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GIP regulates inflammation and body weight by restraining myeloid-cell-derived S100A8/A9

Nature Metabolismvolume 1pages5869 (2019) | Download Citation


Enteroendocrine cells relay energy-derived signals to immune cells to signal states of nutrient abundance and control immunometabolism. Emerging data suggest that the gut-derived nutrient-induced incretin glucose-dependent insulinotropic polypeptide (GIP) operates at the interface of metabolism and inflammation. Here we show that high-fat diet (HFD)-fed mice with immune cell-targeted GIP receptor (GIPR) deficiency exhibit greater weight gain, insulin resistance, hepatic steatosis and significant myelopoiesis concomitantly with impaired energy expenditure and inguinal white adipose tissue (WAT) beiging. Expression of the S100 calcium-binding protein S100A8 was increased in the WAT of mice with immune cell-targeted GIPR deficiency and co-deletion of GIPR and the heterodimer S100A8/A9 in immune cells ameliorated the aggravated metabolic and inflammatory phenotype following a HFD. Specific GIPR deletion in myeloid cells identified this lineage as the target of GIP effects. Furthermore, GIP directly downregulated S100A8 expression in adipose tissue macrophages. Collectively, our results identify a myeloid–GIPR–S100A8/A9 signalling axis coupling nutrient signals to the control of inflammation and adaptive thermogenesis.


The gastrointestinal tract is the largest endocrine organ in the body harbouring enteroendocrine cells (EECs) along its entirety. On sensing ingested nutrients and macromolecules, EECs secrete dozens of hormones, which act in an autocrine, paracrine and endocrine manner to regulate glucose homeostasis and energy balance1. Extensive evidence supports the physiological importance and therapeutic potential of different gut-derived peptides (GDPs) in the control of body weight2. Classic concepts highlight roles for GDPs in conveying the status of nutrient availability to the brain, initiating changes in eating behaviour and energy expenditure to maintain energy balance1. EEC hormones, such as glucagon-like peptide-1 (GLP-1), illustrate the therapeutic potential of understanding gut hormone interorgan cross-talk2. Although less well studied, multiple immune cell subsets express an extended repertoire of receptors for GDPs, suggesting incompletely understood immunoregulatory roles for gut hormones3. Given the growing comprehension of how innate and adaptive immune cell responses in adipose tissue regulate adipocyte function and metabolic homeostasis in health and disease4, delineating the cellular and signalling networks that participate in linking GDPs to the immune and metabolic systems may have immediate translational relevance.

Evidence from rodent and in vitro models has indicated that the gut-derived incretin hormone GIP has anabolic effects by facilitating fat storage in adipocytes5,6,7. Later studies have suggested that GIP represents an important EEC peptide signalling state of nutrient abundance at the immunometabolism interface. Notably, in the setting of diet-induced obesity, augmentation of GIPR signalling by either genetically modified GIP overexpression8 or by continuous administration of a degradation-resistant GIP analogue ([d-Ala2]GIP)9, attenuates adipose tissue infiltration of pro-inflammatory innate and adaptive immune cells. Given the multisystem properties of GIP and the diverse repertoire of cells expressing its receptor10, the cells and mechanisms mediating GIP–immune cell interactions require careful elucidation.

Here we address the role of GIP in bone marrow (BM) myeloid cells. Our study reveals an original facet of GIPR-dependent regulation of energy homeostasis in obesity via myeloid cells and altered expression of the S100 calcium-binding protein heterodimer S100A8/A9.


Aggravated diet-induced obesity and insulin resistance in Gipr −/− BM mice

GIPR is expressed in extra-pancreatic tissues, including hypothalamus and adipose tissues, thus controlling systemic metabolism beyond insulin secretion11. GIPR is also expressed on immune cells, such as monocytes12,13 and macrophages13,14,15, implying unexplored immunoregulatory properties for GIP. To investigate the physiological importance of GIPR in immune cells, we utilized a BM chimerism approach. The BM of lethally irradiated wild-type (WT) recipient mice was differentially reconstituted by transplantation of WT or Gipr −/− BM. Following immune cell reconstitution, mice were subjected to 14 weeks of a high-fat diet (HFD). After 6 weeks of HFD feeding, Gipr −/− BM mice displayed significantly increased weight gain compared with WT BM mice (Fig. 1a and Supplementary Fig. 1a) with similar average calorie consumption (Fig. 1b). Body weight increase was maintained in the Gipr −/− BM mice also after 18 weeks of a HFD (Supplementary Fig. 1d). There was no difference in body weight gain between these groups fed a regular chow diet (Supplementary Fig. 1b,c). Gipr −/− BM mice also exhibited higher epididymal WAT (epiWAT) mass, a reduced percentage of lean mass and an increased percentage of fat mass, greater liver steatosis, and adipocyte hypertrophy in both epiWAT and inguinal WAT (ingWAT) (Fig. 1c–g and Supplementary Fig. 1e). Expression of metabolic genes in liver and epiWAT was not significantly different between the groups (Supplementary Fig. 1f,g). Normal xylose absorption and comparable fat content in the faeces under regular chow and HFD conditions ruled out the possibility that the change in weight gain was secondary to differences in intestinal nutrient absorption (Supplementary Fig. 2a–c). Gipr −/− BM mice also exhibited impaired insulin and glucose tolerance (Fig. 1h,i and Supplementary Fig. 1i) and had significantly higher serum insulin concentrations with comparable glucose levels (Fig. 1j), consistent with insulin resistance. Moreover, serum free fatty acids (FFA) were reduced to a lesser extent by insulin in Gipr −/− BM mice (Fig. 1k), and circulating serum GIP levels were similar in WT versus Gipr −/− BM mice (Fig. 1l). Consistent with their defective insulin action, Gipr −/− BM mice exhibited reduced insulin-induced phosphorylation of protein kinase B (Akt) in both liver and epiWAT (Fig. 1m,n). These results emphasize that the selective impairment of GIPR signalling in BM-derived immune cells increases weight gain and deteriorates glucose homeostasis.

Fig. 1: Gipr −/− BM mice display increased weight gain and insulin resistance.
Fig. 1

Mice were reconstituted with WT BM (blue open circles) or Gipr −/− BM (red triangles) for 6 weeks and then fed a HFD for 14 weeks. a, Weekly measurements of body weight (g) (n = 10, time point week 0–14 P = 0.003, P = 0.007, P = 0.006, P = 0.009, P = 0.021, P = 0.014, P = 0.013, P = 0.005, P = 0.006). b, Average food consumption during a period of 48 h (n = 11). c, Average epiWAT mass (n = 6, P = 0.001). d, Computed tomography analysis of percentage of lean mass (left, n = 5, P = 0.01) and fat mass (right, n = 8, P = 0.014). e, Liver H&E paraffin sections. Original magnification, ×10. Scale bars, 200 µm. f, EpiWAT H&E paraffin. Original magnification, ×20. Scale bars, 100 µm. g, Average adipocyte area quantitated from 30 images taken from 3 distinct mice for each group (P = 0.0001). h, Insulin tolerance test (ITT) showing before and following insulin injection (0.75 U per kg body weight) (for WT BM n = 6, for Gipr −/− BM n = 5, 30 min P = 0.034, 1 h P = 0.014). i, Glucose tolerance test (GTT) assessed by injecting 2 mg per g body weight glucose solution (n = 4, P = 0.029). j, ELISA measurement of fasting serum insulin levels (left, n = 9, P = 0.032) and glucose levels (right, n = 9). k, Serum ΔFFA measurement by ELISA before and after insulin administration (n = 9). l, ELISA measurements of serum GIP levels (n = 9). m,n, Top: representative immunoblots of liver (m) and epiWAT (n) of total Akt and phosphorylated Akt (p-Akt). Mice were injected with insulin 30 min before being killed. Bottom: densitometry calculation showing the ratio of p-Akt/Akt in liver (mn = 5, P = 0.018) and epiWAT (nn = 3, P = 0.002). Data were analysed by unpaired, two-tailed t-test, comparing between WT BM and Gipr −/− BM mice. Data are presented as mean ± s.e.m with significance *P < 0.05, **P < 0.01 and ***P < 0.001. Data in ac, eg and lm are representative of three independent experiments, and data in hl are from one experiment.

Increased myelopoiesis and S100A8 production in Gipr −/− BM mice

A large body of evidence has linked obesity with adipose tissue inflammation and insulin resistance4. We detected increased expression of Ly6Chi monocyte and neutrophil recruitment chemokines Ccl2 and Cxcl1, respectively, in the epiWAT of Gipr −/− BM mice (Fig. 2a). Moreover, epiWAT expression of the gene encoding the pro-inflammatory alarmin S100A8 (S100a8), representing one subunit of the heterodimer S100A8/A9, was increased, while the expression of other inflammatory cytokines was not affected (Fig. 2a). The same pattern of elevated Ccl2, Cxcl1 and S100a8 gene expression was also detected in the livers of Gipr −/− BM mice (Supplementary Fig. 3). Immunoblots demonstrated increased expression of S100A8 in epiWAT of Gipr / BM mice (Fig. 2b), though the serum level of the S100A8/A9 heterodimer was not different (Fig. 2c). Increased S100A8 expression in mice with diabetes or HFD-induced obesity has been mechanistically linked to BM myelopoiesis16,17, and S100A8-driven recruitment of myeloid cells in HFD-fed mice precedes the development of metabolic symptoms18. Indeed, we observed increased representation of CD11b+ myeloid cells over CD11b lymphocytes in the blood and epiWAT of Gipr / BM mice (Fig. 2d,e). In the blood in particular, there was significant neutrophilia (Fig. 2d), and this was further mirrored in the epiWAT with increased representation of both neutrophils and Ly6Chi monocytes (Fig. 2e). Overall myelopoiesis was further supported by a blood count showing elevation in the levels of platelets, erythrocytes, haemoglobin and haematocrit in Gipr / BM mice (Supplementary Fig. 4). Collectively, these results show that impaired GIPR signalling in BM-derived immune cells results in significant myelopoiesis, which may be driven by S100A8/A9.

Fig. 2: epiWAT of Gipr −/− BM mice shows increased expression of S100A8 and myelopoiesis.
Fig. 2

Mice were reconstituted with WT BM (blue open circles) or Gipr −/− BM (red triangles) for 6 weeks and then fed a HFD for 14 weeks. a, Expression of inflammatory mediators in epiWAT was determined by qRT–PCR analysis with normalization to Rplp0 (n = 13, for S100a8 P = 0.005, for Ccl2 P = 0.006). b, Representative immunoblots of epiWAT demonstrating protein expression and quantitation of S100A8. P97 was used as loading control (n = 3 for WT BM, n = 6 for Gipr −/− BM). c, S100A8/A9 levels in the serum were measured by ELISA (n = 10). d,e, Left: representative flow cytometry images depicting the gating strategy for circulating (d) or epiWAT (e) myeloid immune cells. Middle right: graphs showing the ratio of CD11b+ phagocytes versus CD11b lymphocytes in the blood (n = 6, P = 0.012) (d) and epiWAT (n = 6, P = 0.001) (e), as an indication for myelopoiesis. Right: numbers of indicated myeloid cell populations per 50 μl of blood (n = 6, P < 0.00001) (d) or epiWAT mass g (n = 6, for neutrophils P = 0.024, for Ly6Chi monocytes P = 0.014) (e). In the blood, CD45+CD11b+CD115+Ly6G monocytes were dissected into Ly6Chi and Ly6Clo subsets, and neutrophils were defined as CD45+CD11b+CD115Ly6Ghi cells. In the epiWAT and ingWAT, adipose tissue macrophages (ATMs) were defined as CD45+CD11b+MHCII+CD64+F4/80+ cells; neutrophils as CD45+CD11b+MHCIICD64–/loF4/80–/loLy6Ghi cells and Ly6Chi monocytes as CD45+CD11b+MHCIICD64loF4/80loLy6GLy6Chi cells. Data were analysed by an unpaired, two-tailed t-test, comparing between WT BM and Gipr −/− BM groups. Data are presented as mean ± s.e.m with significance *P < 0.05, **P < 0.01 and  ***P < 0.001. Data in a and ce are representative of two independent experiments, and data in b are from one experiment.

Attenuated energy expenditure and beiging in Gipr −/− BM mice

Gipr / BM mice exhibited increased myelopoiesis and S100A8 production (Fig. 2). These mice also gained excessive weight, despite similar food intake (Fig. 1). To determine the underlying mechanisms, whole-body energy expenditure and food intake were simultaneously monitored in individually housed WT BM versus Gipr / BM mice over a 48-h period using automated caging systems. Mice were analysed at 8 weeks following the initiation of a HFD regimen, a time point when Gipr / BM mice display higher body weight gain versus WT BM mice (Fig. 1a). There was no difference in food intake between single-housed mice (Fig. 3a). Moreover, Gipr / BM mice displayed significantly reduced oxygen consumption and heat production (Fig. 3b,c), consistent with reduced energy expenditure. We next examined whether the effect of GIPR deletion in BM-derived cells on energy expenditure is attributable to reduced heat production in brown adipose tissue (BAT). Expression of uncoupling protein 1 (UCP1) and other thermogenesis-related genes was not different in BAT from the two groups (Supplementary Fig. 5a,b). However, adaptive thermogenesis also occurs in ingWAT during diet-induced obesity. Indeed, the reduced heat production in Gipr / BM mice was accompanied by reduced ingWAT gene expression of the transcription factor peroxisome proliferator-activated receptor-γ co-activator 1-α (PGC1α) (Ppargc1a) (Fig. 3d), known to increase both mitochondrial biogenesis and beiging19. UCP1 protein expression was also reduced in the ingWAT of Gipr / BM mice (Fig. 3e). The expression of mitochondrial respiration-associated genes was unchanged (Fig. 3d).

Fig. 3: Gipr −/− BM mice exhibit attenuated energy expenditure.
Fig. 3

Mice were reconstituted with WT BM (blue open circles) or Gipr / BM (red triangles) for 6 weeks and then fed a HFD for 8 or 14 weeks. ac, Mice were housed in metabolic cages (TSE Systems) for estimation of metabolic parameters in dark and light phase over a period of 48 h. Graphs show food intake (n = 5, P = 0.028) (a), oxygen consumption (n = 5, for light, dark and 24 h P = 0.028, P = 0.018 and P = 0.015) (b) and heat production (n = 5, P = 0.016) (c) of WT BM versus Gipr / BM mice after 8 weeks of HFD. d, Expression of thermogenesis genes in ingWAT after 14 weeks of a HFD as analysed by qRT–PCR and normalized to Rplp0 (n = 7, for Ppargc1a P = 0.0009). e, UCP1 expression was assessed by immunoblot analysis and was quantified using P97 as loading control (n = 5 for WT BM group, n = 5 for Gipr / BM group, P = 0.024). f, Representative images of H&E (top) and immunohistochemistry staining for UCP1 (bottom) in mice subjected to 24 h of cold challenge and pretreated with single injection of vehicle (saline) or 5 μg S100A8/A9. Original magnification, ×20. Scale bars, 100 µm. g, PGC1α and UCP1 expression was assessed by immunoblot analysis and was quantified using P97 as loading control (n = 7 for PGC1α, P = 0.037 and n = 4 for UCP1, P = 0.019). Data were analysed by unpaired, two-tailed t-test. Data are presented as mean ± s.e.m with significance *P < 0.05 **P < 0.01 and ***P < 0.001. Data in ac and g represent one experiment. Data in df represent two independent experiments.

The increased inflammatory response, and especially S100A8/A9 production, may be linked to reduced energy expenditure in Gipr / BM mice. To assess whether S100A8/A9 modulates energy expenditure, mice were treated with vehicle (saline) or S100A8/A9 and were exposed to 24 h of cold challenge. Haematoxylin and eosin (H&E) staining and UCP1 immunohistochemistry revealed that S100A8/A9 reduced beiging in ingWAT (Fig. 3f). This was further corroborated by immunoblots displaying reduced expression of the beiging markers PGC1α and UCP1 in the ingWAT of S100A8/A9-treated mice (Fig. 3g). Therefore, increased S100A8/A9 disrupts cold-induced adaptive thermogenesis.

Immune S100A8/A9 drives metabolic phenotype of Gipr −/− BM mice

S100A8 is a key inducer of myeloid pro-inflammatory immune responses in epiWAT with deleterious metabolic implications16,17,18. S100A8 expression was significantly increased in the epiWAT of Gipr / BM mice concomitantly with significant myelopoiesis (Fig. 2). Therefore, the metabolic and immune phenotypes observed in the Gipr / BM mice may be mediated via increased S100A8/A9 production. To test this hypothesis, we reconstituted lethally irradiated mice with BM from Gipr / S100a9 / double knockout mice (Gipr //S100a9 / BM). S100a9 / mice were chosen given the embryonic lethality of S100a8 / mice and since S100a9 / mice do not express a mature S100A8 protein due to its high turnover in the absence of S100A920,21. Notably, the profound increase in body weight gain and epiWAT mass witnessed in the Gipr / BM mice under a HFD was abolished in the Gipr //S100a9 / BM mice (Fig. 4a,b), despite similar food intake (Fig. 4c). In addition, insulin sensitivity was similar between Gipr //S100a9 / BM and WT BM mice (Fig. 4d). Moreover, the decreased gene expression of Ucp1 and Ppargc1a in the ingWAT of Gipr / BM mice was restored in Gipr //S100a9 / BM mice to the control level (Fig. 4e). The enhanced liver steatosis and hepatic triglyceride content detected in Gipr / BM mice were also abolished in the Gipr //S100a9 / BM mice (Fig. 4f,g). Importantly, similar weight gain patterns were observed after 18 weeks of a HFD (Supplementary Fig. 6a). These results highlight a pivotal role for S100A8/A9 in transducing the metabolic phenotype arising in GIPR-deficient BM-derived immune cells following a HFD. Of note, the sole deletion of S100A8/A9 in immune cells (S100a9 / BM) did not induce significant changes in most metabolic parameters, when compared with WT BM mice (Fig. 4a–g), suggesting that loss of basal BM-derived S100A8/A9 is insufficient, by itself, to trigger distinct metabolic phenotypes.

Fig. 4: Double deletion of GIPR and S100A8/A9 from immune cells reverses the metabolic phenotype displayed by Gipr −/− BM mice.
Fig. 4

Mice were reconstituted with WT BM (blue open circles), Gipr / BM (red triangles), Gipr / /S100a9 / BM (green squares) and S100a9 / BM (grey circles) for 6 weeks and then fed with a HFD for 14 weeks. a, Weekly measurements of body weight (n = 6). b, Average epiWAT mass (n = 6). c, Average food consumption during a period of 48 h, presented as kcal per mouse per day (n = 3). d, Insulin sensitivity was estimated by ITT. Graph presents basal blood glucose levels and blood glucose following insulin injection (0.75 U per kg body weight) (n = 7). e, qRT–PCR assessment of the expression of indicated thermogenesis genes in ingWAT normalized to Rplp0 (n = 6). f, Representative images of H&E liver paraffin sections for the assessment of hepatic steatosis. Original magnification, ×10. Scale bars, 200 μm. g, Liver triglyceride content (mg g−1 tissue) (n = 6). h, 3D PCA plot showing the variance between groups in epiWAT and ingWAT (n = 4). i,j, Hierarchical clustering of the differentially expressed genes (fold-change ≥ 2, P < 0.05) performed on mRNA extracted from epiWAT (i) and ingWAT (j). A coloured bar indicating the standardized log2 intensities accompanies the expression profile. Data in a and d were analysed by two-way ANOVA with Bonferroni, and data in b, c, e and g were analysed by one-way ANOVA with Bonferroni. Data are presented as mean ± s.e.m. with significance *P < 0.05, **P < 0.01 and ***P < 0.001. Red or grey stars represent significance of Gipr / BM or S100a9 / BM groups versus WT BM, respectively. Green stars represent significance of Gipr / /S100a9 / BM versus Gipr / BM. Data in ac represent two independent experiments, and data in di are from one experiment.

We next sought to define the consequences of an impaired GIPR–S100A8/A9 axis using microarray-based analysis of gene expression in epiWAT and ingWAT from WT BM, Gipr / BM and Gipr //S100a9 / BM mice after a HFD. Principal component analysis (PCA) revealed a resemblance between the WT BM and Gipr //S100a9 / BM groups, especially in the epiWAT (Fig. 4h). Hierarchical clustering of differentially expressed genes revealed distinct gene expression signatures in Gipr / BM and WT BM mice in both epiWAT and ingWAT, while the gene expression profile of Gipr //S100a9 / BM mice clustered with that of the WT BM group in both tissues (Fig. 4i,j). Further analysis revealed the same clustering pattern in specific metabolic pathways in both epiWAT and ingWAT (Supplementary Fig. 7a,b). Strikingly, 305 genes were differentially expressed between the WT BM and Gipr / BM groups; however, only 39 different genes remained differentially expressed between the WT and Gipr //S100a9 / BM groups (Supplementary Fig. 7c). Therefore, these results support a pivotal role for S100A8/A9 in inducing the transcriptional changes observed in epiWAT and ingWAT of Gipr / BM versus WT BM mice.

Immune cell S100A8/A9 drives myelopoiesis in Gipr −/− BM mice

We next examined whether the impaired GIP–S100A8/A9 axis in immune cells contributes to the increased myelopoiesis observed in Gipr / BM mice (Fig. 2). Indeed, the increased myelopoiesis (CD11b+/CD11b ratio) detected in the blood and epiWAT of Gipr / BM versus WT BM mice was completely diminished in Gipr //S100a9 / BM mice (Fig. 5a). Importantly, recent studies have shown that inflammation in ingWAT impairs thermogenesis/beiging22. In line with this, there was increased myelopoiesis in ingWAT of Gipr / BM mice, which was strongly attenuated in the Gipr //S100a9 / BM mice (Fig. 5a). Specifically, the significant neutrophilia observed in circulation and in the two WAT depots of Gipr / BM mice was abolished in the Gipr //S100a9 / BM mice (Fig. 5b–d). Furthermore, the augmented infiltration of inflammatory Ly6Chi monocytes in epiWAT and ingWAT of Gipr / BM mice was also attenuated in Gipr //S100a9 / BM mice (Fig. 5c,d). Notably, the same pattern of immune cell distribution was also present after 18 weeks of a HFD (Supplementary Fig. 6b,c). Compatible with the augmented myeloid cell infiltration, S100a8, Ccl2 and Cxcl1 genes were significantly increased in epiWAT and ingWAT of Gipr / BM mice, but were similarly expressed between the WT BM and Gipr //S100a9 / BM groups (Fig. 5e,f). S100a9 / BM mice were comparable to WT BM mice in most parameters (Fig. 5). Further transcriptomic profiling of inflammatory pathways in epiWAT and ingWAT revealed the same consistent pattern of similarity between WT BM and Gipr //S100a9 / BM mice as opposed to Gipr / BM mice (Supplementary Fig. 7a,b). Collectively, these results indicate that GIP plays an important immunoregulatory role in fat depot immune cells through its attenuation of S100A8/A9-driven inflammation.

Fig. 5: Combined deletion of GIPR and S100A8/A9 from immune cells reverses the myelopoiesis phenotype of Gipr −/− BM mice.
Fig. 5

Mice were reconstituted with WT BM (blue open circles), Gipr / BM (red triangles), Gipr / /S100a9 / BM (green squares) and S100a9 / BM (grey circles) for 6 weeks and then fed a HFD for 14 weeks. a, Graphs showing as indication for myelopoiesis the ratio of CD11b+ phagocytes versus CD11b lymphocytes in the blood, epiWAT and ingWAT (n = 6). bd, Bar graphs showing numbers of circulating myeloid cells per 50 μl of blood (n = 6) (b), as well as of neutrophils, Ly6Chi monocytes and ATMs normalized per tissue mass in the epiWAT (n = 6) (c) and ingWAT (d) (n = 6). e,f, qRT–PCR assessment of the expression of indicated inflammatory mediators normalized to Rplp0 in epiWAT (n = 6) (e) and ingWAT (n = 6) (f). Data were analysed by one-way ANOVA with Bonferroni. Data are presented as mean ± s.e.m. with significance *P < 0.05, **P < 0.01 and ***P < 0.001. Red or grey stars represent significance of Gipr / BM or S100a9 / BM groups versus WT BM, respectively. Green stars represent significance of Gipr / /S100a9 / BM versus Gipr / BM. Data are from one experiment.

LysM ΔGipr mice have severe metabolic and immune phenotype

Myeloid cells, such as monocytes and neutrophils, are known to be the main producers of S100A8/A923. Therefore, we hypothesized that GIP specifically restrains S100A8/A9 in myeloid cells. Flow cytometry analysis revealed that S100A8 is expressed by CD45+ immune cells in the epiWAT stromal vascular fraction (SVF) and non-parenchymal liver cells of Gipr / BM mice (Fig. 6a,b). S100A8 expression in these tissue fractions was restricted to ATMs and neutrophils, while Kupffer cells (KCs) were negative (Fig. 6a,b). GIPR gene expression was detected in epiWAT Ly6Chi monocytes and ATMs after a HFD (Fig. 6c). Given the combined expression of GIPR and S100A8 by ATMs, we next examined the direct effect of GIP on S100A8 expression in ATMs sorted from HFD epiWAT (Supplementary Fig. 8). Indeed, [d-Ala2]GIP treatment reduced S100a8 expression in sorted ATMs (Fig. 6d). Moreover, mice treated with [d-Ala2]GIP during the last 8 weeks of a 14 week HFD also exhibited reduced expression of S100a8 in epiWAT ATMs (Fig. 6e). Of note, in both cases, [d-Ala2]GIP treatment had no effect on ATM gene expression of the pro-inflammatory mediators interleukin-1 (IL-6; Il6), tumour-necrosis factor-α (Tnf) and IL-1β (Il1b) (Supplementary Fig. 9a,b), further underscoring the uniqueness of the GIP–S100A8/A9 axis in these cells. Lastly, we targeted GIPR deficiency to myeloid cells by crossing Gipr fl/fl mice24 and Lyz2cre mice (LysM ΔGipr). After 14 weeks of a HFD, LysM ΔGipr mice exhibited higher body mass, despite similar food intake (13.5 ± 2.8 versus 13.9 ± 2.5 kcal per mouse per day), profound hepatic steatosis and impaired glucose tolerance (Fig. 6f–i), relative to littermate Gipr fl/fl controls. They also had increased myelopoiesis in the epiWAT (Fig. 6j,k), augmented S100a8 expression in ingWAT and epiWAT (Fig. 6l), and decreased expression of the beiging-associated marker PGC1α in ingWAT at both transcriptional and protein levels (Fig. 6m,n). Collectively, these findings outline a pivotal immunoregulatory role for GIP in myeloid cell S100A8/A9-driven metabolic dysfunction and myelopoiesis.

Fig. 6: GIP negatively regulates S100A8 expression in myeloid cells.
Fig. 6

a, Flow cytometry of S100A8 in epiWAT ATMs and neutrophils. b, Flow cytometry of S100A8 in liver KCs and neutrophils. c, Gipr mRNA expression in sorted Ly6chi monocytes (n = pool of 15 mice), ATMs and Cd11b lymphocytes (n = 3, each repeat from pool of 5 mice). d, qRT–PCR of S100a8 in ATMs sorted from epiWAT of WT mice after 14 weeks of a HFD and following ex vivo 24 h treatment with 100 nM [d-Ala2]GIP or vehicle (n = 3, each repeat from pool of 5 mice, P = 0.017). e, qRT–PCR analysis of S100a8 expression in ATMs sorted from epiWAT of WT mice after 14 weeks of a HFD and daily intraperitoneal injections of [d-Ala2]GIP (0.12 μg per g body weight) or vehicle during the last 8 weeks (n = 2, each repeat from pool of 5 mice). f, Body weight of LysM ΔGipr (orange squares, n = 3) and littermates Gipr fl/fl (black open circles, n = 6, time point weeks 0–14 P=0.05, P = 0.011, P = 0.013, P = 0.002, P = 0.003, P = 0.004, P = 0.006) fed a HFD. g, Body weight after 14 weeks of a HFD (n = 8, P = 0.001). h, Liver H&E paraffin sections. Original magnification, ×10. Scale bars, 200 µm. i, GTT of LysM ΔGipr (n = 5) and Gipr fl/fl (n = 4, 15 min P = 0.02, 30 min P = 0.002, 1 h P = 0.027). jk, Flow cytometry showing myelopoiesis (P = 0.0005) (j) and immune cells in epiWAT (P = 0.021) (k) of LysM ΔGipr (n = 3) and Gipr fl/fl (n = 5). l, S100a8 mRNA expression in epiWAT and ingWAT (n = 8, P = 0.015). m, qRT–PCR of ingWAT thermogenesis genes (n = 8, for Ppargc1a P = 0.04). n, Immunoblot showing PGC1α in ingWAT from LysM ΔGipr (n = 3) and Gipr fl/fl mice (n = 4, P = 0.012), with densitometry presented below. Data was analysed by unpaired, two-tailed t-test. Data are presented as mean ± s.e.m with significance *P < 0.05 **P < 0.01 ***P < 0.001. Data in ad, f, ik and n are from single experiments, and data in g, l and m are from two experiments.


Herein we demonstrate a previously unappreciated role for the incretin hormone GIP in immune cells in the regulation of energy homeostasis and myelopoiesis under conditions of nutrient excess. GIPR deficiency in BM-derived cells impairs energy expenditure as manifested by reduced heat production and oxygen consumption and by impaired ingWAT beiging. This leads to weight gain and impaired insulin action. Moreover, Gipr / BM mice show profound myelopoiesis in the blood, epiWAT and ingWAT. We identify the myeloid lineage as a specific target of GIP regulation. Mechanistically, we show direct inhibition of S100A8 by GIP in ATMs. We also demonstrate that S100A8/A9 directly impairs cold-induced adaptive thermogenesis in ingWAT.

One of the most recognized extra-pancreatic effects of GIP is its anabolic effect on fat depots. Indeed, Gipr / mice exhibit resistance to weight gain and greater insulin sensitivity under conditions of prolonged nutrient excess5. In stark contrast, we report here that GIPR deficiency in myeloid cells unexpectedly leads to greater weight gain and insulin resistance. The question may be raised as to why GIPR deficiency in myeloid cells does not contribute to a deleterious metabolic and inflammatory role in the Gipr / mice. A possible explanation may be that in the absence of obesity-induced inflammation in these lean mice, resident fat depot immune cells (especially ATMs) are reduced in number and do not become inflammatory and other myeloid immune cells, such as neutrophils and Ly6Chi monocytes, are not recruited. Our results uncover cell-specific roles for GIP in the context of dietary energy excess leading to obesity.

Consistent with cell-specific roles for GIPR signalling, a recent study demonstrated that selective GIPR inactivation in cardiomyocytes unexpectedly improves survival and reduces adverse cardiac muscle remodelling after experimental myocardial infarction, whereas GIPR agonism alone had little effect on cardiac outcomes25. Taken together, these studies underscore the importance of defining cell-specific actions of basal GIPR signalling to reveal its diverse physiological roles and understand its therapeutic potential.

The heterodimeric protein alarmin S100A8/A9, which is demonstrated in the current study to be modulated by GIP, belongs to a family of Ca++ binding proteins and is considered an important mediator and biomarker in various inflammatory disorders20,23. S100A8/A9 is mainly found in the cytoplasm of monocytes and neutrophils and locally released at sites of inflammation in a variety of different diseases26. The significant neutrophilia and monocytosis in both epiWAT and ingWAT of Gipr / BM and flow cytometry intracellular staining for S100A8 in neutrophils and ATMs suggest that these cells are the likely sources for the prominent increase in S100A8 expression within these tissues. Several studies have demonstrated that elevated visceral adipose tissue and serum expression of S100A8 is an early marker in type 2 diabetes mellitus (T2DM)27, as well as a novel marker for obesity28 that is reduced following bariatric surgery29. In line with our results, S100A8/A9 is mainly expressed by cells within the SVF in adipose tissue of obese subjects, and its expression significantly correlates with monocyte/macrophage markers29. Mechanistically, recent studies highlighted S100A8/A9 as a key promoter of myelopoiesis linking fat depots and the BM. While in diabetic mice S100A8/A9 directly induces BM myelopoiesis via RAGE signalling17, in obesity, monocytosis and neutrophilia are facilitated via S100A8/A9-mediated inflammasome activation and IL-1β-induced myelopoiesis in the BM16. We show that GIPR deletion in immune cells resulted in an S100A8/A9-induced myelopoiesis in blood and in both epiWAT and ingWAT. A main culprit for obesity that developed in HFD-fed Gipr / BM mice was perturbed heat dissipation, as seen in the metabolic cage experiments and accompanied by impaired beiging in ingWAT. In this respect, we show that S100A8/A9 reduces the expression of UCP1 and PGC1α in ingWAT in a model of cold-induced beiging. Therefore, these results highlight a pathological role for S100A8/A9 in interfering with adaptive thermogenesis, further linking inflammation with metabolic dysfunction. 

Given the established deleterious role of S100A8/A9 in obesity-induced inflammation, it seems likely that under physiological conditions of nutrient overload, intact GIPR signalling restrains the deleterious activity of S100A8/A9 originating from immune cells. It might be expected that targeting S100A8/A9 deficiency to immune cells would independently result in an improved metabolic phenotype under HFD conditions. However, we demonstrate quite similar phenotypes in most metabolic and immunologic parameters in WT and S100a9 / BM mice. In these settings, adipocytes seem to be the main source for pathological S100A8/A918. Moreover, phenotypes arising from basal loss of S100A8/A9 in the BM compartment may reflect compensation from multiple mechanisms independently impacting the extent of local inflammation and whole-body energy homeostasis. Hence, further studies are required to further pinpoint the adipocyte versus immune cell-specific roles of S100A8/A9 in energy homeostasis.

It is generally acknowledged that lethal irradiation, as used here in the generation of BM chimeric mice, may limit weight gain. Yet, the augmented weight gain independently observed in the non-irradiated LysM ΔGipr mice under HFD feeding rules out the importance of irradiation as the major cause for excess weight gain of the Gipr / BM chimeras. We also provide data showing similar xylose absorption and comparable fat content in the faeces of WT and Gipr / BM mice under both regular chow and HFD, thus dismissing the possibility that the change in weight gain stems from differences in intestinal nutrient absorption.

Overall, our studies uncover an exciting aspect of GIPR-dependent regulation of energy homeostasis in obesity via myeloid cells and their altered expression of S100A8/A9. Our findings extend our understanding of how sustained food ingestion upregulates hormonal signals from the gut, exemplified by GIP, which in turn provide unique information to different tissues, thereby contributing to control of nutrient-related inflammation and energy homeostasis. Given the new advance in molecular understanding of the sensory machinery by which EECs detect different nutrients, one of the future challenges is to unravel which of the many gut signals is physiologically relevant and might potentially be exploited therapeutically in the treatment of obesity and T2DM. In this respect, GIP multi-agonists are being clinically tested as novel therapeutic agents for T2DM and the metabolic syndrome11,30,31. Our data highlight the unexpected role(s) of immune cells as targets for GIP-based therapeutics in the control of inflammation and energy homeostasis.



C57BL/6JOlaHsd male mice (Envigo), Gipr / mice (C57Bl/6 background, provided by Dr Y. Yamada, Akita University, Japan), S100a9 / mice (C57Bl/6 background, provided by Prof. T. Vogl, Munster University, Germany), B6.129P2-Lyz2 tm1(cre)Ifo (C57Bl/6 background, Jackson Laboratory) and Gipr fl/fl mice24 (C57Bl/6 background, provided by Prof. D. J. Drucker, Mount Sinai Hospital, University of Toronto, Canada) were maintained in specific pathogen-free animal facility, and experiments were performed according to protocols approved by the Animal Care Use Committee of the Sourasky Medical Center. Mice were housed with 12-h light cycles and a constant temperature of 22 °C. For the generation of BM chimeric mice, 6-week-old recipient C57BL/6JOlaHsd male mice were lethally irradiated with 950 rad using a TrueBeam linear accelerator (Varian Medical Systems). The next day, femurs were dissected from 6-week-old donor male mice (WT, Gipr / and Gipr / /S100a9 / mice). The two ends of the femur were cut and BM cells were flushed out using a 26-G needle containing sterile PBS. After cell counting, 5 × 106 of respective BM cells were injected into the tail vein of the recipients, and mice were allowed to reconstitute their BM-derived immune cells for 6 weeks. After reconstitution, mice were fed with regular chow or a HFD (60% cal from fat, Research Diets, D12492) for 14 weeks.

[d-Ala2]GIP administration

C57BL/6JOlaHsd male mice (6 weeks old) were fed a HFD for 14 weeks and received a single daily intraperitoneal injection of human [d-Ala2]GIP (0.12 μg per g body weight) (synthesized by Bio-Synthesis) or vehicle (saline) during the last 8 weeks. Thirty minutes before they were killed, all mice received an intraperitoneal injection of insulin (Actrapid, NovoNordisk) (0.75 U per kg body weight).

Cold exposure

Six-week-old C57BL/6JOlaHsd male mice were intraperitoneally injected with vehicle (saline) or 5 μg S100A8/A9 (R&D Systems) in 200 μl per mouse, then housed in individual cages and exposed to 4 °C in a temperature controlled refrigerator (4–8 °C) for 24 h. The next day, mice were killed, and their ingWAT was assessed for beiging markers by immunohistochemistry and immunoblotting.

Insulin tolerance test

Five days before the end of the experiments, mice were subjected to 6 h of fasting from 7:00 until 13:00, and basal glucose level was determined using the Accu-Check Performa Sensor. Subsequently, insulin (Actrapid) was administered by intraperitoneal injection at a dose of 0.75 U per kg body weight, and serum glucose was measured at indicated time points following the insulin injection.

Glucose tolerance test

One week before the end of the experiments, mice were fasted for 16 h, and then basal glucose was measured using the Accu-Check Performa Sensor. Mice were injected with 2 mg per g body weight glucose, and serum glucose was measured after 15, 30, 60 and 120 min.

Analysis of serum metabolic markers

Mouse serum was collected and kept at −80 °C until assessed. Serum triglycerides were measured using the Advia 2000 Automatic Analyzer. Serum S100A8/A9 levels were measured by enzyme-linked immunosorbent assay (ELISA) in the lab of Prof. T. Vogl using antibodies prepared in his lab. Serum GIP and insulin levels were measured using murine-specific ELISA kits (EMD Millipore), according to the manufacturer’s instructions. Basal FFA and FFA after insulin administration were measured using a fluorometric ELISA kit (Abcam).

Metabolic cages

BM chimeric mice fed with a HFD for a total of 8 weeks were individually housed in metabolic chambers with free access to food and water, maintaining a 12 h–12 h dark–light cycle. The LabMaster/PhenoMaster caging systems (TSE Systems) consist of a combination of sensitive feeding and drinking sensors for automated online measurement. Mice were acclimatized in metabolic chambers for 48 h before initiation of data collection. The calorimetry system is an open-circuit system that determines the volume of oxygen consumption (VO2) and carbon dioxide production (VCO2), allowing determination of the ratio between fat and glucose utilization for energy. All the parameters were measured continuously and simultaneously for 48 h. Determination of lean and fat mouse mass was performed by using computed tomography (Minispec LF50 Body Composition Analyzer, Bruker).

Determination of d-xylose absorption

After 6 weeks of BM reconstitution, WT BM and Gipr −/− BM chimeric mice were fed with a regular chow diet for two weeks. Subsequently, mice were anaesthetized and administered an aqueous solution of 500 μl containing 0.3075 g of d-xylose into the stomach by gavage. After 3 h, mice were bled 1 ml of blood from the orbital plexus. The serum was collected and stored at −80 °C until analysis. Serum was mixed 1:5 with 5% trichloroacetic acid. Samples were centrifuge at 20,817g. for 10 min at room temperature. Upper phase was reacted 1:10 with reagent (4 g thiourea, 100 ml glacial acetic acid, 3 g para-bromoaniline) and boiled in water bath at 100 °C for 4 min. Optical density at 520 nm was measured using a spectrophotometer.

Determination of fat from faeces

Faeces (100 mg) were collected from WT BM or Gipr −/− BM chimeric mice and dried for 1 h at 70 °C and then incubated with 2 ml of chloroform for 30 min at 60 °C and centrifuged 2,000 r.p.m. for 10 min at room temperature. Water was added to the supernatant, and phase separation was induced by low-speed centrifugation at 2,000 r.p.m. for 10 min. The lower chloroform phase was then transferred to a new tube, and the samples were evaporated to dryness and weighed.

Cell isolation procedure from epiWAT, ingWAT and blood

Epididymal fat pads or inguinal subcutaneous fat were surgically removed from mice, weighted, cut into small pieces and subsequently incubated in digestion buffer (DMEM medium, 12.5 mM HEPES buffer pH 7.4, 2% BSA and 10 mg collagenase type II) for 30 min at 37 °C in a shaking bath at 150 r.p.m. The digested tissue was filtered through a 100-μM mesh and centrifuged at room temperature at 500g for 5 min. The pellet was washed, and erythrocytes were lysed with red blood cells lysis solution. The SVF was used for flow cytometry analysis. Blood peripheral blood mononuclear and polymorphonuclear cells were isolated using the BD FACS Lysing Solution, according to the manufacturer’s instructions.

Flow cytometry analysis and sorting of adipose tissue ATMs

Analysis of myeloid immune cells from SVF and blood was performed according to our previous publication9. The antibodies used were against: CD45, CD64, Ly6G, Ly6C, CX3CR1, CD11b, I-A/I-E and F4/80. With respect to S100A8 staining, after surface antigen staining, cells were fixed and permeabilized with BD Cytofix Cytoperm kit and stained intracellularly with an isotype control or S100A8 (provided by Prof. T. Vogl, Munster University, Germany) antibodies, followed by staining with Alex 647 anti-rabbit antibodies. The list of antibodies is presented in Supplementary Table 1. Multi-parameter flow cytometry analyses were performed using the FACSCanto II machine and the FACS DIVA or FlowJo software. Antibodies used are described in the Supplementary Table 1.

Real-time RT–PCR

Total RNA was extracted from isolated livers using TriReagent (Sigma-Aldrich) and from epiWAT, ingWAT and BAT using the QIAzol and Lipid RNA extraction kit (Qiagen). Total RNA was reverse-transcribed using the High Capacity cDNA RT kit (Applied Biosystems). Real-time RT–PCR was performed with the Fast SYBR Green Master Mix (Applied Biosystems) using the Corbett rotor light cycler. Quantification of the PCR signals of each sample was performed by the ΔCt method normalized to Rplp0 or Tbp housekeeping genes. Murine primers used are shown in Supplementary Table 2.

Protein immunoblots

Total protein from liver, epiWAT, ingWAT and BAT was extracted by homogenization in ice-cold RIPA buffer (PBS, 1% Igepal, 0.5% sodium deoxycholate, 0.1% sodium dodecyl sulfate, protease and phosphatase inhibitors cocktails 1:100). Homogenates were centrifuged for 20 min at 14,000g, supernatants were collected and extracts were normalized to total protein content. Proteins were separated by SDS–PAGE, blotted onto nitrocellulose and blots were blocked for 1 h in 5% milk. Blots were incubated overnight at 4 °C with antibodies to pAkt (Cell Signaling), Akt1/2, (Santa Cruz Biotechnology), S100A8 (rabbit polyclonal prepared in the lab of Prof. T. Vogl), UCP-1 (Abcam), PGC1α (Abcam) or P97 (rabbit polyclonal prepared in the lab of Prof. Boaz Tirosh, Hebrew University, Jerusalem), and then incubated with horseradish-peroxidase-conjugated secondary antibody and subjected to chemi-luminescent detection using the Micro Chemiluminescent imaging system (DNR Bio Imaging Systems). Densitometry was performed using the Image J software and expression of p-Akt was normalized to expression of Akt and of the other proteins normalized to expression of housekeeping gene p97.

Histological staining and immunohistochemistry for UCP-1

Liver, epiWAT and ingWAT pieces were fixed in 4% paraformaldehyde (EMS) for 24 h and embedded in low melting paraffin (Paraplast Plus, Sigma Aldrich). Five-mm-thick sections were cut (rotary microtome HSM55, Microm) and sections were mounted, dehydrated in increasing ethanol series and stained with H&E (Merck). For UCP-1 immunocytochemistry, slices were subjected to citrate-based antigen retrieval and stained with antibodies to UCP-1 (dilution 1:100, Abcam, ab23841) for 1 h at 4 °C in a humidified chamber. Staining was developed using the VECTASTAIN Elite ABC kit (rabbit immunoglobulin-G) (Vector laboratories, PK-6101), according to the manufacturer’s instruction and visualized with a Nikon microscope.

RNA isolation, sample processing and microarray analysis

Total RNA was extracted from epiWAT and ingWAT using the QIAzol Lysis Reagent and Lipid RNA extraction RNeasy Mini Kit (Qiagen), according to the manufacturer’s instructions. The isolated RNA was stored at −80 °C. Gene expression profiling was performed using Clariom S arrays (Thermo Fisher Scientific). RNA quantity and quality were determined by measurement of concentration with absorbance at 260 and 280 nm (NanoDrop 2000/2000c spectrophotometers; Thermo, Fisher Scientific) and by TapeStation Analysis (Software A.02.01, Agilent Technologies). Only high-quality RNA with 260/280 nm ratios between 1.9 and 2.1, ratio of 260/230 nm >1.5 and RNA integrity numbers of 6.2–7.9 were used for further microarray analysis. Samples were prepared according to the standard Affymetrix WT PLUS Reagent Kit protocol and Affymetrix WT Terminal Labeling Kit (ThermoFisher) from 100 ng total RNA starting material. GeneChips were washed and stained in the Affymetrix Fluidics Station 450 according to the standard GeneChip Expression Wash, Stain and Scan protocol. Subsequently, the GeneChips were scanned using the Affymetrix 3000 7G scanner. Microarray analysis was performed using Partek Genomics Suite version 6.6 (Partek). Data were normalized and summarized with the robust multiaverage method34. Functional enrichment analysis was performed on the differentially expressed genes (≥2-fold change, P ≤ 0.05, analysis of variance (ANOVA)) using DAVID32 and WebGestalt33 tools. Heat maps were generated using Partek Genomics Suite software.

Statistical analysis

Results are presented as means ± s.e.m. Statistical significance between two groups was assessed using a two-tailed Student’s t-test. Significance was defined if the P value was less than 0.05 as follows: *P < 0.05; **P < 0.01; ***P < 0.001. Statistical analysis between three groups and above was performed using one-way or two-way ANOVA non-parametric test with Bonferroni correction comparing all pairs of columns. Statistical analyses of gene expression data were performed using the Partek Genomics Suite, DAVID or WebGestalt softwares (ANOVA).

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The microarray datasets generated during the current study are deposited at the National Center for Biotechnology Information Gene Expression Omnibus public database under accession code GSE109371.

Additional information

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Change history

  • 01 March 2019

    In the version of the article originally published, Fig. 3a was missing a y-axis line and key labels, and Fig. 3c,d had illegible text labels overlapping the graphs. The errors have been corrected in the HTML and PDF versions of the article.


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The authors acknowledge support from the Israel Science Foundation for C.V. and S.F. (grant nos 35/12 and 1146/16) and the Canada Research Chairs Program and Canadian Institutes of Health Research Foundation grant 154321, both to D.J.D. Y.K. is the incumbent of the Sarah and Rolando Uziel Research Associate Chair. We thank A. H. Futerman and Y. Pewzner-Jung for their assistance with metabolic cages experiments. Finally, we thank N. Strauss (Sourasky Medical Center) for radiation services.

Author information

Author notes

  1. These authors contributed equally: Fernanda Dana Mantelmacher, Isabel Zvibel.


  1. The Research Center for Digestive Tract and Liver Diseases, Tel-Aviv Sourasky Medical Center and the Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel

    • Fernanda Dana Mantelmacher
    • , Isabel Zvibel
    • , Keren Cohen
    • , Alona Epshtein
    • , Shai Weiss
    • , Chen Varol
    •  & Sigal Fishman
  2. Bioinformatics Unit, Faculty of Life Science, Tel Aviv University, Tel-Aviv, Israel

    • Metsada Pasmanik-Chor
  3. Institute of Immunology, University of Münster, Münster, Germany

    • Thomas Vogl
  4. Department of Veterinary Resources, Weizmann Institute of Science, Rehovot, Israel

    • Yael Kuperman
  5. The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada

    • Daniel J. Drucker
  6. Department of Clinical Microbiology and Immunology, Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel

    • Chen Varol


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F.D.M., I.Z., C.V. and S.F. conceived the study, designed experiments and wrote the manuscript. F.D.M., I.Z and C.V. performed the experiments and analysed the data. M.P-C. performed the bioinformatic analyses. T.V. analysed data, provided reagents and reviewed the manuscript. K.C., A.E. and S.W. performed many of the RT–PCR and immunoblot analyses in the presented experiments. Y.K. greatly assisted in the performance of metabolic cages experiments and analyses. D.J.D. provided key scientific consultation, essential reagents and mouse tools and carefully reviewed and edited the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Sigal Fishman.

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