Autocrine vitamin D signaling switches off pro-inflammatory programs of TH1 cells

Abstract

The molecular mechanisms governing orderly shutdown and retraction of CD4⁺ type 1 helper T (T_H1) cell responses remain poorly understood. Here we show that complement triggers contraction of T_H1 responses by inducing intrinsic expression of the vitamin D (VitD) receptor and the VitD-activating enzyme CYP27B1, permitting T cells to both activate and respond to VitD. VitD then initiated the transition from pro-inflammatory interferon-γ⁺ T_H1 cells to suppressive interleukin-10⁺ cells. This process was primed by dynamic changes in the epigenetic landscape of CD4⁺ T cells, generating super-enhancers and recruiting several transcription factors, notably c-JUN, STAT3 and BACH2, which together with VitD receptor shaped the transcriptional response to VitD. Accordingly, VitD did not induce interleukin-10 expression in cells with dysfunctional BACH2 or STAT3. Bronchoalveolar lavage fluid CD4⁺ T cells of patients with COVID-19 were T_H1-skewed and showed de-repression of genes downregulated by VitD, from either lack of substrate (VitD deficiency) and/or abnormal regulation of this system.

Extended Data Fig. 1. Cellular phenotypes of CD4⁺ T cells in BALF of patients with COVID-19.

a-b, UMAP representation of scRNAseq showing main clusters of cells from bronchoalveolar lavage fluid (BALF) of patients with COVID-19 and healthy controls (a) and dot plot depicting expression of select marker genes for each cluster (b). Highlighted in both a and b are clusters 2 and 12, which represent T lymphocytes. c, Dot plot showing expression of select marker genes for clusters of cells depicted in Fig. 1a. d, UMAP projection of scRNAseq showing sub-clustering of T cells from bronchoalveolar lavage fluid (BALF) of healthy controls (above) and patients with COVID-19 (below). e, GSEA showing genes more highly expressed in bulk RNA-seq of BALF cells obtained from patients (n=8) with COVID-19 compared to healthy controls (n=20) are enriched in Th1 genes. Box and whisker plots (right) shows the expression of IL10 mRNA in these samples, indicating the median and extending to the minimum, maximum, 25% and 75% quartiles. Data in a-b are from n=9 patients with COVID-19 and n=4 healthy controls, sourced from GSE145926 and GSE122960. Data in c-d are from the same sources but with n=8 patients with COVID-19 and n=3 healthy controls (one sample from each was removed due to too low CD4⁺ T cell numbers). Data in e are from n=8 patients with COVID-19 and n=20 healthy subjects, obtained from HRA000143. **** p<0.0001 by two-sided Mann-Whitney U-test.

Extended Data Fig. 2. Circulating CD4⁺ T cells of patients with COVID-19 are not Th1 biased.

a-b, UMAP representation of scRNAseq showing main clusters of cells from peripheral blood mononuclear cells (PBMC) of patients with COVID-19 and healthy controls (a) and dot plot depicting expression of select marker genes for each cluster (b). Highlighted in both a and b are clusters 4, 5 and 6, which represent CD4⁺ T lymphocytes. c, Violin plots showing expressions of Th1, Th2 and Th17 genes, respectively, summarized as module scores, in PBMC CD4⁺ T cells of patients with COVID-19 and healthy controls. Data in a-c are from n=6 patients with COVID-19 and n=6 healthy subjects, obtained from GSE150728.

Extended Data Fig. 3. Expression of activation markers and proliferation of anti-CD3 + anti-CD46-activated cells.

CD4⁺ T cells were activated as before using anti-CD3 + anti-CD46 in culture plates. After 48h cells were stained for IFN-γ and IL-10 and co-stained with CD25 or CD69. Separately, CTV-labelled CD4⁺ T cells were activated in the same manner. a-b, representative flow cytometry histograms (a) and cumulative data from n=3 independent experiments (b) of CD25 and CD69 expression in cells at each stage of cytokine secretion. c-d, representative CTV dilution representing cells that had undergone proliferation (c) and cumulative data from n=3 independent experiments (d). Box and whisker plots in b and d show the medians and extend to the minimum, maximum, 25% and 75% quartiles. * p<0.05 by two-sided ANOVA. All other comparisons were non-significant.

Extended Data Fig. 4. Complement-activated CD4⁺ T cells are enriched in transcription factors.

a-c, Volcano plots showing differentially expressed genes (DEGs) following activation of CD4⁺ T cells with α-CD3+α-CD46, comparing IFN-γ⁺IL-10⁻ cells (a), IFN-γ⁺IL-10⁺ cells (b) and IFN-γ⁻IL-10⁺ cells (c) to IFN-γ⁻IL10⁻ cells, respectively. DEGs are defined as at least 1.5-fold change in either direction at unadjusted p-value <0.05 using two-sided ANOVA. Marked in a-c are the IFNG, IL10, VDR and CYP27B1 genes. d, Heatmap showing expression of the 2023 shared DEGs in a-c. e, ClueGo analysis for molecular function terms in the 2023 DEGs shown in d represented as a Cytoscape visualization. Genes are shown in grey, enriched molecular function terms are in red scaled to reflect fold enrichment and edges link genes to molecular function terms. Node sizes reflect enrichment significance. Related terms grouped as families within yellow circles. Four such families represent transcriptional regulation of gene expression and are shown in the inset on the right. f, The top 14 transcription factor molecular function terms are shown, with associated fold enrichments and FDR q-values. Data in a-f are from n=4 experiments.

Extended Data Fig. 5. Efficiency of silencing of indicated targets by siRNA from Fig 2j.

Shown are cumulative data from n=3 independent experiments. Bars show mean + sem. NT, non-targeting. **p<0.01 ***p<0.001 by unpaired two-sided t-test.

Extended Data Fig. 6. Phenotype of Vitamin D treated cells.

a, Western blot (left) and cumulative data (right) for VDR at days 1, day 3 and day 5, with Hsp90 as loading control, in both carrier and VitD treated CD4⁺ T cells. b, Representative immunoblots for VDR and indicated housekeeping proteins in nuclear and cytoplasmic extracts of carrier and VitD treated CD4⁺ T cells. c, Co-localization of VDR and DAPI in carrier and VitD treated CD4⁺ T cells, measured on day 2 using ImageStream. Shown are representative frequency histograms indicating overlap between VDR and DAPI in the entire population (left), and cumulative data from n=3 independent experiments (right). d, Cell death assessed by live/dead stain and proliferation assessed by CFSE dilution in CD4⁺ T cells treated with carrier or VitD after 3 days of culture. e, GSEA showing genes more highly expressed in CD4⁺ T cells treated with carrier compared to VitD are enriched in Tr1-induced genes (left panel) and genes more highly expressed in CD4⁺ T cells treated with VitD compared to carrier are enriched in Tr1-repressed genes (right panel; curated from GSE139990). f, Representative flow cytometry plot showing CD49b and LAG-3 expression in CD4⁺ T cells, with carrier and VitD treatment (left), and quantification of cumulative data (right). g, Top 5 MSigDB canonical pathways enriched in DEGs of VitD vs carrier treated CD4⁺ T cells (see Fig. 3a). Unless indicated, all cells in Fig. S6 have been activated with α-CD3+α-CD28. Bars represent mean + sem throughout. All experiments have been carried out n=3 times. Shown in e are unadjusted empirical p-values; NES = normalized enrichment score. *p<0.05, ****p<0.0001 by 2-way ANOVA (a, f) and paired t-test (c). All statistical analyses are two-sided.

Extended Data Fig. 7. Hierarchy of cytokines induced by Vitamin D and modulation of IL-6 effects and the phosphor-kinase proteome by Vitamin D.

a, Differentially expressed genes (DEGs) between VitD and carrier treated CD4⁺ T cells (see Figs. 3a-b) ranked by fold change. Each DEG is marked by a blue dot; each differentially expressed cytokine is marked by an orange dot. Select cytokines have been labelled. b, Cytokine concentrations in supernatants of CD4⁺ T cell cultures after 5 days of treatment with carrier or VitD. c, Heatmap showing mRNA expressions (log2 TPM) of the 25-hydroxylase enzymes (CYP2R1 and CYP27A1), the 1α-hydroxylase enzyme (CYP27B1) and the 24-hydroxylase enzyme (CYP24A1), responsible for the two steps of Vitamin D activation and its subsequent inactivation, respectively. Data are from CD4⁺ T cells activated with α-CD3+α-CD28 and cultured with either carrier or VitD, or left unactivated. d, Concentrations of indicated cytokines in culture supernatants of CD4⁺ T cells treated with escalating doses of 25(OH)VitD for 72h. e, IL6 mRNA, fold change compared to day 1 carrier (above), and IL-6 protein concentration in matched supernatants (below), at days 1, 3 and 5 in carrier and VitD-treated CD4⁺ T cell cultures. f, Representative flow cytometry plot (left) and cumulative data (right) of intracellular IL-6 expression in T cells treated with carrier or VitD (assay carried out on day 3). Cells gated based on lymphocyte gate (forward scatter, side scatter), singlets, live cells and CD4⁺ cells. g, IL-10 concentrations in supernatants of CD4⁺ T cells cultured in the presence of increasing concentrations of IL-6 for 72 hours. h, IL-17 concentrations in supernatants of CD4⁺ T cells cultured in the presence of increasing concentrations of IL-6, with and without VitD for 72 hours. i, Volcano plot representing changes in protein phosphorylation on phospho-kinase array comparing VitD-treated versus carrier treated cells. Data are from n=2 independent experiments. Thresholds for significance have been set at 1.2 fold change in phosphorylation in either direction at p-value <0.05. Please also see Figs. 3f-g. Marked are phosphoproteins that show significant changes in phosphorylation on VitD treatment. j, Quantification of pY-STAT3, STAT3, p-c-JUN, c-JUN and Hsp90 from immunoblots of lysates of CD4⁺ T cells treated with carrier or VitD with, and without, Tocilizumab (Toc) at the concentrations shown. Bars show mean + sem from n=3 independent experiments. Please also see Fig. 3h. k-l, Shown are representative Western blots (k) and quantifications (l) of STAT3 and HSP90 in Carrier- and VitD-treated CD4⁺ T cells in the presence and absence of STAT3 siRNA. Data are representative of n=2 experiments carried out. Unless indicated, all cells in Fig. S7 have been activated with α-CD3+α-CD28. Cumulative data in b, d-h, j depict mean + sem. Unless indicated, all experiments have been carried out n=3 times. All statistical tests are two-sided. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001 by one-way ANOVA (d, j), two-way ANOVA (b, e, h) and paired t-test (f). Statistical comparisons in d and h compare VitD-treated (d) or IL-6-treated (h) cells against untreated cells.

Extended Data Fig. 8. Vitamin D recruits key transcription factors.

a, histograms showing average cuts per bp in relation to the summits of c-JUN, STAT3, VDR and BACH2 peaks in VitD-treated CD4⁺ T cells. Shown are data from CUT&RUN (c-JUN) and CUT&Tag (VDR, STAT3 and BACH2) carried out with IgG (cyan) or antibodies specific to each TF (dark blue). p-values from K-S tests are indicated. b, heatmaps showing H3K27Ac and c-JUN signals at VitD-repressed and VitD-induced peaks over time. Time points are indicated. c-d, genome browser tracks at the CTLA4 (c) and STAT3 (d) loci showing H3K27Ac, c-JUN, BACH2, STAT3 and VDR binding in Carrier and VitD-treated cells. Red and blue dots represent peaks in Carrier and VitD-treated cells, respectively. SE denotes super-enhancer regions. Track heights are indicated on the left corner for each track. All cells in Fig. S8 have been activated with α-CD3+α-CD28.

Extended Data Fig. 9. A subset of Vitamin D-regulated genes are dependent on BACH2.

a-c, GSEA comparing the transcriptomes of carrier and VitD-treated CD4⁺ T cells against BACH2-bound BACH2-induced genes (a) and -repressed genes (b). Shown in c are the leading edges of the two GSEA enrichment plots in a-b. Marked in a and c is the IL-6 receptor (IL6R). d, GSEA comparing enrichment in VitD-repressed genes of the transcriptomes of VitD-treated BACH2^WT/WT haplo-sufficient and BACH2^WT/L24P haplo-insufficient CD4⁺ T cells. e, Circos diagram showing VitD-induced and repressed genes in VitD-treated BACH2^WT/WT haplo-sufficient and BACH2^WT/L24P haplo-insufficient CD4⁺ T cells. Cords join shared genes in patient and control. Indicated are the shared VitD-induced genes (CYP24A1, CD38 and IL6) and genes only induced by VitD in the presence of two wild-type copies of BACH2 (IL10 and IL6R). f, Genome browser tracks showing Bach2 ChIP-seq at the IL6ra locus and expression of IL6ra mRNA in CD4⁺ T cells of Bach2 wild-type (Bach2^+/+) and knock-out (Bach2^-/-) mice. Track heights are indicated on the left corner for each track. Source data are from GSE45975. Empirical p-values are shown in a-b and d. NES = normalized enrichment score.

Extended Data Fig. 10. Vitamin D-induced genes do not distinguish CD4⁺ BALF T cells of patients with COVID-19 from healthy controls.

a, Violin plots showing expressions of VitD-induced genes, summarized as module scores, of BALF CD4⁺ T cells of patients with COVID-19 and healthy controls. Data are from n=8 patients with COVID-19 and n=3 healthy controls, sourced from GSE145926 and GSE122960. b, Violin plots showing expressions of VitD-induced and VitD-repressed genes, summarized as module scores, of PBMC CD4⁺ T cells of patients with COVID-19 and healthy controls. Data are from n=6 patients with COVID-19 and n=6 healthy subjects, obtained from GSE150728. c, Venn diagram showing overlap between COVID-induced genes in CD4⁺ BALF T cells that are predicted to be normalized by VitD treatment versus those that are predicted to be normalized by steroid drugs. Please also see Table S7. d, Schematic model of autocrine VitD-driven Th1 contraction program and potential intervention of impaired COVID-19 program with VitD and cortico-steroids. e, representative flow sorting strategy and example of post-sort purity obtained for isolation of memory CD4⁺ T cells in this paper.

Fig 1. COVID-19 CD4⁺ T (Th) cells are Th1 skewed.

a, UMAP projection of scRNAseq showing sub-clustering of T cells from bronchoalveolar lavage fluid (BALF) of n=8 patients with COVID-19 and n=3 healthy controls. Stack bars (right) show cumulative cellularities across samples in patients and controls. Dot plot of marker genes for these clusters are shown in Extended Data Fig.1c. b-c, Heatmap showing differentially expressed genes (DEGs; at least 1.5-fold change in either direction at Bonferroni adjusted p-value <0.05 using two-sided Wilcoxon rank sum test) between Th cells of n=8 patients with COVID-19 and n=3 healthy controls (b) and enrichment of those DEGs in Hallmark MSigDB genesets (c). FDR-corrected p-values in c are from hypergeometric tests. Highlighted in red in c are hallmark interferon-γ response and complement pathways. d, Violin plots showing expressions of Th1, Th2 and Th17 specific genes, respectively, summarized as module scores, in BALF Th cells of patients with COVID-19 and healthy controls. Medians are indicated. Exact p-values have been calculated using two-tailed Wilcoxon tests. e, Heatmap showing mean expression of classic Th1 marker genes in BALF Th cells of patients with COVID-19 and healthy controls. Data are sourced from GSE145926 and GSE122960.

Fig. 2. VDR and CYP27B1 are induced by complement and predicted as regulators of the Th1 program in COVID-19.

a, Representative flow cytometry showing IFN-γ and IL-10 in CD4⁺ T (Th) cells activated with α-CD3+α-CD46 and the four quadrants (A, B, C and D) from which cells were flow-sorted for transcriptome analysis. Live and single cells are pre-gated. b, Venn diagram showing number of DEGs (≥±1.5-fold at unadjusted p-value<0.05 using ANOVA) comparing cells in quadrants B, C and D against A, respectively (n=4 experiments). c, Enrichment of gene ontology molecular function terms in shared DEGs (intersect of Venn diagram in b), ranked by statistical significance. Marked are terms corresponding to transcription factor (TF) activity. d, Heatmap of induced TFs in α-CD3+α-CD46-activated Th cells at each stage of the life-cycle shown in a. Highlighted are VDR and expression of CYP27B1. e, EnrichR-predicted ENCODE and ChEA (ChIP enrichment analysis) TFs regulating the DEGs between COVID-19 vs. healthy donor Th cells (upper panel) and lung biopsies (lower panel). Shown are Benjamini-Hochberg adjusted p-values from hypergeometric tests. f, VDR (left panel) and CYP27B1 (right panel) mRNA in Th cells activated, or not, as indicated, with or without cathepsin L inhibitor (CTSL inh.) (n=5 experiments). g, VDR (left panel) and CYP27B1 (right panel) mRNA in Th cells of a patient with CD46-deficiency, activated, or not, as indicated (n=3 experiments). h-i, Representative flow cytometry (h) and cumulative data from n=6 independent experiments (i) showing IFN-γ and IL-10 in Th cells activated with α-CD3+α-CD46 with, or without, carrier, active [1,25(OH)2D3] or inactive [25(OH)D3] VitD. j, IL10 in Th cells activated with α-CD3+α-CD46 with, or without, inactive [25(OH)D3] VitD, with siRNA targeting VDR, CTSL or CYP27B1, or non-targeting siRNA (NT) (n=5 experiments). Data in a-d are from GSE119416. Data in e, upper panel are from GSE145926 and GSE122960. Data in e, lower panel are from GSE147507. Data in g are from microarrays in. Bars in f-g and i show mean + sem; box plots in j show median value and the range extends from minimum to maximum. All statistical tests are two-sided. *p<0.05 **p<0.01 ***p<0.001 ****p<0.0001 by ANOVA.

Fig. 3. Vitamin D induces IL-10 in Th cells by enhancing IL-6-STAT3 signal transduction.

a, Number of DEGs between VitD and Carrier-treated Th cells (≥±1.5-fold change at FDR<0.05). b, Scatter plot showing mRNA expression (RPKM) of genes in Th cells treated with VitD or carrier. VitD-induced and -repressed genes are depicted in red and blue, respectively. Noteworthy genes are annotated (black), including classical VitD-induced genes (orange). n=3 independent biological experiments. c, Heatmap showing expression of select genes from b. d, Dose-response of indicated cytokines from Th cells treated for 72h with VitD. Stars indicate statistically significant changes in comparison to 0nM of VitD; n=3 experiments. e, Pearson correlation between IL-6 and IL-10 concentrations in culture supernatants of VitD-treated Th cells. Shown is the correlation line, plus 95% confidence interval. f, IL-10 concentrations in supernatants of Th cells cultured with VitD, with and without Tocilizumab; n=3 experiments. g, Representative image from n=2 experiments of a phospho-kinase array (array of 43 kinases in duplicate spots) carried out on 3-day lysates of carrier- or VitD-treated Th cells. Location of STAT3 phosphorylated at lysine 705, c-JUN phosphorylated at serine 63 and reference spot (to which all spots are normalized) are indicated. h, Heatmap showing normalized phosphorylation values of differentially phosphorylated proteins following VitD-treatment (please see also Extended Data Fig.7i) in n=2 donors. i, Immunoblots of lysates of Th cells treated with carrier or VitD with, and without, Tocilizumab (Toc) at the concentrations shown. Shown are representative images from n=3 experiments (quantified in Extended Data Fig.7j). j, IL-10 production from Th cells cultured with carrier or VitD, with or without a STAT3 inhibitor (STAT3i). Genotype of cells (WT, STAT3 WT; DN, STAT3 dominant negative) is indicated. n=3 experiments; each dot represents an individual donor. k, IL-10 production from Th cells transfected with control siRNA or siRNA targeting STAT3. n=5 experiments. Unless indicated, all cells in Fig. 3 have been activated with α-CD3+α-CD28. Cumulative data in d, f and j-k depict mean+sem. All statistical tests are two-sided. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001 by one-way (d, j, k) and two-way ANOVA (f).

Fig. 4. Vitamin D reshapes epigenetic landscape of Th cells.

a, Genome-wide H3K27Ac CUT&RUN peaks 45mins, 48h and 72h after VitD or carrier-treatment of Th cells. b, differential H3K27Ac peaks (signal ≥0.2, ≥1.5-fold change) after VitD or carrier-treatment of Th cells at the indicated time points. c, Scatterplot showing H3K27Ac CUT&RUN peak signal intensities 48h after VitD or carrier-treatment of Th cells. Indicated are VitD-induced peaks (red) and VitD-repressed peaks (blue). Highlighted are select peaks at loci of interest. Data show a representative example from n=2 independent experiments. d, Heatmaps showing H3K27Ac signal at VitD repressed and VitD-induced peaks (below) and histograms showing normalized signals in carrier and VitD treated cells (48h) above. e, Ranked order of H3K27Ac-loaded enhancers induced by VitD in Th cells after 48h. Super-enhancers (SEs) are indicated. Marked are the relative positions, ranked according to signal intensity (higher = greater signal intensity), of enhancers attributed to selected genes. f, Enriched transcription factor (TF) DNA motifs at H3K27Ac peak loci induced by VitD. Shown are TF families on the left and representative TF members enriched in the data on the right. Unless indicated, all in vitro T cell experiments depicted in Fig. 4 have been activated with α-CD3+α-CD28.

Fig. 5. VitD recruits key transcription factors to shape transcriptional output.

a, Histograms (above) and heatmaps of c-JUN, VDR, STAT3 and BACH2 bound loci in Carrier and VitD-treated Th cells, centered on the peaks. p values by two sample K-S test comparing Carrier to VitD are shown. b, Histograms of c-JUN, VDR, STAT3 and BACH2-bound loci centered on VitD-induced H3K27Ac peaks. p-values by two sample K-S test comparing Carrier to VitD are shown. c, Proportion of genes differentially expressed after VitD treatment (DEGs, from n=3 experiments) bound by each indicated TF. ****p<0.0001 by two-sided Fisher exact test compared to all genes. d, Proportion of DEGs bound by 0, 1, 2, 3 or all 4 of the TFs profiled. e, Venn diagram showing overlap in TF binding between VitD DEGs. f, Heatmap of DEGs showing binding of genes by c-JUN, VDR, STAT3 and BACH2. Select genes have been highlighted on the right. g, Network diagram showing TF binding of genes differentially expressed after VitD treatment. Arrows join TFs to bound genes. Heatmap scale indicates fold change expression after VitD treatment compared to Carrier. h, Genome browser tracks at the BACH2 and IL10 loci showing H3K27Ac, c-JUN, BACH2, STAT3 and VDR binding in Carrier and VitD-treated cells. Red and blue dots represent peaks in Carrier and VitD-treated cells, respectively. SE denotes super-enhancer regions. Track heights are indicated on the left corner for each track. i, ChIP-qPCR for VDR or IgG in VitD-treated Th cells from two donors. Anti-VDR ChIP fragments were probed by qPCR for enrichment of promoters of CYP24A1 (left), STAT3 (middle) and IL6 (right). Data are shown separately for each donor; bars show mean+sem of qPCR. All p-values are from two-sided tests.

Fig. 6. BACH2 is an important regulator of the Vitamin D response in Th cells.

a, BACH2 Immunoblot in VitD- and carrier-treated Th cell lysates. Shown is a representative example from n=3 experiments. b-e, Representative dermal images of lesional skin from psoriasis patients with (n=2) and without (n=3) VitD supplementation stained for BACH2 (red) and CD3 (green), showing overview (leftmost) and zoomed images (right three images) (b), number of nuclei/image (c), percentage of BACH2⁺ cells relative to nuclei frequency/image (d) and average number of BACH2 foci/cell (e). For c-e 5-6 images were acquired for each sample and are shown as median values, with minimum, maximum, 25% and 75% quartiles. f-g, Pie-charts comparing percentage of VitD-regulated genes bound and regulated by mouse (f) or human BACH2 (g) against all genes in the genome. The two-sided Fisher exact p-value is shown. h, GSEA showing enrichment in VitD-induced genes comparing transcriptomes of VitD-treated wild-type control (BACH2^WT/WT) with BACH2-haploinsufficient Th cells (BACH2^WT/L24P). Shown is the empirical p-value; NES = normalized enrichment score. i, Sankey diagram showing the relationship between genes up- and down-regulated by VitD in BACH2 BACH2^WT/L24P compared to BACH2^WT/WT Th cells, and the binding of those genes by c-JUN, VDR, BACH2 and STAT3. j, Pie-charts comparing percentage of genes up- and down-regulated by VitD in BACH2 sufficient cells that are differently regulated in BACH2-haploinsufficiency. The percentage of those genes bound by BACH2 are shown underneath. k, Top 3 enriched MSigDB canonical pathways pertaining to the genes bound by BACH2 and differently regulated in BACH2-haploinsufficient cells. Indicated are 3 relevant genes contributing to enriched pathways. l, Heatmap showing expression patterns of select VitD-regulated genes in the transcriptomes of carrier- and VitD-treated BACH2^WT/WT and BACH2^WT/L24P Th cells. m, Genome browser tracks at IL6R locus showing H3K27Ac, c-JUN, BACH2, STAT3 and VDR binding in Carrier and VitD-treated cells. Red and blue dots represent peaks in Carrier and VitD-treated cells, respectively. Track heights are indicated on the left. In vitro T cell experiments depicted in Fig. 6 have been activated with α-CD3+α-CD28. *p<0.01, ****p<0.0001 by unpaired two-tailed t-test.

Fig. 7. VitD is among the top predicted therapeutics to retract the Th1 program in SARS-CoV2-infected lungs.

a, Violin plots showing expressions of VitD-repressed genes, summarized as module scores, in BALF Th cells of patients with COVID-19 and healthy controls. Exact p-values in a have been calculated using two-tailed Wilcoxon tests. b, GSEA showing enrichment in VitD-repressed genes within genes more highly expressed in scRNAseq CD4⁺ BALF T cells of patients with COVID-19 compared to healthy controls. c, Correlation between module scores of Th1-genes and VitD-repressed genes on a per cell basis in BALF Th cells of patients with COVID-19 and healthy controls. Pearson r and exact p-values are shown. d, Receiver operating characteristic (ROC) curve, evaluating the performance of the Th1 and VitD-repressed module scores to distinguish BALF Th cells of patients with COVID-19 from healthy controls. Shown are the area under the curve (AUC) statistics and p-values. e, Analyses showing the performance of all MSigDB canonical and hallmark genesets to distinguish BALF Th cells of patients with COVID-19 from healthy controls, ranked by AUC values. Marked are the top 2 performing genesets in red and the position of the VitD-repressed geneset within the top 1% of all genesets. f, Top 10 drugs predicted (out of 461 significant drugs) to counteract genes induced in BALF Th cells of COVID-19 patients compared to healthy controls, ordered by adjusted p-value. Highlighted in red is alfacalcidol, an FDA-approved active form of VitD. g-h, GSEA showing enrichment in VitD-repressed genes for genes more highly expressed in bulk RNA-seq lung biopsy specimens (g) and bulk RNA-seq BALF cells (h) of COVID-19 compared to healthy controls. Empirical p-values are shown for GSEA in b, g-h; NES = normalized expression value. p-values in d are from the Mann-Whitney U-statistic. Data in a-f are from n=8 patients with COVID-19 and n=3 healthy controls, sourced from GSE145926 and GSE122960. Data in g-h are from GSE147507 and HRA000143, respectively, and n numbers are indicated.