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Disease Markers
Volume 2019, Article ID 7901062, 16 pages
https://doi.org/10.1155/2019/7901062
Research Article

Association between Adipose Tissue Interleukin-33 and Immunometabolic Markers in Individuals with Varying Degrees of Glycemia

1Research Division, Dasman Diabetes Institute, Dasman 15462, Kuwait
2Medical Division, Dasman Diabetes Institute, Dasman 15462, Kuwait
3Department of Medicine, Faculty of Medicine, Kuwait

Correspondence should be addressed to Amal Hasan; gro.etutitsninamsad@nasah.lama

Received 9 October 2018; Accepted 17 December 2018; Published 3 April 2019

Academic Editor: Mariann Harangi

Copyright © 2019 Amal Hasan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Introduction. Interleukin-33 (IL-33), the ligand for the receptor ST2, is abundant in adipose tissue, including preadipocytes, adipocytes, and endothelial cells. The IL-33/ST2 axis is protective against obesity, insulin resistance, and type 2 diabetes (T2D) in animal models. We determined whether adipose tissue IL-33 was associated with glycated hemoglobin (HbA1c), as well as mediators of inflammation and immune regulation and beiging of adipose tissue, among individuals with varying degrees of glycemia. Materials and Methods. A total of 91 adults with normoglycemia, prediabetes, and T2D were included. After measuring their anthropometric and biochemical parameters, subcutaneous adipose tissue samples were isolated and mRNA expression of cytokines, chemokines, chemokine receptors, pattern recognition receptors, and mediators involved in beiging of adipose tissue were measured. Results. Adipose tissue IL-33 was inversely associated with HbA1c in individuals with normoglycemia and T2D but not in those with prediabetes and was inversely correlated with fasting plasma glucose in individuals with T2D and with a better glycemic control. IL-33-to-ST2 ratio was inversely correlated with HbA1c in individuals with normoglycemia but not in those with prediabetes or T2D. IL-33 was directly associated with ST2, CD302, fibrinogen-like protein 2 (FGL2), and PR domain containing 16 (PRDM16) but inversely correlated with chemokine (C-C motif) ligand (CCL) 7 and CCL8 in individuals with normoglycemia. Similarly, IL-33 was directly associated with ST2, CD302, FGL2, PRDM16, and, additionally, toll-like receptor (TLR) 3 and IL-12A in individuals with T2D. However, IL-33 was not associated with any of these mediators but was directly and strongly associated with TLR9 in individuals with prediabetes. Conclusions. IL-33 and/or IL-33/ST2 dynamics and biological functions may play a role in overall glycemia among humans and may represent a novel target by which glucose-lowering managements confer their beneficial effects.

1. Introduction

A state of chronic low-grade inflammation in the adipose tissue plays a major role in the development of metabolic disease, such as insulin resistance and type 2 diabetes (T2D). Although adiposity is often associated with metainflammation and metabolic disorders [1, 2], a group of obese individuals remains metabolically healthy [3]; this reflects the complex and multifactorial nature of metabolic disease development. Adipose tissue is a key regulator of energy balance and is generally divided into two broad categories, namely, white adipose tissue and brown adipose tissue [46]. In addition, white adipose tissue can convert to a brown-like depot, termed “beige” adipose [7, 8]. Brown and beige adipose tissues dissipate energy and influence glucose and VLDL-TG metabolism [9, 10], which may protect against metabolic diseases, such as T2D. Immune cells and their mediators have emerged as a major axis of metabolic regulation [1113], which has given rise to a new paradigm in immune regulation of metabolism. Type 1 immune responses are involved in the induction/exacerbation of metabolic dysfunction, such as obesity and diabetes, whereas type 2 immune programs are responsible for maintaining and fine-tuning tissue function [1315].

Interleukin-33 (IL-33) is an IL-1-like cytokine that signals via the IL-1 receptor-related protein ST2 and plays an important role in Th2-associated immune responses. IL-33, in addition to being secreted as a cytokine, is constitutively expressed in the nucleus of various cell types including preadipocytes and adipocytes, endothelial cells, epithelial cells [1618], and fibroblast-like reticular cells [1923] and may also have transcriptional regulatory capacity [16]. ST2, together with its coreceptor IL-1RAcP [24], form the IL-33 receptor complex. Two splice variants of the ST2 gene have been described; one encodes for a transmembrane isoform (ST2L), and the other one encodes for a secreted soluble isoform (sST2) [25]. ST2L confers the biological effects of IL-33, whereas sST2 serves as an antagonistic decoy receptor [26]. ST2L, initially regarded as a selective marker for Th2 cells [25, 2730], is also expressed by various other immune cells, including Treg cells [31, 32], Th9 cells [33, 34], mast cells [28, 3537], basophiles, eosinophils [38, 39], monocytes [40], macrophages [41, 42], innate lymphoid cell type 2 (ILC2) [43, 44], dendritic cells [45, 46], neutrophils [38, 47], B1 B cells [48], invariant natural killer T (iNKT) cells, NK cells [37, 49], and Tc1 T cells [29, 50, 51].

The IL-33/ST2 pathway plays a protective role against obesity, insulin resistance, and T2D in animal models [18, 52, 53]. The mechanisms by which IL-33 exerts these protective metabolic effects include the reduction of resistin expression [18], accumulation of protective Th2 cells and associated cytokines, and polarization of resident macrophages toward a protective alternatively activated phenotype [18, 52]. These findings have been supported by in vitro studies showing that treatment with IL-33 induces the production of protective Th2 cytokines (mainly IL-5 and IL-13), reduces lipid storage, and decreases the expression of genes associated with lipid metabolism and adipogenesis [18, 52]. Moreover, IL-33 administration to diabetic obese (ob/ob) mice results in the reduction of adiposity and fasting glucose, as well as improved glucose tolerance and insulin levels [18, 52], whereas feeding ST2−/− mice a high-fat diet leads to increased body weight and fat mass, as well as impaired insulin secretion and glucose regulation [18, 52, 53]. In addition, recent evidence suggests that circulating IL-33 may confer protective metabolic effects (body mass index (BMI), lipid profile, and glycated hemoglobin (HbA1c)) in humans, specifically, in individuals with normal weight and glycemia but not in those with obesity and/or T2D [54]. However, little is known about the metabolic effects of adipose tissue IL-33 in humans. Therefore, we investigated the expression level of IL-33 (and ST2) in the adipose tissue, as well as its association with overall glycemia (measured by HbA1c), mediators of inflammation and immune regulation, and beiging of adipose tissue, among individuals with normoglycemia, prediabetes, and T2D.

2. Materials and Methods

2.1. Study Participants

The study was conducted in accordance with the ethical principles of the Declaration of Helsinki and approved by the Ethical Review Committee of Dasman Diabetes Institute, State of Kuwait. All participants had provided written informed consent. The study included 91 adults between the ages of 23 and 72, which were classified based on their HbA1c (in accordance with the American Diabetes Association criteria [55]) into three groups, namely, normoglycemia (HbA1c < 5.7%), prediabetes (HbA1c 5.7-6.4%; not on glucose-lowering medications), and T2D (a diagnosis of T2D with an HbA1c ≥ 6.5%; on any type of glucose-lowering medications) [5557]. Comorbid health conditions included hypertension, dyslipidemia, cardiovascular disease, kidney disease, and neuropathy.

2.2. Anthropometric and Biochemical Measurements

After measuring participant’s body weight (kg) and height (to the nearest 0.5 cm), BMI was then calculated as weight/height2 (kg/m2) and used as an overall index of adiposity. The waist-to-hip ratios were calculated, and the whole-body composition including percentage of body fat (PBF), soft lean mass (SLM), and total body water (TBW) was measured using the IOI 353 Body Composition Analyzer (Jawon Medical). Fasting blood samples were obtained, and plasma glucose, serum total cholesterol, triglycerides (TG), and high-density lipoprotein (HDL) cholesterol were measured using the Siemens Dimension RXL chemistry analyzer (Diamond Diagnostics, Holliston, MA, USA). Low-density lipoprotein (LDL) cholesterol was also estimated. Glycated hemoglobin (HbA1c) was determined using Variant™ (Bio-Rad, Hercules, CA, USA). High-sensitivity C-reactive protein (hsCRP) was measured using the ELISA kit (BioVendor, USA). Optimal values for TG (<1.7 mmol/L), total cholesterol (<5.2 mmol/L), LDL cholesterol (<3.3 mmol/L), and HDL cholesterol (>1.03 mmol/L) were based on the American Heart Association’s guidelines. Anthropometric and biochemical parameters are shown in Table 1.

Table 1: Anthropometric and biochemical characteristics of the study groups.
2.3. Collection of Subcutaneous Adipose Tissue Samples

Human adipose tissues (~0.5 g) were collected via a standard surgical procedure from abdominal subcutaneous fat pads situated lateral to the umbilicus. Briefly, the periumbilical area was sterilized using alcohol swabs and then locally anesthetized using 2% lidocaine (2 mL). A small superficial skin incision (0.5 cm) was then made; after which, fat tissue was collected using punch biopsy. Freshly collected adipose tissues (~50–100 mg) were preserved in RNAlater or embedded in optimal cutting temperature (OCT) compound and stored at −80°C until use.

2.4. RNA Isolation from Adipose Tissue Samples

Total cellular RNA was purified using the RNeasy kit (Qiagen, Valencia, CA, USA) as per manufacturer’s instructions. Briefly, adipose tissue samples (preserved in RNAlater or embedded in OCT) were thawed and homogenized in Qiazol lysis solution (Qiagen, Valencia, CA, USA) using TissueRuptor (Qiagen, Hilden, Germany) at 33,000 rpm for 40 s. The homogenates were then treated with chloroform and separated into aqueous and organic phases by centrifugation at 12,000 ×g for 15 min at 4°C. After the upper aqueous RNA phase was collected, 70% ethanol was then added. Thereafter, the sample was applied to an RNeasy spin column to allow total RNA binding with the membrane and to wash out phenol and other contaminants. High-quality RNA was then eluted in RNase-free water. The quantity and quality of the isolated RNA were determined using the Epoch™ Spectrophotometer System (BioTek, Winooski, VT, USA) and formaldehyde–agarose gel electrophoresis, respectively.

2.5. Real-Time Reverse Transcription-Polymerase Chain Reaction

Real-time reverse transcription-polymerase chain reaction was conducted, as previously described [58]. Briefly, RNA samples (1 μg each) were reverse transcribed to cDNA using random hexamer primers and TaqMan reverse transcription reagents (High-Capacity cDNA Reverse Transcription kit; Applied Biosystems, CA, USA). For real-time reverse transcription-polymerase chain reaction (RT-PCR), cDNA (50 ng) was amplified using TaqMan® Gene Expression MasterMix (Applied Biosystems, CA, USA) and gene-specific 20x TaqMan Gene Expression Assays (Applied Biosystems, CA, USA) containing forward and reverse primers and a target-specific TaqMan® minor groove binder (MGB) probe labeled with 6-fluorescein amidite dye at the 5 end and a nonfluorescent quencher MGB at the 3 end of the probe for 40 cycles of PCR reaction using a 7500 Fast Real-Time PCR System (Applied Biosystems, CA, USA). Each cycle consisted of a denaturation phase for 15 s at 95°C and an annealing/extension phase for 1 min at 60°C. The cycle was started after uracil DNA glycosylase activation (50°C for 2 min) and AmpliTaq Gold enzyme activation (95°C for 10 min). Amplified glyceraldehyde 3-phosphate dehydrogenase (GAPDH) expression was used as an internal control to normalize differences between individual samples. The comparative CT method was used to analyze the relative gene expression of various mediators using the formula 2-ΔΔCT whereby . In this regard, the relative mRNA fold expression for each specific gene was calculated by first normalizing the CT values to an endogenous control (GAPDH) (ΔCT) and then normalizing the ΔCT of each gene expression to the highest ΔCT of normal weight control. The assay IDs (TaqMan® Assays, Thermo Fisher Scientific) of the analyzed genes are shown in Supplementary Materials (available here).

2.6. Statistical Methods

The GraphPad Prism software (version 7.04; San Diego, CA, USA) was used for all statistical analyses. Correlation analysis was conducted using nonparametric Spearman’s test, and results were validated using an extra test wherein an estimated cutoff value for IL-33 (median mRNA expression of IL-33 in individuals with normoglycemia = 1.5) was used to compare between low and high IL-33 in terms of the medians of various biomarkers. To compare between two groups of data, the nonparametric Mann–Whitney test was utilized with a value of less than 0.05 being considered statistically significant.

3. Results

3.1. Adipose Tissue IL-33 and Its Association with ST2

There was no difference in the expression levels of IL-33 or ST2 among individuals with normoglycemia, prediabetes, or T2D (Figures 1(a) and 1(b)). In addition, no difference was observed in the expression levels of IL-33 or ST2 among individuals with T2D who had varying degrees of glycemic control (, HbA1c 6.6–8.0, ) (Figures 1(c) and 1(d)). IL-33 was directly correlated with ST2 in individuals with normoglycemia (; ; ) and T2D (; ; ) but not in those with prediabetes. Similarly, higher levels of IL-33 were associated with significantly higher ST2 in individuals with normoglycemia (; median 2.45, vs. median 1.69, ) and T2D (; median 2.83, vs. median 1.83, ) but not in those with prediabetes (Figures 2(a) and 2(b)).

Figure 1: Expression of adipose tissue IL-33 and ST2 among individuals with varying degrees of glycemia. (a) There was no difference in the level of IL-33 among individuals with normoglycemia, prediabetes, or T2D. (b) There was no difference in the level of ST2 among individuals with normoglycemia, prediabetes, or T2D. (c) There was no difference in the level of IL-33 among individuals with T2D and varying degrees of glycemic control. (d) There was no difference in the level of ST2 among individuals with T2D and varying degrees of glycemic control. IL-33: interleukin-33; ST2: suppressor of tumorigenicity 2; HbA1c: glycated hemoglobin; T2D: type 2 diabetes; Meds: glucose-lowering medications.
Figure 2: Association between adipose tissue IL-33 and ST2 in individuals with varying degrees of glycemia. (a) IL-33 was directly correlated with ST2 in individuals with normoglycemia () and T2D () but not in those with prediabetes (). (b) Higher levels of IL-33 were associated with significantly higher ST2 in individuals with normoglycemia and T2D but not in those with prediabetes. IL-33: interleukin-33; ST2: suppressor of tumorigenicity 2; NL: normoglycemia; T2D: type 2 diabetes; Meds: glucose-lowering medications.
3.2. Association between Adipose Tissue IL-33 and Clinicometabolic Parameters

IL-33 was inversely correlated with HbA1c in individuals with normoglycemia (; ; ) and T2D (; ; ) but not in those with prediabetes. Among individuals with normoglycemia, those who had higher levels of IL-33 had lower (although statistically nonsignificant) HbA1c compared with those who had lower levels of IL-33 (; median 5.2, vs. median 5.4, ). This pattern was not observed among individuals with prediabetes. Among individuals with T2D, those who had higher levels of IL-33 had significantly lower HbA1c compared with those who had lower levels of IL-33 (; median 7.6, vs. median 9, ) (Figures 3(a) and 3(b)). No correlation was observed between ST2 and HbA1c among individuals with normoglycemia, prediabetes, or T2D. The ratio of IL-33 to ST2 was inversely correlated with HbA1c in individuals with normoglycemia (; ; ) but not in those with prediabetes or T2D (Figure 3(c)). To determine whether the inverse correlation between IL-33 and HbA1c in people with T2D was confined to individuals with a better glycemic control, they were divided into three groups based on their level of glycemic control (i.e., , HbA1c 6.6–8.0, and ) and analyzed separately. Accordingly, IL-33 was not correlated with HbA1c in individuals with T2D when stratified for their level of glycemic control. However, IL-33 was inversely correlated with fasting glucose (; ) in individuals with T2D who had a better glycemic control (; ) (Figure 3(d)). No correlation was found between adipose tissue IL-33 and circulating lipids (total cholesterol, LDL, HDL, and TG), BMI, body composition (PBF, SLM %, and TBW %), or waist-to-hip ratio among individuals with normoglycemia, prediabetes, or T2D. No difference in these parameters was observed among individuals with higher versus lower levels of IL-33. Interestingly, IL-33 in individuals with T2D increased with age (; ; ; data not shown).

Figure 3: Association between adipose tissue IL-33 and HbA1c (and fasting glucose in T2D) in individuals with varying degrees of glycemia. (a) IL-33 was inversely correlated with HbA1c in individuals with normoglycemia () and T2D () but not in those with prediabetes (). (b) Higher levels of IL-33 were associated with lower HbA1c in individuals with normoglycemia (although statistically nonsignificant) and T2D (statistically significant) but not in those with prediabetes. (c) The ratio of IL-33 to ST2 was inversely correlated with HbA1c in individuals with normoglycemia () but not in those with prediabetes () or T2D (). (d) IL-33 was inversely correlated with fasting glucose in individuals with T2D who had a better glycemic control (). IL-33: interleukin-33; ST2: suppressor of tumorigenicity 2; HbA1c: glycated hemoglobin; NL: normoglycemia; T2D: type 2 diabetes; Meds: glucose-lowering medications.
3.3. Association between Adipose Tissue IL-33 and Macrophage Markers

The association between adipose tissue IL-33 and macrophage markers, including pattern recognition receptors (PRRs) and chemokine receptors, was assessed in individuals with normoglycemia, prediabetes, and T2D. There was no correlation between IL-33 and CD68 (a universal macrophage marker); however, comparative analysis revealed that higher levels of IL-33 were associated with significantly lower CD68 (; median 2.19, vs. median 3.57, ) in individuals with T2D (Figures 4(a) and 4(b)). There was no correlation between IL-33 and CD11c, CD86, CD163, and chemokine receptors CCR1, CCR2, and CCR5 (data not shown). With regard to PRRs, IL-33 was directly correlated with the C-type lectin receptor CD302 in individuals with normoglycemia (; ; ) and T2D (; ; ) but not in those with prediabetes. Comparative analysis confirmed that higher levels of IL-33 tended to be associated with higher CD302 expression in individuals with normoglycemia (although this did not reach statistical significance, ; median 1.77, vs. median 1.3, ) and T2D (; median 1.57, vs. median 1.07, ) but not in those with prediabetes (Figures 4(c) and 4(d)). There was no association between IL-33 and CLEC7A among individuals with normoglycemia, prediabetes, or T2D (data not shown). There was no correlation between IL-33 and toll-like receptor (TLR) 2, TLR4, TLR7, TLR8, and TLR10 among individuals with normoglycemia, prediabetes, or T2D. However, IL-33 was directly correlated with TLR3 (; ; ) in individuals with T2D, although comparative analysis did not reach statistical significance (Figures 5(a) and 5(b)). Interestingly, IL-33 was directly correlated with TLR9 (; ; ) in individuals with prediabetes; and comparative analysis confirmed that higher levels of IL-33 were associated with significantly higher TLR9 expression (; median 8.84, vs. median 4.6, ) (Figures 5(c) and 5(d)).

Figure 4: Association between adipose tissue IL-33 and CD68 and CD302 in individuals with varying degrees of glycemia. (a) IL-33 was inversely correlated (although statistically nonsignificant) with CD68 in individuals with normoglycemia (), prediabetes (), and T2D (). (b) Higher levels of IL-33 were associated with significantly lower CD68 in individuals with T2D but not in those with normoglycemia or prediabetes. (c) IL-33 was directly correlated with CD302 in individuals with normoglycemia () and T2D () but not in those with prediabetes (). (d) Higher levels of IL-33 were associated with higher CD302 in individuals with normoglycemia (although statistically nonsignificant) and T2D (statistically significant) but not in those with prediabetes. IL-33: interleukin-33; CD68: cluster of differentiation 68; CD302: cluster of differentiation 302; NL: normoglycemia; T2D: type 2 diabetes; Meds: glucose-lowering medications.
Figure 5: Association between adipose tissue IL-33 and TLR3 and TLR9 in individuals with varying degrees of glycemia. (a) IL-33 was directly correlated with TLR3 in individuals with T2D () but not in those with normoglycemia () or prediabetes (). (b) Higher levels of IL-33 were associated with higher TLR3 in individuals with T2D (although statistically nonsignificant) but not in those with normoglycemia or prediabetes. (c) IL-33 was directly correlated with TLR9 in individuals with prediabetes () but not in those with normoglycemia () or T2D (). (d) Higher levels of IL-33 were associated with significantly higher TLR9 in individuals with prediabetes but not in those with normoglycemia or T2D. IL-33: interleukin-33; TLR3: toll-like receptor 3; TLR9: toll-like receptor 9; PreD: prediabetes; T2D: type 2 diabetes; Meds: glucose-lowering medications.
3.4. Association between Adipose Tissue IL-33 and Mediators of Inflammation and Immune Regulation

The association between adipose tissue IL-33 and inflammatory cytokines and chemokines was evaluated in individuals with normoglycemia, prediabetes, and T2D. There was no association between IL-33 and inflammatory cytokines including IL-1β, TNF-α, IL-8, IL-18, or IL-23A. Similarly, there was no association between IL-33 and chemokine (C-C motif) ligand (CCL) 2 (CCL2), CCL5, CCL11, CCL15, CCL19, CCL20, (C-X-C motif) ligand (CXCL) 8, CXCL9, and CXCL10, among individuals with normoglycemia, prediabetes, or T2D. However, IL-33 was inversely correlated with CCL7 in individuals with normoglycemia (; ; ) but not in those with prediabetes or T2D. Comparative analysis confirmed that higher levels of IL-33 were associated with significantly lower CCL7 in individuals with normoglycemia (; median 16.7, vs. median 118.8, ) but not in those with prediabetes or T2D (Figures 6(a) and 6(b)). Similarly, IL-33 was inversely correlated with CCL8 in individuals with normoglycemia (; ; ) but not in those with prediabetes or T2D. However, although higher levels of IL-33 were associated with lower CCL8 in individuals with normoglycemia, this did not reach statistical significance (Figures 6(c) and 6(d)). Interestingly, although IL-33 was not correlated with IL-6 in individuals with normoglycemia, prediabetes, or T2D, higher levels of IL-33 were associated with significantly higher IL-6 in individuals with normoglycemia (; median 8.86, vs. median 3.5, ) but not in those with prediabetes or T2D (Figures 7(a) and 7(b)). Furthermore, IL-33 was directly correlated with IL-12A (IL-12B was not tested) in individuals with T2D (; ; ) but not in those with normoglycemia or prediabetes. Among individuals with T2D, those who had higher levels of IL-33 had higher levels (although statistically nonsignificant) of IL-12A compared with those who had lower levels of IL-33 (; median 5.79, vs. median 4.2, ) (Figures 7(c) and 7(d)).

Figure 6: Association between adipose tissue IL-33 and CCL7 and CCL8 in individuals with varying degrees of glycemia. (a) IL-33 was inversely correlated with CCL7 in individuals with normoglycemia () but not in those with prediabetes () or T2D (). (b) Higher levels of IL-33 were associated with significantly lower CCL7 in individuals with normoglycemia but not in those with prediabetes or T2D. (c) IL-33 was inversely correlated with CCL8 in individuals with normoglycemia () but not in those with prediabetes () or T2D (). (d) Higher levels of IL-33 were associated with lower CCL8 in individuals with normoglycemia (although statistically nonsignificant) but not in those with prediabetes or T2D. IL-33: interleukin-33; CCL7: chemokine (C-C motif) ligand 7; CCL8: chemokine (C-C motif) ligand 8; NL: normoglycemia; T2D: type 2 diabetes; Meds: glucose-lowering medications.
Figure 7: Association between adipose tissue IL-33 and IL-6 and IL-12A in individuals with varying degrees of glycemia. (a) IL-33 was directly correlated with IL-6 (although statistically nonsignificant) in individuals with normoglycemia (), prediabetes (), and T2D (). (b) Higher levels of IL-33 were associated with significantly higher IL-6 in individuals with normoglycemia but not in those with prediabetes or T2D. (c) IL-33 was directly correlated with IL-12A in individuals with T2D () but not in those with normoglycemia () or prediabetes (). (d) Higher levels of IL-33 were associated with significantly higher IL-12A in individuals with T2D (although statistically nonsignificant) but not in those with normoglycemia or prediabetes. IL-33: interleukin-33; IL-6: interleukin-6; IL-12A: interleukin-12A; T2D: type 2 diabetes; Meds: glucose-lowering medications.

Next, we determined whether adipose tissue IL-33 was associated with downstream Th2 cytokines and/or mediators of immune regulation. There was no association between IL-33 and IL-5, IL-10, IL-13, TGF-β, FOXP3, or CD127. However, IL-33 was directly correlated with fibrinogen-like protein 2 (FGL2) in individuals with normoglycemia (; ; ) and T2D (; ; ) but not in those with prediabetes. Among individuals with normoglycemia, those who had higher levels of IL-33 had significantly higher levels of FGL2 (; median 2.0, vs. median 1.44, ) compared with those who had lower levels of IL-33 (Figures 8(a) and 8(b)).

Figure 8: Association between adipose tissue IL-33 and FGL2 in individuals with varying degrees of glycemia. (a) IL-33 was directly correlated with FGL2 in individuals with normoglycemia () and T2D () but not in those with prediabetes (). (b) Higher levels of IL-33 were associated with significantly higher FGL2 in individuals with normoglycemia but not in those with prediabetes or T2D. IL-33: interleukin-33; FGL2: fibrinogen-like protein 2; NL: normoglycemia; T2D: type 2 diabetes; Meds: glucose-lowering medications.
3.5. Association between Adipose Tissue IL-33 and “Beiging” of Adipose Tissue

We determined whether adipose tissue IL-33 was associated with genes involved in beiging of adipose tissue in individuals with normoglycemia, prediabetes, and T2D. Although IL-33 was not correlated with UCP1 or COX7A1, it was directly correlated with PR domain containing 16 (PRDM16) in individuals with normoglycemia (; ; ) and T2D (; ; ) but not in those with prediabetes. Similarly, higher levels of IL-33 were associated with significantly higher PRDM16 in individuals with normoglycemia (; median 7.9, vs. median 3.3, ) and T2D (; median 6.1, vs. median 4.1, ) but not in those with prediabetes (Figures 9(a) and 9(b)).

Figure 9: Association between adipose tissue IL-33 and PRDM16 in individuals with varying degrees of glycemia. (a) IL-33 was directly correlated with PRDM16 in individuals with normoglycemia () and T2D () but not in those with prediabetes (). (b) Higher levels of IL-33 were associated with significantly higher PRDM16 in individuals with normoglycemia and T2D but not in those with prediabetes. IL-33: interleukin-33; PRDM16: PR domain containing 16; NL: normoglycemia; T2D: type 2 diabetes; Meds: glucose-lowering medications.

4. Discussion

Adiposity is associated with increased risk of metabolic disease such as insulin resistance and T2D [1, 2]; interestingly, however, some individuals with increased adiposity remain metabolically healthy [3]. It has been demonstrated that the IL-33/ST2 axis plays a protective role against obesity and/or insulin resistance and T2D in animal models [18, 52, 53]. Moreover, there is evidence to suggest that circulating IL-33 may confer protective metabolic effects in humans, more specifically, in individuals with normal weight and glycemia but not with obesity and/or T2D [54]. However, it is not known whether metabolic effects of adipose tissue IL-33 differ among individuals with normoglycemia, prediabetes, and T2D. In the present study, we investigated the expression level of IL-33 (and ST2) in the adipose tissue, as well as its association with various immune and metabolic parameters, in individuals with normoglycemia, prediabetes, and T2D.

We initially asked whether the level of adipose tissue IL-33 (and/or ST2) was differentially expressed among individuals with normoglycemia, prediabetes, or T2D. Our results showed no difference in the level of IL-33 or ST2 among the study groups. Next, we sought to investigate whether adipose tissue IL-33 was differentially associated with overall glycemia (by measuring HbA1c). As was found with circulating IL-33 [54], adipose tissue IL-33 was inversely associated with HbA1c in individuals with normoglycemia. Interestingly, however, an inverse association was also observed in individuals with T2D but not in those with prediabetes; this finding was unexpected since such an association was not observed with circulating IL-33 in individuals with T2D [54]. Given the importance of IL-33/ST2 axis in protection against metabolic disease, we sought to investigate whether IL-33 was differentially associated with ST2 among individuals with normoglycemia, prediabetes, and T2D. Our data showed that IL-33 was directly associated with ST2 in individuals with normoglycemia and T2D but not in those with prediabetes. Taken together, our results suggested that adipose tissue IL-33 was inversely associated with overall glycemia but directly associated with ST2 and, importantly, that these patterns of associations were only observed in individuals with normoglycemia and T2D but not in those with prediabetes. Thus, there appears to be an important dynamic between IL-33 and ST2 in the adipose tissue; one that influences overall glycemia and may be dysfunctional in individuals with prediabetes. Indeed, the ratio of IL-33 to ST2 was inversely associated with HbA1c, although this was only observed in individuals with normoglycemia. Accordingly, we speculate that the dynamic between IL-33 and ST2 may be equipped with a positive feedback loop that promotes protective IL-33/ST2 axis in the adipose tissue. Further, we speculate that the kinetics of innate and/or adaptive immune cell responses, such as Th2 and Treg cells which are protective against metabolic disease, may be regulated by the ratio of IL-33 to ST2. Taking all these observations into consideration, it seems plausible to surmise that the dynamics and biological functions (rather than the level per se) of endogenous adipose tissue IL-33 and ST2 may play a role in overall glycemia in humans (and irrespective of its influence on body weight).

Since prediabetes is an early-stage metabolic disorder which, if left unmanaged, often progresses to T2D, one would have expected to observe a similar dynamic between IL-33/ST2 and HbA1c among individuals with prediabetes and T2D; however, this was not the case. The lack of an association between IL-33 and overall glycemia in individuals with prediabetes may point toward a potential dysfunction in the IL-33/ST2 axis that may occur early during the development of metabolic disorders/T2D and its potential reversal with glucose-lowering medications as may be occurring in individuals with T2D. In this regard, glucose-lowering medications may have played a role in the observed inverse association between adipose tissue IL-33 and HbA1c in individuals with T2D. Thus, the IL-33/ST2 axis may represent a novel target by which glucose-lowering medications confer their metabolic effects in T2D. This notion is supported by the observation that adipose tissue IL-33 was inversely associated with fasting glucose in individuals with T2D and a better glycemic control, which was not observed in individuals with prediabetes despite having a similar HbA1c range. There are various mechanisms by which glucose-lowering medications confer their metabolic effects; in addition to these, we speculate that these medications may also target and restore the dynamics and biological functions of IL-33/ST2 in the adipose tissue of individuals with T2D. Of note, the previous studies have reported that the inhibition of ST2 internalization enhances IL-33-induced cytokine release in epithelial cells [59]. Therefore, one way in which these medications may target/restore the IL-33/ST2 axis in individuals with T2D is by inhibiting ST2 internalization, thereby leading to increased IL-33/ST2 interaction. In addition, it has been demonstrated that IL-33 can be released in either full length or a cleaved form and that processing by caspase-1, caspase-3, or caspase-7 leads to IL-33 inactivation and abrogation of IL-33-mediated responses [60, 61]. Accordingly, it may be possible to speculate that glucose-lowering medications, through inhibition of caspases, may lead to the activation of IL-33 in individuals with T2D.

It is well known that obesity is associated with a state of chronic low-grade inflammation leading to ongoing activation of the innate immune system [62]. The recruitment of immune cells, specifically macrophages, is a key feature in obesity-induced inflammation [63]. Although the phenotype of macrophages in the adipose tissue is highly dependent on the microenvironment [64], macrophages can generally be divided into two main phenotypes: the alternatively activated (M2) macrophages which are metabolically protective and the classically activated (M1) macrophages which are proinflammatory. However, macrophages can have mixed states that do not fall strictly under the M1/M2 phenotypes [65], and thus, phenotyping may not adequately capture the inflammatory state of the adipose tissue. IL-33 has been shown to be metabolically protective in murine models of obesity in which various mechanisms have been described [18, 52, 53]. One mechanism by which IL-33 exerts these protective metabolic effects is through polarization of resident macrophages toward M2 phenotype and by recruitment of immune cells that are involved in type 2 immune responses [18, 52]. Regarding our findings, given that adipose tissue IL-33 was inversely associated with HbA1c, we speculated that this may have been due to IL-33-induced reduction in adipose tissue inflammation. To address this possibility, we attempted to investigate the association between adipose tissue IL-33 and a select number of markers known to be associated with inflammation and immune regulation. Our data showed that there was no association between IL-33 and CD68 (a universal marker of macrophages), CD86 (regarded as an M1 marker [66]), CD11c (upregulated in M1), or CD163 (upregulated in M2 but can also be expressed by M1; the M2 marker CD206 [67] was not tested). Of note, although there was no correlation between IL-33 and CD68, higher levels of IL-33 were associated with lower CD68 in individuals with T2D which may suggest reduced monocyte/macrophage recruitment in the adipose tissue. Importantly, since this association was only observed in individuals with T2D, this may have been potentiated by glucose-lowering medications. It was not possible to ascertain whether IL-33 was associated with an increased M2 phenotype since this would have required a wide range of markers ascribed to M1 and M2 phenotypes. Future studies addressing this possibility, along with the effect of glucose-lowering medications on M1 versus M2, are required.

In addition, we investigated the association between adipose tissue IL-33 and PRRs, including the C-type lectin receptor CD302 and a range of toll-like receptors. In humans, CD302 is expressed by myeloid-derived cells such as monocytes, macrophages, and myeloid dendritic cells (DCs) [68]. We found a direct association between IL-33 and CD302 in individuals with normoglycemia and T2D but not in those with prediabetes. The previous studies have shown that the activation of monocyte-derived macrophages or DCs leads to decreased expression of CD302 [68]. Thus, the observed direct association between IL-33 and CD302 may reflect a reduced state of macrophage activation, which may translate into reduced adipose tissue inflammation. On the other hand, the previous in vitro studies on M1/M2 macrophage differentiation have shown that M2 macrophages (differentiated with IL-4) increased the expression of CD302 by almost 2-fold when compared with M0 macrophages. Although M1 macrophages also express CD302, cell surface expression was greater in M2 than M1 [69]. Therefore, the direst association between IL-33 and CD302 may reflect the preferential differentiation of M2 macrophages in the presence of increased IL-33 (although it may reflect increased CD302 on myeloid DCs as well). However, these possibilities need to be explored further with mechanistic as well as clinical studies. Out of all the TLRs tested, IL-33 was directly correlated with TLR3 and TLR9 in individuals with T2D and prediabetes, respectively. TLR3 influences glucose homeostasis and beta-cell insulin secretion [70], whereas TLR9 prevents response to self/host DNA, thereby preventing autoinflammation while facilitating access to pathogen/viral DNA [71, 72]. Therefore, there appears to be some potential dynamic between IL-33 and the TLR system, specifically TLR3 and TLR9, during the development of metabolic disease, which may be impacted by glucose-lowering medications.

Studies in animal models suggest that Th1/Tc1 are detrimentally involved in the attraction and differentiation of adipose tissue macrophages, whereas Th2 cells and, mainly, Treg cells are protective [7375]. Our data showed no association between IL-33 and the inflammatory mediators TNF-α, IL-1β, IL-8, IL-18, or IL-23A. Interestingly, however, a direct association was observed between IL-33 and IL-12A in individuals with T2D but not in those with normoglycemia or prediabetes. IL-12A can form the α-subunit (IL-12p35) of proinflammatory IL-12 (the β subunit being IL-12p40) or immunosuppressive IL-35 (the β subunit being Ebi3) [76, 77]; however, since the β subunits of the cytokines IL-12 and IL-35 were not assessed, it was not possible to get a clearer idea as to which of these cytokines were associated with IL-33. Nonetheless, considering that IL-33 is metabolically protective [18, 52, 53], combined with our finding that IL-33 was inversely associated with HbA1c in individuals with T2D, we speculate that this association between IL-33 and IL-12A may reflect its association with IL-35 rather than IL-12. However, this is mere speculation, and further studies are required to specifically assess the association between IL-33 and IL-12 versus IL-35.

It has been shown that Th2 and Treg cells play a protective role in the context of adipose tissue inflammation [7375]. IL-33 induces Th2 cytokine production, mainly IL-5 and IL-13 [36], and has also been associated with Treg cell responses [7880]. Therefore, we assessed whether IL-33 was associated with Th2 cytokines and/or mediators involved in immune regulation. Our data showed that IL-33 was not associated with Th2 cytokines IL-5 and IL-13 or with mediators involved in immune regulation, including IL-10, TGF-β, FOXP3, and CD127. Interestingly, however, IL-33 was directly associated with FGL2 in individuals with normoglycemia and T2D but not in those with prediabetes. Previously, FGL2 had been demonstrated to promote regulatory T cell function, to suppress Th1 and enhance Th2 skewing, and to downregulate antigen presentation [8185]. Therefore, our finding may point toward a novel mechanism by which IL-33 promotes Th2 and Treg cell responses, possibly through interaction with ST2.

Moreover, the previous studies have shown that white adipose tissue can convert to a brown-like depot termed “beige” adipose [7, 8], which mainly occurs in subcutaneous white adipose tissue depots [7, 8]. Beige adipose tissue dissipates energy and influences glucose and VLDL-TG metabolism [9, 10], potentially protecting against metabolic diseases, such as T2D. Although IL-33 has been implicated in promoting beiging of white adipose tissue in animal models [86, 87], no studies have assessed whether IL-33 is associated with beiging of adipose tissue in humans. Given that beige adipose can influence glucose metabolism [9, 10], we speculated that the observed inverse association between IL-33 and HbA1c may be due to IL-33-mediated beiging of white adipose tissue. To this end, we examined the association between IL-33 and genes involved in beiging of white adipose tissue. Accordingly, IL-33 was directly associated with PRDM16 but not with UCP1 or COX7A1 in individuals with normoglycemia and T2D but not in those with prediabetes. Therefore, our results suggest that IL-33 may play a role in glucose metabolism through a mechanism that may involve PRDM16.

5. Conclusions

Endogenous adipose tissue IL-33 and its dynamics with ST2 may play a role in overall glycemia in humans. The lack of an association between IL-33 and overall glycemia in individuals with prediabetes but not in those with T2D may suggest a potential dysfunction in the IL-33/ST2 axis in early-stage metabolic disorders and its potential reversal with glucose-lowering medications, as may be occurring in individuals with T2D. Therefore, our data suggest that it may be beneficial to start glucose-lowering management in the early stages of metabolic disease development. Furthermore, exogenous administration of IL-33 to individuals with prediabetes (and/or T2D) may promote reversal of metabolic derangement. We believe that these results will form a basis for novel mechanistic and clinical studies in people with prediabetes and T2D.

Data Availability

The raw data of the study can be provided upon request with maintenance of confidentiality, privacy, and anonymity of the research participants.

Conflicts of Interest

The authors declare no conflict of interest.

Acknowledgments

This work was conducted on a sample of participants that were recruited as part of the Adult Obesity Program (grant number RA-2010-0003). The program was financially supported by the Kuwait Foundation for the Advancement of Sciences (KFAS). We would like to acknowledge the staff from the Clinical Laboratory Department, Medical Fitness Center, and the National Dasman Diabetes Biobank for all their support. We also acknowledge Ms. Samia Warsame for her involvement in the recruitment of participants and data entry for the Adult Obesity Program. We thank all individuals who participated in the study.

Supplementary Materials

A table showing (1) the assay IDs of the analyzed genes and (2) the values (Kruskal-Wallis test) depicting any differences in the gene expression of the analyzed mediators among the three groups. (Supplementary Materials)

References

  1. S. E. Kahn, R. L. Hull, and K. M. Utzschneider, “Mechanisms linking obesity to insulin resistance and type 2 diabetes,” Nature, vol. 444, no. 7121, pp. 840–846, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. M. F. Gregor and G. S. Hotamisligil, “Inflammatory mechanisms in obesity,” Annual Review of Immunology, vol. 29, no. 1, pp. 415–445, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. N. Barbarroja, R. López-Pedrera, M. D. Mayas et al., “The obese healthy paradox: is inflammation the answer?” The Biochemical Journal, vol. 430, no. 1, pp. 141–149, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Gesta, Y. H. Tseng, and C. R. Kahn, “Developmental origin of fat: tracking obesity to its source,” Cell, vol. 131, no. 2, pp. 242–256, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. E. D. Rosen and B. M. Spiegelman, “What we talk about when we talk about fat,” Cell, vol. 156, no. 1-2, pp. 20–44, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Frontini and S. Cinti, “Distribution and development of brown adipocytes in the murine and human adipose organ,” Cell Metabolism, vol. 11, no. 4, pp. 253–256, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Bartelt and J. Heeren, “Adipose tissue browning and metabolic health,” Nature Reviews Endocrinology, vol. 10, no. 1, pp. 24–36, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Harms and P. Seale, “Brown and beige fat: development, function and therapeutic potential,” Nature Medicine, vol. 19, no. 10, pp. 1252–1263, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Bartelt, O. T. Bruns, R. Reimer et al., “Brown adipose tissue activity controls triglyceride clearance,” Nature Medicine, vol. 17, no. 2, pp. 200–205, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Chondronikola, E. Volpi, E. Borsheim et al., “Brown adipose tissue improves whole-body glucose homeostasis and insulin sensitivity in humans,” Diabetes, vol. 63, no. 12, pp. 4089–4099, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. O. Osborn and J. M. Olefsky, “The cellular and signaling networks linking the immune system and metabolism in disease,” Nature Medicine, vol. 18, no. 3, pp. 363–374, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. D. Mathis, “Immunological goings-on in visceral adipose tissue,” Cell Metabolism, vol. 17, no. 6, pp. 851–859, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. J. I. Odegaard and A. Chawla, “The immune system as a sensor of the metabolic state,” Immunity, vol. 38, no. 4, pp. 644–654, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. J. I. Odegaard and A. Chawla, “Pleiotropic actions of insulin resistance and inflammation in metabolic homeostasis,” Science, vol. 339, no. 6116, pp. 172–177, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. J. R. Brestoff and D. Artis, “Immune regulation of metabolic homeostasis in health and disease,” Cell, vol. 161, no. 1, pp. 146–160, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. V. Carriere, L. Roussel, N. Ortega et al., “IL-33, the IL-1-like cytokine ligand for ST2 receptor, is a chromatin-associated nuclear factor in vivo,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 1, pp. 282–287, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. C. Moussion, N. Ortega, and J. P. Girard, “The IL-1-like cytokine IL-33 is constitutively expressed in the nucleus of endothelial cells and epithelial cells in vivo: a novel ‘alarmin’?” PLoS One, vol. 3, no. 10, article e3331, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. A. M. Miller, D. L. Asquith, A. J. Hueber et al., “Interleukin-33 induces protective effects in adipose tissue inflammation during obesity in mice,” Circulation Research, vol. 107, no. 5, pp. 650–658, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. D. Kolodin, N. van Panhuys, C. Li et al., “Antigen- and cytokine-driven accumulation of regulatory T cells in visceral adipose tissue of lean mice,” Cell Metabolism, vol. 21, no. 4, pp. 543–557, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. A. B. Molofsky, F. van Gool, H. E. Liang et al., “Interleukin-33 and interferon-γ counter-regulate group 2 innate lymphoid cell activation during immune perturbation,” Immunity, vol. 43, no. 1, pp. 161–174, 2015. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Pichery, E. Mirey, P. Mercier et al., “Endogenous IL-33 is highly expressed in mouse epithelial barrier tissues, lymphoid organs, brain, embryos, and inflamed tissues: in situ analysis using a novel Il-33-LacZ gene trap reporter strain,” The Journal of Immunology, vol. 188, no. 7, pp. 3488–3495, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. I. S. Wood, B. Wang, and P. Trayhurn, “IL-33, a recently identified interleukin-1 gene family member, is expressed in human adipocytes,” Biochemical and Biophysical Research Communications, vol. 384, no. 1, pp. 105–109, 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Zeyda, B. Wernly, S. Demyanets et al., “Severe obesity increases adipose tissue expression of interleukin-33 and its receptor ST2, both predominantly detectable in endothelial cells of human adipose tissue,” International Journal of Obesity, vol. 37, no. 5, pp. 658–665, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. W. P. Arend, G. Palmer, and C. Gabay, “IL-1, IL-18, and IL-33 families of cytokines,” Immunological Reviews, vol. 223, no. 1, pp. 20–38, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Löhning, J. L. Grogan, A. J. Coyle et al., “T1/ST2 expression is enhanced on CD4+ T cells from schistosome egg-induced granulomas: analysis of Th cell cytokine coexpression ex vivo,” The Journal of Immunology, vol. 162, no. 7, pp. 3882–3889, 1999. View at Google Scholar
  26. H. Hayakawa, M. Hayakawa, A. Kume, and S. I. Tominaga, “Soluble ST2 blocks interleukin-33 signaling in allergic airway inflammation,” The Journal of Biological Chemistry, vol. 282, no. 36, pp. 26369–26380, 2007. View at Publisher · View at Google Scholar · View at Scopus
  27. V. Trajkovic, M. J. Sweet, and D. Xu, “T1/ST2--an IL-1 receptor-like modulator of immune responses,” Cytokine & Growth Factor Reviews, vol. 15, no. 2-3, pp. 87–95, 2004. View at Publisher · View at Google Scholar · View at Scopus
  28. M. Lohning, A. Stroehmann, A. J. Coyle et al., “T1/ST2 is preferentially expressed on murine Th2 cells, independent of interleukin 4, interleukin 5, and interleukin 10, and important for Th2 effector function,” Proceedings of the National Academy of Sciences of the United States of America, vol. 95, no. 12, pp. 6930–6935, 1998. View at Publisher · View at Google Scholar · View at Scopus
  29. J. Zhang, A. M. Ramadan, B. Griesenauer et al., “ST2 blockade reduces sST2-producing T cells while maintaining protective mST2-expressing T cells during graft-versus-host disease,” Science Translational Medicine, vol. 7, no. 308, article 308ra160, 2015. View at Publisher · View at Google Scholar · View at Scopus
  30. D. Xu, W. L. Chan, B. P. Leung et al., “Selective expression of a stable cell surface molecule on type 2 but not type 1 helper T cells,” The Journal of Experimental Medicine, vol. 187, no. 5, pp. 787–794, 1998. View at Publisher · View at Google Scholar · View at Scopus
  31. C. Schiering, T. Krausgruber, A. Chomka et al., “The alarmin IL-33 promotes regulatory T-cell function in the intestine,” Nature, vol. 513, no. 7519, pp. 564–568, 2014. View at Publisher · View at Google Scholar · View at Scopus
  32. B. M. Matta, D. K. Reichenbach, X. Zhang et al., “Peri-alloHCT IL-33 administration expands recipient T-regulatory cells that protect mice against acute GVHD,” Blood, vol. 128, no. 3, pp. 427–439, 2016. View at Publisher · View at Google Scholar · View at Scopus
  33. L. Blom, B. C. Poulsen, B. M. Jensen, A. Hansen, and L. K. Poulsen, “IL-33 induces IL-9 production in human CD4+ T cells and basophils,” PLoS One, vol. 6, no. 7, article e21695, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. K. Gerlach, Y. Y. Hwang, A. Nikolaev et al., “TH9 cells that express the transcription factor PU.1 drive T cell-mediated colitis via IL-9 receptor signaling in intestinal epithelial cells,” Nature Immunology, vol. 15, no. 7, pp. 676–686, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. K. Xu, R. M. Williams, D. Holowka, and B. Baird, “Stimulated release of fluorescently labeled IgE fragments that efficiently accumulate in secretory granules after endocytosis in RBL-2H3 mast cells,” Journal of Cell Science, vol. 111, Part 16, pp. 2385–2396, 1998. View at Google Scholar
  36. J. Schmitz, A. Owyang, E. Oldham et al., “IL-33, an interleukin-1-like cytokine that signals via the IL-1 receptor-related protein ST2 and induces T helper type 2-associated cytokines,” Immunity, vol. 23, no. 5, pp. 479–490, 2005. View at Publisher · View at Google Scholar · View at Scopus
  37. M. D. Smithgall, M. R. Comeau, B. R. Park Yoon, D. Kaufman, R. Armitage, and D. E. Smith, “IL-33 amplifies both Th1- and Th2-type responses through its activity on human basophils, allergen-reactive Th2 cells, iNKT and NK cells,” International Immunology, vol. 20, no. 8, pp. 1019–1030, 2008. View at Publisher · View at Google Scholar · View at Scopus
  38. M. Suzukawa, M. Iikura, R. Koketsu et al., “An IL-1 cytokine member, IL-33, induces human basophil activation via its ST2 receptor,” The Journal of Immunology, vol. 181, no. 9, pp. 5981–5989, 2008. View at Publisher · View at Google Scholar · View at Scopus
  39. M. Suzukawa, R. Koketsu, M. Iikura et al., “Interleukin-33 enhances adhesion, CD11b expression and survival in human eosinophils,” Laboratory Investigation, vol. 88, no. 11, pp. 1245–1253, 2008. View at Publisher · View at Google Scholar · View at Scopus
  40. S. H. Mun, N. Y. Ko, H. S. Kim et al., “Interleukin-33 stimulates formation of functional osteoclasts from human CD14+ monocytes,” Cellular and Molecular Life Sciences, vol. 67, no. 22, pp. 3883–3892, 2010. View at Publisher · View at Google Scholar · View at Scopus
  41. M. Kurowska-Stolarska, B. Stolarski, P. Kewin et al., “IL-33 amplifies the polarization of alternatively activated macrophages that contribute to airway inflammation,” The Journal of Immunology, vol. 183, no. 10, pp. 6469–6477, 2009. View at Publisher · View at Google Scholar · View at Scopus
  42. A. D. Joshi, S. R. Oak, A. J. Hartigan et al., “Interleukin-33 contributes to both M1 and M2 chemokine marker expression in human macrophages,” BMC Immunology, vol. 11, no. 1, p. 52, 2010. View at Publisher · View at Google Scholar · View at Scopus
  43. D. R. Neill, S. H. Wong, A. Bellosi et al., “Nuocytes represent a new innate effector leukocyte that mediates type-2 immunity,” Nature, vol. 464, no. 7293, pp. 1367–1370, 2010. View at Publisher · View at Google Scholar · View at Scopus
  44. K. Moro, T. Yamada, M. Tanabe et al., “Innate production of TH2 cytokines by adipose tissue-associated c-Kit+Sca-1+ lymphoid cells,” Nature, vol. 463, pp. 540–544, 2010. View at Publisher · View at Google Scholar · View at Scopus
  45. M. A. Rank, T. Kobayashi, H. Kozaki, K. R. Bartemes, D. L. Squillace, and H. Kita, “IL-33-activated dendritic cells induce an atypical TH2-type response,” The Journal of Allergy and Clinical Immunology, vol. 123, no. 5, pp. 1047–1054, 2009. View at Publisher · View at Google Scholar · View at Scopus
  46. H. R. Turnquist, T. L. Sumpter, A. Tsung et al., “IL-1β-driven ST2L expression promotes maturation resistance in rapamycin-conditioned dendritic cells,” The Journal of Immunology, vol. 181, no. 1, pp. 62–72, 2008. View at Publisher · View at Google Scholar
  47. J. C. Alves-Filho, F. Sônego, F. O. Souto et al., “Interleukin-33 attenuates sepsis by enhancing neutrophil influx to the site of infection,” Nature Medicine, vol. 16, no. 6, pp. 708–712, 2010. View at Publisher · View at Google Scholar · View at Scopus
  48. M. Komai-Koma, D. S. Gilchrist, A. N. J. McKenzie, C. S. Goodyear, D. Xu, and F. Y. Liew, “IL-33 activates B1 cells and exacerbates contact sensitivity,” The Journal of Immunology, vol. 186, no. 4, pp. 2584–2591, 2011. View at Publisher · View at Google Scholar · View at Scopus
  49. E. Bourgeois, L. P. van, M. Samson et al., “The pro-Th2 cytokine IL-33 directly interacts with invariant NKT and NK cells to induce IFN-gamma production,” European Journal of Immunology, vol. 39, no. 4, pp. 1046–1055, 2009. View at Publisher · View at Google Scholar · View at Scopus
  50. Q. Yang, G. Li, Y. Zhu et al., “IL-33 synergizes with TCR and IL-12 signaling to promote the effector function of CD8+ T cells,” European Journal of Immunology, vol. 41, no. 11, pp. 3351–3360, 2011. View at Publisher · View at Google Scholar · View at Scopus
  51. W. V. Bonilla, A. Frohlich, K. Senn et al., “The alarmin interleukin-33 drives protective antiviral CD8+ T cell responses,” Science, vol. 335, no. 6071, pp. 984–989, 2012. View at Publisher · View at Google Scholar · View at Scopus
  52. A. M. Miller and F. Y. Liew, “The IL-33/ST2 pathway--a new therapeutic target in cardiovascular disease,” Pharmacology & Therapeutics, vol. 131, no. 2, pp. 179–186, 2011. View at Publisher · View at Google Scholar · View at Scopus
  53. A. M. Miller, D. Xu, D. L. Asquith et al., “IL-33 reduces the development of atherosclerosis,” The Journal of Experimental Medicine, vol. 205, no. 2, pp. 339–346, 2008. View at Publisher · View at Google Scholar · View at Scopus
  54. A. Hasan, F. al-Ghimlas, S. Warsame et al., “IL-33 is negatively associated with the BMI and confers a protective lipid/metabolic profile in non-diabetic but not diabetic subjects,” BMC Immunology, vol. 15, no. 1, p. 19, 2014. View at Publisher · View at Google Scholar · View at Scopus
  55. P. H. Marathe, H. X. Gao, and K. L. Close, “American Diabetes Association standards of medical care in diabetes 2017,” Journal of Diabetes, vol. 9, no. 4, pp. 320–324, 2017. View at Publisher · View at Google Scholar · View at Scopus
  56. American Diabetes Association, “Diagnosis and classification of diabetes mellitus,” Diabetes Care, vol. 37, Supplement 1, pp. S81–S90, 2014. View at Publisher · View at Google Scholar · View at Scopus
  57. The International Expert Committee, “International Expert Committee report on the role of the A1c assay in the diagnosis of diabetes,” Diabetes Care, vol. 32, no. 7, pp. 1327–1334, 2009. View at Publisher · View at Google Scholar · View at Scopus
  58. R. Ahmad, P. K. Shihab, R. Thomas et al., “Increased expression of the interleukin-1 receptor-associated kinase (IRAK)-1 is associated with adipose tissue inflammatory state in obesity,” Diabetology and Metabolic Syndrome, vol. 7, no. 1, p. 71, 2015. View at Publisher · View at Google Scholar · View at Scopus
  59. J. Zhao, J. Wei, R. K. Bowser, R. S. Traister, M. H. Fan, and Y. Zhao, “Focal adhesion kinase-mediated activation of glycogen synthase kinase 3β regulates IL-33 receptor internalization and IL-33 signaling,” The Journal of Immunology, vol. 194, no. 2, pp. 795–802, 2015. View at Publisher · View at Google Scholar · View at Scopus
  60. C. Cayrol and J. P. Girard, “The IL-1-like cytokine IL-33 is inactivated after maturation by caspase-1,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 22, pp. 9021–9026, 2009. View at Publisher · View at Google Scholar · View at Scopus
  61. A. U. Lüthi, S. P. Cullen, E. A. McNeela et al., “Suppression of interleukin-33 bioactivity through proteolysis by apoptotic caspases,” Immunity, vol. 31, no. 1, pp. 84–98, 2009. View at Publisher · View at Google Scholar · View at Scopus
  62. P. A. Tataranni and E. Ortega, “A burning question: does an adipokine-induced activation of the immune system mediate the effect of overnutrition on type 2 diabetes?” Diabetes, vol. 54, no. 4, pp. 917–927, 2005. View at Publisher · View at Google Scholar · View at Scopus
  63. M. Zeyda and T. M. Stulnig, “Adipose tissue macrophages,” Immunology Letters, vol. 112, no. 2, pp. 61–67, 2007. View at Publisher · View at Google Scholar · View at Scopus
  64. P. J. Murray, J. E. Allen, S. K. Biswas et al., “Macrophage activation and polarization: nomenclature and experimental guidelines,” Immunity, vol. 41, no. 1, pp. 14–20, 2014. View at Publisher · View at Google Scholar · View at Scopus
  65. G. Chinetti-Gbaguidi and B. Staels, “Macrophage polarization in metabolic disorders: functions and regulation,” Current Opinion in Lipidology, vol. 22, no. 5, pp. 365–372, 2011. View at Publisher · View at Google Scholar · View at Scopus
  66. J. C. Gensel, T. J. Kopper, B. Zhang, M. B. Orr, and W. M. Bailey, “Predictive screening of M1 and M2 macrophages reveals the immunomodulatory effectiveness of post spinal cord injury azithromycin treatment,” Scientific Reports, vol. 7, no. 1, p. 40144, 2017. View at Publisher · View at Google Scholar · View at Scopus
  67. D. L. Morris, K. Singer, and C. N. Lumeng, “Adipose tissue macrophages: phenotypic plasticity and diversity in lean and obese states,” Current Opinion in Clinical Nutrition and Metabolic Care, vol. 14, no. 4, pp. 341–346, 2011. View at Publisher · View at Google Scholar · View at Scopus
  68. M. Kato, S. Khan, E. d'Aniello, K. J. McDonald, and D. N. J. Hart, “The novel endocytic and phagocytic C-type lectin receptor DCL-1/CD302 on macrophages is colocalized with F-actin, suggesting a role in cell adhesion and migration,” The Journal of Immunology, vol. 179, no. 9, pp. 6052–6063, 2007. View at Publisher · View at Google Scholar · View at Scopus
  69. T. H. Lo, P. A. Silveira, P. D. Fromm et al., “Characterization of the expression and function of the C-type lectin receptor CD302 in mice and humans reveals a role in dendritic cell migration,” The Journal of Immunology, vol. 197, no. 3, pp. 885–898, 2016. View at Publisher · View at Google Scholar · View at Scopus
  70. D. Strodthoff, Z. Ma, T. Wirström et al., “Toll-like receptor 3 influences glucose homeostasis and β-cell insulin secretion,” Diabetes, vol. 64, no. 10, pp. 3425–3438, 2015. View at Publisher · View at Google Scholar · View at Scopus
  71. G. M. Barton, J. C. Kagan, and R. Medzhitov, “Intracellular localization of Toll-like receptor 9 prevents recognition of self DNA but facilitates access to viral DNA,” Nature Immunology, vol. 7, no. 1, pp. 49–56, 2006. View at Publisher · View at Google Scholar · View at Scopus
  72. M. L. Mouchess, N. Arpaia, G. Souza et al., “Transmembrane mutations in Toll-like receptor 9 bypass the requirement for ectodomain proteolysis and induce fatal inflammation,” Immunity, vol. 35, no. 5, pp. 721–732, 2011. View at Publisher · View at Google Scholar · View at Scopus
  73. M. Feuerer, L. Herrero, D. Cipolletta et al., “Lean, but not obese, fat is enriched for a unique population of regulatory T cells that affect metabolic parameters,” Nature Medicine, vol. 15, no. 8, pp. 930–939, 2009. View at Publisher · View at Google Scholar · View at Scopus
  74. S. Nishimura, I. Manabe, M. Nagasaki et al., “CD8+ effector T cells contribute to macrophage recruitment and adipose tissue inflammation in obesity,” Nature Medicine, vol. 15, no. 8, pp. 914–920, 2009. View at Publisher · View at Google Scholar · View at Scopus
  75. S. Winer, Y. Chan, G. Paltser et al., “Normalization of obesity-associated insulin resistance through immunotherapy,” Nature Medicine, vol. 15, no. 8, pp. 921–929, 2009. View at Publisher · View at Google Scholar · View at Scopus
  76. D. A. A. Vignali and V. K. Kuchroo, “IL-12 family cytokines: immunological playmakers,” Nature Immunology, vol. 13, no. 8, pp. 722–728, 2012. View at Publisher · View at Google Scholar · View at Scopus
  77. L. Sun, C. He, L. Nair, J. Yeung, and C. E. Egwuagu, “Interleukin 12 (IL-12) family cytokines: role in immune pathogenesis and treatment of CNS autoimmune disease,” Cytokine, vol. 75, no. 2, pp. 249–255, 2015. View at Publisher · View at Google Scholar · View at Scopus
  78. M. Li, Y. Li, X. Liu, X. Gao, and Y. Wang, “IL-33 blockade suppresses the development of experimental autoimmune encephalomyelitis in C57BL/6 mice,” Journal of Neuroimmunology, vol. 247, no. 1-2, pp. 25–31, 2012. View at Publisher · View at Google Scholar · View at Scopus
  79. H. R. Jiang, M. Milovanović, D. Allan et al., “IL-33 attenuates EAE by suppressing IL-17 and IFN-γ production and inducing alternatively activated macrophages,” European Journal of Immunology, vol. 42, no. 7, pp. 1804–1814, 2012. View at Publisher · View at Google Scholar · View at Scopus
  80. B. M. Matta, J. M. Lott, L. R. Mathews et al., “IL-33 is an unconventional alarmin that stimulates IL-2 secretion by dendritic cells to selectively expand IL-33R/ST2+ regulatory T cells,” The Journal of Immunology, vol. 193, no. 8, pp. 4010–4020, 2014. View at Publisher · View at Google Scholar · View at Scopus
  81. C. W. Y. Chan, L. S. Kay, R. G. Khadaroo et al., “Soluble fibrinogen-like protein 2/fibroleukin exhibits immunosuppressive properties: suppressing T cell proliferation and inhibiting maturation of bone marrow-derived dendritic cells,” The Journal of Immunology, vol. 170, no. 8, pp. 4036–4044, 2003. View at Publisher · View at Google Scholar · View at Scopus
  82. I. Shalev, K. M. Wong, K. Foerster et al., “The novel CD4+CD25+ regulatory T cell effector molecule fibrinogen-like protein 2 contributes to the outcome of murine fulminant viral hepatitis,” Hepatology, vol. 49, no. 2, pp. 387–397, 2009. View at Publisher · View at Google Scholar · View at Scopus
  83. K. Foerster, A. Helmy, Y. Zhu et al., “The novel immunoregulatory molecule FGL2: a potential biomarker for severity of chronic hepatitis C virus infection,” Journal of Hepatology, vol. 53, no. 4, pp. 608–615, 2010. View at Publisher · View at Google Scholar · View at Scopus
  84. Z. Zhao, C. Yang, L. Wang et al., “The regulatory T cell effector soluble fibrinogen-like protein 2 induces tubular epithelial cell apoptosis in renal transplantation,” Experimental Biology and Medicine, vol. 239, no. 2, pp. 193–201, 2014. View at Publisher · View at Google Scholar · View at Scopus
  85. I. Shalev, H. Liu, C. Koscik et al., “Targeted deletion of fgl2 leads to impaired regulatory T cell activity and development of autoimmune glomerulonephritis,” The Journal of Immunology, vol. 180, no. 1, pp. 249–260, 2007. View at Publisher · View at Google Scholar
  86. J. R. Brestoff, B. S. Kim, S. A. Saenz et al., “Group 2 innate lymphoid cells promote beiging of white adipose tissue and limit obesity,” Nature, vol. 519, no. 7542, pp. 242–246, 2015. View at Publisher · View at Google Scholar · View at Scopus
  87. M. W. Lee, J. I. Odegaard, L. Mukundan et al., “Activated type 2 innate lymphoid cells regulate beige fat biogenesis,” Cell, vol. 160, no. 1-2, pp. 74–87, 2015. View at Publisher · View at Google Scholar · View at Scopus