Skip to main content
PLOS One logoLink to PLOS One
. 2023 Jun 5;18(6):e0286470. doi: 10.1371/journal.pone.0286470

Effects of T cell leptin signaling on systemic glucose tolerance and T cell responses in obesity

Kaitlin Kiernan 1, Amanda G Nichols 2, Yazan Alwarawrah 2, Nancie J MacIver 2,3,*
Editor: Sadiq Umar4
PMCID: PMC10241364  PMID: 37276236

Abstract

Background/Objectives

Leptin is an adipokine secreted in proportion to adipocyte mass and is therefore increased in obesity. Leptin signaling has been shown to directly promote inflammatory T helper 1 (Th1) and T helper 17 (Th17) cell number and function. Since T cells have a critical role in driving inflammation and systemic glucose intolerance in obesity, we sought to determine the role of leptin signaling in this context.

Methods

Male and female T cell-specific leptin receptor knockout mice and littermate controls were placed on low-fat diet or high-fat diet to induce obesity for 18 weeks. Weight gain, serum glucose levels, systemic glucose tolerance, T cell metabolism, and T cell differentiation and cytokine production were examined.

Results

In both male and female mice, T cell-specific leptin receptor deficiency did not reverse impaired glucose tolerance in obesity, although it did prevent impaired fasting glucose levels in obese mice compared to littermate controls, in a sex dependent manner. Despite these minimal effects on systemic metabolism, T cell-specific leptin signaling was required for changes in T cell metabolism, differentiation, and cytokine production observed in mice fed high-fat diet compared to low-fat diet. Specifically, we observed increased T cell oxidative metabolism, increased CD4+ T cell IFN-γ expression, and increased proportion of T regulatory (Treg) cells in control mice fed high-fat diet compared to low-fat diet, which were not observed in the leptin receptor conditional knockout mice, suggesting that leptin receptor signaling is required for some of the inflammatory changes observed in T cells in obesity.

Conclusions

T cell-specific deficiency of leptin signaling alters T cell metabolism and function in obesity but has minimal effects on obesity-associated systemic metabolism. These results suggest a redundancy in cytokine receptor signaling pathways in response to inflammatory signals in obesity.

Introduction

Obesity, which is increasing at an alarming rate in the United States and other developed countries [1], is associated with a wide range of comorbidities, including type 2 diabetes, hyperlipidemia, hypertension, heart disease, ischemic stroke and several types of cancer [27]. Numerous studies have demonstrated that the development of many of these obesity-associated comorbidities is promoted by a chronic, low grade inflammatory state [812]. In mouse studies of visceral adipose tissue, pro-inflammatory immune cells are preferentially increased in the adipose tissue of obese mice compared to lean mice. In particular, increases in M1-like pro-inflammatory macrophages, CD8+ T cells, T helper 1 (Th1) cells, and B cells are observed [13]. In contrast, there are decreased percentages of T regulatory (Treg) cells, T helper 2 (Th2) cells, natural killer T (NKT) cells, eosinophils, and type 2 innate lymphoid (ILC2) cells. This influx of inflammatory immune cells into obese adipose tissue promotes the production of inflammatory cytokines such as tumor necrosis factor (TNF), interleukin-6 (IL-6), and interferon gamma (IFN-γ), which contribute to the chronic inflammatory state (metaflammation) observed in obesity [1319]. Moreover, inflammatory cytokine production by adipose tissue immune cells has been shown to induce insulin resistance in obesity.

Although pro-inflammatory macrophages are the most predominant immune cell population in obese adipose tissue [20], multiple studies have now shown that inflammatory T cells also play a key role in driving obesity-associated inflammation. In a study of recombinase activating gene 1 knockout (Rag1-/-) mice, in which adaptive immune cells (lymphocytes) are not present, but the macrophage compartment remains intact, the Rag1-/- mice fed high-fat diet showed increased adiposity and weight gain, but had decreased inflammation in the adipose tissue compared to wildtype mice [21]. In particular, there were decreased inflammatory innate immune cells present in the adipose tissue, and IFN-γ levels were decreased [21]. These data suggest that adaptive immune cells, likely T cells, significantly contribute to the inflammatory environment of the adipose tissue in obesity either by recruitment of inflammatory innate immune cells or maintenance of the inflammatory environment by cytokine production. Moreover, mouse models that knockout the T cell receptor (TCRβ−/−), the Th1-associated transcription factor T-bet, or the Th1-associated cytokine IFN-γ have been shown to be protected against insulin resistance and diabetes when placed on high-fat diet [22,23]. This suggests that not only are T cells required for the inflammatory environment in obesity, but they are also required for the development of the systemic metabolic phenotype associated with obesity.

Adipose-derived cytokines, called adipokines, can contribute to the inflammatory environment of the adipose tissue in obesity. One such adipokine is leptin, which is secreted in proportion to adipose tissue mass and is therefore increased in the setting of obesity and decreased in undernutrition [24]. Leptin is best known for its role in the hypothalamus where leptin receptors are highly expressed and leptin signaling leads to decreased appetite and increased energy expenditure. The roles of leptin in neuroendocrine function, energy homeostasis and metabolism have been thoroughly reviewed [2527]. Interestingly, leptin also has a critical role as an immune modulator. Leptin has been shown to affect multiple immune cell types and mediate varying effects depending on the cell type or activation status of the cell, with particularly significant and striking effects on T cell development and function [24].

To start, leptin is required for early T cell development in the thymus. Double negative, double positive and CD4 single positive, but not CD8 single positive, thymocytes were shown to express leptin receptor [28]. Furthermore, leptin administration to leptin deficient mice rescued CD4+ T cell development, but not CD8+ T cell development [28]. In addition to the developmental requirement, leptin has also been shown to influence CD4+ T cell function and differentiation. In particular, leptin promotes CD4+ T cell differentiation into Th1 and T helper 17 (Th17) functional subsets and increases their IFN-γ and IL-17 production, respectively [29]. One mechanism by which leptin may influence CD4+ T cell function and differentiation is through cellular metabolism [29,30]. Specifically, leptin has been shown to increase expression of the glucose transporter Glut1 and increase glucose uptake and glycolytic metabolism, thereby promoting inflammatory cytokine production [29,30]. Thus, leptin modulates metabolism systemically at the level of the hypothalamus, but also at the level of the immune cell, where it can influence immune cell function.

Given the role of T cells in driving obesity-associated inflammation and insulin resistance and the role of leptin in promoting CD4+ T cell inflammatory function, we asked: what is the role of leptin signaling in driving T cell inflammation in the context of obesity-associated changes in systemic metabolism? To answer this question, we set up the following studies. T cell-specific leptin receptor conditional knockout mice and littermate controls were either fed high-fat diet (60% kcal from fat) or low-fat diet for 18 weeks, starting at weaning. Body weights and blood glucose levels were monitored throughout the study. At the completion of the dietary intervention, a glucose tolerance test was performed, and CD4+ T cells were isolated from spleens to measure cellular metabolism and function.

Materials and methods

Animals

T cell-specific leptin receptor (LepR) knockout mice were generated by crossing CD4Cre transgenic mice with LepR-floxed mice (Jackson Laboratory). The following genotypes were studied: CD4Cre+LepRfl/fl (leptin receptor knockout mice; LepRcKO) and CD4Cre-LepRfl/fl (controls; Ctrl). Mice were weaned at 3 weeks of age onto either low-fat, normal chow (NC, 10-kcal% fat, LabDiet, St. Louis, MO) or high-fat diet (HFD, 60-kcal% fat, Research Diets, New Brunswick, NJ) and remained on this diet for 18 weeks. Mice were group housed (up to 5 per cage), maintained at ambient temperature, and given ad libitum access to food and water. Mouse weights were collected weekly. All animal protocols were approved by the Institutional Animal Care and Use Committees at Duke University or the University of North Carolina at Chapel Hill.

Blood glucose readings and glucose tolerance tests

Blood glucose readings were taken using a One Touch Verio glucometer and strips. Mice were fasted for 4 hours prior to measuring blood glucose. Tails were snipped and one drop of blood deposited onto glucometer strip. To measure glucose tolerance, mice were fasted for 6 hours prior to glucose tolerance test. A baseline blood glucose reading was taken from each mouse. Mice were then injected intraperitoneally with 2 g/kg body weight of glucose. Blood glucose readings were taken at 15 minutes, 30 minutes, 60 minutes, 90 minutes, and 120 minutes following injection.

Tissue collection and processing

Method as described in Alwarawrah et al. 2020 [31]. Mice were euthanized using CO2 inhalation. Spleens were mashed and strained in PBS, washed, and resuspended. A portion of splenocytes were set aside for flow cytometry and the rest were used to isolate CD4+ T cells using the StemCell CD4+ T cell isolation kit (StemCell technologies, Vancouver, BC, Canada). Isolated CD4+ T cells were analyzed by extracellular flux analysis.

Flow cytometry

Method as described in Alwarawrah et al. 2020 [31]. Treg staining: Two million splenocytes were fixed and permeabilized using the Foxp3 Transcription Factor Staining Buffer kit (eBioscience) and stained for Foxp3 following the manufacturer instructions. For the identification of Treg cells, the following antibodies were used: BV421 Armenian Hamster anti-mouse CD3e (Biolegend, San Diego, CA), BV605 rat anti-mouse CD4 (Biolegend), PeCy7 rat anti-mouse CD25 (Biolegend), AF488 rat anti-mouse Foxp3 (Biolegend). Cytokine staining: For the identification of effector T cells (Th1 and Th17) and evaluation of their function, the following antibodies were used: BV421 Armenian Hamster anti-mouse CD3e (BD BioSciences), BV605 rat anti-mouse CD4 (Biolegend), AF488 rat anti-mouse CD8a (Biolegend), APC rat anti-mouse IL-17A (Biolegend), and PE/Cy7 rat anti-mouse IFNγ (Biolegend). Five million splenocytes were stimulated for 4.5 h in complete media containing Golgi Plug (2 μg/ml) (BD Biosciences), PMA (50 ng/ml) (Sigma-Aldrich, St. Louis, MO), and ionomycin (1 μg/ml) (Sigma-Aldrich), then permeabilized and fixed with Cytofix/Cytoperm kit (BD Biosciences) and stained for IFN-γ (Biolegend) and IL-17A (Biolegend) following the manufacturer’s protocol. Samples were acquired on a ThermoFisher Attune NxT flow cytometer, and data were analyzed using FlowJo (Treestar, Ashland, OR).

Metabolic flux assays

Method as described in Alwarawrah et al. 2020 [31]. CD4+ T cells were washed with Seahorse XF RPMI 1640 media (Agilent, Santa Clara, CA) and plated at a density of 250,000 cells/well (50 μL) in a Seahorse XFe96 plate (Agilent) pre-coated with Cell-Tak (Corning, Corning, NY). After spinning down the plate at 200 rpm for 1 min, the plate was incubated for 30 min in a humidified 37°C incubator in the absence of CO2. Seahorse XF RPMI 1640 media (130 μL) was added, and the plate was incubated for an additional 20 min. Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were measured using a Seahorse XFe96 Analyzer (Agilent).

Statistical analysis

Comparisons between groups were analyzed using t-test with Welch’s correction assuming Gaussian distribution. Statistical analysis was performed using GraphPad Prism 9 (GraphPad Software, Inc., La Jolla, CA). All data was determined as significant by p<0.05. Glucose tolerance test (GTT) analyses were performed in SAS 9.4 (SAS Institute Inc., Cary, NC) at a two-tailed significance level of 0.05. All available GTT measurements within the study were included in the analysis. Marginal models using generalized estimating equations (GEE) were implemented to account for the correlation between repeated measurements of GTT levels in each mouse. A GEE-type model with a normal distribution and identity link was implemented to test the association between sex and GTT levels as well as diet and GTT levels. An exchangeable working correlation structure was used along with the robust variance estimator for all models.

Results

Leptin receptor deficiency on T cells does not confer protection from systemic glucose intolerance in diet-induced obesity

To determine the role of leptin signaling in driving T cell inflammation in the context of obesity-associated changes in systemic metabolism, we generated T cell-specific leptin receptor knockout mice by crossing leptin receptor floxed (LepRfl/fl) animals with mice expressing a Cre recombinase transgene under the control of the CD4+ promoter (CD4Cre) on the C57BL/6 background, as we have previously described [29,30]. These mice have a selective deletion of leptin receptor in both CD8+ and CD4+ T cells and in T cell subsets. Given our prior work demonstrating the effect of leptin on CD4+ T cells, particularly Th1 and Th17 cells, as well as the relatively low expression of leptin receptor on CD8+ T cells compared to CD4+ T cells, we anticipated that this deletion would affect the metabolism and function of CD4+ T cells more so than CD8+ T cells [29,30].

T cell-specific leptin receptor knockout mice (CD4Cre+LepRfl/fl) and littermate controls expressing leptin receptor (CD4Cre-LepRfl/fl) were placed on high-fat diet (HFD; 60% kcal from fat) or low-fat, normal chow (NC; 10% kcal from fat) for 18 weeks, starting at weaning at 3 weeks of age. This study design allowed us to collect longitudinal data during the development of obesity. The mice on HFD gained significantly more weight than the mice on NC, but there was no difference in weight gain between T cell-specific leptin receptor knockout mice and littermate controls on the same diet (Fig 1A). These findings were consistent in both male and female mice; however, male mice on HFD showed increased weight gain at an earlier age than female mice, as well as greater weight gain overall (Fig 1B and 1C).

Fig 1. T cell-specific leptin receptor knockout mice and littermate controls have equivalent weight gain following high-fat diet.

Fig 1

T cell-specific leptin receptor knockout mice (LepR cKO) and littermate controls (Ctrl) were fed low-fat, normal chow (NC) or high-fat diet (HFD) for 18 weeks. Body weights were measured weekly and graphed versus time. Data plotted as mean ± standard error. Bar graphs show weights at 16 weeks on diet. Data shown as mean ± standard deviation. All experimental mice (n = 18-20/group) are shown in (a), male mice (n = 9-14/group) are shown in (b), female mice (n = 9-11/group) are shown in (c) over the course of the experiment and then at 16 weeks (bar graphs). Data analyzed using student’s t test with Welch’s correction (*p<0.05; **p<0.01).

Following 18 weeks on diet, mice were fasted for 4 hours to allow for measurement of fasting blood glucoses. Fasting blood glucose levels of control mice on HFD were significantly elevated compared to control mice fed NC; however, these differences were not observed between leptin receptor conditional knockout mice on HFD compared to NC (Fig 2A). When these data were separated by sex, we found that control male, but not female, mice on HFD had significantly higher fasting blood glucose levels than their NC fed counterparts (Fig 2B and 2C). Again, this significant difference in weight gain between NC and HFD-fed male control mice was not observed in male mice with T cell-specific leptin receptor deficiency (Fig 2B).

Fig 2. Leptin receptor knockout prevents impaired fasting glucose levels in male, but not female, mice fed high-fat diet.

Fig 2

T cell-specific leptin receptor knockout mice (LepR cKO) and littermate controls (Ctrl) were fed low-fat, normal chow (NC) or high-fat diet (HFD) for 18 weeks. Fasting blood glucose readings were taken after a 4 hour fast. Readings from all experimental mice (n = 12-16/group) are shown in (a), fasting blood glucose readings from male mice (n = 6-9/group) shown in (b), fasting blood glucose readings from female mice (n = 6-7/group) are shown in (c); analyzed using student’s t test with Welch’s correction (**p<0.01).

Mice from each experimental group were then subjected to a glucose tolerance test. As shown in Fig 3A and 3B, mice fed HFD had significantly impaired glucose tolerance compared to mice fed NC, but there were no significant differences in glucose tolerance between T cell-specific leptin receptor knockout mice or control mice on either NC or HFD. These findings were maintained when mice were separated into male or female groups; however, male mice on HFD had more impaired glucose tolerance than female mice on HFD (Fig 3C and 3D), consistent with previous observations in the literature [3235]. To determine if there were other changes to systemic metabolism that could influence immune cell responses in our experimental groups, we analyzed serum levels of cholesterol, triglycerides, and leptin and found no significant differences (S1 Fig).

Fig 3. T cell-specific leptin receptor knockout mice have similar glucose tolerance to littermate control mice following high-fat diet.

Fig 3

Glucose tolerance test (GTT) was conducted after 18 weeks on low-fat, normal chow (NC) or high-fat diet (HFD). Mice were fasted for 6 hours; baseline blood glucose measured by tail vein bleed (0-minute timepoint). 2 g/kg glucose was injected intraperitoneally, and blood glucose (mg/dL) measured at 15, 30, 60, 90, 120 minutes post-injection. Bar graph shows all experimental mice (n = 10-13/group) mean ± standard error (a), Line graph shows all experimental mice (n = 10-13/group) mean ± standard error (b), male mice (n = 5-7/group) mean ± standard error shown in (c), female mice (n = 5-7/group) mean ± standard error shown in (d); analyzed using generalized estimating equations (GEE) model (****p<0.0001).

Obesity-induced changes in CD4+ T cell oxidative metabolism depend on T cell-specific leptin receptor expression

Two days following glucose tolerance testing, mice were euthanized, and T cell metabolism and function were evaluated. Since CD4+ T cell metabolism has been previously shown to be altered in obesity [31], we sought to characterize the metabolic phenotype of CD4+ T cells from T cell-specific leptin receptor knockout mice versus littermate control mice on either NC or HFD. To that end, CD4+ T cells were isolated from spleens and analyzed using extracellular flux analysis. CD4+ T cells isolated from control mice fed HFD, but not from T cell-specific leptin receptor knockout mice fed HFD, showed significantly increased basal oxygen consumption rate (OCR, a surrogate marker for mitochondrial oxidation) compared to mice fed NC (Fig 4A). In contrast, CD4+ T cells from T cell-specific leptin receptor knockout mice or littermate control mice on either NC or HFD did not show any difference in basal extracellular acidification rate (ECAR, a surrogate marker for glycolytic metabolism) (Fig 4B). The OCR/ECAR ratio is a useful measure to determine what proportion of the energy being produced in the cells is coming from mitochondrial oxidation versus glycolytic metabolism. In our study, control mice on HFD had a significantly increased OCR/ECAR ratio compared to control mice on NC (Fig 4C). However, there was no difference in OCR/ECAR ratio observed between T cell-specific leptin receptor knockout mice on NC or HFD (Fig 4C). These results suggest that leptin signaling is required, at least in part, for changes seen in T cell oxidative metabolism in obesity.

Fig 4. T cell-specific leptin signaling is required for changes in CD4+ T cell metabolism observed in mice fed high-fat diet compared to low fat diet.

Fig 4

T cell-specific leptin receptor knockout mice (LepR cKO) and littermate controls (Ctrl) were fed low-fat, normal chow (NC) or high-fat diet (HFD) for 18 weeks. CD4+ T cells were isolated from spleens and extracellular flux analysis was performed. Basal oxygen consumption rate (OCR) (mean ± standard error) shown in (a), basal extracellular acidification rate (ECAR) (mean ± standard error) shown in (b), OCR/ECAR ratio (mean ±standard error) shown in (c). Data analyzed using student’s t test with Welch’s correction (*p<0.05; **p<0.01; n = 15-19/experimental group).

Obesity-induced changes in CD4+, but not CD8+, T cell function require T cell-specific leptin receptor expression

To investigate T cell function in the experimental groups, splenocytes were analyzed by intracellular flow cytometry to determine the proportion of cells producing the pro-inflammatory cytokines IFN-γ and IL-17 or expressing the Treg-associated transcription factor Foxp3. Overall, control mice fed HFD had significantly increased proportion of IFN-γ producing CD4+ T cells than control mice on NC (Fig 5A). However, no difference was observed in IFN-γ producing CD4+ T cells between T cell-specific leptin receptor conditional knockout mice on NC versus HFD (Fig 5A). Interestingly, both T cell-specific leptin receptor conditional knockout and littermate control mice on HFD had significantly increased proportions of IFN-γ producing CD8+ T cells when compared to mice on NC (Fig 5B). One possible explanation for the difference seen in IFN-γ production by CD4+ versus CD8+ T cells in the knockout mouse may be that CD8+ T cells are less leptin responsive than CD4+ T cells. This is consistent with findings demonstrating reduced leptin receptor expression on CD8+ T cells as compared to CD4+ T cells. Thus, obesity-induced changes in CD4+, but not CD8+, T cell production of IFN-γ depends on T cell-specific leptin receptor expression.

Fig 5. Splenic CD4+ T cells isolated from leptin receptor knockout mice are altered following high-fat diet compared to CD4+ T cells isolated from littermate controls.

Fig 5

T cell-specific leptin receptor knockout mice (LepR cKO) and littermate controls (Ctrl) were fed low-fat, normal chow (NC) or high-fat diet (HFD) for 18 weeks. CD4+ T cells were isolated from spleens and cytokine production or Foxp3 transcription factor was measured by intracellular flow cytometry. Proportion of splenic CD4+ T cells that produce IFN-γ (a), Proportion of splenic CD8+ T cells that produce IFN-γ (b), Proportion of splenic CD4+ T cells that produce IL-17 (c), Proportion of splenic CD4+ T cells that produce CD25 and Foxp3 (Treg cells) (d); analyzed using student’s t test (*p<0.05) with Welch’s correction. Each plotted point represents one mouse; n = 9-12/experimental group.

We found no statistically significant difference in the proportion of CD4+ T cells producing IL-17 in mice fed HFD compared to mice fed NC in both T cell-specific leptin receptor knockout and littermate control mice (Fig 5C). Lastly, we examined changes in Treg cell numbers, as defined by the proportion of CD4+ T cells expressing the transcription factor Foxp3 and the activation marker CD25. The proportion of Treg cells was increased in control mice, but not T cell-specific leptin receptor knockout mice, fed HFD compared to mice fed NC (Fig 5D). This result suggests that increased Treg cell proportions in obesity depend at least partially on leptin receptor expression and signaling. Lastly, we performed multiplex analysis to compare serum cytokine levels in our experimental groups, and we found no significant differences in circulating levels of TNF, IFN-γ, IL-17, IL-1β, and IL-6 (S3 Fig). Altogether, these findings demonstrate that T cell-specific leptin signaling is required for some of the changes in CD4+ T cell metabolism and function observed in obesity but has minimal effects on obesity-associated systemic metabolism.

Discussion

As an increasing proportion of the United States population is diagnosed with obesity, it is becoming critically important to understand the implications of obesity for the health of those individuals. Studying the immune response is an important aspect of this, as obesity-associated changes in immune cells have been shown to drive inflammation which promotes systemic metabolic disease [9]. Specifically, it has been demonstrated that T cells have a critical role in driving obesity-associated inflammation leading to systemic insulin resistance [21]. We have previously shown that leptin is a key regulator of both CD4+ T cell metabolism and effector cell differentiation and function [29,30]. Given the role of T cells in driving obesity-associated inflammation and insulin resistance and the role of leptin in promoting CD4+ T cell inflammatory function, we set out to determine whether leptin signaling in T cells was required for the development of glucose intolerance and insulin resistance in obesity.

Our conditional leptin receptor knockout mouse, which has leptin receptor selectively deleted in T cells, allowed us to interrogate whether leptin signaling to T cells could be promoting T cell metabolic changes, increased inflammatory cytokine production, and subsequent systemic metabolic disease in the context of obesity. We have previously reported metabolic differences between T cells from C57BL/6 mice fed NC versus HFD [31]. The data shown here support our previous work showing that CD4+ T cells from diet-induced obese control mice have increased oxidative metabolism (OCR), as well as an increased ratio of oxidative to glycolytic metabolism (OCR:ECAR) compared to CD4+ T cells from lean control mice. However, CD4+ T cells from HFD versus NC-fed T cell-specific leptin receptor knockout mice did not show the same metabolic changes. These results suggest that direct leptin signaling mediates metabolic remodeling of CD4+ T cells in obesity resulting in significantly increased basal oxidative metabolism and an increase in the ratio of oxidative to glycolytic metabolism characteristic of T cells from obese mice.

We also found differences when we interrogated the proportions of splenic T cells from our experimental groups. An increase in the proportion of IFN-γ producing CD4+ T cells was observed in control mice fed HFD compared to NC, which was not observed in CD4+ T cells from T cell-specific leptin receptor knockout mice. On the other hand, the proportion of IFN-γ producing CD8+ T cells was increased following HFD compared to NC in both T cell-specific leptin receptor knockout mice and littermate controls. This differing result in CD4+ versus CD8+ T cells is consistent with the fact that leptin receptor is expressed at lower levels on CD8+ T cells than CD4+ T cells. Therefore, CD8+ T cells may retain their IFN-γ producing phenotype in the context of HFD-induced obesity, even when leptin receptor is deleted.

To our surprise, deletion of leptin signaling in T cells had a mild influence on fasting blood glucose in obesity but did not significantly affect systemic metabolic disease. Glucose tolerance tests between HFD-induced obese T cell-specific leptin receptor knockout mice and littermate controls did not show any significant differences. One possible explanation for these negative results is a redundancy in signaling pathways downstream of leptin as well as other nutritionally regulated hormones that are increased in the setting of obesity. For example, IL-6 is known to be increased in obesity, shares signaling pathways with leptin, and can have synergistic effects on T cells. In the absence of leptin signaling in obesity, other cytokine signals may be compensatory.

One limitation of our study is that we only investigated the role of leptin receptor signaling directly on T cells. While T cells have been shown to be important for obesity-associated inflammation, there are other immune cells that have been shown to have a role in this process. In particular, macrophages are known to be important drivers of inflammation in the adipose tissue in the setting of obesity. To this point, several studies have shown that reducing macrophage recruitment to or eliminating macrophages from the adipose tissue improves insulin sensitivity [3640]. Furthermore, IL-1 receptor I deficient mice have improved glucose tolerance and reduced inflammation in obesity, suggesting that macrophage production of IL-1β is a critical inflammatory driver in obesity [39]. A study investigating the role of leptin signaling in myeloid cells found that specific deletion of leptin receptor in myeloid cells did not significantly affect glycemic control [41]. However, selective reconstitution of leptin receptor in myeloid cells in mice that were otherwise leptin receptor deficient resulted in improved glucose tolerance. This combination of results suggests that there is a complex signaling network involving immune cells, adipose tissue, and soluble mediators such as leptin and other nutritionally regulated cytokines. Future studies should investigate the roles of other immune cells, endocrine signals, and inflammatory cytokines in driving obesity-associated inflammation and glucose intolerance.

In this study, we set out to understand whether the leptin signal to T cells in obesity is sufficient to drive systemic inflammation and metabolic disease. Using our T cell-specific leptin receptor conditional knockout mouse, we interrogated the role of leptin signaling on the pathogenesis of obesity as driven by the T cell. We found that loss of leptin signaling to T cells was insufficient to influence glucose tolerance in obesity, although leptin deficiency did prevent impaired fasting glucose levels compared to control mice in the HFD setting, in a sex dependent manner. Despite these minimal effects on systemic metabolism, T cells isolated from leptin receptor knockout mice on HFD lacked some of the metabolic and functional changes observed in T cells from control mice on HFD. These results contribute to a body of work that aims to understand the signals influencing immune cell function and inflammation in obesity. Understanding immune cells in obesity is critical for the development of therapeutics to ameliorate the consequences of obesity and subsequent metabolic disease. With an increasing proportion of individuals with obesity in both the United States and around the world, treating the inflammation and downstream consequences of obesity will become an increasing priority.

Supporting information

S1 Fig. T cell-specific leptin receptor knockout mice have similar serum cholesterol, triglyceride, and leptin levels compared to littermate control mice following high-fat diet.

Mouse serum was collected after 18 weeks on low-fat, normal chow (NC) or high-fat diet (HFD) and analyzed for concentration of (A) total cholesterol and (B) triglycerides using clinical chemistry tests performed by Animal Clinical Laboratory Services core facility at University of North Carolina at Chapel Hill, and (C) leptin levels by serum ELISA (ThermoFisher). One way ANOVA corrected for multiple comparisons. No significant differences were observed between the groups. n = 5–7 mice/group.

(TIF)

S2 Fig. Splenic CD4+ T cells isolated from leptin receptor knockout mice are altered following high-fat diet compared to CD4+ T cells isolated from littermate controls.

Representative flow cytometry plots with gating are shown from each group showing cytokine production for CD4+ T cells (top row), CD8+ T cells (middle row), and expression of FoxP3 on CD4+ T cells (bottom row). (A) Pre-gated on lymphocytes, single cells, CD3+, CD4+; percentage IL-17+ and percentage IFN-γ+ shown (B) Pre-gated on lymphocytes, single cells, CD3+, CD8+ (C) Pre-gated on lymphocytes, single cells, CD3+, CD4+; percentage FoxP3+CD25+ shown.

(TIF)

S3 Fig. T cell-specific leptin receptor knockout mice have similar levels of circulating cytokines compared to littermate control mice following high-fat diet.

Multiplex analysis of cytokine levels was performed on selected mouse serum samples using Bio-Rad Bio-Plex Pro Th17 multiplex cytokine analysis assay to examine levels of (A) TNF, (B) IFN-γ, (C) IL-17, (D) IL-1β, and (E) IL-6. IL-10 was also analyzed but levels were below the limit of detection. n = 5–7 mice/group except where below limit of detection, in which case sample was excluded from graph. One way ANOVA corrected for multiple comparisons. No significant differences were observed between groups.

(TIF)

S1 Data

(XLSX)

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

This study was supported by NIH R01-DK106090 (NJM) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.National Health and Nutrition Examination Survey 2017–March 2020 Prepandemic Data Files Development of Files and Prevalence Estimates for Selected Health Outcomes, (2021).
  • 2.Nguyen NT, Magno CP, Lane KT, Hinojosa MW, Lane JS. Association of Hypertension, Diabetes, Dyslipidemia, and Metabolic Syndrome with Obesity: Findings from the National Health and Nutrition Examination Survey, 1999 to 2004. Journal of the American College of Surgeons. 2008;207(6):928–34. doi: 10.1016/j.jamcollsurg.2008.08.022 [DOI] [PubMed] [Google Scholar]
  • 3.Wilson PWF, D’Agostino RB, Sullivan L, Parise H, Kannel WB. Overweight and Obesity as Determinants of Cardiovascular Risk: The Framingham Experience. Archives of Internal Medicine. 2002;162(16):1867–72. [DOI] [PubMed] [Google Scholar]
  • 4.Rexrode KM, Hennekens CH, Willett WC, Colditz GA, Stampfer MJ, Rich-Edwards JW, et al. A Prospective Study of Body Mass Index, Weight Change, and Risk of Stroke in Women. JAMA. 1997;277(19):1539–45. doi: 10.1001/jama.1997.03540430051032 [DOI] [PubMed] [Google Scholar]
  • 5.Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ. Overweight, Obesity, and Mortality from Cancer in a Prospectively Studied Cohort of U.S. Adults. New England Journal of Medicine. 2003;348(17):1625–38. doi: 10.1056/NEJMoa021423 [DOI] [PubMed] [Google Scholar]
  • 6.McTigue K, Larson JC, Valoski A, Burke G, Kotchen J, Lewis CE, et al. Mortality and Cardiac and Vascular Outcomes in Extremely Obese Women. JAMA. 2006;296(1):79–86. doi: 10.1001/jama.296.1.79 [DOI] [PubMed] [Google Scholar]
  • 7.Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study. Circulation. 1983;67(5):968–77. doi: 10.1161/01.cir.67.5.968 [DOI] [PubMed] [Google Scholar]
  • 8.Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of Obesity and Severe Obesity Among Adults: United States, 2017–2018. NCHS Data Brief. 2020(360):1–8. [PubMed] [Google Scholar]
  • 9.Zatterale F, Longo M, Naderi J, Raciti GA, Desiderio A, Miele C, et al. Chronic Adipose Tissue Inflammation Linking Obesity to Insulin Resistance and Type 2 Diabetes. Front Physiol. 2019;10:1607. doi: 10.3389/fphys.2019.01607 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Ohman MK, Shen Y, Obimba CI, Wright AP, Warnock M, Lawrence DA, et al. Visceral adipose tissue inflammation accelerates atherosclerosis in apolipoprotein E-deficient mice. Circulation. 2008;117(6):798–805. doi: 10.1161/CIRCULATIONAHA.107.717595 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Wang Z, Nakayama T. Inflammation, a link between obesity and cardiovascular disease. Mediators Inflamm. 2010;2010:535918. doi: 10.1155/2010/535918 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Khafagy R, Dash S. Obesity and Cardiovascular Disease: The Emerging Role of Inflammation. Front Cardiovasc Med. 2021;8:768119. doi: 10.3389/fcvm.2021.768119 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Alwarawrah Y, Kiernan K, MacIver NJ. Changes in Nutritional Status Impact Immune Cell Metabolism and Function. Front Immunol. 2018;9:1055. doi: 10.3389/fimmu.2018.01055 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Kern PA, Saghizadeh M, Ong JM, Bosch RJ, Deem R, Simsolo RB. The expression of tumor necrosis factor in human adipose tissue. Regulation by obesity, weight loss, and relationship to lipoprotein lipase. J Clin Invest. 1995;95(5):2111–9. doi: 10.1172/JCI117899 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Hotamisligil GS, Arner P, Caro JF, Atkinson RL, Spiegelman BM. Increased adipose tissue expression of tumor necrosis factor-alpha in human obesity and insulin resistance. J Clin Invest. 1995;95(5):2409–15. doi: 10.1172/JCI117936 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Hotamisligil GS, Shargill NS, Spiegelman BM. Adipose expression of tumor necrosis factor-alpha: direct role in obesity-linked insulin resistance. Science. 1993;259(5091):87–91. doi: 10.1126/science.7678183 [DOI] [PubMed] [Google Scholar]
  • 17.Pickup JC, Mattock MB, Chusney GD, Burt D. NIDDM as a disease of the innate immune system: association of acute-phase reactants and interleukin-6 with metabolic syndrome X. Diabetologia. 1997;40(11):1286–92. doi: 10.1007/s001250050822 [DOI] [PubMed] [Google Scholar]
  • 18.Kern PA, Ranganathan S, Li C, Wood L, Ranganathan G. Adipose tissue tumor necrosis factor and interleukin-6 expression in human obesity and insulin resistance. Am J Physiol Endocrinol Metab. 2001;280(5):E745–51. doi: 10.1152/ajpendo.2001.280.5.E745 [DOI] [PubMed] [Google Scholar]
  • 19.Xu E, Pereira MMA, Karakasilioti I, Theurich S, Al-Maarri M, Rappl G, et al. Temporal and tissue-specific requirements for T-lymphocyte IL-6 signalling in obesity-associated inflammation and insulin resistance. Nat Commun. 2017;8:14803. doi: 10.1038/ncomms14803 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Weisberg SP, McCann D, Desai M, Rosenbaum M, Leibel RL, Ferrante AW. Obesity is associated with macrophage accumulation in adipose tissue. The Journal of clinical investigation. 2003;112(12):1796–808. doi: 10.1172/JCI19246 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Liu X, Huh JY, Gong H, Chamberland JP, Brinkoetter MT, Hamnvik OP, et al. Lack of mature lymphocytes results in obese but metabolically healthy mice when fed a high-fat diet. International journal of obesity. 2015;39(10):1548–57. doi: 10.1038/ijo.2015.93 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Khan IM, Dai Perrard XY, Perrard JL, Mansoori A, Wayne Smith C, Wu H, et al. Attenuated adipose tissue and skeletal muscle inflammation in obese mice with combined CD4+ and CD8+ T cell deficiency. Atherosclerosis. 2014;233(2):419–28. doi: 10.1016/j.atherosclerosis.2014.01.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Stolarczyk E, Vong CT, Perucha E, Jackson I, Cawthorne MA, Wargent ET, et al. Improved insulin sensitivity despite increased visceral adiposity in mice deficient for the immune cell transcription factor T-bet. Cell Metab. 2013;17(4):520–33. doi: 10.1016/j.cmet.2013.02.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Kiernan K, MacIver NJ. The Role of the Adipokine Leptin in Immune Cell Function in Health and Disease. Front Immunol. 2020;11:622468. doi: 10.3389/fimmu.2020.622468 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Park H-K, Ahima RS. Physiology of leptin: energy homeostasis, neuroendocrine function and metabolism. Metabolism. 2015;64(1):24–34. doi: 10.1016/j.metabol.2014.08.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Khan SM, Hamnvik O-PR, Brinkoetter M, Mantzoros CS. Leptin as a modulator of neuroendocrine function in humans. Yonsei Med J. 2012;53(4):671–9. doi: 10.3349/ymj.2012.53.4.671 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Rosenbaum M, Leibel RL. 20 years of leptin: role of leptin in energy homeostasis in humans. J Endocrinol. 2014;223(1):T83–T96. doi: 10.1530/JOE-14-0358 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Kim SY, Lim JH, Choi SW, Kim M, Kim ST, Kim MS, et al. Preferential effects of leptin on CD4 T cells in central and peripheral immune system are critically linked to the expression of leptin receptor. Biochem Biophys Res Commun. 2010;394(3):562–8. doi: 10.1016/j.bbrc.2010.03.019 [DOI] [PubMed] [Google Scholar]
  • 29.Gerriets VA, Danzaki K, Kishton RJ, Eisner W, Nichols AG, Saucillo DC, et al. Leptin directly promotes T-cell glycolytic metabolism to drive effector T-cell differentiation in a mouse model of autoimmunity. Eur J Immunol. 2016;46(8):1970–83. doi: 10.1002/eji.201545861 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Saucillo DC, Gerriets VA, Sheng J, Rathmell JC, Maciver NJ. Leptin metabolically licenses T cells for activation to link nutrition and immunity. J Immunol. 2014;192(1):136–44. doi: 10.4049/jimmunol.1301158 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Alwarawrah Y, Nichols AG, Green WD, Eisner W, Kiernan K, Warren J, et al. Targeting T-cell oxidative metabolism to improve influenza survival in a mouse model of obesity. Int J Obes (Lond). 2020;44(12):2419–29. doi: 10.1038/s41366-020-00692-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Casimiro I, Stull ND, Tersey SA, Mirmira RG. Phenotypic sexual dimorphism in response to dietary fat manipulation in C57BL/6J mice. J Diabetes Complications. 2021;35(2):107795. doi: 10.1016/j.jdiacomp.2020.107795 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Hasegawa Y, Chen SY, Sheng L, Jena PK, Kalanetra KM, Mills DA, et al. Long-term effects of western diet consumption in male and female mice. Sci Rep. 2020;10(1):14686. doi: 10.1038/s41598-020-71592-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Pettersson US, Walden TB, Carlsson PO, Jansson L, Phillipson M. Female mice are protected against high-fat diet induced metabolic syndrome and increase the regulatory T cell population in adipose tissue. PLoS One. 2012;7(9):e46057. doi: 10.1371/journal.pone.0046057 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Ingvorsen C, Karp NA, Lelliott CJ. The role of sex and body weight on the metabolic effects of high-fat diet in C57BL/6N mice. Nutr Diabetes. 2017;7(4):e261. doi: 10.1038/nutd.2017.6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Bu L, Gao M, Qu S, Liu D. Intraperitoneal injection of clodronate liposomes eliminates visceral adipose macrophages and blocks high-fat diet-induced weight gain and development of insulin resistance. AAPS J. 2013;15(4):1001–11. doi: 10.1208/s12248-013-9501-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Feng B, Jiao P, Nie Y, Kim T, Jun D, van Rooijen N, et al. Clodronate liposomes improve metabolic profile and reduce visceral adipose macrophage content in diet-induced obese mice. PLoS One. 2011;6(9):e24358. doi: 10.1371/journal.pone.0024358 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Mamane Y, Chung Chan C, Lavallee G, Morin N, Xu LJ, Huang J, et al. The C3a anaphylatoxin receptor is a key mediator of insulin resistance and functions by modulating adipose tissue macrophage infiltration and activation. Diabetes. 2009;58(9):2006–17. doi: 10.2337/db09-0323 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.McGillicuddy FC, Harford KA, Reynolds CM, Oliver E, Claessens M, Mills KH, et al. Lack of interleukin-1 receptor I (IL-1RI) protects mice from high-fat diet-induced adipose tissue inflammation coincident with improved glucose homeostasis. Diabetes. 2011;60(6):1688–98. doi: 10.2337/db10-1278 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Phieler J, Chung KJ, Chatzigeorgiou A, Klotzsche-von Ameln A, Garcia-Martin R, Sprott D, et al. The complement anaphylatoxin C5a receptor contributes to obese adipose tissue inflammation and insulin resistance. J Immunol. 2013;191(8):4367–74. doi: 10.4049/jimmunol.1300038 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Pereira S, Cline DL, Chan M, Chai K, Yoon JS, O’Dwyer SM, et al. Role of myeloid cell leptin signaling in the regulation of glucose metabolism. Sci Rep. 2021;11(1):18394. doi: 10.1038/s41598-021-97549-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Sadiq Umar

17 Mar 2023

PONE-D-23-03100

Effects of T cell leptin signaling on systemic glucose tolerance and T cell responses in obesity

PLOS ONE

Dear Dr. MacIver,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Apr 30 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Sadiq Umar

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at 

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and 

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Thank you for stating the following in the Acknowledgments Section of your manuscript: 

    "This study was supported by NIH R01-DK106090 (NJM)"

We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. 

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows: 

    "This study was supported by NIH R01-DK106090 (NJM)

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

3. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

"Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

4. Please include your full ethics statement in the ‘Methods’ section of your manuscript file. In your statement, please include the full name of the IRB or ethics committee who approved or waived your study, as well as whether or not you obtained informed written or verbal consent. If consent was waived for your study, please include this information in your statement as well. 

5. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: - Authors need to revise the structure of results section. Please structure with proper subsections and describe your results separately; not only with Figures legends/captions. Also, figure legends should be submitted in a different section or along with figures.

- Why you didn't check lipid profile? What about systemic effects of hypercholesterolimia? It greatly trigger cardiovascular and immune response. Please justify.

- You could also take pictures of spleen from each mice. Could help justify splenomegaly!

- Study could elaborate several other factors and parameters of systemic inflammation. Why didn't you do that. Please justify other possible limitations.

Reviewer #2: In this article, the authors have focused on understanding the leptin signaling pathway driving T-cell inflammation in obesity. This is a well-written manuscript with minor correction that needs to be addressed,

Line 61 Expand Rag-/-

Quote reference for all the protocols followed in the method section

Line 148 quote the company

Include Scatter plot for flow cytometry

Give a line of reason for every result stated in the study. For example, why are the metabolism and function affected in CD4+ T cells when compared to CD8+ T cells?

Check for grammatical mistakes throughout the manuscript

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: FARHATH SULTANA

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2023 Jun 5;18(6):e0286470. doi: 10.1371/journal.pone.0286470.r002

Author response to Decision Letter 0


15 May 2023

Response to reviewers

We thank the reviewers for their careful review of our manuscript. We have addressed all reviewer comments below and now present an improved manuscript for review.

Reviewer #1:

1. Authors need to revise the structure of results section. Please structure with proper subsections and describe your results separately; not only with Figures legends/captions. Also, figure legends should be submitted in a different section or along with figures.

Thank you for this suggestion. We agree that adding subsections would improve the structure of the manuscript. In response to this comment, subsections were added to the results section. Figure legends are placed in the text as per PLOS manuscript guidelines.

2. Why you didn't check lipid profile? What about systemic effects of hypercholesterolemia? It greatly trigger cardiovascular and immune response. Please justify.

Thank you for this comment. We agree that systemic effects of hypercholesterolemia could be impacted in our mouse model and subsequently influence T cell responses. In response to this comment, we performed lipid analyses on serum samples from this study. Please see Supplemental Figure 2. We did not see any significant differences in total cholesterol or triglyceride levels between the experimental groups.

3. You could also take pictures of spleen from each mice. Could help justify splenomegaly!

While we did not have photographs of the spleens from these mice, we did record spleen weights and weight of visceral adipose tissue, so we have included that information in the response to reviewers attached file and in the revised cover page.

4. Study could elaborate several other factors and parameters of systemic inflammation. Why didn't you do that. Please justify other possible limitations.

Thank you for this comment. We agree that more markers and parameters of systemic inflammation would be informative. In response to this comment, we performed multiplex analysis on serum samples from our mouse cohort. We assessed serum levels of TNF, IFN-γ, IL-17, IL-1β, and IL-6. We did not find any significant differences in serum cytokine levels between the groups. We also performed a leptin ELISA to measure serum leptin levels in these mice. This data is included in Supplemental Figures 1 and 3.

Reviewer #2:

1. In this article, the authors have focused on understanding the leptin signaling pathway driving T-cell inflammation in obesity. This is a well-written manuscript with minor correction that needs to be addressed,

Line 61 Expand Rag-/-

Quote reference for all the protocols followed in the method section

Line 148 quote the company

Include Scatter plot for flow cytometry

Give a line of reason for every result stated in the study. For example, why are the metabolism and function affected in CD4+ T cells when compared to CD8+ T cells?

Check for grammatical mistakes throughout the manuscript

We thank the reviewer for these helpful comments. In response to the comments from this reviewer, changes to the text were made, including expanding on “Rag-/-” to clarify, referencing our publications where methods have been published previously, and including several sentences explaining the results presented. We also included flow cytometry scatter plots in Supplemental Figure 2.

Attachment

Submitted filename: Response to reviewers.docx

Decision Letter 1

Sadiq Umar

17 May 2023

Effects of T cell leptin signaling on systemic glucose tolerance and T cell responses in obesity

PONE-D-23-03100R1

Dear Dr. MacIver,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Sadiq Umar

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Recommendation for acceptance.

Reviewers' comments:

Acceptance letter

Sadiq Umar

24 May 2023

PONE-D-23-03100R1

Effects of T cell leptin signaling on systemic glucose tolerance and T cell responses in obesity

Dear Dr. MacIver:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Sadiq Umar

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. T cell-specific leptin receptor knockout mice have similar serum cholesterol, triglyceride, and leptin levels compared to littermate control mice following high-fat diet.

    Mouse serum was collected after 18 weeks on low-fat, normal chow (NC) or high-fat diet (HFD) and analyzed for concentration of (A) total cholesterol and (B) triglycerides using clinical chemistry tests performed by Animal Clinical Laboratory Services core facility at University of North Carolina at Chapel Hill, and (C) leptin levels by serum ELISA (ThermoFisher). One way ANOVA corrected for multiple comparisons. No significant differences were observed between the groups. n = 5–7 mice/group.

    (TIF)

    S2 Fig. Splenic CD4+ T cells isolated from leptin receptor knockout mice are altered following high-fat diet compared to CD4+ T cells isolated from littermate controls.

    Representative flow cytometry plots with gating are shown from each group showing cytokine production for CD4+ T cells (top row), CD8+ T cells (middle row), and expression of FoxP3 on CD4+ T cells (bottom row). (A) Pre-gated on lymphocytes, single cells, CD3+, CD4+; percentage IL-17+ and percentage IFN-γ+ shown (B) Pre-gated on lymphocytes, single cells, CD3+, CD8+ (C) Pre-gated on lymphocytes, single cells, CD3+, CD4+; percentage FoxP3+CD25+ shown.

    (TIF)

    S3 Fig. T cell-specific leptin receptor knockout mice have similar levels of circulating cytokines compared to littermate control mice following high-fat diet.

    Multiplex analysis of cytokine levels was performed on selected mouse serum samples using Bio-Rad Bio-Plex Pro Th17 multiplex cytokine analysis assay to examine levels of (A) TNF, (B) IFN-γ, (C) IL-17, (D) IL-1β, and (E) IL-6. IL-10 was also analyzed but levels were below the limit of detection. n = 5–7 mice/group except where below limit of detection, in which case sample was excluded from graph. One way ANOVA corrected for multiple comparisons. No significant differences were observed between groups.

    (TIF)

    S1 Data

    (XLSX)

    Attachment

    Submitted filename: Response to reviewers.docx

    Data Availability Statement

    All relevant data are within the paper and its Supporting information files.


    Articles from PLOS ONE are provided here courtesy of PLOS

    RESOURCES