate the ZAS, 5% zymosan (wt/vol) was incubated in serum for 30 min at 37°C, centrifuged and 10x diluted in DMEM supplemented with 0.1% (wt/vol) BSA. After 8 hours of migration, membranes were fixed with 4% formalin and stained with hematoxylin. Non-migrating cells were removed from the upper surface using a cotton swab. Membranes were mounted on microscope slides and the number of migrated cells on the lower surface was determined in 15-20 representative fields (400x magnification). Four to six separate membranes were analyzed for each condition.

RNA isolation and RT-qPCR analysis.
Total RNA was isolated from adipose tissue using TRIzol (Invitrogen, Carlsbad, CA), according to manufacturer's instructions. RNA was reverse-transcribed (iScript cDNA Synthesis Kit, Bio-Rad Laboratories). The following qPCR was performed using power SYBR green master mix (Applied Biosystems, Foster City, CA) using the StepOne Real-Time PCR System (Applied Biosystems, Foster City, CA). For mice samples, we used 36B4 and cyclophillin as housekeeping genes to normalize the mRNA quantities. For human samples, we used beta-2-microglobulin (B2M) as a housekeeping gene. Specific primer sequences used are listed in Supplemantary Table S2.

Microarray analysis.
Epididymal adipose tissue samples from low fat diet (LFD)-fed WT and Sucnr1 −/− animals (n=4 per genotype) were subjected to genome-wide expression profiling. In brief, total RNA was isolated from adipose tissue samples and integrity was confirmed using a Bio-analyzer (Agilent). Subsequently, RNA was hybridized on Affymetrix Mouse Gene 1.1 ST arrays (Affymetrix, Santa Clara, CA). Packages from the Bioconductor project [2], integrated in an online pipeline [3], were used for quality control and statistical analysis of the array data.
Probe sets were first redefined utilizing current genome annotation information [4]. Probes were reorganized based on the gene definitions available in the GRCm38.p2 mouse genome assembly released by the Genome Reference Consortium (remapped CDF v18). Normalized gene expression estimates were obtained using the robust multi-array analysis (RMA) preprocessing algorithm available in the library 'AffyPLM' using default settings [5]. The dataset was filtered to only include probe sets that were active (i.e. expressed) in at least 4 samples using the universal expression code (UPC) approach (UPC score > 0.50) [6]. This resulted in the inclusion of 8,348 (39%) of the 21,266 probe sets. Differentially expressed probe sets were identified by using linear models and an intensity-based moderated t-statistic [7,8].
Probe sets that satisfied the criterion of P<0.05 were considered to be significantly regulated.
Array data have been submitted to the Gene Expression Omnibus under accession number GSE64104. Detailed information on microarray processing and data analysis is available upon request.

Biological interpretation of array data
Changes in gene expression were related to biologically meaningful changes using gene set enrichment analysis (GSEA) [9]. It is well accepted that GSEA has multiple advantages over analyses performed on the level of individual genes [9][10][11]. Gene sets were retrieved from the expert-curated KEGG, Biocarta, Reactome and WikiPathways pathway databases. Only gene sets consisting of more than 15 and fewer than 500 genes were taken into account.
Genes were ranked on their t-value that was calculated by the moderated t-test. Statistical significance of GSEA results was determined using 1,000 permutations. The Enrichment Map plugin for Cytoscape was used for visualization and interpretation of the GSEA results [12].