Graphical abstract

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Highlights

     * •
       Lupus follicular T (Tfh) cells express a signaling and effector
       gene signature
     * •
       This enhanced gene signature is initiated in naive CD4 T cells (Tn)
     * •
       Lupus Tn and Tfh cells present altered mitochondrial functions
     * •
       Amino acid metabolism and anabolic programs are altered in lupus
       Tfh cells
     __________________________________________________________________

   Physiology; Immunology; Cell biology; Metabolomics; Transcriptomics

Introduction

   Follicular helper T (Tfh) cells are a specialized subset of cluster of
   differentiation (CD) CD4^+ T cells that provide co-stimulatory signals
   to germinal center (GC) B cells to undergo class-switching, somatic
   hypermutation, and differentiation into long-lived plasma cells.
   Class-switched high-affinity autoantibodies are a major mediator of
   tissue damage in systemic lupus erythematosus (SLE). The frequency of
   Tfh cells is expanded in SLE patients proportionally with disease
   activity[40]^1^,[41]^2^,[42]^3 and rises in SLE patients evaluated
   shortly after diagnosis.[43]^4 A higher frequency of Tfh and GC B cells
   has been found in all mouse models of lupus, in which the pharmacologic
   or genetic targeting of interleukin (IL)-21, a cytokine specifically
   produced by Tfh cells, ameliorated disease.[44]^5^,[45]^6

   The molecular mechanisms responsible for Tfh cell differentiation and
   function have been well defined in response to a foreign antigen
   challenge.[46]^7^,[47]^8 However, the specific mechanisms that drive
   the spontaneous expansion of Tfh cells in lupus are largely unknown. To
   address this issue, we have used the B6.Sle1.Sle2.Sle3 triple congenic
   (TC) mouse model, which carries the three NZM2410 susceptibility loci
   that are necessary and sufficient to develop lupus in a C57BL/6 (B6)
   background.[48]^9 In this model, we have identified two genetic
   determinants in the Sle1 locus, which contribute to Tfh cell expansion.
   The Pbx1-d dominant negative splice isoform of Pbx1 in the Sle1a
   susceptibility locus favors Tfh cell expansion at the expense of
   impaired regulatory T cells.[49]^10 Sle1b corresponds to variants in a
   cluster of genes of the signaling lymphocytic activation molecule
   (SLAM) family,[50]^11 which are known to regulate Tfh cell
   responses.[51]^12^,[52]^13 Among them, a different ratio of Ly108
   isoforms promotes the expansion of Tfh cells.[53]^14^,[54]^15

   The cellular metabolism of immune cells is altered in SLE patients and
   mouse models of the disease, and some of these alterations may offer
   therapeutic targets.[55]^16^,[56]^17^,[57]^18 Specifically, we have
   shown that CD4^+ T cells from SLE patients and TC mice are more
   metabolically active, with a greater glycolysis and mitochondrial
   oxidative phosphorylation (OXPHOS). Reducing the metabolic activity of
   CD4^+ T cells with inhibitors of glycolysis and OXPHOS was therapeutic,
   implying that metabolism and effector function were causally linked in
   lupus CD4^+ T cells.[58]^19 We have also shown that naive CD4^+ T cells
   from pre-autoimmune TC mice were more metabolically active than their
   B6 counterparts,[59]^19 suggesting an intrinsic genetic alteration of
   TC CD4^+ T cells that was independent of autoimmune activation.

   Although the field of immunometabolism has exploded in the last decade,
   the metabolic programs that sustain Tfh cells have been relatively less
   studied.[60]^20 Tfh cells are considered as metabolically
   quiescent,[61]^21^,[62]^22 but they require both mammalian target of
   rapamycin complex 1 and 2 (mTORC1 and mTORC2) signaling,[63]^23 and
   their proliferation involves both glycolysis and oxidative
   phosphorylation.[64]^23^,[65]^24 We have shown that spontaneous Tfh
   cells were more glycolytic than naive CD4^+ T cells, and TC Tfh cells
   were more glycolytic than B6 Tfh cells.[66]^25 Accordingly, treatment
   with 2-deoxy-D-glucose (2DG), a non-metabolizable glucose analogue that
   inhibits the first step of glycolysis, reduced the frequency of both
   Tfh and GC B cells and eliminated the production of autoantibodies in
   multiple models of lupus-prone mice. Remarkably, the 2DG treatment
   preferentially targeted spontaneously expanded Tfh cells in lupus-prone
   mice, which contained autoreactive specificities, but did not impair
   the frequency or function of Tfh cells induced by a viral or protein
   antigen challenge in either lupus or control mice. Consistent with this
   result, the deletion of the main glucose transporter, Slc2a1, in CD4^+
   T cells did not impair Tfh cell differentiation in B6 mice; however,
   Slc2a1 overexpression expanded Tfh cells in response to protein
   immunization.[67]^23 Treatment of lupus or control mice with
   6-diazo-5-oxo-L-norleucine (DON), an inhibitor of Gls1, the first
   enzyme in glutaminolysis, reduced the expression of B-cell lymphoma 6
   (BCL-6), the Tfh cell master regulator, and eliminated both spontaneous
   and protein immunization-induced GCs in both lupus and control mice.
   Gls1 deficiency in total CD4^+ T cells altered their Th1 and Th17
   functions.[68]^26^,[69]^27 However, the impact of Gls1 deletion on Tfh
   cell development has not been examined.

   Tfh cells from lupus-prone TC mice have a transcriptional profile
   distinct from B6 controls.[70]^25 The transcriptional signature of
   spontaneous Tfh cells was also different from that of Lymphocytic
   Choriomeningitis Virus (LCMV)-induced Tfh cells in the Sle1.Yaa
   lupus-prone mice.[71]^28 The metabolic profile of Tfh cells has only
   been characterized comparatively to other effector subsets in
   in vitro-polarized cells from B6 mice.[72]^23 The current study was
   performed to further characterize the transcriptomic signature of TC
   Tfh cells comparatively to B6 Tfh cells by including their naive T (Tn)
   cell counterparts. We also integrated metabolic profiling of both these
   subsets. Our results showed that lupus-susceptibility genes drive the
   expression of a specific gene signature that starts in Tn cells and
   expands in Tfh cells toward enhanced signaling and effector programs.
   Metabolically, TC Tfh cells showed profound defects in mitochondrial
   programs that may be at the root of their reliance on glycolysis. TC
   Tfh cells also showed specific anabolic programs with enhanced
   glutamate metabolism and malate-aspartate shuttle. Thus, our study has
   revealed specific metabolic programs that can be targeted to limit the
   expansion of pathogenic Tfh cells.

Results

Spontaneous Tfh cells from TC lupus-prone mice express a robust
transcriptional signature

   Our previous microarray analysis demonstrated that PD1^+CXCR5^+CD4^+
   Tfh cells isolated from TC mice carried a different transcriptional
   program as compared to B6 controls.[73]^25 To extend this finding, we
   performed RNA sequencing (RNA-seq) analyses on splenic Tfh cells gated
   as CD44^+PSGL-1^loPD-1^+CD4^+ (ref. [74]22) from anti-dsDNA
   immunoglobulin G (IgG)-positive TC and age-matched B6 mice
   ([75]Figure S1), and the results were compared with microarray
   analyses. The juxtaposition of the transcriptional profiles obtained
   with different platforms and gating indicated a concerted perturbation
   of gene expression in TC Tfh cells ([76]Figures 1A and 1B). Most
   significant differentially expressed genes (DEGs) shifted concordantly
   higher or lower in TC Tfh cells compared to B6 controls. This was
   especially true for genes related to Tfh development such as Bcl6,
   Gata3, and Il4, which showed a higher expression in TC Tfh cells. and
   interferon regulatory factor 4 (Irf4) and Slam family 1 (Slamf1) that
   showed a higher expression in B6 Tfh cells ([77]Figure 1A). 342 and 409
   DEGs with fold changes (FCs) above 1.5 and false discovery rate
   (FDR)-corrected, p < 0.05, were respectively higher and lower in TC Tfh
   cells concordantly in both cohorts ([78]Figure 1C). This DEG set
   represents a robust transcriptional signature of TC Tfh cells. When
   computed with publicly available gene sets, these shared DEGs matched
   with immune cell function and signaling, proliferation, and amino acid
   transport ([79]Figure 1D), consistent with our previous
   findings.[80]^25 DEGs higher in TC Tfh cells were enriched in multiple
   cellular signaling pathways (e.g., signaling by receptor Tyrosine
   kinase, tyrosine-protein kinase Met, and G protein-coupled receptors
   (GPCR)), suggesting that TC Tfh cells rely on higher signaling networks
   than B6 controls ([81]Figure 1D). Additionally, gene set enrichment
   analyses (GSEAs) performed on these shared DEGs identified several
   metabolic pathways consistently enriched in either TC or B6 strains.
   Surprisingly, most of these pathways, including
   glycolysis/gluconeogenesis, the pentose phosphate pathway (PPP),
   OXPHOS, and the TCA cycle, were suppressed in TC Tfh cells compared to
   B6 controls ([82]Figure 1E). On the other hand, two pathways directly
   involved in T cell activation, glycerophospholipid metabolism, and
   phosphatidylinositol signaling were elevated in TC Tfh cells
   ([83]Figure 1E). To further test the robustness of our transcriptional
   profiling, we incorporated a previously generated Nanostring dataset
   comparing TC and B6 Tfh cells.[84]^25 Again, clearly consistent
   patterns were observed across the three platforms, highlighting an
   elevated expression of Tfh-related genes (e.g., Bcl6, Il4, and Il21), a
   dysregulated expression of SLAM family genes (e.g., Slamf1, Cd48) and
   solute transporters (e.g., Slc1a4, Slc7a10), and an increased
   expression of signaling molecules (e.g., Dusp6, Camk2d) in TC Tfh cells
   ([85]Figure 1F). We have thus identified across three platforms and two
   gating strategies a robust transcriptional signature of TC Tfh cells
   that includes not only Tfh-related genes but also genes involved in
   immune activation as well as metabolism.

Figure 1.

   [86]Figure 1
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   Validated transcriptional programs in TC Tfh cells across platforms,
   gating strategies, and cohorts

   Combined analysis of the RNA-seq dataset (this report) from Tfh cells
   defined as CD44^+ PSGL-1^lo PD-1^+CD4^+ isolated from anti-dsDNA
   IgG-positive TC mice (n = 3) and B6 counterparts (n = 4) and Microarray
   (mArray) and Nanostring data from Tfh cells defined as PD1^hi CXCR5^+
   CD4^+ from TC (n = 5) and B6 (n = 3) mice (ref. [88]^25).

   (A) Heatmap of log2FC comparing TC vs. B6 Tfh cells computed from the
   mArray and RNA-seq datasets. Selected genes related to Tfh function are
   showed on the left. DEGs with upregulation/downregulation consistent
   across platforms between TC and B6 are annotated on the right along
   with genes of interest belonging to two metabolic pathways, OXPHOS, and
   ribosome, (B) Pearson correlation analysis of transcriptional profiles
   represented by t-scores from mArray (X axis) or Wald-statistic scores
   from RNA-seq (Y axis) datasets.

   (C) Venn diagrams visualizing shared DEGs upregulated (left) and
   downregulated (right) in TC relative to B6 Tfh cells between mArray
   (pink) and RNA-seq (green) datasets. DEGs were filtered with
   FDR-corrected p < 0.05 in either direction.

   (D) Gene set overlap test of the canonical pathways representing the
   342 DEGs upregulated in TC and 409 DEGs upregulated in B6 Tfh cells
   shown in C. The color scale indicates levels of significance, and the
   size of the symbols indicates the number of significant genes in each
   pathway. The gene sets are from the canonical pathway database. The
   asterisk (“Amino Acid Transport”) indicates a pathway obtained from a
   separate analysis on DEGs filtered by FDR-corrected p < 0.05 and fold
   change >1.5.

   (E) Significant gene sets derived from GSEA analysis comparing TC and
   B6 Tfh cells in either RNA-seq or mArray, filtered by FDR-corrected
   p < 0.25. The color scale indicates normalized enrichment scores (NES),
   and the size of the symbols indicates levels of significance.

   (F) Heatmaps of DEGs between TC and B6 Tfh cells whose expression was
   concordant across three platforms, RNA-seq , mArray, and Nanostring,
   each in a different cohort of mice. The log2FC for each platform are
   shown in each cell, with the color indicating the size and direction of
   the change according to the scale shown in the upper left corner. DEGs
   were selected if they trended in the same direction between any of two
   assays (FDR-corrected p < 0.1) with a few exceptions with p > 0.1
   labeled with asterisk (∗) and grayed out. The names of DEGs with
   direction consistent across only two platforms are also grayed out.
   DEGs were grouped by functions indicated to the left, with the
   corresponding keys at the bottom of the graph.

TC Tfh cells express a canonical Tfh signature

   To shed light on whether TC Tfh cells undergo a distinct
   differentiation process from naive (Tn) CD4^+ T cells, we compared the
   transcriptional profile of CD44^− Tn cells ([89]Figure S1) to the
   corresponding Tfh cells from the same TC and B6 mice.
   Principal-component analyses (PCAs) revealed distinct transcriptional
   profiles among the 4 groups ([90]Figure S2A). Cell-type differences
   across principal component (PC) 1 explained 82% of the variance and
   strain differences across PC2 accounted for only 12% of the variance.
   Therefore, these data demonstrated a greater difference between Tn and
   Tfh cells in both strains than between lupus-prone and control T cells
   from either subset. Of note, similar PC1 coordinates were shared
   between the clusters of Tfh cells isolated from TC or B6 mice, and
   likewise, similar PC2 coordinates were shared between their progenitor
   Tn cells. It suggests that although TC Tfh cells differ from B6 Tfh
   cells, the differential transcriptional projection from Tn to Tfh cells
   is largely shared between TC and B6 mice. This conclusion was supported
   by their Euclidian distance matrix ([91]Figure S2B). Accordingly, the
   majority of significant DEGs either upregulated or downregulated
   between Tfh and Tn cells were shared between strains ([92]Figure S2C).
   We then performed GSEA analyses and juxtaposed the significant gene
   sets computed on the ranked list of comparisons between Tfh and Tn
   cells from either TC or B6 mice. Expectedly from the PCA, gene sets
   differentially expressed between Tfh and Tn cells were largely shared
   between the TC and B6 strains. Gene sets related to T cell activation
   (e.g., cytokine activity/binding and signaling receptor binding) were
   increased in Tfh cells while gene sets involved in RNA binding
   decreased in Tfh cells ([93]Figure S2D). A heatmap of Tfh-signature
   genes highlighted numerous genes (e.g., Maf, Icos) whose expression
   levels differed to a similar extent between Tfh and Tn cells in both TC
   and B6 strains ([94]Figure S2E). However, some DEGs between Tn and Tfh
   cells showed a different expression between strains ([95]Figure S2F).
   Some Tfh-upregulated DEGs were overexpressed in TC Tfh cells (e.g.,
   Bcl6, Il21), and some in B6 Tfh cells (e.g., Irf4, Batf). A smaller
   group of genes were downregulated in Tfh cells and to a greater extent
   in TC (Ifngr1, Itgb7) or B6 (e.g., Smad4, Prkd2, and Foxp1) Tfh cells.
   These results suggest that the genetic susceptibility to lupus in TC
   mice exerts modulatory effects on the transcriptional profiles of Tfh
   cells. In conclusion, Tfh cells from lupus-prone mice express canonical
   Tfh-signature genes shared with non-autoimmune B6 mice. However, a
   subset of these genes presented a differential expression between the
   two strains.

TC Tfh cells share a majority of DEGs relative to B6 Tfh cells with their Tn
precursors

   To further identify the transcriptional perturbations exerted by the
   lupus-susceptibility genes on Tfh cells, we compared the
   transcriptional profiles from either Tfh or Tn cells isolated from TC
   mice with their counterparts B6 controls and highlighted the
   significant DEGs concordantly upregulated or downregulated between the
   TC and B6 strains across Tfh and Tn cells. A substantial number of DEGs
   in TC Tfh cells trended similarly in TC Tn cells ([96]Figure 2A). This
   was further supported by Pearson correlation analyses between the
   Wald-statistic rank lists of genes computed by comparing TC Tfh to B6
   Tfh cells and the rank list of genes computed by comparing TC Tn to B6
   Tn cells ([97]Figure 2B). Within rigorously defined DEGs obtained from
   the TC to B6 comparisons across Tfh and Tn cells, 714 genes out of 1899
   were co-upregulated by TC T cells (over ⅓ of DEGs in either
   comparison), while 243 genes (about a quarter of DEGs in either
   comparison) were co-downregulated by TC T cells when comparing to B6
   T cells ([98]Figure 2C). To understand the function of these genes, we
   computed the TC-upregulated or B6-upregulated overlapping genes with
   publicly available gene sets. Noticeably, T cell receptor (TCR)
   signaling and mitogen-activated protein kinase (MAPK) signaling
   pathways are upregulated in both TC Tfh and Tn cells, while metabolic
   processes such as OXPHOS and glycolysis were reduced in both TC Tfh and
   Tn cells ([99]Figure 2D), suggesting that numerous signaling and
   metabolic perturbations found in TC Tfh cells ([100]Figure S2D) occur
   early at the Tn state.

Figure 2.

   [101]Figure 2
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   Shared transcriptional signatures of TC Tn and Tfh cells relative to
   their B6 counterparts

   (A) Heatmap showing Z-scores transformed from
   regularized-Log2-converted counts of the whole transcriptional profile
   of DEGs (FDR-corrected p < 0.05, likelihood ratio test) in the 4
   groups. The track on the right shows consistently upregulated DEGs
   comparing TC to B6 in both Tfh and Tn cells as labeled in yellow
   (FC > 0) or red (FC > 1.5); the consistently downregulated counterparts
   are labeled in cyan (FC > 0) or dark cyan (FC > 1.5). DEGs categorized
   in the indicated metabolic pathways are also highlighted in the further
   right track.

   (B) Pearson correlation analyses of transcriptional profiles comparing
   Wald-statistic scores of the comparison of TC Tfh vs. B6 Tfh (Y axis)
   and the scores of the comparisons of TC Tn vs. B6 Tn (X axis).

   (C) Venn diagrams visualizing DEGs (FDR-corrected p < 0.05, FC > 1.5)
   comparing B6 to TC mice shared by Tfh and Tn cells. The non-shared DEGs
   are uniquely (bottom) or up- (top) regulated in Tfh (pink) or Tn
   (green) cells.

   (D) Gene set overlap test of DEGs upregulated in either TC or B6 and
   shared by both Tfh and Tn comparisons with the same cutoff as in panel
   C. The color scale indicates levels of significance, and the size of
   the symbols indicates the number of significant genes in each pathway.
   Pathways are filtered by FDR <0.05, except for pathways with an
   asterisk (FDR <0.15). The pooled gene sets are from the KEGG database
   from the Molecular Signature Database (MSigDB v5.2).

   (E) Bubble plot of gene sets significantly different between TC vs. B6
   mice in both Tfh and Tn cells, filtered by FDR-corrected p < 0.05. The
   inputs are ranked gene lists based on Wald-statistic scores comparing
   TC vs. B6 in either Tfh or Tn cells. The gene sets are from Gene
   Ontology database.

   To corroborate this hypothesis, we performed GSEA analyses on Tfh or Tn
   cells comparing TC and B6 origins. The top six pathways enriched in
   both TC Tfh and Tn cells compared to B6 controls were related to
   histone methylation and RNA splicing regulation, suggesting that a
   specific epigenetic landscape plays a critical role in establishing the
   identity of TC Tfh cells and that it is established early at the Tn
   stage ([103]Figure 2E). A strong chromatin modification signature was
   found in TC Tfh cells, including genes encoding for histone lysine
   methyl transferases (e.g., H3K36me) associated with open chromatin
   marks ([104]Figure S3A). Among them, Smyd2 was overexpressed in TC Tfh
   relative to B6 Tfh cells ([105]Figures S3B and S3C) and corresponded to
   increased relative levels of H3K36m2 in TC Tfh cells ([106]Figures S3D
   and S3E). The inositol phosphate synthesis pathway was also elevated in
   TC Tfh and Tn cells, suggesting a higher TCR to phosphoinositide
   3-kinase (PI3K) signal. On the other hand, the majority of gene sets
   downregulated in both TC Tfh and Tn cells corresponded to metabolic
   pathways, including OXPHOS and redox homeostasis as well as several
   indicators of mitochondrial functions, suggesting that the perturbed
   metabolic mode in Tfh cells is inherited early in their precursor Tn
   cells ([107]Figure 2E). Thus, our results suggest that perturbations in
   the metabolic modes and epigenetic landscape that characterize TC Tfh
   cells have been initiated at the Tn stage. In support of this
   hypothesis, in vitro polarization of Tn cells from pre-autoimmune TC
   mice resulted in a higher frequency of Tfh cells than from age-matched
   B6 mice ([108]Figures S4A and S4B). This suggests that
   lupus-susceptibility genes confer TC CD4^+ T cells a higher intrinsic
   ability to become Tfh cells.

The TC Tfh transcriptional program increases differentiation and effector
functions

   In addition to the large number of DEGs in both TC Tfh and Tn cells as
   compared to B6 T cells, 444 out of 1,158 (38%) Tfh DEGs were uniquely
   upregulated in TC Tfh cells and 377 out of 620 (60%) Tfh DEGs were
   uniquely downregulated in TC Tfh cells ([109]Figure 2C). To better
   understand the contribution of these genes to the TC Tfh cell expansion
   and phenotypes, we scrutinized the expression of specific genes based
   on their known association with T cell functions ([110]Figure 3A). In
   addition, whenever possible with a reliable detection by flow
   cytometry, we compared the protein expression of these genes on an
   independent cohort of mice ([111]Figure 3B). The expression of several
   transcription factors was altered in TC Tfh cells. Among them, the
   decreased expression of Irf4, identified as part of the TC Tfh
   signature ([112]Figure 1), was not found in TC Tn cells, and it was
   confirmed at the protein level. We confirmed that anti-apoptotic BCL-2
   protein levels were higher in TC Tfh than B6 Tfh cells[113]^25 and
   showed here that it was specific to TC Tfh cells ([114]Figures 3A and
   3B).

Figure 3.

   [115]Figure 3
   [116]Open in a new tab

   Gene expression signature specific to Tfh cells of TC origin

   (A) Heatmaps for log2FC of significant DEGs (FDR-corrected p < 0.05) in
   each indicated comparison according to the color scale shown on the
   upper left. Genes were grouped by functional pathways indicated on the
   left of each cluster.

   (B) Heatmaps for log2FC of protein levels of selected DEGs analyzed by
   flow cytometry. In (A) and (B), bold font indicates genes mentioned in
   the text.

   (C) Flow cytometry analysis of SLAMF genes in non-Tfh and Tfh cells.
   Each symbol represents a mouse (n = 6–7) pooled from 2 experiments.
   Means ± s.e.m analyzed by 1-way ANOVA with Dunnett’s T3 multiple
   comparisons tests. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

   Two quintessential Tfh-type cytokines, IL-21 and IL-4, along with
   interferon (IFN) γ and IL-10, two T cell cytokines associated with
   lupus,[117]^29 were highly expressed in TC Tfh cells relative to B6,
   although not as much as in TC Tn cells relative to B6 ([118]Figure 3A).
   A higher expression of IFNγ protein was confirmed in both TC Tn and Tfh
   cells ([119]Figure S4C). We also observed a heightened expression of
   the critical signaling adapter Jak3 acting downstream of both IL-21 and
   IL-4, as well as in genes in the Janus kinases - Signal transducer and
   activator of transcription (JAK-STAT) pathway ([120]Figure S5A). The
   concerted increased expression of cytokines and signaling molecules
   downstream of their receptors may constitute an autocrine feedforward
   loop amplifying Tfh cell development in TC mice. We also observed an
   elevated expression of non-canonical cytokine-like genes such as
   cardiotrophin-like cytokine (Clcf1), a member of the IL-6 family with B
   cell stimulating functions[121]^30 and amphiregulin (Areg) that has
   been reported in Th2-type cells and in tissue-resident Treg
   cells,[122]^31 potentially promoting pathogenic immune responses
   ([123]Figure 3A). There was a small but significant increased
   expression of Il21r and a reduced expression of cytokine receptors
   specific to other lineages (Il2rb and Tnfrsfb1) in TC Tfh cells,
   although only IL-2Rβ protein levels were also significantly reduced in
   TC Tfh cells ([124]Figures 3A and 3B). Interestingly, a reduction in
   both mRNA and protein levels of Tfh-specific chemokine receptors
   including CXCR3 and CXCR5 was observed in TC Tfh cells, suggesting
   dysregulated cellular trafficking and migration ([125]Figures 3A and
   3B).

   Other than cytokines, chemokines, and TCR signals, Tfh cells require
   co-stimulatory molecules to direct their development and sustain their
   activation. Although the levels of Icos, Cd28, and Pdcd1 (encoding for
   PD1) were similar between TC and B6 Tfh cells, the corresponding
   protein levels were higher in TC Tfh cells ([126]Figures 3A and 3B),
   implying post-translation layers of regulation in TC Tfh cells.
   Furthermore, the gene expression of another pivotal co-activating Tfh
   molecule, Cd40lg encoding for CD154, was increased in both TC Tn and
   Tfh cells ([127]Figure 3A). In addition, upregulation of their ligands
   (Cd40 and Icosl) was also observed on TC Tfh cells. The expression of
   other co-stimulatory molecules Tnfrsf4 and Tnfsf8 was downregulated in
   TC Tfh cells, but it was not validated at the protein level for OX40
   and CD30L. Elevated expression of Efnb2/3, which encodes for a
   nonclassical co-signaling molecule,[128]^32 and the reduced expression
   of the TCR-recycling molecule Sdc2[129]^33 were also observed,
   suggesting enhanced signaling rafts on the surface of TC Tfh cells.

   Tfh cells are equipped with a broad array of SLAMF family members that
   regulate their contact with B cells.[130]^34 Indeed, higher protein
   levels of SLAMF1 (CD150), SLAMF3 (Ly9), SLAMF4 (CD244), SLAMF5 (CD84),
   and SLAMF6 (Ly108) were found on Tfh cells compared to non-Tfh cells no
   matter their TC or B6 origin, and this was consistent with the RNA-seq
   data except for Slamf3 ([131]Figures 3A–3C). SLAMF5, one of the highest
   upregulated genes in TC Tfh cells ([132]Figures 3A–3C), has been firmly
   established as positive regulator of interactions between murine Tfh
   and B cells,[133]^35 and it is expressed by pathogenic human peripheral
   helper T (Tph) cells in rheumatoid arthritis.[134]^36 SLAMF3 expression
   was also higher in TC Tfh and Tn cells in both RNA-seq and
   fluorescence-activated cell sorting (FACS) analyses, although whether
   it contributes to B cell help is unknown. On the other hand, the
   expression of SLAMF2 and SLAMF4 was lower in TC than B6 Tfh cells.
   Taken together, Tfh cells of TC origin manifest a perturbed expression
   of SLAMF genes, potentially altering their interactive dynamics with B
   cells.

   TC Tfh cells presented an increased expression of type I and II (Acvr2a
   and Acvr1b) receptors for activin A ([135]Figure S5A), a molecule that
   promotes human Tfh cell differentiation.[136]^37 We also observed a
   higher expression of other genes in the activin receptor signaling
   pathway, such as transforming growth factor β (TGFβ) signaling (Tgfbr3,
   Smad3, and Smad7), suggesting that TC Tfh cells engage in heightened
   activin receptor signaling that intersects with TGFβ signaling. Activin
   A did not, however, enhance TC Tfh differentiation in vitro (data not
   shown). Therefore, the role of activin A in mouse Tfh cells in general
   and specifically in TC Tfh cells remains elusive.

   Finally, TC Tfh cells presented a substantial number of DEGs in the
   highly overlapping mitogen-activated protein kinase (MAPK) and
   Kirsten rat sarcoma (K-Ras) pathways, which were also overexpressed,
   but to a lesser extent, in TC Tn cells ([137]Figures S5A and S5B). This
   enhanced signaling may be secondary to a profound downregulation of the
   Phosphatase and tensin homolog (PTEN) pathway in both Tn and Tfh TC
   cells. These pathways are likely to play a major role in the enhanced
   differentiation, proliferation, and activation of TC Tfh cells. In
   conclusion, in-depth scrutinization of the Tfh-specific effector
   molecules and signaling pathways in TC mice at the transcriptional and
   protein levels revealed substantial alterations that not only may
   account for the accumulation of Tfh cells but also suggest that they
   develop enhanced effector functions.

Both Tn and Tfh cells in TC mice express altered metabolic gene programs

   We have previously reported a global increased mTOR expression in TC
   Tfh cells measured by histology as well as mTORC1 activity measured by
   flow cytometry on one of its targets, phospho-S6 (pS6) ribosomal
   protein.[138]^25 Consistent with this finding, we found here an
   increased gene expression of the upstream p70-S6 kinase 1 Rps6kb1 and
   the sensing amino acid transporters Slc36a1 and Slc38a9
   ([139]Figure S5A), both of which are critical for mTORC1
   activation.[140]^38^,[141]^39 The protein expression of pS6 and p4EBP1,
   another marker of mTORC1 activation, as well as protein kinase B
   phosphorylated at serine 473 (pAKT^S473), a marker of mTORC2
   activation, was elevated in Tfh cells relative to Tn cells
   ([142]Figures S6A–S6C), confirming that Tfh cells rely on both mTORC1
   and mTORC2 activation.[143]^23 Concerning strain-wise differences, we
   found increased levels of pS6 not only in TC Tfh cells but also in TC
   Tn cells relative to their B6 counterparts ([144]Figure S6B). Although
   the gene expression of the mTORC2 central regulator Rictor was elevated
   in TC Tfh cells relative to B6 ([145]Figure S5A), pAKT^S473 expression
   was lower in TC than B6 Tfh cells ([146]Figure S6C). This resulted in a
   skewed pS6/pAKT^S473 ratio between B6 and TC T cells ([147]Figure S6D).
   Overall, these results show that TC CD4^+ T cells are sustained by an
   imbalanced mTORC1 activation that initiates in Tn cells and is
   maintained in Tfh cells.

   Mitochondria directly tailor T cell effector
   functions,[148]^40^,[149]^41 and mitochondrial dysfunctions in T cells
   have been firmly associated with lupus.[150]^17^,[151]^42^,[152]^43
   However, mitochondria have not been specifically characterized in Tfh
   cells. The TC Tfh signature was characterized by decreased TCA cycle
   and OXPHOS pathways as compared to B6 Tfh cells ([153]Figure 1E). A
   GSEA analysis also identified the downregulation of major mitochondrial
   pathways including OXPHOS, TCA cycle, and fatty acid (FA) metabolism in
   both Tfh and Tn cells from TC mice as compared to B6 counterparts
   ([154]Figure 4A), suggesting that mitochondrial defects occurred early
   in TC Tn cells and were maintained when they differentiate into Tfh
   cells. Accordingly, genes essential for tricarboxylic acid (TCA) cycle
   (Mdh1, Ogdhl) and OXPHOS (Cox4i1, Ndufs5, Uqcr10) were downregulated in
   TC Tfh and Tn cells ([155]Figure 4B). Acaa2, responsible for the last
   step of mitochondrial β-oxidation,[156]^44 along with Cpt1a and Aldh2,
   was also downregulated ([157]Figure 4B), indicating an impairment of FA
   oxidation in TC T cells. Cpt1c was upregulated in TC Tfh and Tn cells
   compared with B6 counterparts ([158]Figure 4B). This atypical member of
   the carnitine palmitoyltransferase family functions as a lipid
   metabolism sensor and may favor triglyceride synthesis rather than FA
   oxidation.[159]^45 We subsequently measured the uptake of BODIPY and
   2-N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl) amino-2-deoxyglucose (2-NBDG)
   to verify lipid and glucose metabolic activities in vitro. Tfh cells
   showed a higher lipid content as compared to Tn cells in both strains,
   but it was lower in both TC Tn and Tfh cells than in B6 counterparts
   ([160]Figure S6E). Glucose uptake was also lower in TC T cells, both
   in vitro and in vivo ([161]Figures S6F and S6G). It should be noted
   also that in the more physiological in vivo conditions, glucose uptake
   was globally lower in Tfh than in Tn cells ([162]Figure S6G). These
   results suggest an altered nutrient flow in TC CD4^+ T cells including
   Tfh cells relative to B6 counterparts.

Figure 4.

   [163]Figure 4
   [164]Open in a new tab

   Lupus-susceptibility genes confer mitochondrial defects in Tfh and Tn
   cells

   (A) GSEA plots on the indicated gene sets derived from the Molecular
   Signature Database. The inputs are ranked gene lists (Wald statistic)
   comparing separately TC and B6 Tfh cells on the left and Tn cells on
   the right. The asterisk associated with the FDR value for Glutathione
   metabolism indicates that this pathway did not meet the stringent
   significance threshold of FDR <0.05. However, it was modestly
   significant with the FDR control and shared similar gene-rank profiles
   with TC Tn versus B6 Tn.

   (B) Heatmaps of log2FC for DEGs (FDR-corrected p < 0.05) in the gene
   sets shown in (A).

   Mitochondrial dysfunction manifested not only in OXPHOS and FA
   metabolism but also in oxidative stress and redox imbalance exerted by
   uncontrolled production of reactive oxygen species (ROS), which plays a
   critical role in T cell activation in general[165]^46 and in lupus in
   particular.[166]^47 Indeed, gene sets associated with ROS modulation
   and glutathione metabolism were both dampened in TC Tfh and Tn cells
   compared to B6 T cells ([167]Figure 4A). In agreement with the GSEA
   results, the expression of many genes related to glutathione
   metabolism, such as Gpx1/4, and negative regulation of ROS, such as
   Sod1, Prdx1-6, Atox1, and Romo1, was diminished in TC T cells
   ([168]Figure 4B). Gphx4 expression is required by Tfh cells to
   counterbalance high levels of lipid peroxidation and mitochondrial
   stress.[169]^48 In contrast, we observed an increased expression of the
   gene encoding the mitochondrial (m) ROS-generating enzyme xanthine
   dehydrogenase (Xdh) ([170]Figure 4B). Increase of nicotinamide
   nucleotide transhydrogenase (Nnt) ([171]Figure 4B) may also promote a
   “reverse” electron flux from nicotinamide adenine dinucleotide
   phosphate (NADPH) to NADP^+, consuming ROS-neutralizing NAPDH.[172]^49

   Globally, Tfh cells presented a reduced mitochondrial mass but an
   increased mitochondrial reactive oxygen species (mROS) and
   mitochondrial membrane potential (MMP) relative to Tn cells
   ([173]Figures 5A–5D), indicating a mitochondrial involvement in this
   effector T cell subset. Importantly, differences were observed between
   B6 and TC T cells, with a lower mitochondrial mass in TC Tn and Tfh
   cells than in B6 counterparts and a higher mROS in TC Tfh than B6 Tfh
   cells. A reduced mitochondrial mass combined with an increased mROS
   validates the reduced expression of genes supporting mitochondrial
   metabolism as well as the dysregulation of genes related to antioxidant
   system TC Tfh cells. Due to limitations in Tfh cell numbers, we
   evaluated mitochondria by electron microscopy in CD44^− Tn and CD44^+
   effector T cells (Teff), which confirmed the reduction in mitochondrial
   size in B6 Teff relative to Tn cells, although their number was
   unchanged ([174]Figures 5D–5F). The number of mitochondria increased in
   TC Teff cells, while their size remained similar between TC Tn and Teff
   cells, further supporting differential mitochondrial dynamics between
   B6 and TC CD4^+ T cells. Furthermore, Tn and specially TC Tfh cells
   expressed a robust gene signature that indicates a defective mitophagy,
   including the downregulation of Pink1 and many genes involved in
   Pink/Parkin-mediated autophagy ([175]Figures S7A–S7C). On the other
   hand, TC T cells overexpressed Mterf3, an inhibitor of mDNA
   replication,[176]^50 which is required to maintain functional
   mitochondria. The overexpression of the mediator of fusion Mfn1 in TC
   Tfh cells may be a response to an increased mitochondrial stress. In
   summary, our results indicate that the lupus-susceptibility genes that
   are expressed in TC mice trigger mitochondrial and antioxidant defects
   in Tn cells than are amplified in Tfh cells. This corresponds to the
   oxidative state that has been described in human SLE T cells[177]^51
   and suggests that it is a genetically controlled, intrinsic
   characteristic of lupus T cells that starts at the naive stage.

Figure 5.

   [178]Figure 5
   [179]Open in a new tab

   Mitochondrial alterations in Tn and Tfh TC cells

   Mitochondrial mass (A), mROS (B), and (MMP (C), in Tn and Tfh cells
   from B6 and TC mice. Each symbol represents a mouse (n = 8–15 pooled
   from 2 to 4 cohorts).

   (D–F) Number of mitochondria per cell (D) and mitochondria size (E) in
   Tn and Teff assessed by electron microscopy. Representative images are
   shown in (F) at x15,000–20,000 (top) and ×40,000 (bottom)
   magnification. Black and red arrow heads point to individual B6 and TC
   mitochondria, respectively. In (D), each point represents a cell (n =
   7–10) from 5 mice per group, and (E) represents individual mitochondria
   from (F).

   Means ± SEM analyzed by 1-way ANOVA with’ Dunnett’s T3 multiple
   comparisons tests. ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001.

TC Tfh cells present a distinctive anabolic metabolism

   To further understand the metabolism of Tfh cells, we first reanalyzed
   the metabolomes of in vitro-polarized Tfh cells and activated T cells
   (Tact) from B6 mice.[180]^23 Phosphatidylethanolamine (PE) and
   phosphatidylcholine (PC) synthesis pathways ([181]Figure S8A), of which
   the end-products cytidine diphosphate (CDP)-ethanolamine and
   sn-Glycero-3-phosphocholine accumulated in Tfh cells while the
   substrates or intermediates, such as L-serine and choline, were
   significantly reduced ([182]Figure S8B). Moreover, aspartate, an amino
   acid critical for T cell proliferation[183]^52^,[184]^53 was
   dramatically increased while other amino acids such as glycine and
   serine, fueling purine or PE synthesis,[185]^54 were reduced
   ([186]Figure S8B), consistent with a hyperactive malate-aspartate
   shuttle in Tfh cells driving aspartate synthesis ([187]Figure S8A).
   Phosphatidylinositol phosphate (PIP) metabolism bridging TCR signaling
   with mTOR activation and anabolic processes was specifically heightened
   in Tfh cells. Accordingly, myo-inositol, the critical building block of
   this pathway, accumulated in Tfh cells ([188]Figure S8B). Lactate
   levels were decreased in Tfh cells, indicating that glycolysis is not
   mobilized in B6 Tfh cells. However, TCA cycle intermediates, such as
   succinate, fumarate, and malate, were depleted while TCA byproducts
   itaconate and 2-hydroxyglutarate (2-HG) accumulated in Tfh cells,
   suggesting an active TCA cycle. Itaconate and 2-HG mitigate oxidative
   stress by inhibiting succinate dehydrogenase (Sdh) and β-ketoglutarate
   (KG)-mediated enzymatic reactions, respectively.[189]^55^,[190]^56 FA
   β-oxidation was downregulated in Tfh cells, as indicated by the reduced
   amounts of FA-conjugated carnitines. Tfh cells also presented increased
   levels of several metabolites related to pentose and glucuronate
   interconversions. Since the PPP generates NADPH to maintain the redox
   balance in activated T cells,[191]^57 these data supports the need to
   mitigate stress responses in Tfh cells. Altogether, these observations
   indicate that Tfh cells are supported by mitochondrial metabolism and
   enhanced anabolic processes coupled with antioxidant responses to
   maintain redox balance.

   To understand the metabolic processes that drive the hyperactive status
   of TC Tfh cells, we compared the metabolome of Tfh and Tn cells from TC
   and B6 mice. PCA of the 109 annotated metabolites demonstrated distinct
   profiles among the four groups ([192]Figure 6A). We then performed
   pathway enrichment analyses comparing genotypes within the same cell
   types (TC vs. B6 Tfh and TC vs. B6 Tn) and comparing cell types within
   the same genotype (Tfh vs. Tn from TC mice and Tfh vs. Tn from B6
   mice). A majority of significantly altered pathways, such as PC
   biosynthesis, purine metabolism, and aspartate metabolism, were shared
   between TC and B6 mice when comparing cell types, suggesting that, as
   for the transcriptome, TC Tfh and B6 Tfh cells largely share metabolic
   programs ([193]Figure 6B). However, several pathways were uniquely
   presented by TC Tfh cells compared to B6 Tfh cells, such as ammonia
   recycling, glutamate metabolism, and malate-aspartate shuttle, while PE
   biosynthesis and purine and methionine metabolism, which exist in B6
   Tfh cells, were further enhanced in TC Tfh cells ([194]Figure 6B). An
   increased PE synthesis and decreased degradation are an essential
   feature of Tfh cells to maintain CXCR5 surface expression.[195]^58 A
   comparison of PE levels between Tn and Tfh cells confirmed this finding
   ([196]Figure S9A). More interestingly, TC Tfh cells contained an
   increased amount of PE metabolite as well as an increased relative
   level of surface PE ([197]Figure S9B). This process may compensate for
   a lower CXCR5 expression by TC Tfh cells ([198]Figures 3A and 3B).

Figure 6.

   [199]Figure 6
   [200]Open in a new tab

   Enhancement of anabolic metabolism in TC Tfh cells

   (A–D) GC-MS analysis was conducted on Tn and Tfh cells from TC or B6
   mice from two cohorts. (A) Principal-component analysis (PC2 - PC3) of
   the 109 annotated metabolites. PC1 was ignored as it mainly accounted
   for batch effects, n = 7–9. (B) Pathway enrichment analysis of the
   significant metabolites (FDR-corrected p < 0.25, Student’s t test)
   comparing TC vs. B6 Tfh, Tfh vs. Tn TC, and Tfh vs. Tn B6 cells. The TC
   vs. B6 Tn cell comparison did not show significant metabolite features.
   TC Tfh-specific pathways that are also significantly enriched in
   in vitro-activated Tfh cells are shown in red font (see
   [201]Figure S8). (C) Hierarchical clustering analyses of significant
   metabolites with stringent criteria (FDR-corrected p < 0.05, one-way
   ANOVA) among the four indicated groups of T cells are shown by heatmap.
   (D) Selected metabolites differentially enriched in TC vs. B6 Tfh cells
   (nominal p < 0.05) are shown by boxplots for the four groups of
   T cells. Means ± SEM analyzed by 1-way ANOVA with Dunnett’s T3 multiple
   comparisons tests. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗∗p < 0.0001.

   (E) Metabolic pathways shared between ex vivo Tfh cells compared to Tn
   cells shown in (A-D) and in vitro-polarized activated T cells (Tact)
   and Tfh cells.[202]^23 The heatmaps show log2FC of selected significant
   metabolites, with the absolute numbers labeled in each cell and values
   with log2FC > 2 normalized to 2. DHA: dehydroascorbic acid; P:
   phosphate; PE: phosphatidylethanolamine; PC: Phosphatidylcholine;
   N-Formyl-GAR: 5′-Phosphoribosyl-N-formylglycinamide.

   A hierarchical clustering analysis to visualize metabolites with levels
   that are rigorously altered among the four groups supported the pathway
   analysis by showing increased amounts of aspartate and several purine
   metabolites with Tfh cell differentiation, especially in TC Tfh cells
   ([203]Figure 6C). This indicates enhanced anabolic activities in the
   Tfh cell development in TC mice. Further, PE and glycine levels were
   higher while succinic acid was less abundant in both Tn and Tfh cells
   isolated from TC mice when compared to B6 T cells ([204]Figure 6C). As
   the same alterations in the levels of PE, glycine, and TCA
   intermediates were observed in in vitro-polarized B6 Tfh cells, this
   may indicate, as for gene expression, that metabolic programs in TC Tn
   cells favor Tfh cell development. We then compared the four groups with
   a lower threshold of significance (raw p value <0.05). This analysis
   confirmed the decreased levels of succinic acid in TC Tn and Tfh cells
   ([205]Figure 6D). It also revealed that cholesterol, a critical
   component of lipid rafts in human lupus T cells,[206]^59 was also more
   abundant in TC Tfh cells ([207]Figure 6D). Together with the
   observation of an enhanced PE biosynthesis, this may reflect that TC
   Tfh cells require a robust lipid biosynthesis to facilitate the
   generation of immune synapses with GC B cells.[208]^60 Finally, several
   amino acids other than glycine and aspartate were more abundant in TC
   Tfh cells ([209]Figure 6D). Although glycine and serine that are used
   for anabolic metabolic processes[210]^54 were depleted in B6 Tfh cells
   differentiated in vitro, their increase in ex vivo TC Tfh cells may
   reflect their enhanced capacity for amino acid intake. This corresponds
   to the enrichment in amino acid transporter pathway in TC Tfh cells
   observed in transcriptional analyses ([211]Figure 1). Furthermore, we
   performed a network analysis of the dynamics of amino acid content and
   their transporters (AATs) in Tfh and Tn cells of TC or B6 origin by
   combining RNA-seq and metabolomic results ([212]Figure S10). Expression
   of the majority of AATs was higher in Tfh than Tn cells
   ([213]Figures S10A and S10B). Multiple AATs responsible for
   transporting glutamine (e.g., Slc1a5, Slc38a6, Slc38a2) were
   overexpressed in Tfh cells of both TC and B6 origins, but they were
   further enhanced in TC Tfh cells, consistent with the highest levels of
   glutamic acid detected in TC Tfh cells ([214]Figures 6D and [215]S10C).
   Moreover, the relative distribution of these glutamine transporters was
   different with Slc1a5 dominating B6 Tfh and Slc38a2 dominating TC Tfh
   cells. The activation of Slc1a4 and Slc36a1 in TC Tfh cells may explain
   the increased levels of serine and glycine comparing TC and B6 Tfh
   cells, respectively ([216]Figure S10C).

   Finally, the comparative analysis of the metabolites between the
   in vitro polarization results obtained in B6 Tfh cells[217]^23 and the
   ex vivo results generated in this study confirmed several concerted
   alterations during Tfh cell development ([218]Figure 6E). In addition,
   TC Tfh cells present further increases in nucleotide,
   glycerophospholipid, and amino acid metabolism, which are the hallmarks
   of anabolic processes optimal for T cell activation
   proliferation.[219]^52 TCA cycle intermediates (e.g., succinate) were
   less abundant in TC Tfh cells, as they were in in vitro-polarized B6
   Tfh cells, which aligned with the global downregulation of the
   expression of genes related to the TCA cycle ([220]Figure 2) as well as
   mitochondrial alterations ([221]Figure 4). Although metabolites
   relevant to ascorbate and aldarate metabolism were more abundant in
   both in vivo and in vitro Tfh cells comparing to their respective Tn
   cells, the levels of those metabolites were lower comparing TC Tfh/Tn
   to B6 Tfh/Tn cells. Given the TC Tfh cell phenotypes, such findings may
   confirm an unbalanced redox balance in TC Tfh cell leading to
   dysregulated T cell activation status.

Discussion

   The expansion of Tfh cells represents a common feature of most
   autoimmune diseases. A number of hypotheses have been proposed to
   explain this finding, including the fact that several genes associated
   with susceptibility to autoimmune diseases also regulate the
   development of Tfh cells.[222]^61 Tfh cells from TC mice are highly
   proliferative, resulting in an expansion of their number as the mice
   age.[223]^25 Yet whether the lupus-susceptibility genes also favor
   effective and even pathogenic Tfh cell phenotypes was not fully
   investigated. We confirmed a robust transcriptional signature of TC Tfh
   cells, and we showed that this signature emerges first in TC Tn cells,
   providing strong evidence for a genetic basis. Histone modifications
   are likely to be involved in the TC Tn cells being poised to
   differentiate into Tfh cells since we observed a histone modification
   transcriptional signature in TC Tn cells that is expanded in Tfh cells.

   TC Tfh cells share a canonical Tfh signature with control Tfh
   cells[224]^62 by upregulating key Tfh-specific TFs (e.g., Maf, Ascl2,
   Tox, and Tox2) and surface markers (e.g., Ccr6, Tigit, and Icos). In
   addition, TC Tfh cells manifest a transcriptional state prone to
   increase differentiation and effector functions. The Tfh-specific
   cytokines IL-21 and IL-4 were drastically increased by the TC genetic
   background. TC Tfh cells also express Ifng, as well as Th2-specific
   Gata3 and Areg, suggesting a Tfh2-type phenotype, which correlates with
   disease severity in SLE patients.[225]^63 Therefore, TC Tfh cells are
   characterized by the enhancement of multiple cytokines that empower
   their effector and pathogenic function. These cytokines may participate
   into a feedforward loop by enhanced downstream JAK-STAT and MAPK
   signaling and may result from an increased KRAS signaling that also
   promotes cell proliferation. TC Tfh cells also showed a reduced
   expression of IRF4, which integrates the strength of TCR signaling.
   IRF4 highest expression promotes T helper over Tfh cell
   differentiation.[226]^64 Its decreased expression in TC Tfh cells
   suggests a lower tonic TCR signaling attributed to chronic activation
   by autoantigens, which might enhance Tfh differentiation.[227]^65 Tfh
   cells help GC B cells through an immunological synapse, where they
   interact with each other through membrane-bound signals including from
   the SLAM family (SLAMF) receptors. The requirement for the expression
   of SLAMF genes for Tfh cell differentiation is controversial, in part
   due to their overlapping roles and function as both receptors and
   ligands.[228]^66 We showed here that each of the 6 SLAMF genes is
   upregulated in Tfh cells at both transcriptional and translational
   levels. Furthermore, TC Tfh cells expressed higher levels of SLAMF3 and
   SLAMF5 but lower levels of SLAMF2 and SLAMF4 than B6 Tfh cells. SLAMF
   molecules induce either positive or negative signals in a
   context-dependent manner. The integration of these signals from several
   SLAMF members on both Tfh and B cells determines the functional
   outcomes.[229]^67 The significance of the differential expression of
   SLAMF members on TC Tfh cells will require functional assays with B
   cells from either TC or B6 mice.

   The transcriptomic analysis showed extensive alterations in metabolic
   pathways that started in in TC Tn cells and were amplified in Tfh
   cells. Our initial reporting of increased mTORC1 activation in TC Tfh
   cells[230]^25 was confirmed and extended to mTORC2. mTORC1 and mTORC2
   contribute to specific aspects of Tfh cell differentiation and
   functions,[231]^23 and their enhanced activation in lupus may support
   the same functions. mTORC2 deficiency reduced the frequency of Tfh
   cells along with other alterations in T cell phenotypes in a mouse
   model of autoimmunity.[232]^68 The enhanced mTOR activation in TC Tfh
   cells is therefore not unique to this subset and corresponds to the
   high mTOR activity that characterizes lupus T and B cells.[233]^69

   Besides mTOR activation, mitochondrial pathways were the most affected
   in both TC Tn and Tfh cells. Mitochondrial defects have been reported
   in the T cells of SLE patients with increased mitochondrial mass and
   MMP.[234]^70 TC Tfh cells have a decreased mitochondrial mass,
   unchanged MMP, and increased mROS, which mirrors the phenotype of
   Sle1c2 CD4^+ T cells with reduced Esrrg expression or Esrrg-deficient
   T cells.[235]^71^,[236]^72 It is therefore likely that the low Esrrg
   expression in TC T cells plays a major role in their dysfunctional
   mitochondria. In addition, TC Tfh cells presented an altered mitophagy
   gene signature. A dysfunctional mitophagy has been proposed as a major
   contributor to T cell defects in SLE patients, via mechanisms including
   the unrestrained production of
   mROS.[237]^43^,[238]^70^,[239]^73^,[240]^74 Impaired mitophagy has also
   been linked to autoimmunity through the generation of interferogenic
   mtDNA.[241]^75 Furthermore, TC T cells overexpressed Mterf3, a gene
   that inhibits the initiation of mtDNA transcription. Mitochondrial
   fitness directly depends on mtDNA replication and transcription, which
   when impaired, forces the cells to rely on glycolysis.[242]^76
   Accordingly, TC T cells showed a profound downregulation of the OXPHOS,
   TCA cycle, and FA oxidation pathways, along with an upregulation of
   oxidative stress pathways. Thus, TC mitochondria are likely to be poor
   energy producers, which could be the root cause of the TC Tfh cells
   being highly sensitive to the inhibition of glucose metabolism.[243]^25

   The transcriptional alterations in TC Tfh cells culminated with
   metabolic changes that correlated with differential expression profiles
   of solute transporters, especially for amino acid transport. There is
   increasing evidence that amino acid metabolism represents a critical
   checkpoint in T cell activation.[244]^77 Glutamine metabolism has been
   so far the only amino acid investigated in lupus, including in TC Tfh
   cells,[245]^25 and results obtained here suggest that glutamine import
   play a central role in TC Tfh cells. Specific anabolic pathways such as
   ammonia recycling, glutamate metabolism, and malate-aspartate shuttle
   dominated the metabolic profile of TC Tfh cells. The malate-aspartate
   shuttle provides aspartate to helper T cells that is required for their
   proliferation.[246]^52 Ammonia recycling is a process by which nitrogen
   is repurposed for amino acid synthesis, including aspartate, in highly
   proliferating cells.[247]^78 These two processes are likely to support
   TC Tfh cell proliferation and expansion. The increased glutamate
   metabolism corresponded to a greater glutamine import. It is unlikely
   to correspond to an increased mitochondrial glutamine metabolism due to
   the low expression of TCA genes but most likely to support the
   chromatin modification signature found in TC Tfh cells, as it has been
   shown for Th1 cells.[248]^27

   Overall, the transcriptomic and metabolic analyses of TC Tfh cells
   suggest that lupus Tfh cells expand from naive T cells that are poised
   to differentiate into the follicular helper pathway, potentially
   through epigenetic modifications sustained by glutamine metabolism. TC
   Tfh cells combine the canonical Tfh signature with a gene expression
   program that increases their effector functions. TC Tfh cells also
   undergo a profound metabolic reprograming that impairs mitochondrial
   functions and redirects amino acid metabolism toward the greater
   demands of highly proliferating cells. These alterations may offer
   targets for an elimination of T cell help that licenses pathogenic
   autoantibodies.

Limitations of the study

   This study was limited to one lupus-prone mouse model, and human data
   were not included. Further, mechanistic experiments following up the
   results presented were not performed. This study was designed to
   investigate classical Tfh cells, although we used two different gating
   strategies, one of them using CXCR5, to define the TC Tfh signature.
   The frequency of CXCR5^- CXCR3^+ PD-1^hi Tph cells is also expanded in
   SLE patients with a high capacity to provide B cell
   help.[249]^79^,[250]^80^,[251]^81 On the other hand, the frequency of
   CXCR3^+ Tfh1 is decreased in SLE patients,[252]^82 and the frequency of
   circulating CXCR3^−PD-1^+CD4^+T cells correlates with disease
   activity.[253]^83 Our findings of decreased CXCR3 and CXCR5 may reflect
   an expansion of these corresponding populations in the TC lupus-mouse
   model in which an increased frequency of extrafollicular helper T cells
   has also been reported.[254]^84 The current metabolomics study is based
   on a relatively small number of identified metabolites with a
   preference for volatile organic compounds inherited by the limitations
   of gas chromatography-mass spectrometry (GC-MS) analytical platform.
   Encouraged by the significance of presented metabolic signatures,
   deeper and targeted approaches (e.g., liquid-chromatography-MS-based
   methods) would be of great promise.

STAR★Methods

Key resources table

   REAGENT or RESOURCE SOURCE IDENTIFIER
   Antibodies
     __________________________________________________________________

   BCL-2 PE/Cy7 BioLegend RRID: [255]AB_2565247
   BCL-6 AF647 BD Biosciences RRID: [256]AB_10898007
   CD3e BD Biosciences RRID: [257]AB_394590
   CD4 AF700 Thermo Fisher RRID: [258]AB_493999
   CD4 APC BioLegend RRID: [259]AB_312719
   CD4 eF450 Thermo Fisher RRID: [260]AB_10718983
   CD4 FITC BD Biosciences RRID: [261]AB_394583
   CD16/CD32 BD Biosciences RRID: [262]AB_394655
   CD28 biotin Thermo Fisher RRID; [263]AB_466411
   CD28 BD Biosciences RRID: [264]AB_394763
   CD44 PE BioLegend RRID: [265]AB_312959
   CD48 FITC Thermo Fisher RRID: [266]AB_465076
   CD69PE/Cy7 BioLegend RRID: [267]AB_493564
   CD84 PE BioLegend RRID: [268]AB_2074757
   CD95 biotin BD Biosciences RRID: [269]AB_395328
   CD122 PE Thermo Fisher RRID: [270]AB_465832
   CD134 APC Thermo Fisher RRID: [271]AB_10717260
   CD150 PE/Cy7 BioLegend RRID: [272]AB_439796
   CD152 APC BioLegend RRID: [273]AB_2087653
   CD153 biotin Thermo Fisher RRID: [274]AB_466622
   CD162 AF647 BD Biosciences RRID: [275]AB_2737807
   CD183 BV421 BioLegend RRID: [276]AB_2563100
   CD185 BD Biosciences RRID: [277]AB_394302
   CD197 PE BioLegend RRID: [278]AB_389357
   CD229 PE BioLegend RRID: [279]AB_2137958
   CD244.2 PE/Cy7 Thermo Fisher RRID: [280]AB_257343
   CD267 APC Thermo Fisher RRID: [281]AB_842759
   CD278 PE BD Biosciences RRID: [282]AB_394349
   CD279 eF450 Thermo Fisher RRID: [283]AB_11150068
   CD279 PE/Cy7 Thermo Fisher RRID: [284]AB_10853805
   GATA3 PE/Cy7 Thermo Fisher RRID: [285]AB_2573568
   IFN-y Pacific blues BioLegend RRID: [286]AB_893526
   IFN-y Bio X cell RRID: [287]AB_1107694
   IFN-yR1 Thermo Fisher RRID: [288]AB_2573097
   IL-10R PE BioLegend RRID: [289]AB_313518
   IL-21R APC BioLegend RRID: [290]AB_2295868
   IL-23R PE BioLegend RRID: [291]AB_2572188
   IL-2 BioLegend RRID: [292]AB_315292
   IL-4 Bio X cell RRID: [293]AB_1107707
   IRF4 eF660 Thermo Fisher RRID: [294]AB_2574393
   Ly108 PE Thermo Fisher RRID: [295]AB_763616
   Nur77 PE Thermo Fisher RRID: [296]AB_1257210
   p4E-BP1 AF647 Cell Signaling RRID: [297]AB_2097838
   pAKT PE/Cy7 Thermo Fisher RRID: [298]AB_2688172
   pS6 Pacific blue Cell Signaling RRID: [299]AB_2797646
   RORyT BV421 BD Biosciences RRID: [300]AB_2687545
   T-bet eF450 Thermo Fisher RRID: [301]AB_2784726
   TGF-β R&D Systems RRID: [302]AB_357931
   Anti-rabbit IgG HRP Cell Signaling RRID: [303]AB_2099233
   Anti-mouse IgG HRP Cell Signaling RRID: [304]AB_330924
   H3K36me2 Ab Cell Signaling RRID: [305]AB_1030983
   Histone 3 Ab Cell Signaling RRID: [306]AB_2756816
     __________________________________________________________________

   Chemicals, peptides, and recombinant proteins
     __________________________________________________________________

   Fixable viability dye Thermo Fisher 65-0865
   MitoSOX^TM Red Thermo Fisher [307]M36008
   CM-H2DCFDA Thermo Fisher C6827
   MitoTracker® Red CMXRos Thermo Fisher M7512
   MitoTracker™ Deep Red Thermo Fisher [308]M22426
   2-NBDG Thermo Fisher N13195
   BODIPY Thermo Fisher D3823
   Duramycin-LC Molecular Targeting Technologies D-1003
   Streptavidin PE/Cy7 BD Biosciences 557598
   Streptavidin BV650 BioLegend 405231
   Streptavidin PerCP BD Biosciences 554064
   dsDNA Sigma D8515
   p-nitrophenol phosphate (PNPP) substrate Thermo Fisher 34045
   4 - 20% SDS-PAGE gels Bio-Rad 4561094
   Western ECL substrate Cell Signaling 6883S
   AMPure XP beads Beckman Coulter A63880
   Restore™ Western Blot Stripping Buffer Thermo Fisher 21059
   Foxp3 / Transcription Factor Staining Buffer Set Thermo Fisher
   00-5523-00
   cell signaling lysis buffer Sigma 43-040
   protease inhibitor cocktail Sigma S8820-20
   SYBR® Green Supermix Bio-Rad 1725271
   RPMI-1640 Corning 10-040-CV
   IL-6 Shenandoah Biotech 200-02
   IL-21 Pepro Tech 210-21
   Activin A Thermo Fisher PHC9564
     __________________________________________________________________

   Critical commercial assays
     __________________________________________________________________

   RNeasy Kit Qiagen 74004
   TruSeq Stranded RNA Sample Preparation Kit Illumina 20020597
   PicoGreen assay Thermo Fisher P7589
   CD4 T Cell Isolation Kit Miltenyi Biotec [309]130-104-454
   Naïve CD4 T Cell Isolation Kit Miltenyi Biotec [310]130-104-453
   BCA protein assay kit Pierce 23225
   Impromptu-II™ Reverse Transcription System Promega A3800
     __________________________________________________________________

   Deposited data
     __________________________________________________________________

   Raw RNA-seq data GEO GSE 157648
   Oher raw and processed data Github
   [311]https://github.com/gmhhope/TC_TFH_omics_analysis/.
   Analysis workflows
   [312]https://github.com/gmhhope/TC_TFH_omics_analysis/.
     __________________________________________________________________

   Experimental models: Organisms/strains
     __________________________________________________________________

   B6.NZM-Sle1^NZM2410/AegSle2^NZM2410/AegSle3^NZM2410/Aeg/LmoJ (TC)
   female mice In house and Jackson laboratories # 007228
   C57BL6/J female mice Jackson laboratories #000664
     __________________________________________________________________

   Oligonucleotides
     __________________________________________________________________

   Smyd2 F N/A F: 5′-AAGGATTGTCAAAATGTGGACGG-3′
   Smyd2 R N/A R: 5′-ATGGAGGAGCATTCCAGCTTG-3′
   Hsp90 F N/A F: 5′-GTCCGCCGTGTGTTCATCAT-3′
   Hsp90 R N/A R: 5′-GCACTTCTTGACGATGTTCTTGC-3′
     __________________________________________________________________

   Software and algorithms
     __________________________________________________________________

   FlowJo V10 Tree Star
   [313]https://www.flowjo.com/solutions/flowjo/downloads
   Prism 9.0 Graphpad
   [314]https://www.graphpad.com/scientific-software/prism/
   ImageJ NIH [315]https://imagej.nih.gov/ij/
   STAR aligner v2.7^85 N/A [316]https://github.com/alexdobin/STAR
   FeatureCounts (v2.0) N/A
   [317]https://subread.sourceforge.net/featureCounts.html
   DESeq N/A
   [318]https://bioconductor.org/packages/release/bioc/html/DESeq2.html
   pheatmap R function (version 1.0.12) N/A
   [319]https://www.rdocumentation.org/packages/pheatmap/versions/1.0.12/t
   opics/pheatmap
   Matplotlib-Venn (Version 0.11.5) N/A
   [320]https://packages.ubuntu.com/bionic/python-matplotlib-venn
   javaGSEA (v3.0) N/A [321]https://www.gsea-msigdb.org/gsea/index.jsp
   Molecular Signature Database (MSigDB v5.2) N/A
   [322]https://www.gsea-msigdb.org/gsea/msigdb/
   R function Dotplot in Lattice package (version 0.3-1) N/A
   [323]https://www.rdocumentation.org/packages/lattice/versions/0.3-1/top
   ics/dotplot
   Metamorph software Molecular Devices
   [324]https://www.moleculardevices.com/products/cellular-imaging-systems
   /acquisition-and-analysis-software/metamorph-microscopy
     __________________________________________________________________

   Others
     __________________________________________________________________

   ACE 18-PFP 100 x 2.1 mm, 2 μm column Mac-Mod Analytical N/A
   LSRFortessa BD Biosciences N/A
   FACSAria III BD Biosciences N/A
   [325]Open in a new tab

Resource availability

   This study did not generate new unique reagents.

Lead contact

   Further information and requests for resources and reagents should be
   directed to and will be fulfilled by the lead contact, Dr. Laurence
   Morel ([326]morel@uthscsa.edu).

Experimental model and subject details

   The B6.NZM-Sle1^NZM2410/AegSle2^NZM2410/AegSle3^NZM2410/Aeg/LmoJ (TC)
   congenic mice have been previously described.[327]^9 C57BL/6J (B6) were
   originally purchased from the Jackson Laboratories. B6 and TC mice were
   bred and maintained at the University of Florida in specific
   pathogen-free conditions. Anti-dsDNA IgG positive female TC mice
   between 7- and 9-months old and age-matched B6 controls were used.
   Representative values of the frequency of CD4^+ T cells in these mice
   are shown in [328]Table S1. All procedures were performed under
   protocols approved by the Institutional Animal Care and Use Committees
   of the University of Florida and Vanderbilt University.

Method details

Flow cytometry and cell sorting

   Single-cell suspensions were prepared from spleens using standard
   procedures. After red blood cell lysis, cells were stained in FACS
   staining buffer (2.5% FBS, 0.05% sodium azide in PBS).
   Fluorochrome-conjugated antibodies (Abs) used for flow cytometry are
   listed in the [329]key resources table. Tfh cells were sorted as
   CD44^+PSGL-1^loPD-1^+CD4^+ (ref [330]22) for RNASeq, metabolomic and
   Western blot analyses. Tfh cells were gated as
   PD1^+BCL-6^+CXCR5^+FOXP3^-CD4^+ cells in FACS analyses with CXCR5
   stained in a three-step process using purified anti-CXCR5 followed by
   biotinylated anti-rat IgG then PerCP-labeled streptavidin.[331]^25 For
   intracellular staining, cells were fixed and permeabilized with FOXP3
   staining buffer. Dead cells were excluded with fixable viability dye.
   Mitochondrial ROS was measured by MitoSOX^TM Red and cellular ROS was
   measured with 2′,7′-dichlorodihydrofluorescein diacetate (H2DCFDA).
   Mitochondrial mass and membrane potential were assessed with
   MitoTracker® Red CMXRos and MitoTracker™ Deep Red. To measure glucose
   or lipid uptake, total splenocytes (1 - 2 × 10^6) were stained with
   Tfh-cell-gating fluorescence-conjugated Abs (CD4, PD-1, CXCR5 and
   BCL-6) for 30 min on ice and incubated with 20 mM
   2-N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl) amino-2-deoxyglucose (2-NBDG)
   or 2 mM BODIPY for 20 min at 37°C, respectively. Duramycin-LC-Biotin
   was used for measuring phosphoethanolamine (PE) on cell surface or
   intracellularly following staining with fluorescence-conjugated
   streptavidin. Data were collected on LSRFortessa and analyzed with
   FlowJo software. Tfh cells defined as CD4^+CD44^+PSGL-1^lo/−PD-1^+ and
   Tn cells defined as CD4^+CD44^− were sorted on a FACSAria III
   high-speed cell sorter from CD4^+ T cells purified by negative
   selection with magnetic beads ([332]Figure S1).

RNA sequencing

   RNA was extracted from FACS-sorted Tfh and Tn cells using the RNeasy
   Kit. RNA-seq libraries were prepared with the TruSeq Stranded RNA
   Sample Preparation Kit and the quality was verified with the Agilent
   Bioanalyzer 2100. The amplified libraries were size-selected using
   AMPure XP beads and quantified with the PicoGreen assay. Sequencing was
   performed on an Illumina Novaseq 6000 at the UF Interdisciplinary
   Center for Biotechnology Research (ICBR). Alignment of RNA-seq reads
   were mapped to the mm10 Mus musculus genome with STAR aligner
   v2.7.[333]^85 FeatureCounts (v2.0) was used to calculate gene counts
   for each sample.[334]^86 Significantly differentially expressed genes
   (DEG, FDR P < 0.05) were identified by DESeq2.[335]^87 To understand
   the global distribution of DEG among the 4 groups, principal component
   analysis and Euclidean distance matrix were generated with functions
   embedded in DESeq2. Microarray and Nanostring datasets previously
   described[336]^25 were reanalyzed to compare with the RNA-seq data
   generated in this study. To globally compare results generated from
   these different platforms or different comparisons, Pearson correlation
   analyses were computed on the indicated list of
   Wald-statistics.[337]^25 Regularized-log-transformation of count data
   or log2-transformed fold changes were performed for heatmap plotting
   using the pheatmap R function (version 1.0.12). Venn-diagrams were
   generated using the Python function Matplotlib-Venn (Version 0.11.5).
   Gene set enrichment analyses (GSEA) were performed in javaGSEA (v3.0)
   using rank-lists based on Wald-statistics, which were generated from
   the DESeq2 analysis.[338]^88 GSEA analyses included the GO, KEGG and
   REACTOME databases. Specific gene sets used for analyses were retrieved
   from the Molecular Signature Database (MSigDB v5.2).[339]^89 For gene
   overlap analysis, significant overlaps with KEGG gene sets in MSigDB
   were computed on the MSigDB website
   ([340]https://www.gsea-msigdb.org/gsea/msigdb/annotate.jsp) and were
   plotted using the R function Dotplot in Lattice package (version
   0.3-1).

Western blot and qRT-PCR analysis

   Tn and Teff cells defined as CD62L^-CD44^+ sorted from B6 or TC mice
   were lysed in cell signaling lysis buffer containing a protease
   inhibitor cocktail. After brief sonication, cells were incubated for
   30 min on ice and centrifuged at 12,000 rpm for 20 min at 4°C to
   collect the soluble supernatant. Proteins in the cell lysates were
   quantified with the BCA protein assay kit. 30 μg of cell lysates were
   run on 4 - 20% SDS-PAGE gels and transferred to PVDF membranes.
   Membranes were blocked with 5% non-fat dry milk in Tris-buffered saline
   containing 0.1% Tween-20 for 1 h at room temperature, then incubated
   with a rabbit anti-H3K36me2 Ab overnight at 4°C. Membranes were then
   washed and incubated with horseradish peroxidase-conjugated secondary
   Ab for 1 h. Western ECL substrate was used to develop the immunoblot.
   After bound Abs were removed using Restore™ Western Blot Stripping
   Buffer, membranes were incubated with mouse anti-histone 3 Ab for
   normalization. Band intensity was calculated with Image J. cDNA was
   synthesized with the ImProm-II™ Reverse Transcription System. qRT-PCR
   analysis of Smyd2 expression was performed with the SYBR® Green
   Supermix. Relative expression was calculated using 2^ΔCq values
   normalized to Hsp90.

In vitro Tfh cells polarization

   Tfh cells were generated from splenic naïve CD62L^dim/-CD4^+ T Cells
   purified by negative selection with magnetic beads as previously
   described.[341]^90 Tn cells (1 × 10^6 per well) were activated in
   24-well plates coated with 2 μg/ml anti-CD3e Ab with 1 ml of RPMI-1640
   media (0.2 % glucose, 0.03% glutamine) supplemented with 10% FBS, 1 mM
   sodium pyruvate, 100 U/ml penicillin/streptomycin, and 10 mM HEPES with
   1 μg/ml anti-CD28 Ab for 3 days. The Tfh polarizing cocktail contained
   100 ng/ml IL-6, 50 ng/ml IL-21, 10 μg/ml anti-IL-2, 10 μg/ml anti-IFNγ,
   10 μg/ml anti-IL-4, and 5 μg/ml anti-TGFβ Abs. Activin A was used at
   100 ng/ml.

Electron microscopy

   CD44^+ (Teff) and CD44^- (Tn) CD4^+ T cells isolated with magnetic
   beads were visualized on a Hitachi H-7000 TEM transmission electron
   microscope at the UF ICBR electron microscopy core. For each sample,
   measurements of mitochondria were performed at X 20,000 magnification
   using the Metamorph software.

Metabolomic analyses

   Sorted Tn and Tfh cells (0.5 – 1 x 10^6) were analyzed at the UC Davis
   West Coast Metabolomics Center using untargeted metabolomics by gas
   chromatography-time of flight-mass spectrometry (GC-TOF-MS). A standard
   primary metabolite extraction protocol was applied, with 100 μl of
   3:3:2 acetonitrile:isopropyl alcohol : water added to 0.5 μl cells,
   followed by centrifugation, decanting, desiccation and
   trimethylsilylation for GC-TOF-MS profiling using Fiehn laboratory
   standard operating procedures.[342]^91 Identification of metabolite
   features was performed by comparison to the BinBase database[343]^92
   and their quantity normalized to the cell count in each sample. Cells
   from two cohorts of mice were processed and analyzed separately and
   recombined to obtain a data matrix with 303 metabolites including 110
   unique structurally annotated compounds. Only annotated compound
   features (mTIC) were used for subsequent analyses. Data analysis was
   performed either in R version 3.5.3 using RStudio (Version 1.2.1335) or
   in Python version 3.6.5 using Jupyter Notebook (Version 5.5.0). The
   data matrix was normalized to average mTIC of the samples and
   log2-transformed. PCA analyses were performed on the annotated features
   using MetaboAnalyst 4.0.[344]^93 Statistical analyses included one-way
   ANOVA and pair-wise Student’s t test as indicated in the text using
   Python package SciPy (version 1.1.0). FDR adjusted p values were
   generated for the annotated features using Benjamini-Hochberg Procedure
   with R function p.adjust. The heatmap presentation of the hierarchical
   clustering analyses was performed using Python package Seaborn (version
   0.10.1). Other heatmap presentations were performed in R function
   pheatmap (version 1.0.12). Pathway analyses were performed using
   enrichment analysis function in MetaboAnalyst 4.0. Only the pathways
   with adjusted p-values < 0.25 were showed in bubble plots. Both box
   plots and bubble plots highlighting individual metabolites and pathways
   were plotted using ggplot2 (version 3.3.0). The metabolomic data of
   in-vitro polarized Tfh and activated T cells from B6 mice has been
   previously published.[345]^23 For the network analysis of the dynamics
   of amino acids and their transporters, the network prototype was mainly
   constructed based on annotations of amino acid transporters.[346]^94
   The layout was configured in Cytoscape ([347]Cytoscape.org).

Quantification and statistical analysis

   Differences between two groups are computed with unpaired t tests or
   Mann-Whitney U tests according to the normality of the data. Multiple
   comparisons among more than three groups were performed by one-way
   ANOVA with post-tests multiple comparisons. Statistical significances
   of differences from RNA-seq and metabolomic analyses are computed
   according to their unique context expanded within aforementioned
   methods. Unless specified, graphs show means and standard error of the
   mean (s.e.m.).

Acknowledgments