Journal: bioRxiv
Article Title: Selective epigenetic regulation of IFN-γ signature genes by JAK inhibitor in inflammatory diseases
doi: 10.1101/2024.08.05.606293
Figure Lengend Snippet: (A) Flow cytometry assessment of STAT1 and STAT3 protein levels in THP-1 cells treated with PMA and IFN-γ (or without) at various time points. Kinetics of total STAT and phospho-STAT tyrosine (STAT1, Tyr701; STAT3, Tyr705) measured by flow cytometry in IFN-γ-primed macrophages compared to resting macrophages. (B) Kinetics of total STAT1 (or STAT3) and phospho-STAT1 (or STAT3) measured by flow cytometry in JAK inhibitor-treated macrophages compared to resting and JAK inhibitor-untreated macrophages. Resting and IFN-γ-primed macrophages were differentiated for 24 h and then treated with JAKi (or DMSO) for up to 6 h. (A and B) Mean fluorescence intensity (MFI) represents a fold change compared to unstained samples. (C) RT-qPCR analysis of normalized target mRNA relative to TBP mRNA in THP-1 monocyte-derived macrophages under indicated conditions. IFN-γ-primed macrophages were treated with JAK inhibitor at a concentration of 1 μM for up to 6 h. Data show means ± SD from two independent experiments. (D) K-means clustering of differentially expressed (DE) genes in pairwise comparisons between the four conditions. DE genes identified by EdgeR (FDR adjusted P < 0.05, fold change > 2) were used. TPM values of RNA-seq data were filtered to be greater than 4. Non-significant clusters between replications were removed, resulting in three identified clusters. Clusters are indicated on the left. (E-G) Examples of expression for selected genes from clusters identified in the heatmap. Each dot on the bar plot represents one sample, and error bars denote the standard deviation. Error bars represent means ± SD. (H) Gene ontology (GO) analysis of THP-1 RNA-seq using genes positively correlated with HMDM RNA-seq. Heatmap displays the P-value (-Log10) significance of GO term enrichment for genes in each cluster, with clusters shown at the top. Downregulated by IFN-γ, n = 42; JAKi-sensitive, n = 129; JAKi-insensitive, n = 112. (I) Identification of genes associated with JAKi-sensitive or JAKi-insensitive in RA patients. Clusters are indicated on the left. (J) Identification of genes associated with JAKi-sensitive or JAKi-insensitive in COVID-19 patients. Clusters are indicated on the left. For heatmaps of single cells, mean expression values were used, and hierarchical analysis was performed. (K) GO analysis of overlapping JAKi-sensitive and JAKi-insensitive genes in RA or COVID-19 patients. Clusters are indicated on the left. JAKi-sensitive, n = 51; JAKi-insensitive, n = 24. p < 0.05(*), p < 0.01(**), p < 0.001(***) and p < 0.0001(****) by one-way ANOVA. GO analysis was performed using Metascape ( http://metascape.org/ ).
Article Snippet: Add antibody (Alexa Fluor® 647 Mouse anti-Total Stat1, BD biosciences, 58560; PE anti-STAT1 Phospho (Tyr701), BioLegend, 666404; APC Mouse anti-Total Stat3, BD biosciences, 560392; PE/Cyanine5 anti-STAT3 Phospho (Tyr705), BioLegend, 651014) cocktail(s) to appropriate tubes, vortex to mix, and incubate for 30 minutes at room temperature in the dark.
Techniques: Flow Cytometry, Fluorescence, Quantitative RT-PCR, Derivative Assay, Concentration Assay, RNA Sequencing Assay, Expressing, Standard Deviation