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Article Title: L-Phenylalanine is a metabolic checkpoint of human Th2 cells
Journal: Cell Reports Medicine
doi: 10.1016/j.xcrm.2025.102466
Figure Legend Snippet: Metabolomics of circulating human memory CD4 + T effector and T regulatory cells reveals distinct metabolic profiles, enriched in phenylalanine and arginine metabolic pathways (A and B) Intermediate gate depicting memory (CD45RA − ) and naive (CD45RA + ) CD3 + CD4 + T cells (A) and final sorting gate (B) of circulating memory CD4 + T effector and T regulatory cells. (C) Venn diagram representing intracellular metabolites ( n = 295) detected by untargeted mass spectrometry metabolomics and lipidomics in memory CD4 + Teff (red) and Treg (blue) cells from healthy individuals. (D) PCA model of memory CD4 + Teff and Treg cells, based on all (shared and unique) metabolites. Data were logarithmic transformed and pareto scaled (log x Par). (E) Pie charts representing biochemical composition of all (shared and unique) metabolites detected in Teff and Treg cells, ordered by abundance. (F) Metabolic pathways analysis in memory CD4 + Teff and Treg cells. Significant (−log 10 ( p value) > 1.3) and corresponding pathways ( n = 25) are shown for either Teff, Treg, or both. Over-representation analysis was performed by IMPaLA including shared and unique metabolites. Pathways related to Phe and Arg metabolism are highlighted in bold. (G) PCA model of memory CD4 + Teff and Treg cells of shared metabolites only ( n = 133). Data were log x Par. (H) Heatmap (left) and bar graph of fold changes (Log 2 FC) (right) of identified shared metabolites in memory CD4 + Teff vs. Treg cells. Data were logarithmic transformed and unit variance scaled. ∗ p < 0.05. Metabolites related to Phe and Arg metabolism are highlighted in bold. (A–H) Analysis done in n = 6 different healthy donors. (E) Metabolites are organized in main biochemical classes according to Human Metabolome Database (HMDB v.2022). Examples of metabolites are shown in major classes (>5%). See also and and , , , , , , and .
Techniques Used: Mass Spectrometry, Transformation Assay
Figure Legend Snippet: High level of L-phenylalanine enhances activation-induced glycolysis but inhibits OXPHOS, while arginine enhances activation-induced glycolysis and OXPHOS in human CD4 + T and memory CD4 + T cells (A) Representative glycolytic proton efflux rate (glycoPER) graph of Seahorse glycolytic rate assay (left); quantification of inducible and compensatory glycolysis (right) of CD4 + T cells treated in full medium (containing 1.149 mM Arg) with additional 0.1 (blue) and 1 mM (red) Arg supplementation or vehicle (gray) for 72 h with/without acute CD2, CD3, and CD28 activation. (B) Representative oxygen consumption rate (OCR) graph of Seahorse Mito Stress test (left); quantification of maximum respiratory capacity (right) of CD4 + T cells treated with Arg as in (A). (C) Representative glycoPER graph of Seahorse glycolytic rate assay (left); quantification of inducible and compensatory glycolysis (right) of CD4 + T cells treated in full medium (containing 90.9 μM of Phe) additionally supplemented Phe at concentrations of 0.1 (violet) and 1 mM (green) or vehicle (gray) for 72 h, with/without acute CD2, CD3, and CD28 activation. (D) Representative OCR graph of Seahorse Mito Stress test (left); quantification of maximum respiratory capacity (right) of CD4 + T cells treated with Phe as in (C). (E) Quantification of induced glycolysis, compensatory glycolysis, and maximum respiratory capacity of memory CD4 + T cells treated with Phe as in (C) and (D). (A–D) Data are representative of three independent experiments in three different donors or (E) in one donor. Data were analyzed by one-way ANOVA with Fisher LSD test. Bar graphs represent mean ± SEM. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, and ∗∗∗∗ p < 0.0001. Arg, L-arginine; Phe, L-phenylalanine; Rot/AA, rotenone/antimycin A; 2DG, 2-deoxyglucose; FCCP, carbonyl cyanide- p -trifluoromethoxyphenylhydrazone. All Seahorse measurements were normalized to total protein concentration.
Techniques Used: Activation Assay, Protein Concentration
Figure Legend Snippet: L-phenylalanine inhibits proliferation of human memory CD4 + T cells by induction of interleukin 4 induced gene 1 enzyme (A) Proliferation of memory CD4 + T cells incubated in full medium (containing 90.9 μM of Phe) supplemented with vehicle or 1 mM Phe and activated with CD2, CD3, and CD28 antibody-coated beads for 72 h. n = 3 different subjects. (B) Frequency of live memory CD4 + T cells in the same experiments as in (A). (C) Expression of IL4I1 mRNA in memory CD4 + T cells following similar treatment as in (A). Data from 6 independent experiments in 6 different subjects. (D) IL4I1 mRNA expression (left) and representative WB image of IL4I1 (right) in siRNA knockdown experiments in memory CD4 + T cells. Data show 3 independent experiments in 6 different subjects. One outlier was identified using Grubbs’ test with α = 0.05. One donor was included in two experiments. (E) Proliferation of control siRNA (Ctrl)- and IL4I1 siRNA-treated memory CD4 + T cells from 2 independent experiments in 5 different subjects. One donor was included in both experiments. (F) Viability of control siRNA (Ctrl)- and IL4I1 siRNA-treated memory CD4 + T cells in the same experiments as in (E). (G) Expression of IL4I1 mRNA in in vitro -differentiated human Th1, Th2, Th17, and Treg cells in siRNA knockdown experiments following similar treatment as in (D) ( n = 3 different donors). (H) Proliferation of control siRNA (Ctrl)- and IL4I1 siRNA-treated Th1, Th2, Th17, and Treg cells, incubated in full medium (containing 90.9 μM of Phe) with 1 mM additional Phe and treated with CD2, CD3, and CD28 activation antibody-coated beads for 48 h before flow cytometry ( n = 3 different donors). (A–H) Each dot represents one donor. (E and F) Bar graph shows fold change as compared to activated vehicle-treated cells. Paired t test was used in (A), (B), (C), (G), and (H); Wilcoxon test was used in (D)–(F). All data are presented as mean ± SEM. ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001. See also , , and and .
Techniques Used: Incubation, Expressing, Knockdown, Control, In Vitro, Activation Assay, Flow Cytometry
Figure Legend Snippet: L-phenylalanine inhibits Th2 cell proliferation and mTOR and STAT6 phosphorylation as well as expression of type 2 transcription factors, cytokines, activation, and pathogenicity markers (A) Phe uptake into Th2 cells. In vitro -differentiated Th2 cells from 3 different donors were incubated in indicated conditions for 6 h, and intracellular Phe was colorimetrically quantified in lysates. (B and C) Proliferation (B) and viability (C) of in vitro differentiated Th2 cells subjected to high doses of additional Phe. Bar graphs show fold changes compared to vehicle-treated, activated cells. n = 3 different donors. (D and E) IL4I1 mRNA expression (D) and representative WB image of IL4I1 protein expression (E) in in vitro -differentiated Th2 cells following incubation in increasing doses of Phe with/without concurrent activation. mRNA ( n = 6–8 different donors) and protein expression ( n = 3 different donors). (F) Volcano plot of differentially expressed genes (DEGs, raw p value < 0.05) between activated Th2 cells treated with Phe (1mM) vs. vehicle for 24 h, obtained by RNA-seq analysis ( n = 5 different donors). Genes related to STAT6/mTOR/AMPK signaling, critical for T cell activity, are highlighted in boxes. (G and H) Significantly enriched downregulated (G) and upregulated (H) GO processes in Th2 cells following treatment as in (F). STRING analysis was conducted with significantly changed DEGs (raw p value < 0.05), and relevant enriched pathways are presented. (I–M) Representative WB image (I and K) and quantification (J, L, and M) of phosphorylation of STAT6 and mTOR, respectively, in in vitro -differentiated Th2 cells ( n = 3 different donors) treated with CD2, CD3, and CD28 activation antibodies with/without additional supplementation of 1 mM of Phe for indicated time points. (N) Heatmap of mRNA expression of critical transcription factors, cytokines, and activation markers in activated in vitro -differentiated Th2 cells treated with increasing doses of Phe. mRNA expression was determined using RT-qPCR. n = 6–8 different donors. Data are analyzed using one-way ANOVA with Dunnett’s correction. Z scores were determined and plotted as heatmap with different genes mentioned as rows. Data are row normalized. (O) Frequency of activated IL4 + Th2 cells with/without additional supplementation of 1 mM of Phe ( n = 4 different donors). (P) Frequency of activated CD3 + CD4 + CCR4 + GATA3 + CD161 + Th2 cells following incubation with/without supplementation of 1 mM Phe for 24 h ( n = 3 different donors). (A–D, J, L, M, N, O, and P) Each dot represents one donor. (A–C, J, L, M, O, and P) Paired t test was used for analysis. Bars represent mean ± SEM. ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001. See also and , , and .
Techniques Used: Phospho-proteomics, Expressing, Activation Assay, In Vitro, Incubation, RNA Sequencing, Activity Assay, Quantitative RT-PCR
Figure Legend Snippet: Low intracellular L-phenylalanine levels in pathogenic memory CD4 + T effector cell populations in severe allergic patients (A–C) Representative flow cytometry dot plots of Th2a cells (A-left) and ILC1, ILC2, and ILC3 (C-left). Number of Th2a cells (A-right); memory CRTH2 + Treg cells and PD1 + Treg cells (B); and ILC1, ILC2, and ILC3 (C-right) in controls ( n = 9) and patients with mild ( n = 7) and severe ( n = 11) allergy (Cohort A). (D) Differentially expressed proteins in serum of controls ( n = 10) and mild ( n = 9) and severe ( n = 10) allergic patients (Cohort A) assessed with PEA technology and presented as NPX. (E) Heatmaps with hierarchical clustering analysis of all metabolites measured in memory CD4 + Teff ( n = 195, left) and Treg ( n = 233, right) cells in controls ( n = 6) and allergic ( n = 11) subjects. A, controls; B, mild allergy; C, severe allergy (from Cohort A). (F) Normalized abundance of Phe in Teff cells in group 1 (control, n = 1; severe allergy, n = 4) and 2 (control, n = 5; mild allergy, n = 1, severe allergy, n = 6) (from Cohort A). (G) t-distributed stochastic neighbor embedding (tSNE) plot of unbiased 2-dimensional flow cytometric analysis of memory CD4 + Teff cells (CD3 + CD4 + CD45RA − CD127 + CD25 − ) from patients with severe allergy (subset of cohort A, n = 9) identifying seven subpopulations based on CD161 and PD-1. (H) Pearson correlation of normalized abundance of intracellular Phe, in memory CD4 + Teff cells from patients with severe allergy ( n = 9), with counts of CD161 + populations within memory CD4 + Teff cells (from Cohort A). Mann-Whitney U test (A–C), one-way ANOVA with Fisher’s LSD test (D), and unpaired t test (F) were used to compare differences among groups. Graphs represent mean ± SEM. ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001. Clinical characteristics of Cohort A are shown in and . NPX, normalized protein expression; Pop, population. See also and , , , , , , , , , and .
Techniques Used: Flow Cytometry, Control, MANN-WHITNEY, Expressing
Figure Legend Snippet: Decreased expression of large amino acid transporters in Th2 cells of allergic patients correlates with elevated serum levels of L-phenylalanine (A–C) Top significantly enriched pathways (A) and upregulated (B) and downregulated (C) metabolic networks within differentially expressed genes (DEG, p < 0.05) in allergic asthma patients compared to controls (control n = 15, allergic asthma n = 37) from GEO: GSE75011 (Cohort B). Black line represents ratio of genes in experiment over complete pathway set. (D) Phe metabolism and transport pathway heatmap showing fold change (Log 2 FC) of DEGs in allergic asthma patients ( n = 37) compared to controls ( n = 15) from GEO: GSE75011 (Cohort B). ∗ p < 0.05. Pathway curated and adapted from GSEA and MSigDB Database . (E) Phe metabolism and transport schematic highlighting DEGs in Th2 cells of allergic asthma and controls (GEO: GSE75011 ) (Cohort B). Significantly upregulated (red) and downregulated (blue) genes; detected but not significantly different (black); not detected in original dataset (black and underlined). Adapted from KEGG pathway. (F) Serum Phe concentration in controls ( n = 8) and mild ( n = 30) and severe ( n = 37) allergic patients (Cohort D) quantified by targeted metabolomics. Kruskal-Wallis test was used for analysis. (G) SLC7A5 (LAT1), SLC7A8 (LAT2), and SLC3A2 (CD98; LAT3) mRNA expression in in vitro -differentiated Th2 cells treated with additional Phe with/without CD2, CD3, and CD28 activation for 24 h. Following incubation, mRNA expression was determined using RT-qPCR ( n = 6–8 different donors). (H and I) Representative WB image of LAT1 expression (H) and LAT1 protein quantification in 8 different donors (I) in in vitro -differentiated Th2 cells treated with additional Phe with/without CD2, CD3, and CD28 activation for 24 h. Expression of LAT1 presented as relative ratio normalized to β-actin. (J) Phe uptake into in vitro -differentiated Th2 cells was quantified colorimetrically. Cells were incubated in full medium with/without SLC7A5 inhibitor (KYT0353) and activation of CD2, CD3, and CD28 for 6 h. Data were analyzed using paired t test ( n = 3 different donors). (K) Spearman correlation of serum Phe concentration and relative LAT1 expression in CD4 + T cells in allergic asthma patients (left) and controls (right) (Cohort E). (G–I) Data are analyzed by one-way ANOVA with Dunnett’s correction. (F–J) Bars represent mean ± SEM. Each dot represents one donor. ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001. See also and and , , , , , , , , and .
Techniques Used: Expressing, Control, Concentration Assay, In Vitro, Activation Assay, Incubation, Quantitative RT-PCR
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