Progesterone Inhibits the Establishment of Activation-Associated Chromatin During TH1 Differentiation

TH1-mediated diseases such as multiple sclerosis (MS) and rheumatoid arthritis (RA) improve during pregnancy, coinciding with increasing levels of the pregnancy hormone progesterone (P4), highlighting P4 as a potential mediator of this immunomodulation. Here, we performed detailed characterization of how P4 affects the chromatin and transcriptomic landscape during early human TH1 differentiation, utilizing both ATAC-seq and RNA-seq. Time series analysis of the earlier events (0.5-24 hrs) during TH1 differentiation revealed that P4 counteracted many of the changes induced during normal differentiation, mainly by downregulating key regulatory genes and their upstream transcription factors (TFs) involved in the initial T-cell activation. Members of the AP-1 complex such as FOSL1, FOSL2, JUN and JUNB were particularly affected, in both in promoters and in distal regulatory elements. Moreover, the changes induced by P4 were significantly enriched for disease-associated changes related to both MS and RA, revealing several shared upstream TFs, where again JUN was highlighted to be of central importance. Our findings support an immune regulatory role for P4 during pregnancy by impeding T-cell activation, a crucial checkpoint during pregnancy and in T-cell mediated diseases, and a central event prior to T-cell lineage commitment. Indeed, P4 is emerging as a likely candidate involved in disease modulation during pregnancy and further studies evaluating P4 as a potential treatment option are needed.

. Data analysis pipeline. Pipeline for the workflow for ATAC-seq and RNA-seq analysis. Each steps gives a brief description and if needed, the specified R package used (A) Raw data analysis to differentially accessible peaks and genes. (B) Downstream analysis of peaks and genes derived from correlating the ATAC-and RNA-seq data. Further details regarding the methods can be found in the materials and methods. P4= progesterone, SNP = single nucleotide polymorphism, TH1= T helper 1 cells. Figure S2. Gene overlap in TH1 versus TH1+P4. The overlap of significantly differentially expressed genes for each time point in TH1 versus TH1+P4. Overlapping genes assigned from (A) promoter peaks and (B) distal peaks shown as line plots. The ratio of P4 affected genes overlapping with respective to the TH1 affected genes are shown at each time point. Pearson correlation was used to determine correlation between the log2fold change over control (timepoint 0) of each peak and gene in the ATAC-seq and RNA-seq data respectively. The gene with the highest (absolute) average correlation was assigned to the peak. Significant genes were then determined based on ATAC-seq log2fold change (>0.5) for each time point. P4= progesterone, TH1= T helper 1 cells.

Supplementary Tables
Supplemental Table S1. Overview of the different GWAS data sets used in Figure 4 and SNPs used from each disease. Disease SNPs (P < 10^-5) in linkage disequilibrium (LD) (LD threshold = 0.8, acquired from SNiPA) were used for disease-enrichment analysis. Note: Not all data sets are available for public download.
Supplemental Table S2. Network of RA-and MS-associated targets and transcription factors. The "Associatin type" displays in which type of peaks, promoter or distal, that the target genes occur. MS= multiple sclerosis, RA =rheumatoid arthritis Supplemental Table S3. Peak-to gene correlation for promoter regions. For each peak the gene with the highest absolute mean correlation was chosen. distanceToTSS describes the distance from the edge of the peak to the transcription start site (TSS) of the gene. If the TSS was within the peak distance was considered as 0. Negative values are upstream, positive values are downstream of TSS. Th1 and P4 columns describe whether the peak (ATAC) or gene (RNA) was significantly changed (maSigPro, FDR < 0.05) during the time series.
Supplemental Table S4. Peak-to gene correlation for distal regions. For each peak the gene with the highest absolute mean correlation was chosen. distanceToTSS describes the distance from the edge of the peak to the transcription start site (TSS) of the gene. If the TSS was within the peak distance was considered as 0. Negative values are upstream, positive values are downstream of TSS. Th1 and P4 columns describe whether the peak (ATAC) or gene (RNA) was significantly changed (maSigPro, FDR < 0.05) during the time series.
Supplemental Table S5. Top 10 transcription factors in promoter peaks over the time series in between genes associated to peaks that are overlapping between genes upregulated during TH1 and downregulated by progesterone. Log p values for each TF motis is given as well as the rankning of the TF, number of targets and the names of the targets. Related to Figure 3 in the manuscript. Log p values below -3 was considered as statistically significant. ns= non significant Supplemental Table S6. Top 10 transcription factors in distal peaks over the time series in between genes associated to peaks that are overlapping between genes upregulated during TH1 and downregulated by progesterone. Log p values for each TF motis is given as well as the rankning of the TF, number of targets and the names of the targets. Related to Figure 3 in the manuscript. Log p values below -3 was considered as statistically significant. ns= non significant Supplemental Table S7. Odds ratio and p-values for the disease enrichment in overlapp between the peaks upregulated during TH1 and downregulated with P4. The observed overlapping SNPs were mapped to the nearest gene using ChiPseeker. Significant p-values are highlighted in bold. The total amount of peaks observed in the intersect between up Th1 and down with P4 was used for the enrichment analysis which consisted of 5284 promoter peaks and 8771 distal peaks. This table relates to Figure 4.