The integration of quantitative metabolic and proteomic analysis uncovers an augmentation of the sphingolipid biosynthesis pathway during T-cell differentiation

doi:10.21203/rs.3.rs-2706645/v1

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The integration of quantitative metabolic and proteomic analysis uncovers an augmentation of the sphingolipid biosynthesis pathway during T-cell differentiation

https://doi.org/10.21203/rs.3.rs-2706645/v1

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published 23 May, 2024

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Recent studies have highlighted the significance of cellular metabolism in the initiation of clonal expansion and effector differentiation of T cells. Upon exposure to antigens, naïve CD4⁺ T cells undergo metabolic reprogramming to meet their metabolic requirements. However, only few studies have simultaneously evaluated the changes in protein and metabolite levels during T cell differentiation. Our research seeks to fill the gap by conducting a comprehensive analysis of changes in levels of metabolites, including sugars, amino acids, intermediates of the TCA cycle, fatty acids, and lipids. By integrating metabolomics and proteomics data, we discovered that the quantity and composition of cellular lipids underwent significant changes in different effector Th cell subsets. Especially, we found that the sphingolipid biosynthesis pathway was commonly activated in Th1, Th2, Th17, and iTreg cells and that inhibition of this pathway led to the suppression of Th17 and iTreg cell differentiation. Additionally, we discovered that Th17 and iTreg cells enhance glycosphingolipid metabolism, and inhibition of this pathway also results in the suppression of Th17 and iTreg cell generation. These findings demonstrate that the utility of our combined metabolomics and proteomics analysis in furthering the understanding of metabolic transition during Th cell differentiation.

Biological sciences/Immunology/Adaptive immunity/Cellular immunity/Lymphocyte differentiation

Biological sciences/Molecular biology/Transcriptomics

Biological sciences/Biotechnology/Proteomics

Biological sciences/Biological techniques/Metabolomics

T cells play a central role in acquired immunity by recognizing foreign antigens through T-cell antigen receptor (TCR). After antigenic stimulation, quiescent naïve CD4⁺ T cells drive rapid proliferation and acquire an effector phenotype^1,2. In particular, lineage-specifying cytokines determine the differentiation of naïve CD4⁺ T cells into functionally distinct subsets, including Th1, Th2, Th17, and regulatory T cells (Tregs)^1,2. The differentiation of Th1 cells is induced by treatment with IL-12, leading to an increase in the production of IFNγ for anti-viral responses. Th2 cell differentiation, in the presence of IL-4, results in the production of IL-4, IL-5, and IL-13, thereby regulating both parasite infections and allergic inflammation. The differentiation of Th17 cells requires stimulation with both IL-6 and TGFβ, which leads to the upregulation of IL-17A and IL-17F and confers protection against fungal pathogens. The differentiation of Treg cells, crucial for maintaining immunological tolerance and immune homeostasis, is dependent on TGFβ and IL-2 stimulation. Dysregulation of effector T cell response has been associated with the progression of cancer and autoimmune diseases^1–3.

In recent years, increasing evidence has highlighted the significance of cellular metabolism in many aspects of T-cell biology^1–3. In naïve CD8⁺ T cells, the metabolic switch from oxidative phosphorylation (OXPHOS) to glycolysis is essential for the acquisition of effector function following antigenic stimulation⁴. It is also reported that cell intrinsic lysosomal lipolysis is necessary for the generation and maintenance of memory CD8⁺ T cells⁵. In CD4⁺ T cells, studies have demonstrated that the changes in intracellular metabolism are closely linked to the regulation of immune function^1,2,6,7. Activated Th cells have been observed to induce heightened nutrient uptake and metabolic rates, resulting in a significant increase of glucose and amino acid transport^6,7. The glucose transporter Glut1, has been found to play a critical role in T cell activation, as its deficiency has been shown to prevent increased glucose uptake and glycolysis, leading to the suppression of TCR stimulation-induced proliferation and the inhibition of effector T cell survival and differentiation^6,7. Additionally, a deficiency in the glutamine transporter, ASCT2, has been found to negatively impact both Th1 and Th17 cell specification, while promoting the generation of Treg cells⁸. Furthermore, the restriction of serine metabolism and the inhibition of serine hydroxymethyltransferase 1 (SHMT1) caused a suppression of optimal cell proliferation⁹.

Besides the regulation of glucose and amino acid metabolism, lipid metabolism has been identified as a key regulator in controlling T cell responses. The mTOR-PPARγ axis-mediated fatty acid metabolic reprogramming is required for the early activation of CD4⁺ T cells¹⁰. The Acetyl-CoA Carboxylase 1 (ACC1) enzyme, which acts as a rate-limiting enzyme in fatty acid biosynthesis, serves as a marker of the memory potential of individual CD4⁺ T cells. Pharmacological inhibition or genetic deletion of ACC1 have been shown to impair the differentiation of pathogenic Th2 and Th17 cells^11,12. Middle- and long-chain fatty acids are crucial for Th1 and Th17 cell differentiation, whereas short-chain fatty acids promote Treg cell differentiation¹³. Recent study also highlights the dependence of Tregs on fatty acid oxidation (FAO) and OXPHOS for their homeostatic maintenance and suppressive capacity¹⁴. Despite numerous studies exploring the relationship between immune function and metabolism in CD4⁺ T cells, limited research has been conducted to evaluate the metabolic changes occurring during T-cell differentiation, specifically at the protein and metabolites levels.

The growing application of various omics technologies led us to explore different layer of biological regulation, including transcriptome, proteome, and metabolome^15–19. Previously, quantitative proteomic analyses were performed to explore the influence of TCR activation on protein ubiquitination and phosphorylation^15,16. A combined analysis of protein expression and ubiquitination status was performed to establish a predictive framework to assess the relationship between ubiquitylation and protein abundance¹⁵. Furthermore, analyses of whole proteomes and phospho-proteomes have revealed early dynamic phosphorylation events following two hours of TCR stimulation and a subsequent amplification of both protein phosphorylation and expression¹⁶. Omics analyses have also been performed to determine the underlying regulatory mechanisms of T cell differentiation^17–19. An integrative analysis of proteomics, bulk RNA-seq, and single-cell RNA-seq was performed to compare the differences in cytokine responses between human naïve and memory CD4⁺ T cells¹⁷. Recently, Sen et al. conducted an integrative analysis of quantitative metabolome and published gene expression data to investigate metabolic changes specific to effector T cell subsets¹⁸. Although the multitude of omics analyses that have been conducted, only few studies have simultaneously evaluated the changes in protein and metabolite levels during T cell differentiation.

In this study, we have conducted deeper untargeted metabolomics analysis to investigate the fluctuating alterations in cellular metabolites during the differentiation of effector Th cells. We then integrated the metabolomic data with our previously published proteomic data, allowing us the opportunity to evaluate the metabolic shifts in both metabolite levels and synthase¹⁹. The results of the integrative analysis revealed that TCR stimulation significantly elevated the biosynthesis pathway of sphingolipids. Furthermore, Th17 and iTreg cells were found to augment glycosphingolipid metabolism and the inhibition of sphingolipid or glycosphingolipid biosynthesis prevented the differentiation of Th17 and iTreg cells. Our findings thus demonstrate that the biosynthesis of sphingolipids and glycosphingolipids regulates the generation of Th17 and iTreg cells.

TCR stimulation caused dynamic changes in the intracellular metabolism, including glucose, amino acid, intermediates of TCA cycle, and fatty acid metabolism to fuel biosynthetic processes.

To evaluate the changes in metabolic pathways during T cell differentiation, we first analyzed the amounts of intracellular metabolites and expression of metabolic proteins using a mass-spectrum-based metabolomics approach and previously established proteogenomic database¹⁹ (Fig. 1a). Naïve CD4⁺ T cells were stimulated with immobilized anti-TCR mAb and anti-CD28 mAb for 48 h under Th0, Th1, Th2, Th17, or iTreg cell culture conditions. Dead cell removal was performed before sample preparation for omics analysis. As reported previously, our omics analysis showed that TCR stimulation elicited a rapid induction of glycolysis at the level of metabolites and proteins^6,7 (Fig. 1b). The intermediate of glycolysis, G3P, was decreased, while the end product, lactate, displayed a substantial increase upon TCR stimulation (Fig. 1b). Proteomics data revealed significant increases in the expression of glucose transporters and glycolytic enzymes, including GLUT1, GLUT3, HK1, GPI, PKM, and LDHA. Furthermore, we observed that TCR stimulation enhanced the utilization of glucogenic amino acids synthesis, which was synthesized at lower levels in resting naïve CD4⁺ T cells (Fig. 1b lower panel, and Supplementary Fig. 1a). While the amount of serine was reduced by 1.5-fold in Th0, Th1, and Th2 cells and increased by 1.5-fold in Th17 and iTreg cells, the amount of glycine and cysteine was significantly elevated in effector Th cell subsets (Fig. 1b, upper panel). In accordance with this result, protein expression of glucogenic amino acid synthase was highly upregulated. These amino acids serve as a primary source for the one carbon pathway, an essential component for the biosynthesis of both proteins and DNA. Thus, these findings suggest that activated Th cells acquire one carbon metabolism to support their rapid proliferation. Next, we focused on TCA cycle and its associated metabolites. Despite observed minimal variation in the intermediates of the TCA cycle, such as succinate, fumarate, malate, and citrate, between naïve CD4⁺ T cells and effector Th cell subsets, the amount of glutamine was greatly reduced following TCR stimulation (Fig. 1c). This finding was supported by the decrease in expression of the enzyme glutamine synthase (GLUL) and the concurrent increase of degradative enzyme (GLS2) in effector Th cell subsets, demonstrating the validity of our multi-omics analysis in reflecting the activation of glutaminolysis in effector Th cells²⁰. In addition, the level of glutamate was increased in Th17 cells, whereas no significant change was observed in the amount of glutamate in the other effector Th cell subsets. Furthermore, there was a significant increase in the levels of several amino acids, including alanine, lysine leucine, threonine, aspartate, asparagine, phenylalanine, tyrosine, and proline. Correspondingly, we also noted an up-regulation in the expression of their synthase and transporters, indicating that amino acid biosynthesis and uptake programs were activated during T cell activation (Figs. 1b and 1c, and Supplementary Fig. 1b). It is noteworthy that we observed a marked increase in the amount of β-alanine, a crucial component for the production of acetyl-CoA, in effector Th cell subsets (Fig. 1d). Since acetyl-CoA was required for the fatty acid biosynthesis, we next investigated the effect of TCR-stimulation on fatty acid biosynthesis pathway. Initially, we detected minor alterations in the expression of enzymes associated with fatty acid elongation, such as ELOVL1, ELOVL5, ELOVL6, and ELOVL7 in Th cell subsets (Fig. 1e). In contrast, TCR stimulation resulted in a marked upregulation of proteins involved in fatty acid desaturation, including SCD1, SCD2, FADS1, and FADS2 (Fig. 1e). Subsequently, we sought to assess the levels of fatty acid saturation, focusing on saturated fatty acid (SFA), monounsaturated fatty acid (MUFA), and polyunsaturated fatty acid (PUFA). In quiescent naïve CD4⁺ T cells, SFA accounted for 92.5% of total fatty acids, while MUFAs and PUFAs comprised relatively smaller proportions (MUFA: 2.68%, PUFA: 4.85%) (Fig. 1f). Activated Th cells exhibited changes in the degree of fatty acid saturation, with the MUFA composition of activated Th cell subsets increasing to 26–37% (Fig. 1f). These findings are reflected in the observation that while most fatty acids were largely increased by TCR-stimulation, the amount of MUFAs increased the most (Fig. 1g). Taken together, these data indicate that TCR stimulation confers activated Th cells with an increased ability to engage in glycolysis and amino acid synthesis and generates a diversity of fatty acid composition.

Effector Th cell subsets altered the composition of cellular lipids compared to naïve CD4 ⁺ T cells.

Since activated Th cells dramatically altered fatty acid metabolism, we next analyzed lipidomic data to assess the changes in cellular lipid profiles after TCR stimulation. Our comprehensive cellular lipidomics analysis identified a total of 567 lipid species. These lipids were further classified based on their structure into cholesterol, free fatty acids, glycerolipids, lysophospholipids, phospholipids, and sphingolipids, with 27, 37, 132, 66, 196, and 109 species, respectively (Fig. 2a). We first observed that activated Th cells increased the overall quantity of these lipid species to over twice that of naïve CD4⁺ T cells (Fig. 2b). A deeper analysis revealed that the amount of most lipid species was largely increased in Th cell subsets as compared to naive CD4⁺ T cells, the numbers of which were 504, 504, 487, 522, and 511 in Th0, Th1, Th2, Th17, and iTreg cells, respectively (Figs. 2c and 2d). A principal-component analysis (PCA) demonstrated that TCR-mediated activation had a significant impact on cellular lipid profiles (Fig. 2e). Additionally, Th17 and iTreg cells formed a cluster that was distinct from other activated Th cells including Th0, Th1, and Th2 cells. The stimulation of TCR not only led to the upregulation of lipid biosynthesis, but also resulted in a change in cellular lipid composition as well as free fatty acid levels as shown in Fig. 1g. The TCR stimulation increased the ratio of glycerolipids with values of 15.4% for naïve CD4⁺ T cells, 28% for Th0 cells, 28.3% for Th1 cells, 27.1% for Th2 cells, 23.9% for Th17 cells, and 24.7% for iTreg cells (Fig. 2f). In a group of glycerolipid, effector Th cell subsets increased the proportion of triacylglycerol (TAG), which are incorporated into lipid droplet as energy storage with values of 26.2% for naïve CD4⁺ T cells, 54.4% for Th0 cells, 52.3% for Th1 cells, 57.1% for Th2 cells, 44.9% for Th17 cells, and 51.0% for iTreg cell (Fig. 2g). The stimulation of TCR has only a minor impact on the composition of cholesteryl ester (ChE) (as depicted in Fig. 2h, with the percentages of 91.2% for naïve CD4⁺ T cells, 83.1% for Th0 cells, 84.0% for Th1 cells, 88.3% for Th2 cells, 88.3% for Th17 cells, and 86.0% for iTreg cells). The percentage of phosphatidylcholine (PC) was nearly comparable across each T cell subset (as depicted in Fig. 2i, with the percentages of 37.5% for naïve CD4⁺ T cells, 46.0% for Th0 cells, 46.6% for Th1 cells, 45.8% for Th2 cells, 46.2% for Th17 cells, and 46.3% for iTreg cells). In comparison to naive CD4⁺ T cells, activated Th cells were found to alter the composition of phosphatidylethanolamine (PE) and phosphatidylserine (PS) from 27.0% to 14–16% or from 11.1% to 6–9%, respectively (Fig. 2i). We also observed substantial changes in the category of lysophospholipids and sphingolipids. Naïve CD4⁺ T cells exhibited the greatest proportion of LPC, yet TCR stimulation resulted in the most prominent lipid shifting from LPC to LPI. In naïve CD4⁺ T cells, LPC and LPI comprised 53.0% and 7.83%, respectively. In activated Th cell subsets, LPC and LPI occupied 23–34% or 27–39% of total lysophospholipids, respectively. TCR stimulation also largely changed the composition of cellular sphingolipids, with the most abundant lipid shifting from sphingomyelin (SM) to ceramide (Cer). In naïve CD4⁺ T cells, SM and Cer comprised 58.0% and 8.34%, respectively, while in activated Th cells subsets, SM and Cer occupied 14–20% or 34–43% of total sphingolipids, respectively (Fig. 2k). In addition, TCR-stimulation led to the production of sphingoglycolipids, including ganglioside GM1 (GM1) and ganglioside GD1 (GD1) (Fig. 2k). These findings demonstrate that T-cell activation dynamically changes the quantity and composition of cellular lipids, which suggests the establishment of a metabolic environment favorable to the regulation of T cell homeostasis.

The activation of a specific Th cell subset resulted in a significant change of metabolites and proteins involved in the biosynthesis pathway of sphingolipids and glycosphingolipids

Subsequently, we seek to determine the species of lipids that influence the generation of effector Th cell subsets. To this end, we conducted an initial evaluation of the expression of 269 proteins involved in lipid metabolism in naïve CD4⁺ T cells, Th0, Th1, Th2, Th17, and iTreg cells. Our proteomic analysis revealed that protein expression levels related to lipid metabolism underwent dynamic changes following TCR stimulation (Figs. 3a and 3b). As indicated by the PCA plots of lipid metabolites (Fig. 2e), Th0, Th1, and Th2 cells displayed similar protein profiles, whereas Th17 and iTreg cells exhibited greater similarity to each other than to the other cell states within the PCA space. We then sought to identify proteins that were commonly changed among the various T cell subsets. Our observations indicated that the expression of diacylglycerol kinase (DGKA, DGKB, DGKZ), phosphatidylinositol kinase (PIK3CB, PIK3CD, PIP4K2A, PIP4K2B), and phospholipase (PLBD1, PLCB2, PLCB3, PLCL1) was decreased in a common manner among effector Th cell subsets (Supplementary Fig. 2a). Each Th cell subset commonly upregulated proteins that are essential for the biosynthesis of fatty acids (ACC1, FASN), elongation of fatty acids (ELOVL1, ELOVL6), desaturation of fatty acids (FADS1, FADS2, SCD2), fatty acid binding protein (FABP5), and cholesterol synthesis (HMGCS1 and HMGCR) (Fig. 3c). Especially, the proteins predominantly associated with the biosynthesis of lipids, primarily sphingolipids, that were commonly upregulated include CERS5, DEGS1, and B4GALT5 (Fig. 3c). In accordance with upregulation of these proteins, the quantity of ceramides (Cer) was considerably increased in effector T cell subsets (Fig. 3d). Lipidomics analysis further revealed substantial elevations in the amounts of cardiolipin (CL) and phosphatidylglycerol (PG) in glycerophospholipid, bis monoacylglycero phosphate (BMP), LPI, and LPG in lysophospholipid, TAG in glycerolipid (Supplementary Figs. 2b-2d). Nevertheless, the synthases of these lipids were not among the commonly altered proteins in effector Th cell subsets except for LPGAT1. Thus, we next focused on the sphingolipid synthesis pathway for the following reasons: Proteomic analysis indicated that proteins related to the sphingolipid synthesis pathway were upregulated in a general manner, and lipidomic analysis showed substantial increase in the quantity of Cer and significant changes in the composition of sphingolipids. Subsequently, we conducted a detailed evaluation of the changes in sphingolipids during T-cell activation. We first observed that the amounts of ceramides and their precursor, sphinganine, were greatly increased (Fig. 3d). Cer serves as the hub of sphingolipid metabolism and is responsible for the generation of sphingomyelin, sphingosine, and glycosphingolipids. Although the amount of SM remained largely unchanged, there were increases in the amounts of hexosyl Cer (HexCer) and di-hexosyl Cer (DiHexCer), which are types of glycosphingolipids (Fig. 3d). In addition, although many gangliosides, which are metabolites of these glycosphingolipids, were barely detectable in naive CD4⁺ T cells, effector Th cell subsets increased the levels of ganglioside, such as GM2, GM3 and GT1a (Fig. 3e). Taken together, these data suggest that TCR activation enhances the utilization of the sphingolipid synthesis pathway and confers the regulation of glycosphingolipid metabolism.

Activated sphingolipid metabolism is required for the proper generation of Th17 and iTreg cells

Next, to explore the influence of sphingolipid biosynthesis on the differentiation of effector Th cell subsets, we inhibited sphingolipid production through the utilization of myriocin, a molecule known to inhibit the initial step of sphingolipid biosynthesis. Our observations indicate that such inhibition of sphingolipid biosynthesis resulted in a moderate decline in the number of iTreg cells and a substantial reduction in the number of Th17 cells (Fig. 4a). However, no significant changes were observed in the number of Th0, Th1, and Th2 cells (Supplementary Fig. 3a). These finding are in consistent with our subsequent observations, which showed myriocin-treated Th17 cells were unable to undergo TCR stimulation-induced cell division (Figs. 4b and 4c). In addition, the inhibition of sphingolipid biosynthesis caused a moderate suppression of cell proliferation in iTreg cells (Figs. 4b and 4c). Subsequently, the effect of myriocin on cell cycle progression was analyzed via the evaluation of BrdU incorporation into Th17 and iTreg cells. As a consequence, myriocin treatment prevented Th17 and iTreg cells from entering the S-phase following TCR stimulation (Figs. 4d and 4e). Furthermore, treatment of myriocin resulted in the inhibition of the cytokine production of Th17 cells, with no effect observed in Th0, Th1, and Th2 cells (Figs. 4f and 4g, and Supplementary Figs. 3b-3d). Our findings also indicated that the inhibition of sphingolipid biosynthesis led to a decrease in PD-1 expression on iTreg cells (Figs. 4h and 4i). The genetic deletion of Sptlc1 and Sptlc2, which represent a pharmacological target of myriocin, moderately suppressed the differentiation of Th17 cells and the expression of PD1 on iTreg cells (Figs. 4j-4m, Supplementary Figs. 3e and 3f). Collectively, our combined analysis demonstrates that effector Th cell subsets exhibit enhanced sphingolipid and glycosphingolipid metabolism. Additionally, the generation of both Th17 and iTreg cells was inhibited by the ceramide biosynthesis inhibitor.

Glycosphingolipid metabolism was facilitated in Th17 and iTreg cells compared to Th0, Th1, and Th2 cells.

The inhibition of sphingolipid biosynthesis selectively suppressed the generation of Th17 and iTreg cells, suggesting characteristic differences in cellular lipid metabolism between these effector Th cell subsets and Th0, Th1, and Th2 cells. To further investigate this cellar metabolic profile, we examined global lipidomic profiling and analyzed protein expression related to lipid metabolism among the various effector Th cells subsets. Results from PCA revealed that Th0, Th1, and Th2 cells showed similar lipid and protein profiles, while Th17 and iTreg cells were more alike to each other than to the other cell states in PCA space (Figs. 5a and 5b). The analysis also revealed elevated amounts of sphingolipids in Th17 and iTreg cells compared to non-polarizing Th0 cells, as well as decreased levels of 3-ketosphinganine, an intermediate in sphingolipid synthesis, in Th17 and iTreg cells (Figs. 5c and 5d). These results led us to focus on sphingolipids and their downstream metabolic pathways, where we observed increased levels of HexCer, DiHexCer, GD1, and GD1[O-acetyl NeuAc] (AcGD1) (Fig. 5e). Although total amount of ceramide lipid levels did not significantly differ in Th17 and iTreg cells compared to Th0 cells, a closer examination revealed that the proportion of individual ceramide species in the most of HexCer, DiHexCer, GD1, and AcGD1 was altered in Th17 and iTreg cells (Figs. 5f-5h). Furthermore, inhibition of glycosphingolipid through pharmacological inhibitor, Genz-123346, suppressed the number of Th17 and iTreg cells as well as myriocin treatment (Figs. 5i-5k and Supplementary Figs. 4a and 4b). In accordance with these data, Genz-123346 inhibited the cytokine production of Th17 cells and PD1 expression on Treg cells, further demonstrating the importance of the glycospingolipid biosynthesis pathway in the differentiation of Th17 and iTreg cells (Figs. 5l and 5m, and Supplementary Figs. 4c and 4d).

In this study, we performed a comprehensive analysis to investigate the impact of TCR and cytokine stimulation on intracellular metabolism in terms of protein and metabolite levels. Our multi-omics analysis revealed that activated Th cells significantly enhance amino acid, glucose, TCA cycle, and fatty acid metabolic pathway, as reported previously⁶. Especially, there was a marked change in the quantity of β-alanine, which serves as a precursor for acetyl-CoA, upon T cell activation. Effector Th cells dramatically increased the production of SFA, MUFA, and PUFA, and modify FA composition. Although most lipid production was elevated, TCR stimulation particularly heightened sphingolipid biosynthesis. The profiles of lipid metabolites and proteins related to lipid metabolism in Th17 and iTreg cells were significantly different from those of Th0, Th1, and Th2 cells. The inhibition of sphingolipid and glycosphingolipid biosynthesis pathways arrested the cell cycle and cell proliferation of Th17 and iTreg cells, but not Th0, Th1, and Th2 cells. Furthermore, inhibition of sphingolipid biosynthesis led to the suppression of cytokine production of Th17 cells and PD1 expression on iTreg cells. Thus, our research provides novel insights into the utility of our combined metabolomics and proteomics analysis in furthering the understanding of metabolic transition during Th cell differentiation.

TCR stimulation exhibited a significant impact on the sphingolipid biosynthesis pathway. Especially, activated Th cells substantially elevated the levels of Cer, which acts as a key hub in sphingolipid metabolism, leading to the generation of sphingomyelin, sphingosine, and glycosphingolipids. Our proteomic analysis revealed that changes in the protein expression of CerS family upon TCR stimulation. Specifically, we observed almost 2-fold increase in CerS2 and Cers5 expression in activated Th cells, which synthesize C22-/C24-/C26-Cer or C14-/C16-Cer, respectively^21,22. In contrast, only slight changes were observed in the expression of CerS4 and CerS6, which produce C18-/C20-Cer or C14-/C16-Cer, respectively^21,22. Consistent with the upregulation of CerS2, the proportion of Cer (d18:1/24:0) in ceramide lipids was significantly increased following TCR stimulation (from 8.19% in naïve CD4⁺ T cells to 26.9% in Th0 cells, 26.2% in Th1 cells, 21.4% in Th2 cells, 17.5% in Th17 cells, and 16.8% in iTreg cells). Furthermore, we found that in naïve CD4⁺ T cells, HexCer (d18:1/24:0) and DiHexCer (d18:1/24:0) accounted for 9.9% and 0.0%, respectively, whereas in activated Th cell subsets, HexCer (d18:1/24:0) and DiHexCer (d18:1/24:0) occupied 29–39% or 63–73% of theses lipids, respectively. These results suggest that activated Th cells upregulate CerS2 expression to produce Cer (d18:1/24:0) for glycosphingolipid biosynthesis, including HexCer (d18:1/24:0) and DiHexCer (d18:1/24:0). Importantly, a previous study reported that mice deficient in CerS2 exhibited reduced sensitivity to ovalbumin-induced allergic asthma, accompanied by impaired Th2 responses²³. Upon antigen stimulation, the TCR aggregates into plasma membrane domains known as lipid rafts, which primarily comprise sphingolipids, glycosphingolipids, cholesterol, and phospholipids. The impairment of downstream TCR signaling in CerS2 or CerS6 deficient CD4⁺ T cells highlights the importance of Cer for the regulation of TCR stimulation^22,23. However, detailed investigations are necessary to elucidate the specific roles of Cer, HexCer, and DiHexCer in the regulation of TCR stimulation.

We have previously reported that the PPARγ-dependent FA uptake program is essential for a rapid proliferation of CD4⁺ T cells¹⁰. In the current study, we also observed substantial changes in the fatty acid biosynthesis pathway following TCR stimulation. Activated Th cells exhibited an increase in the amount of SFA, MUFA、and PUFA, thus leading to modifications in the desaturation status of fatty acids. Importantly, in consistent with the substantial upregulation of SCD2, there was a marked augmentation in the amount of MUFAs. Among the MUFAs, TCR stimulation exhibited large impact on the level of oleic acid with values of 1.83% for Naïve CD4⁺ T cells, 19.7% for Th0 cells, 20.0% for Th1 cells, 16.6% for Th2 cells, 20.3% for Th17 cells, and 24.7% for iTreg cells. MUFA metabolism is required to maintain mitochondrial metabolism and mTOR activity, avoiding excessive autophagy and ER stress in proliferating B cells²⁴. MUFA metabolism is also necessary for triggering the type I-IFN response, thereby activating anti-viral responses²⁵. This report showed that Th1 cell exhibited the greatest proportion of oleic acids (16.8%) as major components of cellular lipids. Gene deletion of Scd2 decreases the amount of oleic acid containing TAG, resulting in the I-IFN production. The supplementation of oleic acid suppresses the type I interferon response in Scd2-deficient Th1 cells, suggesting the importance of oleic acid in anti-viral responses.

The profiles of lipid metabolites in Th17 and iTregs was far different from those in Th0, Th1, and Th2 cells, particularly with regard to the activation of sphingolipid and glycosphingolipid biosynthesis pathways. A recent study demonstrated that liver X receptor (LXR) regulates genes associated with glycosphingolipid biosynthesis in primary human T cells. Thus, in turn, LXR affects T cell plasma membrane lipid composition, subsequent immune synapse formation, and TCR-mediated signaling²⁶. Activation of LXR by pharmacological means in this study increased the amounts of glycosphingolipids by directly binding to the Ugcg locus, which encodes transcripts of UDP-glucose ceramide glucosyltransferase (GCS). Full activation of LXR in mouse embryonic fibroblasts requires TGFβ, which recruits a complex containing RAP250 that can interact with both LXR/RXR and Smad2/3²⁷. TGFβ, a cytokine commonly used to induce both Th17 and iTreg cell differentiation, may therefore contribute to the profiles of sphingolipids and glycosphingolipids in these subsets. Taken together, it could be possible that the TGFβ-RAP250-LXR axis contributes to the construct unique lipid profiles in Th17 and iTreg cells.

The inhibition of sphingolipid pathway has been shown to suppress the proliferation and differentiation of Th17 and iTreg cells, but not of Th0, Th1, and Th2 cells. Several studies have explored the role of sphingolipid biosynthesis pathway in regulating specific Th cell subsets^28–30. Inhibition of acid sphingomyelinase (Asm) has also been demonstrated to suppress Th17 cell differentiation²⁸. Another study reported that gene deletion of Asm in T cells led to the enhancement of Treg cells both in vivo and in vitro settings²⁹. Ceramide-mediated activation of PP2A endows Treg cells with the necessary phosphatase activity to control mTORC1 and establish their distinctive tolerogenic metabolic and cytokine profiles³⁰. Although a series of research investigated the role of sphingolipid biosynthesis pathway for Th17 and iTreg differentiation, detailed molecular mechanism has remained unclear.

In conclusion, we carried out a combined metabolomic and proteomics analysis to investigate alterations in the metabolic profiles during T cell differentiation. Our findings demonstrate that inhibition of sphingolipid or glycosphingolipid biosynthesis impairs the differentiation of Th17 and iTreg cells. Further studies focusing on the molecular mechanism behind these observations could significantly advance our understanding of the regulation of effector Th cell subsets through lipid metabolism.

Mice

C57BL/6 mice were purchased from CLEA Japan. All mice were used at 6–8 weeks old and were maintained under specific-pathogen-free conditions. Almost equal number of male and female animal was used for this study. The animal experiments were performed with protocols approved by the Institution Animal Care and Use Committee of KAZUSA DNA research institute (Registration number: 30-1-002). Experiments and animal care were performed according to the guidelines of Kazusa DNA Research Institute.

Cell Preparation

Splenic naïve CD4⁺ T cells were obtained by the negative selection using the Mojo Sort Mouse CD4 T Cell Isolation Kit (Biolegend #480006) and positive selection using CD62L MicroBeads, mouse (Miltenyi Biotec #130-049-701). Naïve CD4⁺ T cells were plated onto 24-well tissue culture plates (Costar #3526) pre-coated with 10 mg/ml anti-TCRβ antibody (H57-597, BioLegend) for 2 days. Culture medium contained 1 µg/ml anti-CD28 antibody (clone 37.51, BioLegend) and cytokine that induce differentiation of Th cell subsets. Th0 cell cultures contained 15 ng/ml IL-2 (WAKO), 1 µg/ml anti-IL-4 antibody (BioLegend) and 1 µg/ml anti-IFNγ antibody (BioLegend). Th1 cell cultures contained 15 ng/ml IL-2, 10 ng/ml recombinant mouse IL-12 (WAKO) and 1 µg/ml anti-IL-4 antibody. Th2 cell cultures contained 15 ng/ml IL-2, recombinant mouse 10 ng/ml IL-4 (WAKO) and 1 µg/ml anti-IFNγ antibody. Th17 cell cultures contained 10 ng/ml IL-6 (PeproTech 216 − 16), 1 ng/ml TGFβ (PeproTech 100-21C), 1 µg/ml anti-IL-2 antibody (BioLegend), 1 µg/ml anti-IL-4 antibody, and 1 µg/ml anti-IFNγ antibody. Regulatory T cell cultures contained 30 ng/ml IL-2, 10 ng/ml TGFβ, 1 µg/ml anti-IL-4 antibody and 1 µg/ml anti-IFNγ antibody.

Meta-analysis Of Proteomics Data

The data was obtained from our previous reports deposited to the ProteomeXchange Consortium via the jPOST partner repository (http://www.proteomexchange.org/) with the dataset identifier PXD036065³¹. The value of protein intensity was transformed to log2, and then each protein was filtered to contain more than 70% valid value in at least one group. The remaining missing values were imputed by random numbers drawn from a normal distribution (width, 0.3; downshift, 2.8) in Perseus v1.6.15.0³². 2.0-fold changed genes was defined as differentially expressed proteins. PCA analysis and heatmap were depicted with R software (https://cran.r-project.org/) (v 3.6.0).

Metabolome Analysis

The samples, including naïve CD4⁺ T, Th0, Th1, Th2, Th17, and iTreg cells, were prepared as described in “Cell preparation”. After dead cells were excluded using Dead Cell Removal Kit (Miltenyi Biotec #130-090-101), 2.5×10⁶ cells of each sample were collected and washed with PBS (-) twice. After removal of supernatant, the pellet was frozen at -70°C.

Targeted metabolomics was performed by gas chromatograph-mass spectrometer (GC-MS) with methoxime and trimethylsilyl derivatization methods³³. The frozen samples (2.5×10⁶ cells) were suspended in a solvent mixture (475 µl) of water, methanol and chloroform (1: 2.5: 1.25) and mixed by vortex mixer. Subsequently, 125 µl water and 125 µl chloroform were added to the suspension. After centrifugation at 20,400 ×g for 10 minutes, 100 µl of the upper layer was collected and dried under N2-gas flow. The dried samples were dissolved in 40 µl of 20 mg/ml methoxyamine hydrochloride dissolved in pyridine and incubated at 30˚C for 90 minutes. Finally, 40 µl of N-methyl-N-(trimethylsilyl)-trifluoroacetamide was added to the solution and the solution was incubated further at 37˚C for 30 minutes. The derivatized samples were analyzed by GC-MS (GCMS QP-2010 Ultra; Shimadzu Corporation) equipped with an Agilent DB-5 column (0.25 mm × 30 m, 1 µm-film thickness; Agilent).

Lipidome Analysis

Untargeted lipidomics was performed as reported previously with some modifications³⁴. Briefly, the frozen pellets (2.5×10⁶ cells) were re-dissolved in 150 µl of chloroform:methanol (1:2) containing EquiSPLASH (Avanti Polar Lipids) for internal standards. After sonication for 30 sec, 10 µL of water was added and vigorously agitated for at 750 rpm for 20 min at 20 ˚C. The supernatant was collected by centrifugation at 1670 g for 10 min at 20 ˚C and transferred to a LC vial. LC-MS/MS analysis was carried out using a quadruple time-of-flight (Q TOF)/MS (TripleTOF 6600; SCIEX) coupled with an ACQUITY UPLC system (Waters). The LC separation was performed with gradient elution of mobile phase (A) [methanol/acetonitrile/water (1:1:3, v/v/v) containing 5 mM ammonium acetate (Wako Chemicals) and 10 nM EDTA (Dojindo)] and mobile phase (B) [isopropanol (Wako Chemicals) containing 5 mM ammonium acetate and 10 nM EDTA]. The flow rate was 300 µl/min at 45°C using a L-column3 C18 (50 x 2.0 mm i.d., particle size 2.0 µm; Chemicals Evaluation and Research Institute). The solvent composition started at 100% (A) for the first 1 min and was changed linearly to 64% (B) at 7.5 min, where it was held for 4.5 min. The gradient was increased linearly to 82.5% (B) at 12.5 min, followed by 85% (B) at 19 min, 95% (B) at 20 min, 100% (A) at 20.1 min, and 100% (A) at 25 min. The raw data files from Q TOF/MS were converted to MGF files using the program of SCIEX MS converter for the quantitative analysis with 2DICAL (Mitsui Knowledge Industry). Identification of the molecular species was accomplished by comparison with retention times and MS/MS spectra data from the information-dependent acquisition (IDA) mode³⁵.

Cas9 Mediated-genome-editing

The short guide RNA was designed using the online tool provided by CHOPCHOP (http://chopchop.cbu.uib.no)³⁶. Freshly isolated naïve CD4 T cells were activated with plate bound anti-CD3 and CD28 antibodies. Cas9 proteins were prepared immediately before experiments by incubating 1µg Cas9 with 0.3 µg sgRNA in transfection buffer at room temperature for 10 min. 24 hours after T cell activation, these cells were electroporated with a Neon transfection kit and device (Thermo).

Target sequences used in this study are shown below.

sgSptlc1 : 5'- TTTGTCGTAGAATCCTCGCAAGG-3'

sgSptlc2 : 5'- GCGGAACATTGGTGTAGTTGTGG-3'

Quantitative Real-time Pcr

Total RNA was isolated with the TRIzol reagent (Invitrogen #15596-018). cDNA was synthesized with an oligo (dT) primer and Superscript II RT (Invitrogen #18064-014). Quantitative RT-PCR was performed using TB Green Real Time PCR kit (Takara #RR820A). Primers were purchased from Thermo Fisher Scientific. Gene expression was normalized with the Hprt mRNA signal or the 18S ribosomal RNA signal. Primer sequences used in this study are shown below.

18S_FW : 5'-AAATCAGTTATGGTTCCTTTGGTC-3'

18S_RV : 5'-GCTCTAGAATTACCACAGTTATCCAA-3'

Hprt_FW : 5'-TCCTCCTCAGACCGCTTTT-3'

Hprt_RV : 5'-CCTGGTTCATCATCGCTAATC-3'

Il17a_FW : 5'-CAGGGAGAGCTTCATCTGTGT-3'

Il17a _RV : 5'-GCTGAGCTTTGAGGGATGAT-3

Il17f_FW : 5'-CCCAGGAAGACATACTTAGAAGAAA-3'

Il17f _RV : 5'-CAACAGTAGCAAAGACTTGACCA-3

Facs Analysis

For surface staining, anti-PD-L1 PE (1:200, J43, BD Biosciences #551892) was stained for 30 min on ice and dead cell was stained with Propidium iodide (1:2000, DOJINDO #341–07881) before FACS analysis. For intracellular staining, dead cells were first stained with Fixable Viability Dye eFluor 780 (1:1000, eBioscience #65-0865-14) for 10 min. For cytokine staining, sample preparation was conducted with 4% PFA for 10 min at 4°C and incubated with permeabilization buffer for 10 min on ice (50mM NaCl, 5mM EDTA, 0.5% Triton-X). After incubating with blocking buffer for 15 min on ice (0.3% BSA), the cells were stained with anti-TNFα PE (1:200, MP6-XT22, Biolegend# 506306), anti-IFNγ FITC (1:50, XMG1.2, BD Biosciences #554411), anti-IL-2 APC (1:1000, JES6-5H4, BD Biosciences #554429), anti-IL-4 BV421 (1:200, 11B11, BD Biosciences #562915), anti-IL-17A BV421 (1:200, TC11-18H10.1, Biolegend#506925), anti-IL-17F APC (1:200, O79-289, BD Biosciences #561630), or for 30 min in the dark. Cell proliferation and cell cycle were analyzed with Proliferation dye e670 (Invitrogen #65-0840-90) or BrdU Flow kit (BD Biosciences #557891) as according to the manufacture’s protocol. Flow cytometric data were analyzed after removal of dead cells and doublets cells with Flowjo software (version 10.4).

Data Availability

Proteomic data used in this study has been deposited to the ProteomeXchange Consortium via the jPOST partner repository³¹ (http://www.proteomexchange.org/) with the dataset identifier PXD036065.

Statistics And Reproducibility

Data are expressed as mean ± SD. The data were analyzed with the Graphpad Prism software program (version 7). Differences were assessed using unpaired two-tailed student t tests or one-way ANOVA followed by tukey’s multiple comparisons test. Differences with P values of < 0.05 were considered to be statistically significant. No data were excluded from the analysis of experiments. Mice were commercially sourced and randomized into experimental groups upon arrival, and all animals within a single experiment were processed at the same time. For metabolomics, the investigator was blinded. Data display similar variance between groups and are normally distributed where parametric tests are used.

Acknowledgements

This work was supported by grants from the Ministry of Education, Culture, Sports, Science and Technology (MEXT Japan) (Grants-in-Aid: Grant-in-Aid for Scientific Research on Innovative Areas #18H04665, Scientific Research [B]#20H03455, Challenging Research (Exploratory) #20K21618, Early-Career Scientists #21K15476, and Young Scientists (Start-up) #21K20766). The Nakajima Foundation, TERUMO Life Science Foundation, The Tokyo Biochemical Research Foundation, Kato Memorial Bioscience Foundation, The Hamaguchi Foundation for the Advancement of Biochemistry, Suzuken Memorial Foundation, Kanae Foundation for the Promotion of Medical Science, Takeda Science Foundation, Mochida Memorial Foundation for Medical and Pharmaceutical Research, GSK Japan Research Grant 2019, SENSHIN Medical Research Foundation, Sumitomo Foundation, Koyanagi Foundation, Kishimoto Foundation 2019, Uehara Memorial Foundation, Nakatomi Foundation, Research Foundation for Pharmaceutical Sciences Group A, Cell Science Research Foundation, The Astellas Foundation for Research on Metabolic Disorders, MSD Life Science Foundation, Public Interest Incorporated Foundation, NAGASE Science Technology Foundation, The Canon Foundation, ONO Medical Research Foundation, the Research Grant of the Princess Takamatsu Cancer Research Fund, The Yasuda Medical Foundation, and Toray Science Foundation.

Author contributions

T.K., R.K., M.S., Y.K., Y.H., K.I., O.O., and Y.E., conceived and directed the project, designed experiments, interpreted the results, and wrote the manuscript. T.K., R.K., M.S., A.K., Y.K., Y.H., K.I., O.O., and Y.E., designed the project, and analyzed main experiments. M.S., A.K., K.M., T.N., S.Y, S.S., A.K.H., and J.O., developed experimental protocols and performed experiments.

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published 23 May, 2024

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The integration of quantitative metabolic and proteomic analysis uncovers an augmentation of the sphingolipid biosynthesis pathway during T-cell differentiation

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Abstract

Figures

Introduction

Results

Activated sphingolipid metabolism is required for the proper generation of Th17 and iTreg cells

Discussion

Method Details

Mice

Cell Preparation

Meta-analysis Of Proteomics Data

Metabolome Analysis

Lipidome Analysis

Cas9 Mediated-genome-editing

Quantitative Real-time Pcr

Facs Analysis

Data Availability

Statistics And Reproducibility

Declarations

References

Additional Declarations

Supplementary Files

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Journal Publication

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