To determine whether intracellular metabolic processes in iNKT cells play important roles in allergic asthma, iNKT cells sorted from the lungs of OVA-induced asthma model mice were assessed for the expression of key enzymes involved in glycolysis, the tricarboxylic acid (TCA) cycle, β-oxidation, and fatty-acid synthesis (Fig. 1). Indeed, OVA challenge significantly increased iNKT-cell expression of β-oxidation and fatty-acid synthesis enzymes and tended to elevate TCA-cycle enzyme expression. However, glycolysis-enzyme expression remained low (Fig. 1A, B). The significantly upregulated fatty-acid synthesis enzymes were Acc1, Fasn, and Oxsm: Acc1 and Fasn play a particularly key role in de novo fatty-acid synthesis. Notably, when purified murine iNKT cells and conventional CD4⁺ and CD8⁺ T cells were activated in vitro with anti-CD3/CD28, the iNKT cells expressed much higher levels of Acc1 and Fasn than the other cells. By contrast, the CD4⁺ and CD8⁺ T cells expressed higher levels of TCA cycle (Sdh2b and Idh1a) and glycolysis (Hk2 and Pkm2) enzymes, respectively (Fig. 1C). This is consistent with the previous Gene Set Enrichment Analysis of Oh et al.: when they compared unstimulated murine iNKT and CD4⁺ T cells to each other, they found that genes involved in the ‘lipid, fatty acid and steroid metabolism’ pathway were enriched in iNKT cells (Fig. S1A) ³². Thus, iNKT cells express Acc1 and Fasn when they are stimulated, including in asthma, whereas conventional CD4⁺ and CD8⁺ T cells do not.

We then focused on ACC1. To test whether ACC1-mediated de novo fatty-acid synthesis in iNKT cells shapes AHR and allergic immune responses, we generated Cd4-CreAcc1^fl/fl mice: the CD4-positive cells (i.e. conventional CD4⁺ T and iNKT cells) in these mice cannot express ACC1. Since conventional CD4⁺ T cells did not upregulate ACC1 when stimulated in vitro or in vivo (Fig. 1B, C), the knockout is likely to be physiologically relevant for iNKT cells only. When the Cd4-CreAcc1^fl/fl and Acc1^fl/fl control mice were induced to undergo OVA-mediated asthma, we indeed observed that the loss of ACC1 expression attenuated AHR (Fig. 1D) and the total immune-cell numbers in the lung and the bronchoalveolar lavage fluid (BALF) (Fig. S1B). These changes associated with significantly decreased total lymphocyte, neutrophil, eosinophil, and macrophage frequencies in the BALF (Fig. S1C) and lower eosinophil frequencies in the lung (Fig. S1D). The lung CD8⁺ T-cell and alveolar- and interstitial-macrophage frequencies did not change. Importantly, the total lung CD4⁺ T-cell population (excluding iNKT cells) also did not change significantly (Fig. S1D). By contrast, there was a sharp drop in iNKT cells in the lung; this was also associated with a significant increase in PI staining (Fig. 1E). qRT-PCR analysis of the conventional lung CD4⁺ T-cell population (excluding iNKT cells) showed that loss of ACC1 expression associated with lower Gata3, Il4, Il5, and Il13 expression but similar Tbx21, Rorc, Foxp3, Ifng, Il-17a, and Il-10 expression (Fig. S1E, F). Indeed, when the lung iNKT cells and conventional CD4⁺ T cells were separately isolated, both iNKT cells and the conventional CD4⁺ T cells demonstrated dramatically lower Il4 and Il13 expression but similar Ifng and Il17a expression (Fig. 1F and Fig. S1F). Notably, all of these findings were recapitulated when we conducted the same analyses in mice that underwent HDM-induced asthma (Fig. 1G–I and Fig. S1G–K). Thus, the inability of lung iNKT cells to express ACC1 induced iNKT-cell death during allergen-induced asthmogenesis. This also reduced their expression of Gata3, Il4, Il5, and Il13 in lung conventional CD4⁺ T cells: these effects markedly reduced the eosinophilia and AHR. These effects were independent of the inability of conventional lung CD4⁺ T cells to express ACC1 in the mice.

The experiments above suggest that iNKT cells alone mediate ACC1-mediated regulation of airway inflammation. To test this further, we challenged Cd4-CreAcc1^fl/fl and Acc1^fl/fl mice intranasally with α-GalCer, which has been shown previously to induce iNKT cell activation-mediated AHR ³³. Consistent with the results from OVA- and HDM-induced asthma models, the Cd4-CreAcc1^fl/fl mice showed the following: less AHR and lung and BALF inflammation; significantly fewer lymphocytes, neutrophils, and macrophages in the BALF; less eosinophilia in the BALF and lungs; no difference in lung CD4⁺ and CD8⁺ T-cell frequencies but significantly fewer lung iNKT cells and greater PI staining of these cells; lower expression by the lung conventional CD4⁺ T-cell population of Gata3, Il4, Il5, and Il13 but not Tbx21, Rorc, Foxp3, Ifng, IL17a, or Il10; and similar Ifng and Il17a expression but dramatically reduced Il4 and Il13 expression by the lung iNKT cells (Fig. 2A–H). Thus, ACC1-deficient iNKT cells also failed to promote α-GalCer-induced AHR.

To further confirm that it is the ACC1 in iNKT cells that regulated AHR, we adoptively transferred WT iNKT cells (i.e. from Acc1^fl/fl mice) or ACC1-deficient iNKT cells into Jα18 KO mice, which lack NKT cells ³⁴. The mice were then subjected to OVA-induced asthma. The WT iNKT cells, but not the ACC1-deficient iNKT cells, restored the following features: AHR (Fig. 3A); the high total immune-cell numbers in the BALF and lungs (Fig. 3B); the high lymphocyte, neutrophil, eosinophil, and macrophage frequencies in the BALF and lungs (Fig. 3C, D); the high Gata3, Il4, Il5, and Il13 expression by the lung CD4⁺ T-cell population (Fig. 3E, F). As expected, neither adoptively transferred cell type affected the Tbx21, Rorc, Foxp3, Ifng, Il17a, and Il10 expression by the lung conventional CD4⁺ T-cell population (Fig. 3E, F). Moreover, the transfer of WT iNKT cells restored the iNKT cell-numbers in the lungs and their expression of Il4 and Il13 without changing their Ifng and Il17a expression; by contrast, transfer of ACC1-deficient iNKT cells led to very low iNKT-cell frequencies in the lung and low expression of all cytokines (Fig. 3G, H). In addition, the adoptively transferred ACC1-deficient iNKT cells in the lung displayed more apoptosis than the transferred WT iNKT cells (Fig. 3I). Lastly, transfer of the WT iNKT cells associated with significantly higher histological lung scores than transfer of the ACC1-deficient iNKT cells (Fig. 3J). Thus, ACC1-deficient iNKT cells cannot promote AHR in allergic asthma models.

To investigate the effects of ACC1-mediated metabolic programming on the development and function of iNKT cells, we examined the thymic development and peripheral homeostasis of iNKT cells in Cd4-CreAcc1^fl/fl mice. Mature Cd4-Acc1^fl/fl mice had fewer iNKT cells in the thymus, spleen, and liver than Acc1^fl/fl mice (Fig. S2A). By contrast, the conventional CD4⁺ and CD8⁺ T-cell numbers in the thymus and spleen were not affected by Cd4-Acc1^fl/fl (Fig. S2B). However, the ACC1 deletion did not alter the frequencies of the NKT1, NKT2, and NKT17 subsets in the iNKT-cell population at any of the developmental stages, namely, stage S1 (CD24⁺c-Kit^-CD44^-), stage S2 (CD24⁺c-Kit^-CD44⁺), and stage S3 (CD24⁺c-Kit⁺CD44⁺) (Fig. S2C). Thus, ACC1 deficiency appeared to reduce iNKT-cell numbers.

This was tested further by generating mixed bone marrow (BM) chimera mice: thus, a 1:1 ratio of CD45.1⁺ WT BM cells and CD45.2⁺ Cd4-Acc1^fl/fl BM cells were transferred into lethally irradiated CD45.2⁺ WT recipient mice (Fig. S2D). The ACC1-deficient BM cells generated markedly fewer iNKT cells in the thymus, spleen, and liver than the WT BM cells (Fig. S2E, F). This confirmed that ACC1 deficiency induced an intrinsic defect in iNKT-cell thymic development and peripheral homeostasis. Moreover, when iNKT cells sorted from the livers of Cd4-Acc1^fl/fl and Acc1^fl/fl mice underwent TCR ligation with anti-CD3/CD28 in vitro, the ACC1-deficient cells produced less CD69 activation marker and cytokine (Fig. S2G, H).

Thus, ACC1 plays a cell-intrinsic role in the regulation of iNKT-cell thymic development, peripheral homeostasis, and activation.

To investigate the mechanism(s) by which ACC1-mediated metabolic programming of iNKT cells promotes allergic asthma, we subjected the sorted unstimulated iNKT cells from Cd4-CreAcc1^fl/fl and Acc1^fl/fl mice to transcriptome and metabolome analyses (Fig. 4A–D). The ACC1-deficient iNKT cells displayed distinct metabolic reprogramming compared to WT iNKT cells. Specifically, they exhibited low fatty-acid synthesis but high glycolysis. Indeed, compared to WT iNKT cells, their extracellular acidification rate (ECAR), glucose uptake, and expression of Glut1 and other glycolysis-related genes were higher while their expression of gluconeogenesis genes were lower. However, their oxygen consumption rate (OCR) was similar (Fig. 4E–H). Furthermore, the transcriptome analysis revealed significantly altered levels of cell death-related genes in ACC1-deficient iNKT cells (Fig. 4C). These observations suggest that ACC1-deficient iNKT cells are prone to dying and that this may correlate positively with glycolysis. Indeed, the ECAR and cell death in ACC1-deficient iNKT cells correlated positively (Fig. 4I). This relationship was confirmed by treating the ACC1-deficient iNKT cells with 2-deoxy-D-glucose (2-DG), which inhibits glycolysis ³⁵: this decreased their cell death and expression of the pro-apoptotic genes while increasing their expression levels of anti-apoptotic genes. These effects were not observed in WT iNKT cells (Fig. 4J, K). Thus, the high levels of glycolysis in ACC1-deficient iNKT cells promote their cell death.

To identify the target genes that regulate the metabolic reprogramming and cell death in ACC1-deficient iNKT cells, we analyzed the transcription factors in the transcriptome data described above. This showed that sorted unstimulated ACC1-deficient iNKT cells expressed Pparg at significantly lower levels than WT iNKT cells (Fig. 5A, B). This was also observed when the cells were stimulated with anti-CD3/CD28: the treatment elevated Pparg expression in both cell types but much less in the ACC1-deficient cells (Fig. 5B). Thus, PPAR-γ may play a critical role in ACC1-mediated modification of iNKT-cell functions. This is consistent with the fact that PPAR-γ is important for activating genes involved in lipid biosynthesis and the maintenance of metabolic homeostasis ^{36, 37}. Indeed, when anti-CD3/CD28-stimulated ACC1-deficient iNKT cells were treated with the PPARγ agonist pioglitazone, their expression of Pparg was not only significantly restored, their expression of the gluconeogenesis genes Cebpa, Fbp1, and G6pase rose while their 2-NBDG uptake, expression of Glut1 and the glycolysis genes Hk2 and Pkm2, and cell death dropped (Fig. 5B–D).

ACC1 catalyzes the carboxylation of acetyl-CoA to malonyl-CoA, which is then converted to the long-chain fatty acid palmitate ³⁸. We found that treatment with palmitate had similar effects on CD3/CD28-stimulated ACC1-deficient iNKT cells as pioglitazone, namely, it increased Pparg and gluconeogenesis gene expression and downregulated glycolysis (Fig. 5E). These effects were also observed when unstimulated ACC1-deficient iNKT cells were treated with palmitate: Pparg expression rose (Fig. 5G) while glucose uptake and cell death dropped (Fig. 5H). Thus, restoring the levels of palmitate, the downstream product of ACC1 activity, restored PPARγ expression, which in turn downregulated glycolysis and thereby protected the cells from cell death.

PPARγ activity can be regulated by a variety of ligands, including fatty acids, whose transport to the nucleus is actively facilitated by fatty acid-binding proteins (FABPs) ^39–41. The unstimulated ACC1-deficient iNKT cells showed depressed expression of the genes encoding FABP1, FABP3, and FABP5 (Fig. 5F). Thus, ACC1 deficiency downregulated the expression of these genes. We then asked whether circumventing the ACC1 deficiency by treating the unstimulated cells with palmitate could improve the expression of these FABPs, as exogenous treatment of palmitate has been shown to induce FABPs in macrophages: indeed, Fabp1, Fabp3, and especially Fabp5 expression were upregulated in iNKT cells as well (Fig. 5F).

We then determined whether the FABPs mediate the proglycolytic and pro-apoptotic effects of ACC1 (and palmitate) deficiency in iNKT cells by culturing unstimulated palmitate-treated ACC1-deficient iNKT cells with a FABP inhibitor, FABP-IN-1, which is known to inhibit FABP 3, 5 and 7. Indeed, the inhibitor blocked the ability of palmitate to restore PPARγ expression (Fig. 5G) and suppress glucose uptake and cell death (Fig. 5H). These findings were also observed when CD3/CD28-stimulated ACC1-deficient iNKT cells were treated with palmitate with and without FABP inhibitor: the FABP inhibitor reversed the ability of palmitate to increase Pparg and gluconeogenesis gene expression and downregulate glycolysis (Fig. 5E).

Since FABP-inhibitor treatment did not affect the PPARγ expression, glucose uptake, and cell death in WT iNKT cells (Fig. 5E, G, H), it seems that the ACC1 deficiency in iNKT cells mediated their metabolic reprogramming by abolishing palmitate production: this downregulated their FABP expression, which in turn suppressed PPARγ expression, which increased glycolysis and thereby promoted cell death (Fig. 5I).

On a side note, the WT and ACC1-deficient iNKT cells did not differ in terms of the methylation of the promoter site of PPARγ (Fig. S3A), and treatment of the unstimulated cells with histone deacetylase (HDAC) inhibitor did not alter their cell-death rates or expression of Pparg, Hk2, Bcl2, or Bak (Fig. S3B, C). Thus, the regulation of PPARγ expression in ACC1-deficient iNKT cells may not involve epigenetic changes such as methylation and acetylation.

To confirm that ACC1-mediated metabolic programming in iNKT cells regulates the development of AHR, we pretreated WT or ACC1-deficient iNKT cells with palmitate or pioglitazone, adoptively transferred them into Jα18 KO mice, and then subjected the mice to OVA-induced asthma (Fig. 6) or HDM-induced asthma (Fig. S4). As shown above (Fig. 3), transfer of untreated WT iNKT cells was able restore AHR, airway inflammation scores, and high lymphocyte, neutrophil, eosinophil, and macrophage frequencies in the lungs and BALF. It also generated a CD4⁺ T-cell population in the lungs that expressed high levels of Gata3, Il4, Il5, and Il13 and low levels of Tbx21, Rorc, Foxp3, Ifng, Il17a, and Il10. Moreover, the sorted iNKT cells cells in the lungs expressed high levels of Il4 and Il13 but low levels of Ifng and Il17a. By contrast, transfer of the untreated ACC1-deficient iNKT cells did not achieve any of these pathological changes or lung CD4⁺ T-cell or iNKT-cell gene-expression profiles. However, when the ACC1-deficient iNKT cells were pretreated with palmitate or pioglitazone, all pathological and gene-expression changes were restored (Fig. 6A–G and Fig. S4).

As shown above (Fig. 3I), the transfer of untreated WT iNKT cells into mice that were then subjected to OVA-induced asthma led to a moderate iNKT-cell population in the lungs that showed low apoptosis. By contrast, the transfer of untreated ACC1-deficient iNKT cells led to low lung iNKT cell numbers that frequently took up PI. However, pretreatment of the ACC1-deficient iNKT cells with palmitate or pioglitazone increased lung iNKT-cell numbers and reduced their apoptosis (Fig. 6H, I). This is consistent with our in vitro experiments with the unstimulated and anti-CD3/CD28-stimulated iNKT cells above (Fig. 5). Notably, the sorted iNKT-cell population in the lungs of Jα18 KO mice that received untreated ACC1-deficient iNKT cells also demonstrated less PPARγ expression and more glucose uptake and Glut1 expression compared to when untreated WT iNKT cells were transferred, and this was reversed when the ACC1-deficient iNKT cells were pretreated with palmitate or pioglitazone (Fig. 6J). Thus, treatment with palmitate or pioglitazone increased the survival of adoptively transferred ACC1-deficient iNKT cells in Jα18 KO mice, which restored their ability to induce AHR in allergic asthma.

Altogether, these results show that ACC1-mediated metabolic programming in iNKT cells maintains their PPARγ expression, which downregulates their glycolysis and thereby increases their survival. This allows these cells to promote AHR and airway inflammation in allergic asthma models.

We next asked whether patients with allergic asthma demonstrate altered expression of fatty-acid synthesis-related metabolic enzymes in iNKT cells compared to healthy volunteers. Thus, we sorted the iNKT, Treg, CD4⁺ T, and CD8⁺ T cells from the PBMCs of 10 healthy volunteers, 10 patients with allergic asthma, and 10 patients with nonallergic asthma and examined their mRNA expression of ACC1 and PPARG. We also examined the expression of FASN because it, like ACC1, is a key de novo fatty-acid synthesis enzyme and our experiment with CD3/CD28-stimulated murine iNKT cells and conventional CD4⁺ and CD8⁺ T cells showed that the iNKT cells expressed much higher levels of Fasn as well as Acc1 than the other cells (Fig. 1C). Indeed, circulating iNKT cells from allergic asthma patients expressed significantly higher levels of ACC1, FASN, and PPARG than iNKT cells from healthy controls and nonallergic asthma patients (Fig. S5A). Similarly, the iNKT cells from the allergic asthma patients expressed significantly higher FBP1 levels than the control iNKT cells, although this difference was not observed for HK2 (Fig. S5B). These changes in ACC1, FASN, PPARG, and FBP1 expression were much more pronounced in the iNKT cells from allergic asthma patients than in the CD4⁺ T cells, CD8⁺ T cells, and Tregs from the same patients (Fig. S5). Thus, iNKT cells from allergic asthma patients express higher ACC1, FASN and PPARG levels and lower levels of a glycolysis gene than iNKT cells from healthy controls and nonallergic asthma patients. These findings suggest that the ACC1-PPAR-γ axis in iNKT cells may also be involved in human allergic asthma.

Female C57BL/6 (B6) mice were obtained from the Orient Company (Seoul, Korea). Cd4-Cre mice were purchased from the Jackson Laboratory (stock no. 017336; The Jackson Laboratory, Bar Harbor, ME, USA). Acc1^fl/fl mice were originally generated and kindly provided by Dr. Salih J. Wakil of the Baylor College of Medicine (Houston, TX, USA). To generate Cd4-CreAcc1^fl/fl mice, the Acc1^fl/fl mice were crossed with Cd4-Cre mice. The animals were bred and maintained under specific-pathogen-free conditions at the Biomedical Research Institute of Seoul National University Hospital (Seoul, Korea). All experiments were conducted on mice that were 8–10 weeks old. All animal experiments were approved by the Institutional Animal Care and Use Committee at Seoul National University Hospital (SNUH-IACUC). The animals were maintained in an AAALAC International (#001169)- accredited facility in accordance with the Guide for the Care and Use of Laboratory Animals 8th edition.

In total, 30 human PBMC samples were obtained from healthy control subjects and patients with nonallergic and allergic asthma. The PBMCs were obtained from 30 mL peripheral blood by using Ficoll-Paque (GE Healthcare, Uppsala, Sweden). The iNKT, CD4⁺ T, Foxp3⁺ Tregs, and CD8⁺ T cells in the PBMCs were sorted by FACSAria II (Becton Dickinson, San Jose, CA, USA; purity < 95%). All subjects provided informed written consent to participate in the study. The Institutional Review Board of Seoul National University Bundang Hospital approved the human studies (IRB #: B-1901/517–304).

To determine the mRNA expression, transcriptome, metabolome, ECAR, and OCR of T and iNKT cells, the cells were obtained from mice as follows. CD4⁺ or CD8⁺ T cells from the thymi, spleens, lungs, and livers of Acc1^fl/fl and Cd4-CreAccl1^fl/fl mice were enriched by FACSAria II (Becton Dickinson). To obtain iNKT cells, the mononuclear cells of the livers of Acc1^fl/fl and Cd4-CreAccl1^fl/fl mice were isolated by Percoll gradient centrifugation. Thereafter, the iNKT cells were isolated by using APC-conjugated α-GalCer/CD1d tetramers-unloaded or loaded with PBS-57 from the tetramer facility of the National Institutes of Health (Bethesda, MD, USA) followed by sorting using FACS (purity < 95%). Alternatively, liver mononuclear cells were labeled with APC-conjugated -GalCer/CD1d tetramers, bound to anti-APC magnetic beads, and enriched on a MACS separator (Miltenyi Biotec, Auburn, CA, USA; purity 89%). iNKT, CD4⁺ T, and CD8⁺ T cells were stimulated with plate coated anti-CD3 and CD28 antibodies overnight.

iNKT cells (6.0 × 10⁵) obtained from the lungs of Acc1^fl/fl and Cd4-CreAccl1^fl/fl mice were adoptively transferred into experimental mice via the intratracheal route.

The antibodies used in flow cytometry are summarized in Table S1. The PPAR agonist pioglitazone (E6910), the histone deacetylase (HDAC) inhibitor MG-132 (474790), 2-deoxy-D-glucose (2-DG, D6134), and palmitic acid (C16:0, P9767) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Briefly, palmitic acid was dissolved in sterile water using a vortex and heated to 70°C for 10 min. Palmitic acid was conjugated to bovine serum albumin (BSA, Biosesang, Seongnam, Korea) in serum-free RPMI containing 5% non-esterified fatty acids (NEFA)-free BSA immediately after dissolving as described previously ⁵⁹. The conjugated-palmitic acid was shaken at 140 rpm at 40°C for 1 h before being added to the cells. Serum-free RPMI containing 5% NEFA-free BSA was used as the vehicle control. Cells were treated with pioglitazone at 10 μM, 2-DG at 10 mM, MG-132 at 5 μM, or palmitate at 100 μM.

Single-cell suspensions were preincubated with mouse anti-Fc receptor antibodies (BD Biosciences, Franklin Lakes, NJ, USA), stained with antibodies for 30 min at 4, washed twice with 1 × PBS, and analyzed using a BD LSRII flow cytometer (BD Biosciences). For intracellular cytokine staining, cells were stained for surface markers, permeabilized, and then stained for intracellular proteins. Cells were permeabilized and fixed with Intracellular Fixation & Permeabilization Buffer (eBioscience, Waltham, MA, USA), according to the manufacturer’s protocols. Data were analyzed with FlowJo software (version 10; TreeStar, Ashland, OR, USA).

To assess the uptake of the fluorescent glucose analog 2-NBDG, cells were incubated for 30 min with 50 µM 2-NBDG (Cambridge Bioscience, Cambridge, UK)-supplemented glucose-free RPMI 1640 or RPMI containing 10% (v/v) FBS supplemented with 12.5 nM MitoTracker Deep Red (Thermo Fisher Scientific, Waltham, MA, USA) for 30 min at 37℃, 5% (v/v) CO₂. The cells were washed with 1 × PBS, fixed with 0.5% (w/w) paraformaldehyde (PFA, Sigma), and then captured on a BD LSR Fortessa flow cytometer (BD Biosciences).

BM cells were prepared from the femurs and tibias of WT B6 (CD45.1⁺ background) or Cd4-Acc1^fl/fl B6 (CD45.2⁺ background) donor mice. Recipient mice (CD45.2⁺ background) were lethally irradiated (800 rads) and injected intravenously with a 1:1 mixture of BM cells from WT and Cd4-CreAcc1^fl/fl mice (1 ×10⁶ cells) in total. The chimeras were analyzed 8 weeks after BM transplantation.

To induce the OVA asthma model, OVA antigen (Sigma) was dissolved in saline to a 1 mg/mL concentration. The mice were injected intraperitoneally with 4 mg aluminum hydroxide (Thermo Fisher Scientific) mixed with 100 µL OVA in saline (1 mg/mL solution) on days 0 and 7. On days 14, 15, 16, and 17, the isoflurane-anesthetized mice were challenged with 50 µg OVA (1 mg/mL solution) intranasally. Twenty-four h after the last OVA instillation, the mice were sacrificed to measure the AHR. To induce the HDM asthma model, HDM (B82; Greer) ws dissolved in saline to a final concentration of 1 mg/mL. Isoflurane-anesthetized mice were then treated intranasally on days 1–14 with 25 µg HDM solution and, 96 h after the final intranasal instillation, the AHR and other inflammatory variables were measured.

Mice were inoculated intranasally with 200 ng of α-GalCer dissolved in 50 µL PBS and, 24 h after the last instillation, the mice were sacrificed to measure the AHR.

AHR was measured by administering increasing doses of methacholine (0, 5, 10, 20, and 40 mg/mL) using the forced oscillation technique (FlexiVent System; SCIREQ, Montreal, Quebec, Canada). A snapshot perturbation maneuver was imposed to measure the resistance of the entire respiratory system.

Mouse lungs were diced into small pieces using razor blades and incubated for 1 h in digestion medium (RPMI-1640 with 1 mg/mL collagenase type 4, Sigma) at 37°C. The lung cells were then filtered and treated with red blood cell lysis buffer. BALF cells were acquired by administering 1 mL PBS supplemented with 2% FBS 3 times using a tracheal cannula. Lavage fractions were pooled together for total and differential cell counts. Differential cell counts were conducted with cytospin preparations after Giemsa staining and were validated by flow cytometric analysis using a BD Fortessa (BD Biosciences).

iNKT cells from Acc1^fl/fl and Cd4-CreAcc1^fl/fl mice were sorted and total RNA was extracted using RNeasy with QIAshredders (Qiagen, Germantown, MD, USA). RNA quality and quantity were determined using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) and Nanodrop (Nanodrop Technologies, Wilmington, DE, USA). RNA-seq analyses were performed at the Theragen Bio Institute (Suwon, Korea). A Gene Set Enrichment Assay (GSEA) was performed to identify significant expression differences under the two biological conditions and to identify the gene classes that were overexpressed or underexpressed in iNKT cells from Cd4-CreAcc1^fl/fl mice relative to those from WT mice. The transcriptome data were from sorted iNKT cells from eight mice/group that were pooled together. We also analyzed transcriptome data from our previous study to compare the iNKT cells to conventional CD4⁺T cells (GSE103190) ³². These analyses were performed using GenePattern (https://genepattern.broadinstitute.org/).

iNKT cells were sorted and pooled from eight WT mice, or eight Cd4-CreAccl1^fl/fl mice. The metabolites were extracted from each cell group as previously described ^{60, 61}, followed by MeOX and MTBSTFA derivatization. Metabolic profiling was performed by GC-MS/MS dynamic multiple reaction monitoring (dMRM) analyses using the Agilent 7890/7000 GC triple quadrupole mass spectrometer system. Gas chromatography was conducted using an Agilent J&W HP-5ms UI 15 m × 0.25 mm × 0.25 µm (P/N 19091S-431UI) capillary column. Helium was used as the carrier gas with a constant column flow rate of 1.5 mL min^-1. The initial oven temperature of 60°C was increased to 320°C at a rate of 10°C min^-1. The mass spectrometer was operated in the electron ionization mode at 70 eV. The analyses were performed in the dMRM mode. The area ratios of the analyte and the internal standard were used for the evaluation. The split/splitless injector temperature was set to 280°C. The samples were injected in the splitless mode with an autosampler Agilent 7683 A injector. Agilent MassHunter Data Acquisition Software (ver. B.04.00) and MassHunter Workstation Software for Quantitative Analysis (QQQ) were used for data acquisition and the quantitative analysis, respectively. The results were normalized to the iNKT-cell numbers and/or total protein concentrations.

To analyze gene-expression levels in the metabolic pathways, immune cells were sorted and pooled from four-eight mice/group, and used in the analysis. Total RNA was isolated with TRIzol reagent (Thermo Fisher Scientific) according to the manufacturer’s protocol. RNA was reverse-transcribed into cDNA using Maloney murine leukemia virus reverse transcriptase Taq polymerase (Promega, Madison, WI, USA). For quantitative RT-PCR, gene-specific PCR products were quantified by an Applied Biosystems 7500 Sequence Detection System (Applied Biosystems, Foster City, CA, USA). The list of primers and their sequences used are summarized in Table S2. Gene-expression levels were normalized to those of Actb for mice and ACTB for humans.

Lung tissues were obtained from WT and J 18 KO mice that did or did not undergo transfer of iNKT cells from Acc1^fl/fl and Cd4-CreAcc1^fl/fl mice during OVA- or HDM-induced asthma. The tissues were fixed in 10% formalin, embedded in paraffin, and sectioned, followed by staining with hematoxylin and eosin or periodic acid-Schiff (PAS). Lung inflammation and goblet cell hyperplasia were graded using a semi-quantitative scoring system as previously described ^{62, 63}. Briefly, inflammatory-cell numbers were graded as follows: 0: normal; 1: few cells; 2: a ring of inflammatory cells that was 1 cell-layer deep; 3: a ring of inflammatory cells that was 2–4 cell-layers deep; and 4: a ring of inflammatory cells that was >4 cell-layers deep. The goblet-cell numbers in the airway were graded as follows: 0: < 0.5% PAS-positive cells; 1: < 25%; 2: 25–50%; 3: 50–75%; and 4: > 75%. Two fields were counted for each slide and mean score was calculated for each group. Two pulmonary pathologists reviewed and analyzed representative images.

iNKT cells were sorted from eight mice/group and pooled together. ECAR and OCR assays were performed using a Seahorse Extracellular Flux Analyzer XF24e (Seahorse Bioscience Inc., Santa Clara, CA, USA) according to the manufacturer’s instructions. Briefly, the cells were seeded in a Seahorse plate and cultured overnight to 70–80% confluence. The culture medium was then replaced with cellular assay medium supplemented with 1 mM glutamine for the ECAR assay and 1 mM pyruvate, 2 mM glutamine, and 10 mM glucose for the OCR assay. The cells were incubated for 1 h in a CO₂-free incubator before measurement. The assays were performed according to the Seahorse protocols with final concentrations of 10 mM glucose, 2 μM oligomycin, and 50 mM 2-DG for ECAR, and 2 μM oligomycin, 1 μM carbonyl cyanide-4 (trifluoromethoxy) phenylhydrazone (FCCP), and 1 μM antimycin A for OCR. These assays were performed in at least three independent experiments on separate days.

Genomic DNA was converted with bisulfite by using the EZ DNA Methylation Kit (Zymo Research, Irvine, CA, USA) according to the manufacturer’s protocol. PyroMarkTM software was used to design the pyrosequencing primers. In brief, bisulfite-converted DNA was subjected to PCR amplification in a total volume of 25 µL. DNA methylation was assessed using a PyroMark Q48 Autoprep system (Qiagen) and PyroMark Q48 Advanced CpG Reagents (Qiagen). The nucleotide dispensation order was generated by entering the sequences into the PyroMark Q48 Autoprep software ver. 2.4.2 (Qiagen). The methylation at each CpG site was determined using the PyroMark Q48 Autoprep software set in CpG mode. The mean methylation of all CpG sites within the target region was determined by using the methylation at the individual CpG sites.

GraphPad Prism 7 (GraphPad Software, La Jolla, CA, USA) was used for the statistical analyses and graphical display of the data. Unpaired 2-tailed t-tests or Mann-Whitney U test were performed to compare the groups. P values < 0.05 were considered statistically significant.