Male Bovine GH Transgenic Mice Have Decreased Adiposity With an Adipose Depot-Specific Increase in Immune Cell Populations

Abstract

White adipose tissue (WAT) is composed of mature adipocytes and a stromal vascular fraction (SVF), which contains a variety of cells, including immune cells that vary among the different WAT depots. Growth hormone (GH) impacts immune function and adiposity in an adipose depot-specific manner. However, its effects on WAT immune cell populations remain unstudied. Bovine GH transgenic (bGH) mice are commonly used to study the in vivo effects of GH. These giant mice have an excess of GH action, impaired glucose metabolism, decreased adiposity, increased lean mass, and a shortened lifespan. Therefore, the purpose of this study was to characterize the WAT depot-specific differences in immune cell populations in the presence of excess GH in vivo. Three WAT depots were assessed: inguinal (sc), epididymal (EPI), and mesenteric (MES). Subcutaneous and MES bGH WAT depots showed a significantly higher number of total SVF cells, yet only MES bGH WAT had higher leukocyte counts compared with control samples. By means of flow cytometry analysis of the SVF, we detected greater macrophage and regulatory T-cell infiltration in sc and MES bGH WAT depots compared with controls. However, no differences were observed in the EPI WAT depot. RNA-sequencing confirmed significant alterations in pathways related to T-cell infiltration and activation in the sc depot with fewer significant changes in the EPI bGH WAT depot. These findings collectively point to a previously unrecognized role for GH in influencing the distribution of WAT immune cell populations in a depot-specific manner.

White adipose tissue (WAT) is a complex tissue composed of adipocytes as well as assorted cells within the stromal vascular fraction (SVF), including various immune cells. The resident and infiltrating immune cells within WAT vary in type, number, activation status, and production of various cytokines based on the level of adiposity as well as the anatomical location of the WAT depot (1–3). In a lean state, WAT has a higher prevalence of anti-inflammatory M2-like macrophages, myeloid-derived suppressor cells, regulatory T (Treg) cells, T helper (TH)2 cells, and eosinophils. In contrast, obese WAT has increased proinflammatory M1-like macrophages, dendritic cells, neutrophils, T cells (including both CD4⁺ TH1 and CD8⁺ effector T cells), B cells, and mast cells (2, 4, 5). There is also evidence that the leukocyte population in WAT is depot dependent, with proinflammatory cells being more abundant in visceral depots (1). In fact, Cohen et al (1) show that various intraabdominal WAT depots, which he subdivides as “true” visceral (eg, mesenteric [MES] that is drained by the portal vein) vs “nonvisceral” (eg, epididymal [EPI] and retroperitoneal), have distinct immune profiles. This suggests that each individual fat pad can be considered distinct miniorgans at least with respect to their leukocyte populations (1). This is not surprising considering that different depots have been shown to have distinct contributions to metabolism with inherent health ramifications (6–8). Although the roles of the immune cell types in different depots of obese WAT are not resolved, it is likely that the modulation of WAT-associated immune cells is a contributing factor to the chronic inflammation and insulin resistance present in the obese state.

A better characterization of the factors that contribute to the recruitment and establishment of specific immune cell subsets in select AT depots is warranted. Growth hormone (GH) is likely one of these factors as this hormone has potent lipolytic and antilipogenic effects on adipocytes, which result in a dramatic reduction of WAT mass in humans and various rodent models (9–11). In addition, GH receptor is known to be expressed in a wide array of innate and adaptive immune cells (12). GH is also important in conditions of impaired or activated immune systems, with most published data focusing on the hormone's ability to promote thymic growth as well as T-cell development and function (as reviewed in Ref. 12). Furthermore, recent studies report GH action on macrophages controls adipogenesis (13), glucose homeostasis, and WAT inflammation during diet-induced obesity (14). Therefore, GH signaling in WAT could be an important factor in establishing, maintaining, or activating the immune microenvironment and cytokine profile of this tissue. Collectively, this prompted us to explore the effect of an excess in GH-induced intracellular signaling on immune cell populations, numbers, and distribution in various WAT depots.

The bovine GH transgenic (bGH) mouse mimics the clinical condition of acromegaly (15). These transgenic mice have been extensively characterized (15–27). They have elevated plasma levels of GH and IGF-I. They also exhibit accelerated somatic growth and are larger than littermate controls throughout life (16–18). Young, adult bGH mice exhibit insulin resistance and hyperinsulinemia, although improvements in these variables occur with advancing age in some studies (16–19). Importantly, these mice also have a drastically reduced lifespan (20), similar to humans with acromegaly. The decrease in longevity for bGH mice is multifactorial with defects in cardiovascular function (18, 21–23), marked kidney damage (24, 25), and greater incidence of tumors (26). With regard to their WAT, bGH mice are lean with a significant reduction in the mass of all depots (16, 27). Furthermore, they have altered adipokine and cytokine levels with reduced circulating leptin and adiponectin and increased circulating resistin, monocyte chemoattractant protein-1 (MCP-1) and IL-6 levels as well as increased TNF-α and IL-6 mRNA expression levels in WAT as compared with control mice (18, 27–29). In terms of immune function, data show altered immune parameters, particularly T-cell abundance and activation, in the blood, thymus, and spleen of bGH mice (30–33). Thus, bGH mice represent a valuable tool to study the depot-specific impact of chronically elevated GH levels on the WAT microenvironment.

Here, we describe the initial characterization of the GH-dependent and depot-specific differences in immune cells present in bGH WAT. Our results show that an excess of GH action influences the distribution of different leukocyte subsets in WAT depots in association with decreased adiposity. In addition, deep RNA sequence analysis reveals intriguing targets for future studies.

Materials and Methods

Animals

bGH and wildtype (WT) mice in a C57BL/6J background strain were generated and bred at the Edison Biotechnology Institute at Ohio University as previously described (16, 27). Mice were housed 2–4 per cage with controlled light cycles (14-h light/10 h dark), temperature (22 ± 2°C), and ad libitum access to water and rodent chow (ProLab RMH 3000; PMI Nutrition International, Inc). Male bGH mice and WT littermate controls at 5 months of age were used for all studies (n = 6 bGH and 6 WT mice for fluorescence activated cell sorting [FACS] analysis; a separate cohort of n = 3 bGH and 3 WT mice for RNA sequencing [RNA-Seq] analyses). Animal protocols for the mice were approved by Ohio University's Institutional Animal Care and Use Committee.

Body weight and body composition

Body weight and body composition measurements were taken 2 weeks before tissue collection. A bench top quantitative nuclear magnetic resonance apparatus (Minispec; Bruker Optics) was used to analyze body composition as previously described (16).

SVF isolation

Animals were fasted for 12 hours overnight before euthanasia. WAT samples were dissected immediately, weighed, and placed in Krebs-Henseleit buffer solution on ice until further processing. Three depots were collected: 1 sc (inguinal [sc]) and 2 intraabdominal (MES, a true visceral depot, and EPI, a nonvisceral depot). The retroperitoneal pad, a depot commonly collected in our studies, did not provide sufficient SVF cells for proper cell counting. The SVF was obtained as previously described (34). Briefly, WAT samples were treated with 1.1 mg of collagenase type I (Worthington BioChemicals) per 1 g of WAT for tissue digestion. After incubation at 37°C for 45 minutes in a shaker, samples were filtered through mesh and centrifuged (1000 rpm, 10 minutes) to isolate the SVF cells from the adipocytes. The final SVF pellet was resuspended in Krebs-Henseleit buffer solution on ice and prepared for flow cytometry.

Flow cytometry

Nonspecific antibody binding was blocked with Fc block (BD Biosciences) in FACS buffer (PBS with 2% fetal bovine serum and 0.05% sodium azide). Fluorochrome-conjugated monoclonal antibodies against CD206 (MR5D3) (AbDSerotec), Ly-6C (HK1.4), F4/80 (BM8), CD36 (72–1), CD11b (M1/70), NK1.1 (PK136), CD3 (145–2C11), CD25 (PC61.5), CD4 (RM4–5), CD45 (30-F11) (all eBioscience), CD45 (30-F11), NKT (U5A2–13), CD62L (MEL-14) (all BD Biosciences), and CCR2 Phycoerythrin (475301) (R&D Systems, Inc) at a 1:100 dilution were used. Biotinylated MHC-II (M5/114.15.2) or CD44 (IM7), both eBioscience, followed by incubation with streptavidin PE-Texas Red (BD Pharmingen, BD Bioscience) at a 1:400 dilution were also used in our studies. After incubation, samples were subjected to multicolor flow cytometry on a FACSAria flow cytometer (BD) using FACSDiva software (Becton Dickinson). A total of 10 000 to 100 000 events were collected per sample. Output data were recorded by the FACSDiva software and were further examined using FlowJo flow cytometry analysis software (Tree Star, Inc).

RNA isolation and sequencing

Subcutaneous and EPI WAT depots were dissected from 12-hour fasted mice, flash frozen in liquid nitrogen, and stored at −80°C until further processing. Total RNA was isolated from WAT depots with TRIzol reagent, following the manufacturer's protocol (Life Technologies). Subsequently, mRNA was isolated with a Dynabeads mRNA Purification kit (Life Technologies). Quantity and quality of the isolated mRNA were determined by an Agilent 2100 Bioanalyzer from Agilent Technologies. The mRNA used for RNA-Seq had an integrity number higher or equal to 7. A cDNA library was produced using the Ion Total RNA-Seq kit (Life Technologies) and then sequenced using a single end method in an Ion Torrent Personal Genome Machine (Life Technologies).

RNA-Seq analysis

RNA-Seq analysis was performed using the online tool Galaxy (35). Reads were mapped independently using TopHat version 1.5.0 against the mouse genome mm10. Gene expression was calculated by running Cufflinks version 2.1.0 on the alignments from TopHat (36). P values with false discovery rate correction were used for analysis. To ensure quality and to visualize the RNA-Seq data, CummeRbund v2.9 package (37) and the FastQC program (38) were used in which we assessed the homogeneity of the sample's expression and assured a Fred score higher or equal to 20. Pathway analysis of the RNA-Seq data was made using QIAGEN's Ingenuity Pathway Analysis (QIAGEN Redwood City). This tool allowed for the characterization of the relevant canonical pathways that were significantly altered in the WAT depots of bGH mice when compared with WT controls.

Statistical analysis

All data are represented as mean ± SEM. All data analyses except RNA-Seq were done with SPSS 18.0 (IBM). A two-way ANOVA with Tukey's honest significant difference post hoc test was used to identify differences in genotype and depot, and a factorial ANOVA with Tukey's post hoc test for total SVF, SVF per gram of tissue, and the identified immune cell populations was used. An independent T test was used to examine differences in body weight and body composition. For RNA-Seq Ingenuity Pathway Analysis, a Fisher exact test was used to calculate pathways that were significant. Differences for all experiments were considered significant at a P < .05.

Results

As expected based on previous studies, bGH mice were larger than littermate controls. As evident in Figure 1A, bGH mice had higher body weight (1.6×) than their WT counterparts. The excess body weight in bGH mice was due primarily to a significantly greater amount of lean mass (1.6×), without significant changes in either fat or fluid mass (Figure 1A). However, when normalized to body weight, there was no significant increase in percent lean mass (lean mass represented 80.1% of total body weight in bGH mice vs 77.8% in WT mice) (Figure 1B), suggesting that the increased absolute lean mass was proportional to the larger body size of the bGH mouse. Accordingly, fat mass normalized to body weight revealed significantly lower adiposity in bGH mice (4.3% in bGH mice vs 9.7% in WT mice).

Figure 1.

Body composition of male WT and bGH mice at 5 months of age. A, Body weights and absolute body composition values in grams of male WT and bGH mice at 5 months of age. B, Body composition as percentages normalized to body weight of male WT and bGH mice at 5 months of age. Data are expressed as mean ± SEM. Means within a genotype or depot with a common letter do not differ; P ≥ .05; n = 6/group.

Subcutaneous, EPI, and MES WAT depots were excised from bGH mice and their age matched WT controls in order to isolate and analyze their SVFs. As shown in Figure 2A, the mass of the EPI WAT depot was significantly greater in both genotypes than the sc and MES WAT depots. In addition, absolute tissue weight for EPI and MES WAT depots were significantly lower in the bGH mice compared with littermate WT controls, whereas the sc fat pad followed the same trend but did not reach statistical significance. When expressed as a percent of body weight, the difference in tissue weight between genotypes became more pronounced, with a significantly lower mass observed in all WAT depots in bGH mice (Figure 2B).

Figure 2.

WAT depot weight and SVF cellularity for male WT and bGH mice at 5 months of age. A. Tissue weight expressed as values in grams of male WT and bGH mice at 5 months of age. B, Tissue weight as percentages normalized to body weight of male WT and bGH mice at 5 months of age. C, Total SVF cells for adipose tissue depots of male WT and bGH mice at 5 months of age. D, Total SVF cells normalized to gram of adipose tissue of male WT and bGH mice at 5 months of age. Data are expressed as mean ± SEM. Means within a genotype or depot with a common letter do not differ; P ≥ .05. Comparisons are not made between different depots of different genotypes.

We next investigated the proportion of nonadipocyte cells present in these 3 WAT depots by isolating the SVF of each depot and comparing the absolute amount and the proportion of cells per gram of tissue (Figure 2, C and D). Significant differences were found between genotypes for total SVF cells and SVF cells per gram of tissue in sc and MES WAT depots. For both depots, bGH mice had a greater number of SVF cells in a given WAT mass as compared with WT tissue. Intragenotype differences among the depots were found only within the bGH genotype, with the EPI values of total SVF cells and SVF cells per gram significantly lower compared with sc and MES WAT depots. This demonstrates that GH exerts a differential effect on the SVF content of WAT depots.

In order to characterize the immune population present in the different WAT depots, we performed flow cytometry analysis of the SVF. Total leukocyte populations were identified as CD45⁺ cells present in these fractions. As shown in Figure 3A, significant differences between genotypes were seen in the MES and EPI depots where the proportion of CD45⁺ cells was higher and lower, respectively in bGH mice when compared with WT controls. Further, within the bGH genotype, the MES depot contained higher levels of leukocytes than the sc and EPI depots. In contrast, the sc depot in WT mice had the highest leukocyte infiltration (Figure 3A).

Figure 3.

Macrophage population in the SVF of male WT and bGH mice WAT at 5 months of age. A, Total leukocyte population; gated as cells positively identified as CD45⁺ normalized to total SVF of each depot. B, Total ATMs as cells positively identified for F480⁺ and CD11b⁺ normalized to total CD45⁺ leukocytes of each depot. C, Quantification of M2 ATMs as cells coexpressing CD206 and CD36 within the macrophage gate. Values were normalized to total ATMs of the CD45⁺ leukocyte population of each depot for. Data are expressed as mean ± SEM. Means within a genotype or depot with a common letter do not differ; P ≥ .05. Comparisons are not made between different depots of different genotypes.

To assess the effect of GH on WAT macrophages (adipose tissue macrophage [ATM]), CD45⁺CD11b⁺F480⁺ cells were quantified by flow cytometry and found to represent a larger proportion of total SVF in bGH sc and MES depots compared with WT controls (Figure 3B). In addition, specific differences were seen among depots of the bGH mice. As shown in Figure 3B, the sc and MES depots had a significantly higher fraction of ATMs compared with the EPI depot. No significant differences were found between depots in WT mice. To examine the possibility that ATM subtypes were influenced by the levels of GH, the proportion and numbers of anti-inflammatory (M2-like) macrophages were investigated by quantifying CD206⁺CD36⁺ ATMs as previously defined in WAT studies in mice (14, 34). As shown in Figure 3C, increased levels of ATM expressing M2 markers were observed in all depots of the bGH mice compared with controls, with higher values observed in the sc and MES WAT.

We then investigated the capability of GH excess to modulate T-cell composition in WAT depots. As depicted in Figure 4A, no significant differences between genotypes or among depots were observed in the percentage of T cells within the WAT leukocyte population. Interestingly, we were able to detect differences in the proportions of specific T-cell subpopulations. In particular, sc WAT TH cells were significantly higher in bGH mice compared with WT controls (Figure 4B). In addition, as shown in Figure 4C, Treg cells were significantly higher in the sc and MES WAT depots in bGH mice compared with controls. Intragenotype depot differences in T-cell subpopulations were also observed in the bGH mice but, interestingly, not in WT mice. Treg cells were significantly higher in both sc and MES WAT depots but lower levels were observed in the EPI depot within the bGH genotype. Finally, genotype differences in cytotoxic T cells were found within the EPI and MES WAT depots. As depicted in Figure 4D, a significantly higher level of cytotoxic T cells was observed in the bGH EPI depot, whereas lower levels were observed in the bGH MES WAT depot compared with controls. Within the bGH genotype, cytotoxic T cells were significantly higher in the EPI depot compared with the MES depot. Strikingly, these differences between depots were not observed in WT controls.

Figure 4.

T-cell population in the SVF of male WT and bGH mice WAT at 5 months of age. A. Total T cells positively identified for CD45⁺ and CD3⁺. B, TH cells: CD45⁺, CD3⁺, and CD4⁺. C, Treg cells: CD45⁺, CD3⁺, CD4⁺, and CD25⁺. D, Cytotoxic T cells: CD45⁺, CD3⁺, and CD4⁻. All subpopulation values normalized to total T cells (CD45⁺, CD3⁺) of each depot. Data are expressed as mean ± SEM. Means within a genotype or depot with a common letter do not differ; P ≥ .05. Comparisons are not made between different depots of different genotypes.

Further, we explored the distribution of natural killer (NK) and NKT cells in our mice. As shown in Figure 5A, no significant differences were found in the proportion of NKT cells between bGH and WT controls. On the contrary, intragenotype depot-specific differences were significant between MES and sc depots of the bGH mice but not in the WT controls. With respect to NK cells, significant differences in the proportion of these cells between bGH and WT mice were only observed at the level of the EPI WAT depot (Figure 5B). Particularly, lower levels of NK cells were seen in the EPI depot in bGH mice compared with controls. Depot-specific differences were detected between MES and EPI depots in the bGH genotype, with a higher proportion of NK cells being present in the MES depot compared with the EPI WAT depot. This is contrary to what happens in WT mice in which NK levels are higher in the EPI WAT depot compared with the MES WAT depot.

Figure 5.

NK and NKT cells in the SVF of male WT and bGH mice WAT at 5 months of age. A, Total NKT cells positively identified for NK1.1⁺ and CD3⁻ normalized to total CD45⁺ leukocytes of each depot. B, Total NK cells positively identified for NK1.1⁺ and CD3⁻ normalized to total CD45⁺ leukocytes of each depot. Data are expressed as mean ± SEM. Means within a genotype or depot with a common letter do not differ; P ≥ .05. Comparisons are not made between different depots of different genotypes.

Finally, because there were many changes in the SVF and leukocyte population that were depot and genotype dependent, we decided to take a broader, more comprehensive look at the pathways that were significantly altered in 2 depots using deep RNA-Seq analysis followed by biological pathway analyses. This broader look would provide insight as to the most appropriate next steps in deciphering the immune cell changes in the WAT of bGH mice. Two depots were chosen for RNA sequencing, EPI and sc, as they routinely show the most difference with respect to GH action in the data presented in Figures 3–5. As shown in Table 1, the significantly altered pathways in the EPI depot of bGH mice were related to immune cell biology, basic metabolism, and lipid metabolism. Pathways such as “agranulocyte adhesion and diapedesis,” which includes genes that are involved in lymphocyte/monocyte adhesion and migration from the bloodstream to the lymphatic system, and “tight junction signaling,” which includes genes that influence the impermeable barriers between epithelial cells to regulate polarity, proliferation, and differentiation, suggest that high levels of circulating GH have an impact in the transit and infiltration of immune cells to or from the EPI depot. The pathway analysis of the sc WAT depot showed many more changes, and the changes were more highly significant (Table 2). Interestingly, 9 of the 10 most significantly altered pathways in the sc depot of bGH mice vs controls were related with immune cell biology. More specifically, the immune pathways that were significantly altered in this depot suggest that excess GH may alter the action of TH cells and T cytotoxic cells.

Table 1.

The 10 Most Altered Biological Pathways in the EPI WAT Depot of bGH Mice as Compared With EPI WAT From WT Controls

Canonical pathway	P value	Ratioa
Agranulocyte adhesion and diapedesis	3.13E-03	0.043 (4/92)
Calcium signaling	4.23E-03	0.040 (4/100)
Integrin-linked kinase signaling	5.93E-03	0.036 (4/100)
Mechanisms of viral exit from host cells	1.36E-02	0.074 (2/27)
Methylglyoxal degradation I	1.98E-02	0.333 (1/3)
Hepatic fibrosis/hepatic stellate cell activation	2.49E-02	0.032 (3/95)
Tight junction signaling	2.77E-02	0.030 (3/99)
Prostanoid biosynthesis	3.92E-02	0.167 (1/6)
Transforming growth factor-β signaling	4.48E-02	0.039 (2/51)
Oleate biosynthesis II (animals)	4.56E-02	0.140 (1/7)

^aSignificantly changed genes within a pathway altered in bGH mice divided by the total number of molecules in the pathway; the numbers used to calculate the ratios are provided in the parentheses.

Table 2.

The 10 Most Altered Biological Pathways in the sc WAT Depot of bGH Mice as Compared With sc WAT From WT Controls

Canonical pathway	P value	Ratioa
Gluthatione redox reactions I	3.99E-05	0.545 (6/11)
Inducible costimulator-inducible costimulator ligand signaling in TH cells	4.68E-05	0.224 (15/67)
B cell development	5.32E-05	0.438 (7/16)
Triacylglycerol degradation	5.32E-05	0.438 (7/16)
Protein kinase Cθ signaling in T lymphocytes	9.52E-05	0.211 (15/71)
Primary immunodeficiency signaling	2.09E-04	0.320 (8/25)
T-cell receptor signaling	2.99E-04	0.210 (13/62)
Cytotoxic T-lymphocyte-associated protein 4 signaling in cytotoxic T lymphocytes	3.44E-04	0.218 (12/55)
CD28 signaling in TH cells	3.84E-04	0.188 (15/80)
OX40 signaling pathway	4.98E-04	0.286 (8/28)

^aSignificantly changed genes within a pathway altered in bGH mice divided by the total number of molecules in the pathway; the numbers used to calculate the ratios are provided in the parentheses.

Discussion

This study was designed to determine whether excess GH influences the distribution of the leukocyte population in selected WAT depots. Previous studies have evaluated GH transgenic mice with respect to thymic, splenic and circulating immune cell function (30–33, 39). However, this is the first study to characterize the immune cells population of bGH WAT compared with age-matched WT control WAT.

The basic phenotype of bGH mice used in this study is similar to what has been reported in the literature. That is, bGH mice have increased body weight and altered body composition with significantly increased lean mass and decreased fat mass when normalized to body weight, as we reported previously (16, 17, 27, 40). bGH mice also have a reduction in the mass of all WAT depots, although the degree of the reduction varies according to the depot. That is, the MES depot shows the greatest reduction in absolute mass followed by the EPI depot in bGH mice compared with WT. All depots were significantly reduced when the depot tissue weight was normalized to body weight, although again MES was the most dramatically reduced. Depot-specific differences in tissue weight can be attributed to the well-documented variability in cellularity, metabolic activity, and endocrine function of the WAT depots due to alterations in the GH/IGF-I axis (41–45).

WAT is primarily comprised of adipocytes but also harbors many other cell types within the SVF, including leukocytes, stem cells, fibroblasts, and endothelial cells. As shown here, bGH mice have higher SVF cell numbers compared with littermate controls in the sc and MES WAT depots. Thus, an excess in GH-induced signaling is able to modulate the amount of SVF cells in WAT, and this effect is depot dependent. In this study, we particularly focused in defining the profile of one of the SVF cell constituents, the immune cells. However, it is important to note that the sc depot had more total SVF cells but did not have a statistically significant increase in the leukocyte population, indicating other cell types besides immune cells are increased in this particular depot. Although determination of the other SVF cell types was not the focus of this study, these data do suggest a more significant impact on sc WAT in bGH mice than would be projected based on mass alone. Determination of the other SVF cell types is the focus of current studies in our laboratory.

In the present study, macrophages accounted for approximately 30% of total CD45⁺ leukocytes in WT WAT, similar to what have been previously reported (46). In WAT from bGH mice, ATMs comprised a greater percentage of the total leukocytes in 2 depots (sc and MES), which suggests that GH has the capability to regulate macrophage accumulation in WAT. Curiously, GH did not have an effect on the EPI depot with respect to total macrophage content, despite being the depot most commonly studied with respect to immune cell content and WAT (46–48). It has been previously reported that most ATM are of the M2 phenotype in lean animals (49). Herewith, we observed that ATM bearing markers of M2 macrophages, as previously defined in other studies (14, 34), are present at higher levels in all WAT depots of bGH mice when compared with their WT counterparts. In this context, it is important to highlight that our previous work show that the levels of circulating MCP-1 and IL-6 are significantly higher in these bGH mice than in their WT counterparts (18). Several publications indicate that MCP-1 is responsible for macrophage recruitment to tissues, but there is a matter of debate regarding the role of this molecule in inducing a M1 or a M2 phenotype (50–54). In particular, although some studies indicate that MCP-1 is associated with increased M2 macrophage infiltration (53), other studies indicate that MCP-1 is associated with M1 macrophage recruitment (54). As described in models of obesity or lung inflammation, MCP-1 might be mostly responsible for monocyte/macrophage attraction and then the phenotype of these cells towards M1 or M2 defined upon other microenvironmental signals (50, 51). Interestingly, it has been reported that MCP-1 and IL-6 can act in combination directly on monocytes to induce their differentiation towards M2 macrophages (52). Thus, in our model, the heightened levels of both immune metabolites in circulation might contribute to shaping the M2 macrophage profile that we observed in bGH WAT. Further studies regarding the cytokine profile of the bGH WAT deposits will help identify other immune molecules that can be involved in this process.

Our observations did not include functional analysis of isolated ATMs, which preclude us from considering that the cells are functional anti-inflammatory M2 macrophages. Nevertheless, our data are in line with recent reports by Lu et al (14) using mice lacking expression of GHR on macrophages. This research group was able to determine that under high-fat diet conditions, macrophages impervious to the effect of GH accumulate in WAT and show a skewed M1 phenotype. Thus, our combined studies suggest a role of GH in defining the phenotype of WAT macrophages, particularly promoting a M2 phenotype. Subcutaneous and MES WAT depots in the bGH mouse display greater levels of ATMs, particularly M2 macrophages, than WT controls. bGH depot-specific differences in total ATM and M2 ATM content are similar to those seen in HFD mice, with sc and MES WAT depots showing higher levels of M2 macrophages (51).This phenotypic shift seen in mice on an extended HFD is due to extensive tissue remodeling and exposure to microenvironmental cues, such as lipids, cytokines, and hypoxia (55), and may be contributing to the M2 macrophage shift observed in bGH mice.

In the current study, we were also able to detect depot-specific modifications in the proportions of distinct T-cell populations. The interplay between T cells and adipocytes is a multifaceted relationship. T cells have been implicated in the development of obesity-related insulin resistance (56). That is, in obese humans, visceral WAT contains more T cells compared with sc WAT, which is most likely due to increased recruitment or expansion within the tissue. CD4⁺ T cells include TH cells, which can be stimulated by and produce a variety of cytokines. There are 3 primary lineages of TH cells: TH1, TH2, and TH17, which are recognized by the cytokines they respond to and produce. TH1 and TH2 subpopulations are found in WAT and vary in number based on depot and the state of obesity (57–60). The TH1 and TH2 populations orchestrate cytokine production from macrophages and participate in the adaptive immune response, yet the role of these cells in WAT inflammation and obesity is unknown (58). The involvement of adipose CD4⁺ T cells in insulin sensitivity has been previously described. In particular, it has been shown that although WAT infiltrating TH1 T cells induce insulin resistance, this effect is counterbalanced by infiltrating TH2 cells (60). An imbalance in these populations towards a TH1 profile is associated with insulin resistance and obesity. The potential insulin sensitizing action of CD4⁺ T cells in WAT is interesting, considering that bGH mice are regarded as insulin resistant at the age of mice used in this study. Possibly, the significant increase in the TH cell population in the bGH mouse model stalls the development of overt diabetes. Because the imbalance in the TH1, TH2 cells present in WAT may contribute to the pathogenesis of obesity and insulin resistance, it is important to further characterize the different populations of TH cells in future studies using the bGH model. Most importantly, pathway analysis from RNA-Seq data confirms a striking alteration in the T-cell population in the sc depot of bGH mice. That is, although few highly significant changes were seen in the EPI depot and most had no direct connection with immune biology, RNA-Seq analysis of the sc depot showed highly significant and consistent activation of the TH and T cytotoxic cells in the sc depot of bGH mice. Importantly, these data support our flow cytometry analysis, showing that GH increases the percentage of TH cells in the sc depot of bGH mice with less impact in the EPI depot.

Obese visceral and sc WAT in both humans and mice has been found to contain greater levels of NK cells and cytotoxic T cells, which suggests a relationship between obesity, immune cell infiltration, and resulting tissue inflammation and remodeling (7, 48). Indeed, Rausch et al (48) found the WAT in DIO mice and ob/ob obese mice displayed hypoxic areas with an increase in macrophage and cytotoxic T-cell infiltration compared with lean controls, which established a link to insulin resistance, and diabetes pathogenesis. Surprisingly, few studies have examined WAT NK cell frequency in obesity. O'Rourke et al (7) examined the NK cell populations in sc and visceral WAT of obese humans and found a significant increase in NK cell populations compared with nonobese WAT. These cells are thought to be a possible source of inflammatory cytokines that induce WAT inflammation and proinflammatory (M1-like) macrophage infiltration in obesity. Thus, the lower levels of NK cells observed in bGH WAT compared with WT mice could be associated with the higher levels of anti-inflammatory (M2-like) macrophages observed in bGH mice and their lower adiposity. Together, these data argue for further studies on the functional properties of WAT macrophages, T cells, and NK cells in bGH mice.

We also investigated whether GH overexpression was able to modify the levels of NKT cells in WAT depots. NKT cells are critical therapeutic targets in many disease models, but their role in obesity and the development of associated comorbidities is not well understood. The NKT cells possess similar characteristics of the CD4⁺ T cells in that they can produce TH1 and TH2 cytokines. NKT cells interact with CD1d-related lipid and glycolipid antigens, which may provide a link between obesity and inflammation in WAT (61, 62). Recent studies have demonstrated that NKT cells may contribute to obesity through the use of NKT-deficient mice on a high-fat diet. These mice had better glucose tolerance and decreased macrophage infiltration compared with controls (63). However, opposite effects of NKT cell function have been observed in the transplantation of NKT cells to obese WAT and result in mitigation of the metabolic dysfunction of the tissue (64). A recent study examined the relationship between NKT cells, macrophages, and glucose tolerance. They report that activation of NKT cells by a lipid agonist enhances macrophage polarization towards the M2 phenotype; in turn, this improves glucose homeostasis in animals in different stages of obesity (65). Herewith, we did not observe a modification of NKT levels between WT and bGH animals or between different adipose depots in bGH mice, thus suggesting that the increase in GH (and IGF-I action) induces a particular immune cell profile in WAT with no detectable effects on NKT cells at the ages tested.

In closing, the major findings from our study are that GH excess results in WAT depot changes in ATM content with polarization towards a M2-like phenotype, together with depot-specific modifications in the proportions of T cells and NK cells. With the profound changes in immune-related signaling pathways at the level of RNA, our data strongly suggests an important role of GH on shaping the immunological environment in WAT on a depot-specific level. In turn, alterations in the leukocyte population of bGH WAT is likely contributing to the metabolic dysfunction and shortened lifespan well documented for these GH transgenic mice. Functional analyses as well as the impact of age and sex on the immune cell populations in adipose tissue would be valuable future experiments to explore the role that these cells play in the accelerated aging of bGH mice