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antibodies against irf1 (R&D Systems)

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R&D Systems antibodies against irf1

(A) Experimental design for multiomic assessment of WT and <t>IRF1</t> KO bone marrow–derived macrophages (BMDMs) response to IFNγ stimulation. Time-resolved profiling by ATAC-seq, ChIP-seq, Hi-ChIP, SLAM-seq and metabolomics via GC/LC-MS is performed. (B) Venn diagram summarizing ATAC-seq–identified accessible chromatin regions, filtered for high-confidence peaks and IFNγ-responsiveness (n=38,564); this set is used for downstream clustering and differential analyses. (C) Heatmap of normalized ATAC-seq signal (rows = individual accessible site; columns = time points), grouped into eight clusters by k-means clustering. Clusters C1-C3 show IRF1-depedent increase in accessibility in response to IFNγ; highlighted in red. PU.1 ChIP-seq binding signal is also shown, with Cluster C1 lacking detectable PU.1 occupancy. (D) Ribbon plots of relative ATAC-seq peak height (each peak scaled to its maximum) over matched time points; lines indicate mean accessibility and shaded ribbons show ± SD for WT (black) and IRF1 KO (red). (E) Boxplots of normalized ATAC-seq counts in WT BMDMs at heterochromatin regions, and at clusters C1–C8 and unresponsive ATAC-seq sites; median with interquartile range are shown.

Antibodies Against Irf1, supplied by R&D Systems, used in various techniques. Bioz Stars score: 85/100, based on 4 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/antibodies+against+irf1/Mouse+IRF1+Antibody/bio_rxiv__64898__2026__02__27__708404-259-15-18
Average 85 stars, based on 4 article reviews

antibodies against irf1 - by Bioz Stars, 2026-10

85/100 stars

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1) Product Images from "Pioneer factor IRF1 unlocks latent enhancers to rewire chromatin and immunometabolism in inflammatory macrophages"

Article Title: Pioneer factor IRF1 unlocks latent enhancers to rewire chromatin and immunometabolism in inflammatory macrophages

Journal: bioRxiv

doi: 10.64898/2026.02.27.708404

(A) Experimental design for multiomic assessment of WT and IRF1 KO bone marrow–derived macrophages (BMDMs) response to IFNγ stimulation. Time-resolved profiling by ATAC-seq, ChIP-seq, Hi-ChIP, SLAM-seq and metabolomics via GC/LC-MS is performed. (B) Venn diagram summarizing ATAC-seq–identified accessible chromatin regions, filtered for high-confidence peaks and IFNγ-responsiveness (n=38,564); this set is used for downstream clustering and differential analyses. (C) Heatmap of normalized ATAC-seq signal (rows = individual accessible site; columns = time points), grouped into eight clusters by k-means clustering. Clusters C1-C3 show IRF1-depedent increase in accessibility in response to IFNγ; highlighted in red. PU.1 ChIP-seq binding signal is also shown, with Cluster C1 lacking detectable PU.1 occupancy. (D) Ribbon plots of relative ATAC-seq peak height (each peak scaled to its maximum) over matched time points; lines indicate mean accessibility and shaded ribbons show ± SD for WT (black) and IRF1 KO (red). (E) Boxplots of normalized ATAC-seq counts in WT BMDMs at heterochromatin regions, and at clusters C1–C8 and unresponsive ATAC-seq sites; median with interquartile range are shown.

Figure Legend Snippet: (A) Experimental design for multiomic assessment of WT and IRF1 KO bone marrow–derived macrophages (BMDMs) response to IFNγ stimulation. Time-resolved profiling by ATAC-seq, ChIP-seq, Hi-ChIP, SLAM-seq and metabolomics via GC/LC-MS is performed. (B) Venn diagram summarizing ATAC-seq–identified accessible chromatin regions, filtered for high-confidence peaks and IFNγ-responsiveness (n=38,564); this set is used for downstream clustering and differential analyses. (C) Heatmap of normalized ATAC-seq signal (rows = individual accessible site; columns = time points), grouped into eight clusters by k-means clustering. Clusters C1-C3 show IRF1-depedent increase in accessibility in response to IFNγ; highlighted in red. PU.1 ChIP-seq binding signal is also shown, with Cluster C1 lacking detectable PU.1 occupancy. (D) Ribbon plots of relative ATAC-seq peak height (each peak scaled to its maximum) over matched time points; lines indicate mean accessibility and shaded ribbons show ± SD for WT (black) and IRF1 KO (red). (E) Boxplots of normalized ATAC-seq counts in WT BMDMs at heterochromatin regions, and at clusters C1–C8 and unresponsive ATAC-seq sites; median with interquartile range are shown.

Techniques Used: Derivative Assay, ChIP-sequencing, HiChIP, Liquid Chromatography with Mass Spectroscopy, Binding Assay

(A) Network diagrams of transcription factor motif frequency (node size) and co-occurrence (edge thickness) within ±100 bp of ATAC-seq peak centre for clusters C1-C3 and unresponsive sites. “IRF1–IRF1” denotes sites with ≥2 IRF motifs, and node/edge scales reflect motif frequency and co-occurrence. (B) Volcano plots of TOBIAS differential binding scores for 879 mammalian TFs in WT BMDMs comparing 0.5, 3 and 48 h post-IFNγ versus non-treated (0 h); significant TFs are highlighted [Bonferroni-corrected FDR < 0.05; log2 FC > |0.5|]. (C) Heatmap of centered TOBIAS TF footprinting intensity in WT BMDMs, grouped into four clusters by k-means clustering. (D) Density plots (top) and motif-centered TOBIAS footprint heatmaps (bottom) in WT and IRF1 KO BMDMs showing aggregated IRF1-centered footprinting signal at Cluster 1 sites (rows = individual sites; columns = base position around motif). (E) Representative Western blots in WT BMDMs showing IRF1 protein and GAPDH control across time points (0–48 h) post-IFNγ stimulation.

Figure Legend Snippet: (A) Network diagrams of transcription factor motif frequency (node size) and co-occurrence (edge thickness) within ±100 bp of ATAC-seq peak centre for clusters C1-C3 and unresponsive sites. “IRF1–IRF1” denotes sites with ≥2 IRF motifs, and node/edge scales reflect motif frequency and co-occurrence. (B) Volcano plots of TOBIAS differential binding scores for 879 mammalian TFs in WT BMDMs comparing 0.5, 3 and 48 h post-IFNγ versus non-treated (0 h); significant TFs are highlighted [Bonferroni-corrected FDR < 0.05; log2 FC > |0.5|]. (C) Heatmap of centered TOBIAS TF footprinting intensity in WT BMDMs, grouped into four clusters by k-means clustering. (D) Density plots (top) and motif-centered TOBIAS footprint heatmaps (bottom) in WT and IRF1 KO BMDMs showing aggregated IRF1-centered footprinting signal at Cluster 1 sites (rows = individual sites; columns = base position around motif). (E) Representative Western blots in WT BMDMs showing IRF1 protein and GAPDH control across time points (0–48 h) post-IFNγ stimulation.

Techniques Used: Binding Assay, Footprinting, Western Blot, Control

(A) Heatmaps of IRF1 occupancy (ChIP-seq), ATAC-seq accessibility and H3K4me1, H3K4me3 and H3K27ac signals across in response to IFNγ for sites in Clusters 1–3 in WT and IRF1 KO BMDMs. (B) Hi-ChIP arc plots showing loop contacts (arc width represents number of contacts) between IRF1-bound enhancers and promoters at C8 and unresponsive sites in WT and IRF1 KO BMDMs. [FitHiChIP thresholds FDR < 0.1; loop FC > 6, CPM > 6] (C) Graph of the temporal changes for ChIP-seq and ATAC-seq signals at Cluster 1. Half-time (t½) to reach 50% of each signal’s maximum was calculated by normalizing each trajectory to its maximum and extracting the pseudo-time at half-max. (D) Heatmap of ChIP-seq for IRF1, BRG1, ARID1A, BRD9 and PHF10 across Clusters 1–3 at 0, 1, and 4 h post TLR4 activation. ( E ) BRG1 ChIP–qPCR enrichment (fold over input) at four enhancers ( Wdr7 (C1), Shtn1 (C2), Clic5 (C2) , Nos2 (C3)) in WT and IRF1 KO BMDMs, untreated and 4 h post-IFNγ. (F) Boxplots of normalized ATAC-seq counts in Clusters 1–3 in WT BMDMs TLR4 activated with LipidA, with or without and BRG1 inhibition (BRM014).

Figure Legend Snippet: (A) Heatmaps of IRF1 occupancy (ChIP-seq), ATAC-seq accessibility and H3K4me1, H3K4me3 and H3K27ac signals across in response to IFNγ for sites in Clusters 1–3 in WT and IRF1 KO BMDMs. (B) Hi-ChIP arc plots showing loop contacts (arc width represents number of contacts) between IRF1-bound enhancers and promoters at C8 and unresponsive sites in WT and IRF1 KO BMDMs. [FitHiChIP thresholds FDR < 0.1; loop FC > 6, CPM > 6] (C) Graph of the temporal changes for ChIP-seq and ATAC-seq signals at Cluster 1. Half-time (t½) to reach 50% of each signal’s maximum was calculated by normalizing each trajectory to its maximum and extracting the pseudo-time at half-max. (D) Heatmap of ChIP-seq for IRF1, BRG1, ARID1A, BRD9 and PHF10 across Clusters 1–3 at 0, 1, and 4 h post TLR4 activation. ( E ) BRG1 ChIP–qPCR enrichment (fold over input) at four enhancers ( Wdr7 (C1), Shtn1 (C2), Clic5 (C2) , Nos2 (C3)) in WT and IRF1 KO BMDMs, untreated and 4 h post-IFNγ. (F) Boxplots of normalized ATAC-seq counts in Clusters 1–3 in WT BMDMs TLR4 activated with LipidA, with or without and BRG1 inhibition (BRM014).

Techniques Used: ChIP-sequencing, HiChIP, Activation Assay, ChIP-qPCR, Inhibition

$(A) Heatmaps of IRF1 ChIP–seq and normalized ATAC–seq at sites grouped by IRF1 signal strength (very strong to weak) in response to IFNγ. (B) Line plots of average IRF1 ChIP–seq signal in WT BMDMs for each binding-strength category. (C) Heatmap of relative enrichment of IRF1 binding classes across ATAC clusters (enrichment is relative to the maximum site overlap). (D) Stacked bar plots showing proportions of sites with 0, 1, 2 or ≥3 IRF1 motifs per ATAC cluster. (E) Aggregate plots of IRF1 motif frequency across ±100 bp around IRF1 peaks for each ATAC cluster. (F) Heatmap of IRF1 ChIP–seq signal at 3 h post–IFNγ for sites stratified by IRF1 motif count, as determined in D). (G) Scatter plot of fraction of sites forming IRF1 Hi-ChIP loops versus motif count, with a fitted trend line shown. [FitHiChIP thresholds FDR < 0.1; loop FC > 6, CPM > 6] (H) Genome browser tracks at the Jdp2 locus showing IRF1, PU.1 and H3K27ac ChIP–seq, Hi-ChIP interactions and ATAC–seq in WT and IRF1 KO BMDMs. The cluster to with each ATAC-seq peak belong is indicated [C1 = cluster 1; UR = Unresponsive]. Insets display the array of IRF1 motifs at the C1 site and a SLAM-seq Jdp2 expression plot across the IFNγ time course.$
Figure Legend Snippet: (A) Heatmaps of IRF1 ChIP–seq and normalized ATAC–seq at sites grouped by IRF1 signal strength (very strong to weak) in response to IFNγ. (B) Line plots of average IRF1 ChIP–seq signal in WT BMDMs for each binding-strength category. (C) Heatmap of relative enrichment of IRF1 binding classes across ATAC clusters (enrichment is relative to the maximum site overlap). (D) Stacked bar plots showing proportions of sites with 0, 1, 2 or ≥3 IRF1 motifs per ATAC cluster. (E) Aggregate plots of IRF1 motif frequency across ±100 bp around IRF1 peaks for each ATAC cluster. (F) Heatmap of IRF1 ChIP–seq signal at 3 h post–IFNγ for sites stratified by IRF1 motif count, as determined in D). (G) Scatter plot of fraction of sites forming IRF1 Hi-ChIP loops versus motif count, with a fitted trend line shown. [FitHiChIP thresholds FDR < 0.1; loop FC > 6, CPM > 6] (H) Genome browser tracks at the Jdp2 locus showing IRF1, PU.1 and H3K27ac ChIP–seq, Hi-ChIP interactions and ATAC–seq in WT and IRF1 KO BMDMs. The cluster to with each ATAC-seq peak belong is indicated [C1 = cluster 1; UR = Unresponsive]. Insets display the array of IRF1 motifs at the C1 site and a SLAM-seq Jdp2 expression plot across the IFNγ time course.

Techniques Used: ChIP-sequencing, Binding Assay, HiChIP, Expressing

(A) Line plots of nascent RNA-seq log2 fold-change (FC) for genes within ±10 kb of ATAC cluster regions in WT and IRF1 KO BMDMs in response to IFNγ stimulation. (B) Heatmap of GO biological process enrichment for genes within ±50 kb of ATAC peaks. Categories with clusterProfiler FDR < 0.05 for at least one cluster are shown. (C) Line plots of nascent RNA counts per million (CPM; mean ± SD) for selected genes across IFNγ time points; WT vs IRF1 KO comparison by two-way ANOVA and pairwise post-hoc testing at each time point. (D) Genome browser tracks at the Kmt2c locus showing IRF1 and PU.1 ChIP-seq, Hi-ChIP arcs and ATAC-seq signal for WT and IRF1 KO BMDMs. [UR = Unresponsive] (E) Line plots of RNA-seq CPM (mean ± SD) for selected genes at 0, 1 and 4 h post-LipidA treatment in WT BMDMs, with BRM014 treatment at the 4 h time point. [Student T-test; n = 3] (F) Bar plot of log2 odds ratio of downregulated genes (FC < 0.5 and FDR < 0.05) after BRM014 treatment (4 h Lipid A) across clusters. * < 0.05, ** < 0.01, *** < 0.001

Figure Legend Snippet: (A) Line plots of nascent RNA-seq log2 fold-change (FC) for genes within ±10 kb of ATAC cluster regions in WT and IRF1 KO BMDMs in response to IFNγ stimulation. (B) Heatmap of GO biological process enrichment for genes within ±50 kb of ATAC peaks. Categories with clusterProfiler FDR < 0.05 for at least one cluster are shown. (C) Line plots of nascent RNA counts per million (CPM; mean ± SD) for selected genes across IFNγ time points; WT vs IRF1 KO comparison by two-way ANOVA and pairwise post-hoc testing at each time point. (D) Genome browser tracks at the Kmt2c locus showing IRF1 and PU.1 ChIP-seq, Hi-ChIP arcs and ATAC-seq signal for WT and IRF1 KO BMDMs. [UR = Unresponsive] (E) Line plots of RNA-seq CPM (mean ± SD) for selected genes at 0, 1 and 4 h post-LipidA treatment in WT BMDMs, with BRM014 treatment at the 4 h time point. [Student T-test; n = 3] (F) Bar plot of log2 odds ratio of downregulated genes (FC < 0.5 and FDR < 0.05) after BRM014 treatment (4 h Lipid A) across clusters. * < 0.05, ** < 0.01, *** < 0.001

Techniques Used: RNA Sequencing, Comparison, ChIP-sequencing, HiChIP

(A) Bar plot of the proportion of genes in selected metabolic pathways that harbor IRF1 ChIP-seq peaks; red intensity denotes average number of peaks per gene in each pathway. (B) Genome browser tracks of the Hk1 locus showing normalized IRF1 and PU.1 ChIP-seq, Hi-ChIP interactions and ATAC-seq; an inset shows the annotated intragenic enhancer and promoter contact. [UR = Unresponsive] (C) Line plots of nascent RNA CPM (mean ± SD) for selected genes in glycolysis, PPP and TCA pathways WT and IRF1 KO BMDMs; two-way ANOVA and post-hoc testing; * < 0.05, ** < 0.01, *** < 0.001. (D) Oxygen consumption rates (OCR; fmol mm⁻² s⁻¹) for untreated and IFNγ–stimulated WT and IRF1 KO BMDMs [n = 4/group]; adjacent heatmap shows Student t-test p-values for each time point measured. ( E ) Ribbon plots of relative glycolysis metabolite intensity (mean ± SD) detected by GC-MS in response to IFNγ in WT and IRF1 KO BMDMs [n = 3/group]. (F) Diagram of glycolysis, pentose phosphate pathway (PPP) and Krebs cycle highlighting genes and pathway components significantly dysregulated in IRF1 KO BMDMs for at least 1 timepoint.

Figure Legend Snippet: (A) Bar plot of the proportion of genes in selected metabolic pathways that harbor IRF1 ChIP-seq peaks; red intensity denotes average number of peaks per gene in each pathway. (B) Genome browser tracks of the Hk1 locus showing normalized IRF1 and PU.1 ChIP-seq, Hi-ChIP interactions and ATAC-seq; an inset shows the annotated intragenic enhancer and promoter contact. [UR = Unresponsive] (C) Line plots of nascent RNA CPM (mean ± SD) for selected genes in glycolysis, PPP and TCA pathways WT and IRF1 KO BMDMs; two-way ANOVA and post-hoc testing; * < 0.05, ** < 0.01, *** < 0.001. (D) Oxygen consumption rates (OCR; fmol mm⁻² s⁻¹) for untreated and IFNγ–stimulated WT and IRF1 KO BMDMs [n = 4/group]; adjacent heatmap shows Student t-test p-values for each time point measured. ( E ) Ribbon plots of relative glycolysis metabolite intensity (mean ± SD) detected by GC-MS in response to IFNγ in WT and IRF1 KO BMDMs [n = 3/group]. (F) Diagram of glycolysis, pentose phosphate pathway (PPP) and Krebs cycle highlighting genes and pathway components significantly dysregulated in IRF1 KO BMDMs for at least 1 timepoint.

Techniques Used: ChIP-sequencing, HiChIP, Gas Chromatography-Mass Spectrometry

(A) Volcano plots from differential metabolite abundance analysis for GC-MS data (n=3/group), comparing 48 h versus 0 h in WT cells (left) and WT versus IRF1 KO at 48 h (right). (B) Top: bar plots of normalized GC-MS intensity for sedoheptulose 7-P at 3 h post IFNγ, xylulose at 12 h, and erythrose 4-P at 48 h. Bottom: ribbon plots of normalized MS signal over time with mean ± SD. (C) Top: normalized LC-MS GSH intensity at 24 h post-IFNγ stimulation. Bottom: ribbon plots of GSH/GSSG ratios over time (mean ± SD) calculated from normalized LC-MS intensities [n=3/group]. (D) Genome browser tracks at the Acod1 locus showing normalized IRF1 and PU.1 ChIP-seq, Hi-ChIP interactions and ATAC-seq [UR = Unresponsive]. Adjacent panels show Acod1 nascent RNA expression and itaconic acid levels. (E) Ribbon plots of normalized GC-MS signal for TCA metabolites in response to IFNγ in WT and IRF1 KO BMDMs. (F) Diagram of glycolysis, PPP and TCA cycle metabolic pathways with dysregulated intermediates denoted in red.

Figure Legend Snippet: (A) Volcano plots from differential metabolite abundance analysis for GC-MS data (n=3/group), comparing 48 h versus 0 h in WT cells (left) and WT versus IRF1 KO at 48 h (right). (B) Top: bar plots of normalized GC-MS intensity for sedoheptulose 7-P at 3 h post IFNγ, xylulose at 12 h, and erythrose 4-P at 48 h. Bottom: ribbon plots of normalized MS signal over time with mean ± SD. (C) Top: normalized LC-MS GSH intensity at 24 h post-IFNγ stimulation. Bottom: ribbon plots of GSH/GSSG ratios over time (mean ± SD) calculated from normalized LC-MS intensities [n=3/group]. (D) Genome browser tracks at the Acod1 locus showing normalized IRF1 and PU.1 ChIP-seq, Hi-ChIP interactions and ATAC-seq [UR = Unresponsive]. Adjacent panels show Acod1 nascent RNA expression and itaconic acid levels. (E) Ribbon plots of normalized GC-MS signal for TCA metabolites in response to IFNγ in WT and IRF1 KO BMDMs. (F) Diagram of glycolysis, PPP and TCA cycle metabolic pathways with dysregulated intermediates denoted in red.

Techniques Used: Gas Chromatography-Mass Spectrometry, Liquid Chromatography with Mass Spectroscopy, ChIP-sequencing, HiChIP, RNA Expression

(A) Schematic of experimental timeline for the long-term wash-and-rest assay. Cells are plated for seven days, pulsed with 24 h IFNγ (400 U/mL) at specified times (24 h, 48 h, 6 d) with defined washout intervals and a final 1 h re-stimulation. On day 7, cells are harvested for ChIP-seq (IRF1, H3K4me1, H3K27ac and H3K9me2). (B) Heatmaps of normalized ChIP-seq signal for IRF1, H3K4me1, and H3K27ac at Clusters 1–3. (C) Aggregate coverage plots of H3K4me1 ±1 kb from ATAC peak centers for UT, 24 h IFNγ, 6 d washout and 6 d + 1 h restimulation; insets show putative nucleosomal configurations. (D) Bar plots of fold-change in H3K27ac (mean ± SEM) comparing naïve and IFNγ-trained cells after 1 h restimulation; statistical comparison using Wilcoxon test. (E) Volcano plot of H3K4me1 differential enrichment for Cluster 1–3 (control versus IFNγ washout); points = enhancers, color key: red = increased, blue = decreased, yellow = pioneered genes; labeled enhancers meet log₂FC > 1 and CPM > 5. (F) Hif1a locus showing normalized IRF1, H3K27ac, and H3K4me1 ChIP-seq, and ATAC-seq in WT and IRF1 KO BMDMs [UR = Unresponsive]. Normalized SLAM-seq nascent RNA expression for Hif1a is shown; * p < 0.05.

Figure Legend Snippet: (A) Schematic of experimental timeline for the long-term wash-and-rest assay. Cells are plated for seven days, pulsed with 24 h IFNγ (400 U/mL) at specified times (24 h, 48 h, 6 d) with defined washout intervals and a final 1 h re-stimulation. On day 7, cells are harvested for ChIP-seq (IRF1, H3K4me1, H3K27ac and H3K9me2). (B) Heatmaps of normalized ChIP-seq signal for IRF1, H3K4me1, and H3K27ac at Clusters 1–3. (C) Aggregate coverage plots of H3K4me1 ±1 kb from ATAC peak centers for UT, 24 h IFNγ, 6 d washout and 6 d + 1 h restimulation; insets show putative nucleosomal configurations. (D) Bar plots of fold-change in H3K27ac (mean ± SEM) comparing naïve and IFNγ-trained cells after 1 h restimulation; statistical comparison using Wilcoxon test. (E) Volcano plot of H3K4me1 differential enrichment for Cluster 1–3 (control versus IFNγ washout); points = enhancers, color key: red = increased, blue = decreased, yellow = pioneered genes; labeled enhancers meet log₂FC > 1 and CPM > 5. (F) Hif1a locus showing normalized IRF1, H3K27ac, and H3K4me1 ChIP-seq, and ATAC-seq in WT and IRF1 KO BMDMs [UR = Unresponsive]. Normalized SLAM-seq nascent RNA expression for Hif1a is shown; * p < 0.05.

Techniques Used: ChIP-sequencing, Comparison, Control, Labeling, RNA Expression

Incubation:

Article Title: Pioneer factor IRF1 unlocks latent enhancers to rewire chromatin and immunometabolism in inflammatory macrophages
Article Snippet: 40 μg of proteins were separated on 10% stain-free polyacrylamide gels (Bio-Rad) and transferred onto PDVF membranes using the Trans-Blot Turbo system (Bio-Rad). .. Membranes were blocked in 5% skim milk in PBS Tween-20 0.1% and incubated with primary antibodies against IRF1 (R&D Systems, Cat. #AF4715, 1:200) or GAPDH (Cell Signaling Technologies, Cat. #2118) as loading control, followed by appropriate HRP-conjugated secondary antibodies. .. Signals were detected using Bio-Rad ECL substrate and imaged with the Vilber FX7 Gel Documentation System.

Cell-Signaling:

Control:

Image Search Results

Journal: bioRxiv

Article Title: Pioneer factor IRF1 unlocks latent enhancers to rewire chromatin and immunometabolism in inflammatory macrophages

doi: 10.64898/2026.02.27.708404

Figure Lengend Snippet: (A) Experimental design for multiomic assessment of WT and IRF1 KO bone marrow–derived macrophages (BMDMs) response to IFNγ stimulation. Time-resolved profiling by ATAC-seq, ChIP-seq, Hi-ChIP, SLAM-seq and metabolomics via GC/LC-MS is performed. (B) Venn diagram summarizing ATAC-seq–identified accessible chromatin regions, filtered for high-confidence peaks and IFNγ-responsiveness (n=38,564); this set is used for downstream clustering and differential analyses. (C) Heatmap of normalized ATAC-seq signal (rows = individual accessible site; columns = time points), grouped into eight clusters by k-means clustering. Clusters C1-C3 show IRF1-depedent increase in accessibility in response to IFNγ; highlighted in red. PU.1 ChIP-seq binding signal is also shown, with Cluster C1 lacking detectable PU.1 occupancy. (D) Ribbon plots of relative ATAC-seq peak height (each peak scaled to its maximum) over matched time points; lines indicate mean accessibility and shaded ribbons show ± SD for WT (black) and IRF1 KO (red). (E) Boxplots of normalized ATAC-seq counts in WT BMDMs at heterochromatin regions, and at clusters C1–C8 and unresponsive ATAC-seq sites; median with interquartile range are shown.

Article Snippet: Membranes were blocked in 5% skim milk in PBS Tween-20 0.1% and incubated with primary antibodies against IRF1 (R&D Systems, Cat. #AF4715, 1:200) or GAPDH (Cell Signaling Technologies, Cat. #2118) as loading control, followed by appropriate HRP-conjugated secondary antibodies.

Techniques: Derivative Assay, ChIP-sequencing, HiChIP, Liquid Chromatography with Mass Spectroscopy, Binding Assay

Journal: bioRxiv

Article Title: Pioneer factor IRF1 unlocks latent enhancers to rewire chromatin and immunometabolism in inflammatory macrophages

doi: 10.64898/2026.02.27.708404

Figure Lengend Snippet: (A) Network diagrams of transcription factor motif frequency (node size) and co-occurrence (edge thickness) within ±100 bp of ATAC-seq peak centre for clusters C1-C3 and unresponsive sites. “IRF1–IRF1” denotes sites with ≥2 IRF motifs, and node/edge scales reflect motif frequency and co-occurrence. (B) Volcano plots of TOBIAS differential binding scores for 879 mammalian TFs in WT BMDMs comparing 0.5, 3 and 48 h post-IFNγ versus non-treated (0 h); significant TFs are highlighted [Bonferroni-corrected FDR < 0.05; log2 FC > |0.5|]. (C) Heatmap of centered TOBIAS TF footprinting intensity in WT BMDMs, grouped into four clusters by k-means clustering. (D) Density plots (top) and motif-centered TOBIAS footprint heatmaps (bottom) in WT and IRF1 KO BMDMs showing aggregated IRF1-centered footprinting signal at Cluster 1 sites (rows = individual sites; columns = base position around motif). (E) Representative Western blots in WT BMDMs showing IRF1 protein and GAPDH control across time points (0–48 h) post-IFNγ stimulation.

Techniques: Binding Assay, Footprinting, Western Blot, Control

Journal: bioRxiv

Article Title: Pioneer factor IRF1 unlocks latent enhancers to rewire chromatin and immunometabolism in inflammatory macrophages

doi: 10.64898/2026.02.27.708404

Figure Lengend Snippet: (A) Heatmaps of IRF1 occupancy (ChIP-seq), ATAC-seq accessibility and H3K4me1, H3K4me3 and H3K27ac signals across in response to IFNγ for sites in Clusters 1–3 in WT and IRF1 KO BMDMs. (B) Hi-ChIP arc plots showing loop contacts (arc width represents number of contacts) between IRF1-bound enhancers and promoters at C8 and unresponsive sites in WT and IRF1 KO BMDMs. [FitHiChIP thresholds FDR < 0.1; loop FC > 6, CPM > 6] (C) Graph of the temporal changes for ChIP-seq and ATAC-seq signals at Cluster 1. Half-time (t½) to reach 50% of each signal’s maximum was calculated by normalizing each trajectory to its maximum and extracting the pseudo-time at half-max. (D) Heatmap of ChIP-seq for IRF1, BRG1, ARID1A, BRD9 and PHF10 across Clusters 1–3 at 0, 1, and 4 h post TLR4 activation. ( E ) BRG1 ChIP–qPCR enrichment (fold over input) at four enhancers ( Wdr7 (C1), Shtn1 (C2), Clic5 (C2) , Nos2 (C3)) in WT and IRF1 KO BMDMs, untreated and 4 h post-IFNγ. (F) Boxplots of normalized ATAC-seq counts in Clusters 1–3 in WT BMDMs TLR4 activated with LipidA, with or without and BRG1 inhibition (BRM014).

Techniques: ChIP-sequencing, HiChIP, Activation Assay, ChIP-qPCR, Inhibition

Journal: bioRxiv

Article Title: Pioneer factor IRF1 unlocks latent enhancers to rewire chromatin and immunometabolism in inflammatory macrophages

doi: 10.64898/2026.02.27.708404

Figure Lengend Snippet: (A) Heatmaps of IRF1 ChIP–seq and normalized ATAC–seq at sites grouped by IRF1 signal strength (very strong to weak) in response to IFNγ. (B) Line plots of average IRF1 ChIP–seq signal in WT BMDMs for each binding-strength category. (C) Heatmap of relative enrichment of IRF1 binding classes across ATAC clusters (enrichment is relative to the maximum site overlap). (D) Stacked bar plots showing proportions of sites with 0, 1, 2 or ≥3 IRF1 motifs per ATAC cluster. (E) Aggregate plots of IRF1 motif frequency across ±100 bp around IRF1 peaks for each ATAC cluster. (F) Heatmap of IRF1 ChIP–seq signal at 3 h post–IFNγ for sites stratified by IRF1 motif count, as determined in D). (G) Scatter plot of fraction of sites forming IRF1 Hi-ChIP loops versus motif count, with a fitted trend line shown. [FitHiChIP thresholds FDR < 0.1; loop FC > 6, CPM > 6] (H) Genome browser tracks at the Jdp2 locus showing IRF1, PU.1 and H3K27ac ChIP–seq, Hi-ChIP interactions and ATAC–seq in WT and IRF1 KO BMDMs. The cluster to with each ATAC-seq peak belong is indicated [C1 = cluster 1; UR = Unresponsive]. Insets display the array of IRF1 motifs at the C1 site and a SLAM-seq Jdp2 expression plot across the IFNγ time course.

Techniques: ChIP-sequencing, Binding Assay, HiChIP, Expressing

Journal: bioRxiv

Article Title: Pioneer factor IRF1 unlocks latent enhancers to rewire chromatin and immunometabolism in inflammatory macrophages

doi: 10.64898/2026.02.27.708404

Figure Lengend Snippet: (A) Line plots of nascent RNA-seq log2 fold-change (FC) for genes within ±10 kb of ATAC cluster regions in WT and IRF1 KO BMDMs in response to IFNγ stimulation. (B) Heatmap of GO biological process enrichment for genes within ±50 kb of ATAC peaks. Categories with clusterProfiler FDR < 0.05 for at least one cluster are shown. (C) Line plots of nascent RNA counts per million (CPM; mean ± SD) for selected genes across IFNγ time points; WT vs IRF1 KO comparison by two-way ANOVA and pairwise post-hoc testing at each time point. (D) Genome browser tracks at the Kmt2c locus showing IRF1 and PU.1 ChIP-seq, Hi-ChIP arcs and ATAC-seq signal for WT and IRF1 KO BMDMs. [UR = Unresponsive] (E) Line plots of RNA-seq CPM (mean ± SD) for selected genes at 0, 1 and 4 h post-LipidA treatment in WT BMDMs, with BRM014 treatment at the 4 h time point. [Student T-test; n = 3] (F) Bar plot of log2 odds ratio of downregulated genes (FC < 0.5 and FDR < 0.05) after BRM014 treatment (4 h Lipid A) across clusters. * < 0.05, ** < 0.01, *** < 0.001

Techniques: RNA Sequencing, Comparison, ChIP-sequencing, HiChIP

Journal: bioRxiv

Article Title: Pioneer factor IRF1 unlocks latent enhancers to rewire chromatin and immunometabolism in inflammatory macrophages

doi: 10.64898/2026.02.27.708404

Figure Lengend Snippet: (A) Bar plot of the proportion of genes in selected metabolic pathways that harbor IRF1 ChIP-seq peaks; red intensity denotes average number of peaks per gene in each pathway. (B) Genome browser tracks of the Hk1 locus showing normalized IRF1 and PU.1 ChIP-seq, Hi-ChIP interactions and ATAC-seq; an inset shows the annotated intragenic enhancer and promoter contact. [UR = Unresponsive] (C) Line plots of nascent RNA CPM (mean ± SD) for selected genes in glycolysis, PPP and TCA pathways WT and IRF1 KO BMDMs; two-way ANOVA and post-hoc testing; * < 0.05, ** < 0.01, *** < 0.001. (D) Oxygen consumption rates (OCR; fmol mm⁻² s⁻¹) for untreated and IFNγ–stimulated WT and IRF1 KO BMDMs [n = 4/group]; adjacent heatmap shows Student t-test p-values for each time point measured. ( E ) Ribbon plots of relative glycolysis metabolite intensity (mean ± SD) detected by GC-MS in response to IFNγ in WT and IRF1 KO BMDMs [n = 3/group]. (F) Diagram of glycolysis, pentose phosphate pathway (PPP) and Krebs cycle highlighting genes and pathway components significantly dysregulated in IRF1 KO BMDMs for at least 1 timepoint.

Techniques: ChIP-sequencing, HiChIP, Gas Chromatography-Mass Spectrometry

Journal: bioRxiv

Article Title: Pioneer factor IRF1 unlocks latent enhancers to rewire chromatin and immunometabolism in inflammatory macrophages

doi: 10.64898/2026.02.27.708404

Figure Lengend Snippet: (A) Volcano plots from differential metabolite abundance analysis for GC-MS data (n=3/group), comparing 48 h versus 0 h in WT cells (left) and WT versus IRF1 KO at 48 h (right). (B) Top: bar plots of normalized GC-MS intensity for sedoheptulose 7-P at 3 h post IFNγ, xylulose at 12 h, and erythrose 4-P at 48 h. Bottom: ribbon plots of normalized MS signal over time with mean ± SD. (C) Top: normalized LC-MS GSH intensity at 24 h post-IFNγ stimulation. Bottom: ribbon plots of GSH/GSSG ratios over time (mean ± SD) calculated from normalized LC-MS intensities [n=3/group]. (D) Genome browser tracks at the Acod1 locus showing normalized IRF1 and PU.1 ChIP-seq, Hi-ChIP interactions and ATAC-seq [UR = Unresponsive]. Adjacent panels show Acod1 nascent RNA expression and itaconic acid levels. (E) Ribbon plots of normalized GC-MS signal for TCA metabolites in response to IFNγ in WT and IRF1 KO BMDMs. (F) Diagram of glycolysis, PPP and TCA cycle metabolic pathways with dysregulated intermediates denoted in red.

Techniques: Gas Chromatography-Mass Spectrometry, Liquid Chromatography with Mass Spectroscopy, ChIP-sequencing, HiChIP, RNA Expression

Journal: bioRxiv

Article Title: Pioneer factor IRF1 unlocks latent enhancers to rewire chromatin and immunometabolism in inflammatory macrophages

doi: 10.64898/2026.02.27.708404

Figure Lengend Snippet: (A) Schematic of experimental timeline for the long-term wash-and-rest assay. Cells are plated for seven days, pulsed with 24 h IFNγ (400 U/mL) at specified times (24 h, 48 h, 6 d) with defined washout intervals and a final 1 h re-stimulation. On day 7, cells are harvested for ChIP-seq (IRF1, H3K4me1, H3K27ac and H3K9me2). (B) Heatmaps of normalized ChIP-seq signal for IRF1, H3K4me1, and H3K27ac at Clusters 1–3. (C) Aggregate coverage plots of H3K4me1 ±1 kb from ATAC peak centers for UT, 24 h IFNγ, 6 d washout and 6 d + 1 h restimulation; insets show putative nucleosomal configurations. (D) Bar plots of fold-change in H3K27ac (mean ± SEM) comparing naïve and IFNγ-trained cells after 1 h restimulation; statistical comparison using Wilcoxon test. (E) Volcano plot of H3K4me1 differential enrichment for Cluster 1–3 (control versus IFNγ washout); points = enhancers, color key: red = increased, blue = decreased, yellow = pioneered genes; labeled enhancers meet log₂FC > 1 and CPM > 5. (F) Hif1a locus showing normalized IRF1, H3K27ac, and H3K4me1 ChIP-seq, and ATAC-seq in WT and IRF1 KO BMDMs [UR = Unresponsive]. Normalized SLAM-seq nascent RNA expression for Hif1a is shown; * p < 0.05.

Techniques: ChIP-sequencing, Comparison, Control, Labeling, RNA Expression

(A) Pearson’s correlation analysis and regression line of long non-coding RNA ( GSEC , LINC02422 , CARINH ) and proximal coding gene ( ST3GAL4 , RESF1 , IRF1 , respectively) expression in whole blood of patients with influenza A virus (IAV, n = 41). (B, C) Time course of GSEC , LINC02422 , CARINH (B), and ST3GAL4 , RESF1 , IRF1 (C) expression (FPKM) in human monocyte-derived macrophages infected for 3, 6, 12, or 18 h (TOI: time of infection) with influenza A/California/04/09 (H1N1), influenza A/Wyoming/03/03 (H3N2), or influenza A/Vietnam/1203/2004 (H5N1), or mock-infected. Data are the mean ± SEM for two independent experiments. P -values were calculated by repeated-measures two-way ANOVA with Tukey’s multiple-comparisons test between IAV infection and mock control (B, C). # P < 0.1; * P < 0.05; ** P < 0.01; *** P < 0.001.

Journal: Life Science Alliance

Article Title: lncRNA CARINH regulates expression and function of innate immune transcription factor IRF1 in macrophages

doi: 10.26508/lsa.202403021

Figure Lengend Snippet: (A) Pearson’s correlation analysis and regression line of long non-coding RNA ( GSEC , LINC02422 , CARINH ) and proximal coding gene ( ST3GAL4 , RESF1 , IRF1 , respectively) expression in whole blood of patients with influenza A virus (IAV, n = 41). (B, C) Time course of GSEC , LINC02422 , CARINH (B), and ST3GAL4 , RESF1 , IRF1 (C) expression (FPKM) in human monocyte-derived macrophages infected for 3, 6, 12, or 18 h (TOI: time of infection) with influenza A/California/04/09 (H1N1), influenza A/Wyoming/03/03 (H3N2), or influenza A/Vietnam/1203/2004 (H5N1), or mock-infected. Data are the mean ± SEM for two independent experiments. P -values were calculated by repeated-measures two-way ANOVA with Tukey’s multiple-comparisons test between IAV infection and mock control (B, C). # P < 0.1; * P < 0.05; ** P < 0.01; *** P < 0.001.

Article Snippet: In brief, deparaffinized sections underwent a 20-min heat retrieval in Leica ER2 buffer (pH 9, AR9640) followed by Rodent Block (RBM961 L; Biocare) before a 1-h incubation with IAV NP protein antibody (Mouse Anti-Influenza A, Nucleoprotein-UNLB, Cat #10780-01; Southern Biotech) and an antibody against mouse IRF1 (Cat #8478, 1:50; Cell Signaling).

Techniques: Expressing, Virus, Derivative Assay, Infection, Control

(A) Schematic representation of the human CARINH locus on chromosome 5, which encodes three splice variants ( CARINH-V1-3 ) located antisense to IRF1. Solid boxes indicate exonic sequences. Arrows indicate the direction of transcription. (B) qRT–PCR analysis of CARINH and IRF1 in CD14 + primary human macrophages treated with 500 U/ml IFNβ for 8 h. (C) Hi-C heatmap of chromatin interactions at the genomic location of CARINH in THP1 macrophages. Black writing indicates genes on the forward strand, and blue writing indicates genes on the reverse strand. Dashed lines indicate the area of interaction. (D) Distribution of CARINH binding sites within specific loci of the human genome. (E) Volcano plot showing enriched loci bound by CARINH (n = 232). (F) ChIRP-seq reads for IRF1, IL18BP. Top row: input; second row: LacZ control; third row: CARINH probe set 1; and bottom row: CARINH probe set 2. Data are the mean ± SEM for three independent experiments (B). P -values were calculated by a one-sample t and Wilcoxon test (B). * P < 0.05.

Journal: Life Science Alliance

Article Title: lncRNA CARINH regulates expression and function of innate immune transcription factor IRF1 in macrophages

doi: 10.26508/lsa.202403021

Figure Lengend Snippet: (A) Schematic representation of the human CARINH locus on chromosome 5, which encodes three splice variants ( CARINH-V1-3 ) located antisense to IRF1. Solid boxes indicate exonic sequences. Arrows indicate the direction of transcription. (B) qRT–PCR analysis of CARINH and IRF1 in CD14 + primary human macrophages treated with 500 U/ml IFNβ for 8 h. (C) Hi-C heatmap of chromatin interactions at the genomic location of CARINH in THP1 macrophages. Black writing indicates genes on the forward strand, and blue writing indicates genes on the reverse strand. Dashed lines indicate the area of interaction. (D) Distribution of CARINH binding sites within specific loci of the human genome. (E) Volcano plot showing enriched loci bound by CARINH (n = 232). (F) ChIRP-seq reads for IRF1, IL18BP. Top row: input; second row: LacZ control; third row: CARINH probe set 1; and bottom row: CARINH probe set 2. Data are the mean ± SEM for three independent experiments (B). P -values were calculated by a one-sample t and Wilcoxon test (B). * P < 0.05.

Techniques: Quantitative RT-PCR, Hi-C, Binding Assay, Control

(A, B) qRT–PCR analysis of CARINH and IRF1 in THP1 macrophages after treatment with 500 U/ml IFNβ (A) or 1 μg/ml poly(I:C) (B) compared with baseline (0 h) for times indicated. (C, D) Pearson’s correlation analysis and regression line of CARINH and neighboring coding gene RAD50 (C) and SLC22A5 (D) expression in whole blood of patients with influenza A virus (n = 41). Data are the mean ± SEM for three independent experiments (A, B). P -values were calculated by one-way ANOVA with Dunnett’s multiple-comparisons test (A, B). # P < 0.1; * P < 0.05; ** P < 0.01.

Journal: Life Science Alliance

Article Title: lncRNA CARINH regulates expression and function of innate immune transcription factor IRF1 in macrophages

doi: 10.26508/lsa.202403021

Figure Lengend Snippet: (A, B) qRT–PCR analysis of CARINH and IRF1 in THP1 macrophages after treatment with 500 U/ml IFNβ (A) or 1 μg/ml poly(I:C) (B) compared with baseline (0 h) for times indicated. (C, D) Pearson’s correlation analysis and regression line of CARINH and neighboring coding gene RAD50 (C) and SLC22A5 (D) expression in whole blood of patients with influenza A virus (n = 41). Data are the mean ± SEM for three independent experiments (A, B). P -values were calculated by one-way ANOVA with Dunnett’s multiple-comparisons test (A, B). # P < 0.1; * P < 0.05; ** P < 0.01.

Techniques: Quantitative RT-PCR, Expressing, Virus

(A) FISH of CARINH (red) RNA in THP1 macrophages treated with GapCTRL, or GapmeRs targeting CARINH , or an RNase control, and DNA counterstain (DAPI, blue). The scale bar is 50 μm. (B) qRT–PCR analysis of CARINH and IRF1 in CARINH -depleted (Gap CARINH -treated) and control (GapCTRL-treated) CD14 + primary human macrophages (left) or THP1 macrophages (right). (C) qRT-PCR array–based gene expression profiling of 84 type I interferon response genes in THP1 macrophages treated with Gap CARINH versus GapCTRL. (C, D) Interactome of significant differentially expressed genes shown in (C). Red/blue colors indicate belonging to the indicated canonical pathway. (E) Cytometric bead array of IFNβ, IFNγ, IFNλ2/3, and IFNα protein levels in the supernatant of THP1 in Gap CARINH versus GapCTRL-treated cells, and subsequent treatment with poly(I:C) (1 μg/ml) for 24 h. (F) Western blot analysis of IRF1 in Gap CARINH -treated and GapCTRL-treated THP1 macrophages. (G) Reporter assay for IRF-driven transcription in human THP1-Lucia reporter macrophages transfected with Gap CARINH or GapCTRL and treated with or poly(I:C) or IFNβ (500 U/ml). Relative luciferase expression (relative units [RU]) is normalized to time 0 h, set at 100%. (H) Representative whole-well microscopy images of immunofluorescent staining for influenza A–infected (H1N1, green) THP1 macrophages transfected with Gap CARINH and GapCTRL counterstained for nuclear DNA (DAPI, blue). Quantification shown on right as a percentage of infected cells per total number of viable cells in CARINH -depleted and control THP1 macrophages. The scale bar is 500 μm. (I) Integrated model depicting control of IRF1 expression by CARINH leading to alteration of the antiviral immune response. Data are the mean ± SEM for three independent experiments (A, E, F, G) or representative of three independent experiments. P -values were calculated by a one-sample t and Wilcoxon test (B, F), a t test (E, H), and repeated-measures two-way ANOVA with significant group differences between Gap CARINH and GapCTRL (G). * P < 0.05; ** P < 0.01; *** P < 0.001.

Journal: Life Science Alliance

Article Title: lncRNA CARINH regulates expression and function of innate immune transcription factor IRF1 in macrophages

doi: 10.26508/lsa.202403021

Figure Lengend Snippet: (A) FISH of CARINH (red) RNA in THP1 macrophages treated with GapCTRL, or GapmeRs targeting CARINH , or an RNase control, and DNA counterstain (DAPI, blue). The scale bar is 50 μm. (B) qRT–PCR analysis of CARINH and IRF1 in CARINH -depleted (Gap CARINH -treated) and control (GapCTRL-treated) CD14 + primary human macrophages (left) or THP1 macrophages (right). (C) qRT-PCR array–based gene expression profiling of 84 type I interferon response genes in THP1 macrophages treated with Gap CARINH versus GapCTRL. (C, D) Interactome of significant differentially expressed genes shown in (C). Red/blue colors indicate belonging to the indicated canonical pathway. (E) Cytometric bead array of IFNβ, IFNγ, IFNλ2/3, and IFNα protein levels in the supernatant of THP1 in Gap CARINH versus GapCTRL-treated cells, and subsequent treatment with poly(I:C) (1 μg/ml) for 24 h. (F) Western blot analysis of IRF1 in Gap CARINH -treated and GapCTRL-treated THP1 macrophages. (G) Reporter assay for IRF-driven transcription in human THP1-Lucia reporter macrophages transfected with Gap CARINH or GapCTRL and treated with or poly(I:C) or IFNβ (500 U/ml). Relative luciferase expression (relative units [RU]) is normalized to time 0 h, set at 100%. (H) Representative whole-well microscopy images of immunofluorescent staining for influenza A–infected (H1N1, green) THP1 macrophages transfected with Gap CARINH and GapCTRL counterstained for nuclear DNA (DAPI, blue). Quantification shown on right as a percentage of infected cells per total number of viable cells in CARINH -depleted and control THP1 macrophages. The scale bar is 500 μm. (I) Integrated model depicting control of IRF1 expression by CARINH leading to alteration of the antiviral immune response. Data are the mean ± SEM for three independent experiments (A, E, F, G) or representative of three independent experiments. P -values were calculated by a one-sample t and Wilcoxon test (B, F), a t test (E, H), and repeated-measures two-way ANOVA with significant group differences between Gap CARINH and GapCTRL (G). * P < 0.05; ** P < 0.01; *** P < 0.001.

Techniques: Control, Quantitative RT-PCR, Gene Expression, Western Blot, Reporter Assay, Transfection, Luciferase, Expressing, Microscopy, Staining, Infection

(A) Schematic representation of the Carinh locus on mouse chromosome 11, which encodes four splice variants. Solid boxes indicate exonic sequences. Arrows indicate the direction of transcription. (B) Synteny analysis of the human CARINH and mouse Carinh loci showing regions of conservation in pink, connected by green bands. (C, D) qRT-PCR analysis of Carinh and Irf1 in bone marrow–derived macrophages (BMDMs) after 8-h treatment with poly(I:C) (1 μg/ml, (C)) or IFNβ (1,000 U/ml, (D)) compared with vehicle control. (E) qRT–PCR analysis of Carinh and Irf1 in Carinh -depleted (Gap Carinh -treated) and control (GapCTRL-treated) BMDMs. (F) Western blot analysis of IRF1 in Gap Carinh -treated and GapCTRL-treated BMDMs. (G) Reporter assay for IRF-driven transcription in mouse RAW-Lucia reporter macrophages transfected with Gap Carinh or GapCTRL and subsequently treated with poly(I:C) (1 μg/ml) or IFNβ (1,000 U/ml). Relative luciferase expression (relative units [RU]) is normalized to time 0 h, set at 100%. Data are the mean ± SEM for three independent experiments (C, D, E, F, G). P -values were calculated by a one-sample t and Wilcoxon test (C, D, E, F); or repeated-measures two-way ANOVA with significant group differences between Gap Carinh and GapCTRL (G). * P < 0.05; ** P < 0.01.

Journal: Life Science Alliance

Article Title: lncRNA CARINH regulates expression and function of innate immune transcription factor IRF1 in macrophages

doi: 10.26508/lsa.202403021

Figure Lengend Snippet: (A) Schematic representation of the Carinh locus on mouse chromosome 11, which encodes four splice variants. Solid boxes indicate exonic sequences. Arrows indicate the direction of transcription. (B) Synteny analysis of the human CARINH and mouse Carinh loci showing regions of conservation in pink, connected by green bands. (C, D) qRT-PCR analysis of Carinh and Irf1 in bone marrow–derived macrophages (BMDMs) after 8-h treatment with poly(I:C) (1 μg/ml, (C)) or IFNβ (1,000 U/ml, (D)) compared with vehicle control. (E) qRT–PCR analysis of Carinh and Irf1 in Carinh -depleted (Gap Carinh -treated) and control (GapCTRL-treated) BMDMs. (F) Western blot analysis of IRF1 in Gap Carinh -treated and GapCTRL-treated BMDMs. (G) Reporter assay for IRF-driven transcription in mouse RAW-Lucia reporter macrophages transfected with Gap Carinh or GapCTRL and subsequently treated with poly(I:C) (1 μg/ml) or IFNβ (1,000 U/ml). Relative luciferase expression (relative units [RU]) is normalized to time 0 h, set at 100%. Data are the mean ± SEM for three independent experiments (C, D, E, F, G). P -values were calculated by a one-sample t and Wilcoxon test (C, D, E, F); or repeated-measures two-way ANOVA with significant group differences between Gap Carinh and GapCTRL (G). * P < 0.05; ** P < 0.01.

Techniques: Quantitative RT-PCR, Derivative Assay, Control, Western Blot, Reporter Assay, Transfection, Luciferase, Expressing

(A) CRISPR/Cas9 strategy to introduce an indel in the first exon of Carinh . (B) Genotyping PCR of Carinh WT (+/+), heterozygous (+/−), and Carinh −/− (−/−) mice. (C) RNA-seq reads of Carinh in BMDM isolated from Carinh WT and Carinh −/− mice. The expression of genes was visualized with Integrative Genomics Viewer 2.9.4. (top) or as normalized counts (bottom). (D) Hierarchical clustering heatmap showing Z-scores of genes driven by IRF1 in BMDM isolated from Carinh WT and Carinh −/− mice. (E) Immunofluorescent staining of BMDMs isolated from Carinh WT and Carinh −/− mice and treated with a vehicle control, poly(I:C) (1 μg/ml), or IFNβ (1,000 U/ml). Cells were stained for ACTIN (red) and IRF1 (green) with DAPI used for nuclear staining. Scale bars: 50 μm (Merge) and 10 μm (Inset). Quantification of IRF1 is shown at the right; dots are individual fields from independent replicates (n = 3). P -values were calculated by DESeq2 (C, D) or one-way ANOVA with prespecified columns (E). * P < 0.05; *** P < 0.001; **** P < 0.0001.

Journal: Life Science Alliance

Article Title: lncRNA CARINH regulates expression and function of innate immune transcription factor IRF1 in macrophages

doi: 10.26508/lsa.202403021

Figure Lengend Snippet: (A) CRISPR/Cas9 strategy to introduce an indel in the first exon of Carinh . (B) Genotyping PCR of Carinh WT (+/+), heterozygous (+/−), and Carinh −/− (−/−) mice. (C) RNA-seq reads of Carinh in BMDM isolated from Carinh WT and Carinh −/− mice. The expression of genes was visualized with Integrative Genomics Viewer 2.9.4. (top) or as normalized counts (bottom). (D) Hierarchical clustering heatmap showing Z-scores of genes driven by IRF1 in BMDM isolated from Carinh WT and Carinh −/− mice. (E) Immunofluorescent staining of BMDMs isolated from Carinh WT and Carinh −/− mice and treated with a vehicle control, poly(I:C) (1 μg/ml), or IFNβ (1,000 U/ml). Cells were stained for ACTIN (red) and IRF1 (green) with DAPI used for nuclear staining. Scale bars: 50 μm (Merge) and 10 μm (Inset). Quantification of IRF1 is shown at the right; dots are individual fields from independent replicates (n = 3). P -values were calculated by DESeq2 (C, D) or one-way ANOVA with prespecified columns (E). * P < 0.05; *** P < 0.001; **** P < 0.0001.

Techniques: CRISPR, Introduce, RNA Sequencing, Isolation, Expressing, Staining, Control

(A) Live/Dead analysis using annexin V and propidium iodide in bone marrow cells derived from Carinh −/− and WT mice. P -values were calculated by repeated-measures two-way ANOVA with Tukey’s multiple-comparisons test. (B) Immunofluorescent staining of peritoneal macrophages isolated from Carinh WT and Carinh −/− mice treated with a vehicle control or poly(I:C) (1 μg/ml). Cells were stained for ACTIN (red) and IRF1 (green) with DAPI-stained nuclei. Scale bars: 10 μm. Quantification of IRF1 is shown at the right, and dots are individual fields of view from independent replicates (n = 3). (C) Volcano plot showing differentially expressed genes in lungs of Carinh −/− and WT mice 4 d after viral infection. The dashed line indicates P -adj = 0.05, n = 4 (left). (C, D) Predicted upstream cytokine regulators of differentially expressed genes in (C); dashed lines indicate Z-score = ±1.75 and P -adj = 0.05, n = 4 (right). (E) Immunoblotting analysis of IRF1 in whole lungs of Carinh WT and Carinh −/− mice 4 d post-infection with IAV (n = 3). P -values were calculated by one-way ANOVA with prespecified columns (B), ** P < 0.01.

Journal: Life Science Alliance

Article Title: lncRNA CARINH regulates expression and function of innate immune transcription factor IRF1 in macrophages

doi: 10.26508/lsa.202403021

Figure Lengend Snippet: (A) Live/Dead analysis using annexin V and propidium iodide in bone marrow cells derived from Carinh −/− and WT mice. P -values were calculated by repeated-measures two-way ANOVA with Tukey’s multiple-comparisons test. (B) Immunofluorescent staining of peritoneal macrophages isolated from Carinh WT and Carinh −/− mice treated with a vehicle control or poly(I:C) (1 μg/ml). Cells were stained for ACTIN (red) and IRF1 (green) with DAPI-stained nuclei. Scale bars: 10 μm. Quantification of IRF1 is shown at the right, and dots are individual fields of view from independent replicates (n = 3). (C) Volcano plot showing differentially expressed genes in lungs of Carinh −/− and WT mice 4 d after viral infection. The dashed line indicates P -adj = 0.05, n = 4 (left). (C, D) Predicted upstream cytokine regulators of differentially expressed genes in (C); dashed lines indicate Z-score = ±1.75 and P -adj = 0.05, n = 4 (right). (E) Immunoblotting analysis of IRF1 in whole lungs of Carinh WT and Carinh −/− mice 4 d post-infection with IAV (n = 3). P -values were calculated by one-way ANOVA with prespecified columns (B), ** P < 0.01.

Techniques: Derivative Assay, Staining, Isolation, Control, Infection, Western Blot

(A) Experimental approach for influenza A/PR8/34 infection via intranasal inoculation in Carinh −/− or Carinh WT mice. (B, C, D) Percentage survival (B), body weights (C), and disease activity score (D) of Carinh WT , Carinh −/− (n = 5–6/group), and mock-infected mice (n = 2) after challenge with a sublethal dose of influenza A/PR8/34. (E, F) Representative microscopic images (E) of IAV nucleocapsid (IAV, green), IRF1 (red), and DNA counterstain (DAPI, blue). (F) Scale bar is 200 μm and qRT–PCR analysis of viral Pr8 RNA (F) in whole lungs and in lungs of Carinh WT and Carinh −/− 4 d after challenge with a sublethal dose of influenza A/PR8/34. (G) Heatmaps of levels of cytokine/chemokine levels in longitudinal nasal shedding samples for 4 d. Data are 4–6 mice/group (B, C, D, E, F, G). P -values were calculated by a t test (F); by a log-rank test for trend in increased survival from Carinh −/− , to Carinh WT , to mock (B); by repeated-measures two-way ANOVA with significant differences between Carinh −/− and Carinh WT (C); by repeated-measures two-way ANOVA with Sidak’s multiple-comparisons test (D); or by two-way ANOVA with Tukey’s multiple-comparisons test between Carinh −/− and WT mice (G). * P < 0.05; *** P < 0.001; **** P < 0.0001.

Journal: Life Science Alliance

Article Title: lncRNA CARINH regulates expression and function of innate immune transcription factor IRF1 in macrophages

doi: 10.26508/lsa.202403021

Figure Lengend Snippet: (A) Experimental approach for influenza A/PR8/34 infection via intranasal inoculation in Carinh −/− or Carinh WT mice. (B, C, D) Percentage survival (B), body weights (C), and disease activity score (D) of Carinh WT , Carinh −/− (n = 5–6/group), and mock-infected mice (n = 2) after challenge with a sublethal dose of influenza A/PR8/34. (E, F) Representative microscopic images (E) of IAV nucleocapsid (IAV, green), IRF1 (red), and DNA counterstain (DAPI, blue). (F) Scale bar is 200 μm and qRT–PCR analysis of viral Pr8 RNA (F) in whole lungs and in lungs of Carinh WT and Carinh −/− 4 d after challenge with a sublethal dose of influenza A/PR8/34. (G) Heatmaps of levels of cytokine/chemokine levels in longitudinal nasal shedding samples for 4 d. Data are 4–6 mice/group (B, C, D, E, F, G). P -values were calculated by a t test (F); by a log-rank test for trend in increased survival from Carinh −/− , to Carinh WT , to mock (B); by repeated-measures two-way ANOVA with significant differences between Carinh −/− and Carinh WT (C); by repeated-measures two-way ANOVA with Sidak’s multiple-comparisons test (D); or by two-way ANOVA with Tukey’s multiple-comparisons test between Carinh −/− and WT mice (G). * P < 0.05; *** P < 0.001; **** P < 0.0001.

Techniques: Infection, Activity Assay, Quantitative RT-PCR

(A, B) ALEKSIN inhibits IFNα and IFNy-induced gene expression of IRF1 and IRF1 target genes Stat1, Ifit3, and Isg15 in a concentration-dependent manner. HMECs were pre-treated with 10 μM or 20 μM of ALEKSIN for 24 h and (A) 200 U/mL of IFNα for 4 h or (B) 10 ng/mL of IFNy for 8 h. RNA was isolated and subjected to qPCR analysis. Experiments were performed in two individual repeats and compared by a t -test. * p < 0.05, ** p < 0.01, and *** p < 0.001. (C, D) ALEKSIN inhibits IFNα and IFNy-induced protein expression of STAT1, STAT2, IRF1, and IRF9 and partial phosphorylation of STAT1 and STAT2. HMECs were treated with 10 μM of ALEKSIN for 24 h and (C) 200 U/mL of IFNα or (D) 10 ng/mL of IFNy for 0, 2, 4, 8, and 24 h. Protein extracts were collected, and levels of IRF1, IRF9, pSTAT1, pSTAT2, tSTAT1, tSTAT2, and α-tubulin were assessed by Western blotting.

Journal: Frontiers in Pharmacology

Article Title: Identification of ALEKSIN as a novel multi-IRF inhibitor of IRF- and STAT-mediated transcription in vascular inflammation and atherosclerosis

doi: 10.3389/fphar.2024.1471182

Figure Lengend Snippet: (A, B) ALEKSIN inhibits IFNα and IFNy-induced gene expression of IRF1 and IRF1 target genes Stat1, Ifit3, and Isg15 in a concentration-dependent manner. HMECs were pre-treated with 10 μM or 20 μM of ALEKSIN for 24 h and (A) 200 U/mL of IFNα for 4 h or (B) 10 ng/mL of IFNy for 8 h. RNA was isolated and subjected to qPCR analysis. Experiments were performed in two individual repeats and compared by a t -test. * p < 0.05, ** p < 0.01, and *** p < 0.001. (C, D) ALEKSIN inhibits IFNα and IFNy-induced protein expression of STAT1, STAT2, IRF1, and IRF9 and partial phosphorylation of STAT1 and STAT2. HMECs were treated with 10 μM of ALEKSIN for 24 h and (C) 200 U/mL of IFNα or (D) 10 ng/mL of IFNy for 0, 2, 4, 8, and 24 h. Protein extracts were collected, and levels of IRF1, IRF9, pSTAT1, pSTAT2, tSTAT1, tSTAT2, and α-tubulin were assessed by Western blotting.

Article Snippet: Rabbit polyclonal antibodies against STAT1-pTyr701, tSTAT1, tSTAT2, IRF1, and IRF9 were obtained from Santa Cruz, and STAT2-pTyr689, from Merck.

Techniques: Gene Expression, Concentration Assay, Isolation, Expressing, Phospho-proteomics, Western Blot

(A, B) ALEKSIN and STATTIC inhibit IFNy-induced expression of IRF1 and IRF9 and phosphorylation of STAT1. HMECs were pre-treated with 20 μM (A) or 10 μM (B) of ALEKSIN for 24 h and 10 μM (B) of STATTIC for 8 h and 10 ng/mL of IFNγ for 2 h. Protein extracts were collected, and levels of IRF1, IRF9, pSTAT1, and α-tubulin were assessed by Western blotting. (C) Docking of ALEKSIN to the SH2 domain of STAT1, STAT2, and STAT3. Graphical representation describes in detail the binding mode of top scored conformation of the inhibitor in the active pocket of the STATs-SH2 domain. Models are presented in cartoon representation with a visible secondary structure, alpha helices, and beta sheets, in yellow for STAT1, tv-orange for STAT2, and deep purple for STAT3. The target pocket for virtual screening with an idealized active-site ligand (protomol) in the SH2 domain of STAT1, STAT2, and STAT3 is shown in transparent surface representation in cyan blue. Top-scored binding conformations of ALEKSIN in the STAT1, 2, and 3-SH2 domains are shown in stick representation (with the highest BS) and colored according to the atomic structure. Docking simulations were performed using Surflex-Dock 2.6 program and were visualized in PyMOL.

Journal: Frontiers in Pharmacology

Article Title: Identification of ALEKSIN as a novel multi-IRF inhibitor of IRF- and STAT-mediated transcription in vascular inflammation and atherosclerosis

doi: 10.3389/fphar.2024.1471182

Figure Lengend Snippet: (A, B) ALEKSIN and STATTIC inhibit IFNy-induced expression of IRF1 and IRF9 and phosphorylation of STAT1. HMECs were pre-treated with 20 μM (A) or 10 μM (B) of ALEKSIN for 24 h and 10 μM (B) of STATTIC for 8 h and 10 ng/mL of IFNγ for 2 h. Protein extracts were collected, and levels of IRF1, IRF9, pSTAT1, and α-tubulin were assessed by Western blotting. (C) Docking of ALEKSIN to the SH2 domain of STAT1, STAT2, and STAT3. Graphical representation describes in detail the binding mode of top scored conformation of the inhibitor in the active pocket of the STATs-SH2 domain. Models are presented in cartoon representation with a visible secondary structure, alpha helices, and beta sheets, in yellow for STAT1, tv-orange for STAT2, and deep purple for STAT3. The target pocket for virtual screening with an idealized active-site ligand (protomol) in the SH2 domain of STAT1, STAT2, and STAT3 is shown in transparent surface representation in cyan blue. Top-scored binding conformations of ALEKSIN in the STAT1, 2, and 3-SH2 domains are shown in stick representation (with the highest BS) and colored according to the atomic structure. Docking simulations were performed using Surflex-Dock 2.6 program and were visualized in PyMOL.

Article Snippet: Rabbit polyclonal antibodies against STAT1-pTyr701, tSTAT1, tSTAT2, IRF1, and IRF9 were obtained from Santa Cruz, and STAT2-pTyr689, from Merck.

Techniques: Expressing, Phospho-proteomics, Western Blot, Binding Assay

Journal: Frontiers in Cellular and Infection Microbiology

Article Title: Cytosolic DNA sensors activation of human astrocytes inhibits herpes simplex virus through IRF1 induction

doi: 10.3389/fcimb.2024.1383811

Figure Lengend Snippet: Primer pairs for the real-time PCR.

Article Snippet: Antibodies against IFI16, IRF1 and IRF9 were obtained from Santa Cruz (Dallas, TX, USA).

Techniques: Sequencing

Review

antibodies against irf1 (R&D Systems)

Structured Review

Images

1) Product Images from "Pioneer factor IRF1 unlocks latent enhancers to rewire chromatin and immunometabolism in inflammatory macrophages"

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