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Fasting-induced FOXO4 blunts human CD4+ T helper cell responsiveness

Nature Metabolism volume 3, pages 318–326 (2021)Cite this article

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Abstract

Intermittent fasting blunts inflammation in asthma1 and rheumatoid arthritis2, suggesting that fasting may be exploited as an immune-modulatory intervention. However, the mechanisms underpinning the anti-inflammatory effects of fasting are poorly characterized3,4,5. Here, we show that fasting in humans is sufficient to blunt CD4+ T helper cell responsiveness. RNA sequencing and flow cytometry immunophenotyping of peripheral blood mononuclear cells from volunteers subjected to overnight or 24-h fasting and 3 h of refeeding suggest that fasting blunts CD4+ T helper cell activation and differentiation. Transcriptomic analysis reveals that longer fasting has a more robust effect on CD4+ T-cell biology. Through bioinformatics analyses, we identify the transcription factor FOXO4 and its canonical target FK506-binding protein 5 (FKBP5) as a potential fasting-responsive regulatory axis. Genetic gain- or loss-of-function of FOXO4 and FKBP5 is sufficient to modulate TH1 and TH17 cytokine production. Moreover, we find that fasting-induced or genetic overexpression of FOXO4 and FKBP5 is sufficient to downregulate mammalian target of rapamycin complex 1 signalling and suppress signal transducer and activator of transcription 1/3 activation. Our results identify FOXO4–FKBP5 as a new fasting-induced, signal transducer and activator of transcription–mediated regulatory pathway to blunt human CD4+ T helper cell responsiveness.

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Fig. 1: RNA-seq analysis shows distinct separation of the three caloric load conditions.
Fig. 2: Fasting/feeding differentially regulate CD4+ T-cell activation and differentiation.
Fig. 3: Fasting-induced FOXO4 effects.
Fig. 4: FKBP5 induction mimics FOXO4 overexpression and fasting effects on T-cell activation.

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Data availability

The mRNA-seq datasets and data linked to the participants’ body mass index and sex can be accessed at the GEO database under accession number GSE165149 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE165149). All code used in the study can be found at https://github.com/NHLBI-BCB/PTNA. Source data are provided with this paper.

Code availability

All code used in the study can be found at https://github.com/NHLBI-BCB/PTNA. Please also remove this text from the Data availability section.

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Acknowledgements

This work was supported by the Intramural Research Program of the NHLBI (NIH). We thank J. Mann and R. Shi in the Center of Human Immunology, National Institute of Allergy and Infectious Diseases for the assistance with project management and sample collection, respectively. We thank and acknowledge the assistance of the NHLBI DNA Sequencing and Genomics Core in performing the RNA library sequencing. This trial is registered at ClinicalTrials.gov (NCT02719899, NCT01143454) and NIH Clinical Center blood bank (NCT00001846). This study was funded by the Division of Intramural Research of the NHLBI (NIH; HL005102, HL005199 and HL006199) (M.N.S.).

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Author notes

  1. Angélique Biancotto

    Present address: Department of Precision Immunology, Sanofi, Cambridge, MA, USA

  2. These authors contributed equally: Kim Han, Komudi Singh.

Authors and Affiliations

  1. Laboratory of Mitochondrial Biology and Metabolism, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA

    Kim Han, Komudi Singh, Matthew J. Rodman, Shahin Hassanzadeh, Kaiyuan Wu, An Nguyen & Michael N. Sack

  2. Bioinformatics and Computational Core Facility, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA

    Komudi Singh, Fayaz Seifuddin & Mehdi Pirooznia

  3. Cardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA

    Rebecca D. Huffstutler, Nehal N. Mehta & Michael N. Sack

  4. Flow Cytometry Core Facility, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA

    Pradeep K. Dagur, Ankit Saxena & J. Philip McCoy

  5. Center of Human Immunology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA

    Jinguo Chen, Angélique Biancotto & Katherine E. R. Stagliano

  6. Laboratory of Cardiometabolic Disease and Inflammation, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA

    Heather L. Teague & Nehal N. Mehta

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Contributions

K.H., K.S. and M.N.S. conceived the project. M.N.S. secured the funding. K.H., K.S., R.D.H., K.E.R.S., M.P. and M.N.S. designed the experiments. K.H., K.S., M.J.R., A.N., S.H., K.W., R.D.H., A.S., P.K.D., A.B. and H.L.T. carried out the experiments. K.H., K.S., F.S., P.K.D., A.S., J.C. and H.L.T. analysed the data. K.H., K.S. and M.N.S. wrote the manuscript. M.J.R., J.P.M., K.E.R.S., N.N.M. and M.P. edited the manuscript.

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Correspondence to Michael N. Sack.

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The authors declare no competing interests.

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Peer review information Nature Metabolism thanks Koji Hase and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: George Caputa.

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Extended data

Extended Data Fig. 1 Initial analysis of RNA-seq data acquired from the 3 nutritional-load conditions.

a, Individual points (symbols) and means ± SEM (lines) of glucose, insulin and growth hormone levels following 24-hr. fasting and 3-hrs. following a fixed caloric meal. Dot plots represent mean ± SEM with value of each subject (n = 26–28 subjects). The values represent average of duplicates. Two-sided, paired Student’s t-tests. Sera glucose (n = 28), Sera insulin (n = 26), Sera growth hormone (n = 26). b, Table showing number of DE genes (p < 0.05) identified in the indicated comparisons. PBMCs’ RNA from 21 subjects used to generate RNA-seq data. c, Unsupervised principal component analysis (PCA) performed on DE genes (p < 0.05) for indicated comparisons (n = 21 subjects). d, Combined PCA analysis of the top 1000 DE genes (p < 0.05) from all 3 groups. e, Top 10 pathways in which the DE genes (p < 0.05) from the indicated comparisons. The q values (p values adjusted for false discovery rate (FDR)) from the enrichment result are represented by negative log10 scale (x axis). The most significant pathways predominantly align with lymphocyte and T cell differentiation and activation comparison with refed state. f, g, Box and whisker plots show range of fold change of the subset of 114 DE genes (p < 0.05, RNA-seq analysis) that were either downregulated (f) or upregulated (g) to a greater degree following the 24-hr. fast vs. refeeding than baseline (overnight fast) vs. refeeding. The box and whiskers plots show median and upper/lower quartile of the relative gene expression. The whiskers show Turkey distribution and the outlier levels are shown as individual genes (n = 21 subjects). h, Pathway enrichment analysis of 844 DE genes exclusively identified in the 24-hr. fasted versus refed state shown with q values (p value adjusted for FDR) for each pathway represented by negative log 10 scale (x axis). Statistical Source Data of Extended Data Fig. 1.

Source data

Extended Data Fig. 2 Flow cytometry using PBMCs exhibit differential fasting and refeeding cell-surface receptor expression levels.

a, Nutrient-load dependent CD45+ PBMC flow cytometry distribution. Schematic representation of cytometric labelling to distinguish cell type distributions at baseline, following 24-hr. fasting and refeeding (n = 19 subjects). b, Cytometric plots and gating strategies to measure cell surface markers on T helper cells. c-j, Dot and line plots show cell populations of each subject (Wilcoxon two-sided, paired analysis to compare groups, n = 19 subjects). c, Flow plots of activated classical monocytes from representative subject comparing 3 nutrient conditions. The plots show relative cell population frequencies of classical monocytes (CD14highCD16−) and median fluorescence intensity (MFI) of activated classical monocytes (HLADR+ in CD14highCD16−). d, Representative flow plots showing gating and quantitation of activated CD8+ T cells. Plots show expression of activated CD8+ T cells (CD38+HLADR+ in CD8+) showing significant increases in refed samples compared to baseline and fasting. e, Dot and line plots showing significant blunting in refed samples compared to baseline and fasting in activated DCs (HLADR+ in CD16−CD56−), myeloid DCs (CD11c+CD123−) and plasmacytoid cells (CD11c−CD123+). f, Representative flow plots showing gating and quantitation of regulatory T cells and plots show no difference in Treg cells (FOXP3+). g, Follicular helper T cell (CD4+CXCR5+) levels show no difference in three caloric-load conditions. h, Plots showing no difference in activated NK cells (HLADR+ in CD16−CD56+, CD16+CD56+, and CD16+CD56−). i, Plots showing no difference in immature B cells (CD19+CD20−) and mature B cells (CD19+CD20+). j, Quantifying relative cell population frequencies with specific CD8+ T (Tc) markers. Cell populations of Tc1 (CXCR3+CCR6−) and Tc17 (CXCR3−CCR6+) cells show no change in three caloric-load conditions. The antibody information of BD lyoplate and 18-color panel and gating strategy of flow cytometry is shown in Supplementary Tables 1 and 2 and Supplementary Data 3.

Extended Data Fig. 3 Weighted Gene Co-expression Network Analysis (WGCNA) identifies distinct and coordinate gene expression patterns in the fasting and refed states.

a, Modules of genes with correlated expression patterns were clustered using WGCNA. The modules, with their distinct color designations using the fasted and refed data are shown on the x and y axes. Numerical assignment aligned to module colors represent number of genes per module. The correlated modules from fasting and refed were aligned to determine gene overlap (significance of the overlap determined by Fisher’s exact test where red shading denotes significance - darker shade > significance). b, Representative cell type enrichment analysis (CTen) result of a fasting cluster showing enrichment of CD8+ and CD4+ T cells encoding genes (p-value depicted by red geometric plot extending from the center of the figure towards enriched cell types – representing -log10 Benjamini-Hochberg adjusted P. c, GeneMANIA derived protein-protein interaction (PPI) networks. Significant DE genes fold change information where blue circles represent increased, and red circles decreased expression during fasting compared to refeeding. d, qRT-PCR validation of selected network genes. Bar graph represent mean ± SEM with value of each subject (n = 14 subjects following 3-4 replicates using two-sided paired Student’s t-test). e, Relative FOXO4 RNA expression in CD4+ T cells in response to siRNA (Bar graph represent mean ± SEM, n = 6 health volunteers, two-sided, paired Student’s t-test). f, Representative protein blot show FOXO4 expression in FOXO4-siRNA treated CD4+ T cells from healthy subjects (n = 3), 3 days following TCR activation (+). g-h, Representative immunoblots showing expression levels of (g) FOXO4- and (h) FKBP5-overexpression in CD4+ T cells from healthy subjects (n = 3). Open arrowheads - endogenous protein bands, overexpressed tagged-proteins - solid arrowheads. Source Data Extended Data Fig. 3: Unprocessed immunoblots.

Source data

Extended Data Fig. 4 Fasting and refed modules identified by WGCNA analysis.

*CTen, Cell type enrichment; †PPI, Protein-protein interactions; §TF, Transcription factors.

Extended Data Fig. 5 Evaluating FKBP5 effect on T cell activation.

a, Top 5 variable importance in prediction (VIP) genes by partial least squares discrimination analysis (PLS-DA) of RNA-seq data from three caloric-load conditions. b, Representative protein blots and quantitative changes normalized by Actin of canonical TFs of Th1 (TBX21), Th2 (GATA3), and Th17 (RORC) in FKBP5-overexpression (OE) in CD4+ T cells isolated from healthy volunteers, 3 days following TCR activation. Bar graph represent mean ± SEM with data point of each health volunteer (n = 8 subjects, Wilcoxon two-sided, paired analysis). c-d, CD4+ T cells were isolated from healthy volunteers and transfected with FKBP5 siRNA and scrambled controls. c, Relative FKBP5 RNA expression in CD4+ T cells (Bar graph represent mean ± SEM with normalized value to scrambled control, n = 6 subjects, values represent average of quadruplicates, two-sided, paired Student’s t-test). d, Cytokine release of IFNγ, IL-4 and IL-17 following FKBP5 knockdown (KD) in CD4+ T cells isolated from healthy volunteers, 3 days following TCR activation. The dot and line graphs represent mean value of each subject (n = 6 biologically independent subjects, values represent average of duplicates, two-sided, ratio paired Student’s t-tests). Source Data Extended Data Fig. 5: Unprocessed immunoblots.

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2, and Data 1–3

Reporting Summary

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 3

Unprocessed western blots.

Source Data Fig. 4

Unprocessed western blots.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 3

Unprocessed western blots.

Source Data Extended Data Fig. 5

Unprocessed western blots.

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Han, K., Singh, K., Rodman, M.J. et al. Fasting-induced FOXO4 blunts human CD4+ T helper cell responsiveness. Nat Metab 3, 318–326 (2021). https://doi.org/10.1038/s42255-021-00356-0

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