Cellular and molecular players in adipose tissue inflammation in the development of obesity-induced insulin resistance

Abstract

There is increasing evidence showing that inflammation is an important pathogenic mediator of the development of obesity-induced insulin resistance. It is now generally accepted that tissue-resident immune cells play a major role in the regulation of this obesity-induced inflammation. The roles that adipose tissue (AT)-resident immune cells play have been particularly extensively studied. AT contains most types of immune cells and obesity increases their numbers and activation levels, particularly in AT macrophages (ATMs). Other pro-inflammatory cells found in AT include neutrophils, Th1 CD4 T cells, CD8 T cells, B cells, DCs, and mast cells. However, AT also contains anti-inflammatory cells that counter the pro-inflammatory immune cells that are responsible for the obesity-induced inflammation in this tissue. These anti-inflammatory cells include regulatory CD4 T cells (Tregs), Th2 CD4 T cells, and eosinophils. Hence, AT inflammation is shaped by the regulation of pro- and anti-inflammatory immune cell homeostasis, and obesity skews this balance towards a more pro-inflammatory status. Recent genetic studies revealed several molecules that participate in the development of obesity-induced inflammation and insulin resistance. In this review, the cellular and molecular players that participate in the regulation of obesity-induced inflammation and insulin resistance are discussed, with particular attention being placed on the roles of the cellular players in these pathogeneses. This article is part of a Special Issue entitled: Modulation of Adipose Tissue in Health and Disease.

Keywords: ABCA; AIM; APC; ASC; AT; AT macrophage; ATM; ATP-binding cassette transporter; Adipose tissue; Adipose tissue immune cell; Apoptosis inhibitor of macrophage; BLT; BM; BMT; Bregs; C-C chemokine receptor type 2; C-X-C motif chemokine 5; C-X-C motif receptor 2; CCL5; CCR2; CLSs; CTLs; CX3C chemokine receptor 1; CX3CR1; CXCL5; CXCR2; Casp1; Caspase 1; Chemokine (C-C motif) ligand 5; DAMPs; DC; Damage-associated molecular pattern molecules; Dendritic cells; ECM; FABP; FFA; Free fatty acids; G-protein coupled receptor 120; GFP; GLUT4; GPR120; Glucose transporter type 4; HFD; HMGB1; IKKb; IR; IRS; Insulin receptor substrate; Insulin resistance; JNKs; Jun N-terminal kinases; KLF4; Krueppel-like factor 4; MAPK; MCP-1; MGL1; MHC; MPO; Major histocompatibility complex; NE; NFkB; NKT; NLS; NOD-like receptor family, pyrin domain containing 3; Natural Killer T cell; Nlrp3; Obesity-induced Inflammation; PBMC; PKC; PPAR; PRRs; Pattern recognition receptors; Protein kinase C; RAGs; RANTES; ROS; Reactive oxygen species; Recombination activating genes; Regulated on Activation Normal T cell Expressed and Secreted; SOCS1; SVC; Sorbin and SH3 domain-containing protein 1; Sorbs1; Suppressor of cytokine signaling 1; T cell receptors; T2D; TAMs; TCRs; TLR; TNF; TZDs; Toll-like receptors; Treg; Tumor necrosis factors; Type 2 Diabetes; adipose tissue; antigen-presenting cell; apoptotic speck protein containing a caspase recruitment domain; bone marrow; bone marrow transplantation; crown-like structures; cytotoxic T cells; double stranded DNA; dsDNA; extracellular matrix; fatty-acid-binding protein; green fluorescent protein; high fat diet; high-mobility group box 1; inhibitor of κB kinase-β; insulin receptor; leukotriene B4 receptor; macrophage galactose-type lectin 1; mitogen-activated protein kinase; monocyte chemotactic protein-1; myeloperoxidase; neutrophil elastase; nuclear factor kappa-light-chain-enhancer of activated B cells; nuclear localization sequence; peripheral blood mononuclear cell; peroxisome proliferator-activated receptors; regulator T cells; regulatory B cells; stromal vascular cell; thiazolidinediones; tumor-associated macrophages.

Fig. 1. The roles of AT immune cells in the development of obesity-induced inflammation

This diagram is based on the current published findings that are discussed in this review. The blue fonts represent cells that suppress inflammation, while the red fonts represent cells that induce inflammation. Since there are many contradictory reports regarding the role of NKT cells, NKT cells are not categorized in this diagram.

Fig. 2. Examples of flow cytometric analyses of mouse AT and circulating immune cells

A. Gating strategies for ATMs and AT DCs. After SVCs were isolated from AT by using a collagenase method, they were stained with antibodies against cell-specific markers and subjected to flow cytometric analysis with LSR II. The data were analyzed by FlowJo software with the following gating strategy. Since ATMs have high granularity (SSC), we used the logistic scale for the SSC gating. After the appropriate cells were selected by FSC-A (size)/SSC (granularity) gating (1), the aggregated cells were removed by FSC-A/FSC-W gating (2). The total leukocytes were then identified by positivity for CD45 (3). Thereafter, the lymphocytes and RBCs were excluded by removing the CD3⁺ (T cells), CD19⁺ (B cells), NK1.1⁺ (NK and NKT cells) and TER119⁺ (RBC) cells (4). The AT DCs were selected as CD11b⁻ and CD11c⁺ (5). Finally, ATMs were determined by identifying the CD11b⁺ and F4/80⁺ cells (6). B. Gating strategies for circulating immune cells. Whole blood was incubated with FcBlock to block the non-specific binding of Abs and then stained with antibodies against cell-specific surface markers. After staining, the RBCs were removed by lysing with FACS Lysing buffer (BD Biosciences). The cells were then analyzed with LSR II. The data were analyzed by FlowJo software with the following gating strategy. After the appropriate cells were selected by FSC-A (size)/SSC (granularity) gating (1), the aggregated cells were removed by FSC-A/FSC-W gating (2). The total leukocytes were then selected by positivity for CD45 (3). The neutrophils were selected as Gr-1⁺ and SSC^hi, while monocytes were selected as Gr-1⁺ and SSC^lo (4). It should be noted that this gating strategy selects only the Ly6C⁺ (Gr-1⁺) monocyte subpopulation and that the total monocyte population containing both the Ly6C⁺ and Ly6C⁻ monocyte subpopulations should be selected on the basis of their CD11b and/or CD115 positivity. Thereafter, the lymphocyte population was selected as Gr-1⁻ and SSC^lo (4). B cells were then selected as CD19⁺ from the lymphocyte population (5). Finally, NK, NKT and T cells from the CD19⁻ non-B cell population were further determined on the basis of their CD3 and/or NK1.1 expression levels (6). Cell surface markers that were used to define specific cell types by these gating strategies were listed in the boxes. Potential negative selection markers that can be used together were also listed with the parentheses at the end of positive selection markers. It should be also noted that the examples of the gating strategies shown here are only for the mouse immune cell analyses and that they are only for the general guidance.