Ketogenesis activates metabolically protective γδ T cells in visceral adipose tissue

Abstract

Ketone bodies are essential alternative fuels that allow humans to survive periods of glucose scarcity induced by starvation and prolonged exercise. A widely used ketogenic diet (KD), which is extremely high in fat with very low carbohydrates, drives the host into using β-hydroxybutyrate for the production of ATP and lowers NLRP3-mediated inflammation. However, the extremely high fat composition of KD raises the question of how ketogenesis affects adipose tissue to control inflammation and energy homeostasis. Here, by using single-cell RNA sequencing of adipose-tissue-resident immune cells, we show that KD expands metabolically protective γδ T cells that restrain inflammation. Notably, long-term ad libitum KD feeding in mice causes obesity, impairs metabolic health and depletes the adipose-resident γδ T cells. In addition, mice lacking γδ T cells have impaired glucose homeostasis. Our results suggest that γδ T cells are mediators of protective immunometabolic responses that link fatty acid–driven fuel use to reduced adipose tissue inflammation.

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Fig. 1: Metabolic response to 1 week of KD feeding.
Fig. 2: Characterization of tissue-resident CD45+ cell populations in Efat by scRNA-seq.
Fig. 3: KD alters the adipose-resident immune compartment.
Fig. 4: γδ T cells in visceral fat increase in response to KD and are uniquely tissue-resident.
Fig. 5: RNA-seq from bulk-sorted adipose γδ T cells reveals KD induces tissue-protective gene signatures in γδ T cells.
Fig. 6: Loss of γδ T cells contributes to metabolic dysregulation induced by long-term KD.

Data availability

Sequencing data associated with this study have been deposited to the Gene Expression Omnibus with accession numbers GSE137073 and GSE137076. The source data for Extended Data Figs. 6 and 7 are provided with the paper.

Code availability

Codes are publicly available in the relevant citations and custom script is available on request.

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Acknowledgements

We thank Y. Zhuang from the Duke University Medical School for providing the adipose tissue from Tcrd−/− reporter mice. We thank D. Gonzalez and A. Haberman for assistance with two-photon microscopy. E.L.G is funded by grant no. K99AG058801. The Dixit laboratory is supported in part by NIH grant nos. P01AG051459, AI105097, AR070811 and AG043608.

Author information

E.L.G performed experiments and data analysis and prepared the manuscript. I.S. performed scRNA-seq analysis and assisted in manuscript preparation. J.L.A. performed parabiosis experiments and assisted in manuscript preparation. S.S. performed bulk RNA-seq analysis and assisted in manuscript preparation. M.N.A. conceived RNA-seq experiments, oversaw analyses and assisted in data interpretation. V.D.D. conceived the project, and helped with data interpretation and manuscript preparation.

Correspondence to Vishwa Deep Dixit.

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The authors declare no competing interests.

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Editor recognition Primary Handling Editor: Elena Bellafante.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Workflow of single-cell RNAseq analysis.

Normalized gene expression from Efat tissue-resident CD45+ cells was used to identify most variable genes for principal component analysis. Data were visualized by tSNE plots. Unique markers were used to identify the cell type/lineage represented within each cluster.

Extended Data Fig. 2 Further identification of tissue-resident populations.

(a) tSNE plot of tissue-resident immune cells from chow- and KD-fed samples merged displaying expression of selected genes. (b) tSNE plot as in S2A showing average z-scores of genes in cell cycle pathway (Reactome database). (c) tSNE plot of proliferating cells only (cluster 10). Expression of selected markers is displayed. For (a-c) expression is based on pooled data from chow and KD samples (each containing n=3 pooled biological samples into 1 technical sample for each diet). (d) Violin plots of Il1b expression within all cells from each cluster. Expression is pooled from chow (n=4 pooled biological samples into 1 technical sample) and KD (n=3 pooled biological samples into 1 technical sample) and total number of cells in each cluster is indicated on the figure. Overlaid box plots indicate median and 25th-75th percentiles; whiskers extend no further than 1.5xIQR from either upper or lower hinge, as described in Materials and Methods.

Extended Data Fig. 3 Adipose immune compartment changes induced by 1 week of KD feeding.

Abundance of macrophages, eosinophils, and Tregs in Efat of chow (n=4) vs KD-fed (n=5) mice. Statistical differences were calculated by 2-way ANOVA with Sidak’s correction for multiple comparisons. Each symbol represents an individual mouse and all data are expressed as mean±SEM. Data are representative of at least 2 independent experiments. Exact p-values are shown whenever possible, ****p<0.0001.

Extended Data Fig. 4 γδ T cell expansion induced by KD is independent of NLRP3 and FGF21.

(a) Blood BHB levels, (b) Efat γδ T cells, and (c) spleen γδ T cells in Nlrp3/- and Fgf21-/- mice after 1 week of KD feeding (n=6 biological independent mice per group). Statistical differences were calculated by unpaired 2-tailed t-tests within each genotype for each graph. All data are represented as mean±SEM and each symbol represents an individual mouse. Exact p-values are shown whenever possible, ****p<0.0001.

Extended Data Fig. 5 Workflow of bulk γδ T cell RNAseq analysis.

(a) Differentially-expressed genes were identified within all annotated transcribed murine gene loci. From this gene list we performed GSEA to distinguish pathways significantly altered by KD within epididymal adipose tissue γδ T cells. Differential expression gene list was further filtered to identify genes of interest. (b) GSEA enrichment score curve of SASP-related genes.

Extended Data Fig. 6 Long term KD feeding causes obesity in mice.

(a) Body weight, (b) lean mass, (c) fat mass, and (d) liver mass were measured in WT mice fed chow (n=8) or KD (n=9) for 4 months. For (a-d) Statistical differences were calculated by unpaired 2-tailed t-tests. Data are representative of 3 independent experiments. (e) Representative H&E-stained liver sections after long-term KD. Sections are representative of 2 independent experiments each with n=5 mice/group. (f) Western blot of liver AKT phosphorylation after insulin injection into fasted mice. Each lane represents an individual mouse. In KD group mice are ordered from greatest to smallest body weight (ranging from 59-37g). (g) Profile of visceral adipose hematopoietic compartment after 4 months chow (n=8) vs KD (n=8) feeding. Statistical differences were calculated by 2-way ANOVA with Sidak’s correction for multiple comparisons. All data are expressed as mean±SEM and each symbol represents an individual mouse. Exact p-values are shown whenever possible, ****p<0.0001. Source data

Extended Data Fig. 7 Metabolic profiling of Tcrd-/- mice after long-term KD feeding.

(a) Lean mass and (b) body fat in WT (n=10) vs Tcrd-/- (n=11) mice after 4 months KD. Statistical differences were calculated by nonparametric Mann-Whitney 2-tailed test because the Tcrd-/- data are not normally distributed. Data are representative of 3 independent experiments. (c) Insulin tolerance test of WT (n=10) vs Tcrd-/- (n=7) mice after 3.5 months KD. Statistical differences were calculated by paired 2-way ANOVA. (d) Western blot of liver AKT phosphorylation after insulin injection into fasted mice. Each lane represents an individual mouse. (e) Total CD45+ cellularity in Efat from WT (n=11) vs Tcrd-/- (n=11) KD-fed mice. Statistical differences were determined by unpaired 2-tailed t-test. (f) CD45+ composition in epidydimal adipose tissue in WT (n=11) vs Tcrd-/- (n=11) mice after 4 months KD. Data are pooled from 2 independent experiments with a total of n=11 WT and n=11 Tcrd-/- mice analyzed. Statistical differences were calculated by 2-way ANOVA with Sidak’s correction for multiple comparisons. Each symbol represents an individual mouse and data are represented as mean±SEM. Exact p-values are shown whenever possible, ****p<0.0001. Source data

Extended Data Fig. 8 Flow cytometry gating strategies.

Representative gating strategies are shown for each cell population analyzed throughout the experiments. (a) Gating strategy used to define all cell lineages analyzed in Fig. 4d, Fig. 6e, f, o, Extended Data Fig. 3, Extended Data Fig. 4b, c, Extended Data Fig. 6g, Extended Data Fig. 7e, f. (b) Gating strategy used to identify IL-17-producing γδ T cells and Tregs analyzed in Fig. 4h, Fig. 6p. (c) Gating strategy used to identify ILC2 analyzed in Extended Data Fig. 7c.

Extended Data Fig. 9 Comparison of bulk sorted γδ T cell RNAseq with scRNAseq dataset.

(a) tSNE plot of tissue-resident immune cells from chow- and KD-fed samples shown separately to demonstrate macrophage-specific genes differentially expressed in multiple lymphoid clusters as a result of macrophage frequency change (b) tSNE plot with overlaid color that represents expression of down-regulated genes identified from bulk RNAseq data if filtered only by padj. (c) tSNE plot with overlaid color that represents expression of down-regulated (left panel) and up-regulated (right panel) genes identified from bulk RNAseq data when filtered by padj and log2FC. For all plots (a-c) data are derived from a single technical sample generated by pooling n=3 independent samples prior to sequencing for each diet group.

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Supplementary Information

Supplementary Tables 1 and 2

Reporting Summary

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Source Data Extended Data Fig. 6

Unprocessed western blots

Source Data Extended Data Fig. 7

Unprocessed western blots

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Goldberg, E.L., Shchukina, I., Asher, J.L. et al. Ketogenesis activates metabolically protective γδ T cells in visceral adipose tissue. Nat Metab 2, 50–61 (2020). https://doi.org/10.1038/s42255-019-0160-6

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