Journal: bioRxiv

Article Title: Selective Immune Silencing by Targeted TGF-β Agonists

doi: 10.64898/2026.01.19.700410

Figure Lengend Snippet: Design and characterization of mouse T cell-targeted TGF- β agonists. a. Schematic of the plasmid construct encoding the MSA–TGM1-targeting-arm fusion protein. b. SDS–PAGE analysis showing the molecular weight and purity of the indicated recombinant proteins. c. SDS–PAGE analysis showing the molecular weight and purity of the indicated recombinant proteins. d. Representative flow cytometry plots showing mouse CD4 and CD8α expression in mouse CD4- or CD8α-transduced HEK293 cells. e. Dose-dependent curves showing GITR, IRF4, and T-bet expression levels in in vitro –cultured CD4 T cells treated with the indicated proteins on day 3. f. Dose-dependent curves showing GITR, IRF4, and T-bet expression levels in in vitro –cultured CD8 T cells treated with the indicated proteins on day 3. g. Dose-dependent curves showing percentages of IFN-γ– and TNF-α–producing cells among in vitro –cultured CD4 T cells treated with the indicated proteins on day 3. h. Dose-dependent curves showing percentages of IFN-γ– and TNF-α–producing cells among in vitro –cultured CD8 T cells treated with the indicated proteins on day 3. i. Dose-dependent curves showing Foxp3 cell percentages in in vitro -cultured CD4 T cells, and Granzyme B–producing cell percentages in in vitro -cultured CD8 T cells treated with the indicated proteins on day 3. Data are presented as mean ± s.e.m.

Article Snippet: The following antibodies were purchased from Miltenyi Biotec: human IgA (130-113-480) and IRF4 (130-100-909).

Techniques: Plasmid Preparation, Construct, SDS Page, Molecular Weight, Recombinant, Flow Cytometry, Expressing, In Vitro, Cell Culture

Journal: bioRxiv

Article Title: Selective Immune Silencing by Targeted TGF-β Agonists

doi: 10.64898/2026.01.19.700410

Figure Lengend Snippet: a. Frequencies of immune cell subsets. b. SCENIC prediction of IRF4 and Blimp1 transcription factor activity. c. Immunoglobulin gene expression. d. UMAP plots showing cell cycle gene signature scores. e. Quantification of cell cycle gene signature scores. f. Scaled signature scores for TGF-β–inducible genes, senescence genes, and apoptosis-related genes, aggregated by sample.

Article Snippet: The following antibodies were purchased from Miltenyi Biotec: human IgA (130-113-480) and IRF4 (130-100-909).

Techniques: Activity Assay, Gene Expression

Journal: Bioactive Materials

Article Title: Immunomodulatory supramolecular hydrogel for rheumatoid arthritis management via adenosine A2A receptor-mediated macrophage remodeling

doi: 10.1016/j.bioactmat.2025.11.031

Figure Lengend Snippet: Macrophage remodeling via MTX@PNSH therapy elicits the reestablishment of immune homeostasis at the transcriptional level. (A) qRT-PCR analysis of relative mRNA of Irf3 with different treatments. (B) qRT-PCR analysis of relative mRNA of Irf4 with different treatments. (C) qRT-PCR analysis of relative mRNA of Irf5 with different treatments. (D) qRT-PCR analysis of relative mRNA of Irf8 with different treatments. (E) Heatmap analysis was performed to compare the gene expression of Irf3 , Irf4 , Irf5 , and Irf8 across different treatment groups. (F) Western blotting analysis of IRF4, and IRF8 levels in macrophages after 24 h of different treatment. (G) Quantitative analysis of IRF4 protein expression. (H) Quantitative analysis of IRF8 protein expression. Data are presented as mean ± SD, n = 3. Statistical significance was determined using one-way ANOVA, followed by Dunnett's post hoc test for comparisons between groups.

Article Snippet: Subsequently, 10 mg of protein was loaded into 10 % SDS-polyacrylamide gel electrophoresis for detection of adenosine A2AR (sc-32261, Santa Cruz), IRF4 (#62834S, Cell Signaling Technology, MA, USA), IRF8 (#5628T, Cell Signaling Technology, MA, USA) and GAPDH (TA-08, ZSGB-BIO, Beijing, China).

Techniques: Quantitative RT-PCR, Gene Expression, Western Blot, Expressing

Journal: STAR Protocols

Article Title: Protocol for mapping murine transcription factor interactomes and composite motifs combining affinity purification mass spectrometry and ChIP-seq

doi: 10.1016/j.xpro.2025.104184

Figure Lengend Snippet: Generation of the IRF4 Bio mouse strain and in vitro differentiation of primary T cells into Th17 cells (A) Schematic overview of the bacterial artificial chromosome (BAC) transgene production for the generation of the IRF4 Bio mouse strain. (B) Cross-breeding of animals: IRF4 Avi mice, containing the BAC transgene, are crossed with ROSA26 BirA mice, ubiquitously expressing BirA ligase under the ROSA26 promoter, resulting in offsprings that express in vivo biotinylated IRF4 (IRF4 Bio ). (C) Western blot analyses of full cellular lysates and nuclear extracts demonstrate successful in vivo biotinylation of IRF4 (left, anti-IRF4 antibody; right, horseradish peroxidase-conjugated streptavidin [SA-HPO]). (D) Workflow for in vitro differentiation of primary Th17 cells.

Article Snippet: IRF4 Avi-tag mice (BAC transgenic mice; strain C57BL/6J- Irf4 em1Bop ) were obtained from Cyagen ( https://www.cyagen.com ), which provides transgenic mouse services with custom-tailored genetic modifications.

Techniques: In Vitro, Expressing, In Vivo, Western Blot

Journal: STAR Protocols

Article Title: Protocol for mapping murine transcription factor interactomes and composite motifs combining affinity purification mass spectrometry and ChIP-seq

doi: 10.1016/j.xpro.2025.104184

Figure Lengend Snippet: Integrated analysis of IRF4 interactors and double-motif occurrence (A) Double-motif occurrence of IRF4 interactors in Th17 ChIP-seq peaks. Heatmap shows normalized co-occurrence frequencies (0–1) for IRF4 together with transcription factors detected by AP-MS, considering only motifs occurring within <5 bp spacing in the same peak. Asterisk (∗): GTF2IRD1-isoform2. (B) Volcano plot of IRF4 interactors identified by AP-MS comparing biotinylated IRF4 (“Bio”) and control (“Ctrl”) conditions in Th17 cells. Labeled interactors indicate TFs with double-motif occurrences in Th17. (C) Sequence logos of the individual TF motifs used for double-motif annotation. (D) STRING-based IRF4 interaction network displaying interactors classified as TFs according to the criteria by Lambert et al. (STRING DB default settings). See also .

Article Snippet: IRF4 Avi-tag mice (BAC transgenic mice; strain C57BL/6J- Irf4 em1Bop ) were obtained from Cyagen ( https://www.cyagen.com ), which provides transgenic mouse services with custom-tailored genetic modifications.

Techniques: ChIP-sequencing, Protein-Protein interactions, Control, Labeling, Sequencing

Journal: Advanced Science

Article Title: Targeting Immunoproteasome in Polarized Macrophages Ameliorates Experimental Emphysema Via Activating NRF1/2‐P62 Axis and Suppressing IRF4 Transcription

doi: 10.1002/advs.202405318

Figure Lengend Snippet: ONX‐0914 Suppresses M1 Polarization via NRF1 and NRF2‐P62 Signaling Pathway. A) Differential binding of transcription factors between M1+ONX‐0914 and M1 predicted by TOBIAS. This plot indicates a significant increase in NRF1 (MAFG::Nfe2l1) and NRF2 (MAF::Nfe2l2) binding score following ONX‐0914 treatment. B) Mouse genome NRF1 and NRF2 motifs obtained from JASPAR 2022 database. C) Gene Ontology enrichment analysis of genes upregulated by ONX‐0914 treatment in M1 phenotype of PMs. The y‐axis represents the GO terms, and the x‐axis indicates the gene count. D) Heatmaps displaying proteasome‐related genes, the NRF1 downstream regulated genes, in M0, M0+ONX‐0914, M1, and M1+ONX‐0914 group (n = 4). E) Volcano plot showing DEGs between ONX‐0914‐treated and untreated M1 AMs. Genes meeting dual thresholds of FDR <0.05 and log 2 (fold change) >1 are highlighted in red, indicating significant upregulation or downregulation after ONX‐0914 treatment. F) Protein expression level of NRF1 analyzed by Western blotting in ONX‐0914‐treated M1 AMs. Data was obtained from 3 independent samples in each group. G) Heatmaps displaying anti‐oxidative stress‐related genes, the NRF2 downstream regulated genes, in M0, M0+ONX‐0914, M1, and M1+ONX‐0914 group of PMs (n = 4). H) Representative IF images of NRF2 after applying 0.2 µM ONX‐0914 to AMs for 24 h. I) After AMs were pretreated with 0.2 µM ONX‐0914 for 6 h, and then stimulated with 250 µg mL −1 dosage of CSE for 24 h, the gene Nfe2l2 was examined by RT‐qPCR (n = 3). J and K) Transcriptional changes in genes associated with NRF1‐increased binding sites (J) and NRF2‐increased binding sites (K) after ONX‐0914 treatment in M1 of AMs. The plot demonstrates that the regulatory activity of upregulated genes significantly surpasses that of downregulated genes (n = 3). L) After silencing the expression of NRF1 with siRNA and treating M1‐polarized AMs with 0.2 µM ONX‐0914, the expression of Nfe2l1, Sqstm1 , and M1 marker genes ( Nos2, Il12b ) were detected by RT‐qPCR (n = 3). M) After silencing the expression of NRF2 with siRNA and treating M1‐polarized AMs with 0.2 µM ONX‐0914, the expression of Nfe2l2, Sqstm1 , and M1 marker genes ( Nos2, Il12b, Il1b, Tnf ) were detected by RT‐qPCR (n = 3). N) Integrative Genomics Viewer (IGV) plot illustrates the binding landscape of the NRF2 transcription factor to Sqstm1 in M0, M0+ONX‐0914, M1 and M1+ONX‐0914. The upper 4 tracks display the signal intensity of NRF2 binding to Sqstm1 , and the lower 4 tracks display the specific locations where NRF2 binding peaks were identified, which suggest the potential regulatory roles in Sqstm1 gene expression after ONX‐0914 treatment in M1 macrophages. O) CUT&Tag‐qPCR shows that the binding affinity of NRF2 to Sqstm1 enhancer and promoter is significantly increased under ONX‐0914 treatment in M1 macrophages. P) RT‐qPCR analysis detected Nfe2l2 , Sqstm1 and M1 marker genes Nos2 , Il12b , Il1b , and Il6 in each group (n = 3). After silencing the expression of NRF2 with siRNA and overexpression of P62 with p3xFLAG‐P62 var2 plasmid, 0.2 µM ONX‐0914 was used to treat M1‐polarized AMs, and the expression of Nfe2l2 , Sqstm1 , and M1 marker genes ( Nos2 , Il12b , Il1b and Il6 ) were detected by RT‐qPCR (n = 3). Q) After silencing the expression of NRF2 with siRNA and using 0.2 µM ONX‐0914 pretreated CSE (250 µg mL −1 ) stimulation of AMs, Sqstm1 and M1 marker genes ( Nos2, Il12b, Il1b, Tnf ) were detected by RT‐qPCR (n = 3). *p < 0.05, **p < 0.01, ***p < 0.001.

Article Snippet: The plasmids used in this study included p3xFLAG‐P62 var2 (#204576, Addgene) and pMIG‐IRF4 (#58987, Addgene).

Techniques: Binding Assay, Expressing, Western Blot, Quantitative RT-PCR, Activity Assay, Marker, Gene Expression, Over Expression, Plasmid Preparation

Journal: bioRxiv

Article Title: Deep Visual Proteomics Unveils Precision Medicine Insights in Composite Small Lymphocytic and Classical Hodgkin Lymphoma

doi: 10.1101/2024.06.12.598635

Figure Lengend Snippet: (A) Proteins ranked by their expression differences in HRS compared to GC. The top ten differentially expressed proteins in HRS are listed, with known lymphoma-related markers in bold. (B) Same as A but for SLL compared to GC. (C) Principal component analysis of GC/HRS/SLL cell proteomes. (D) Volcano plot of the proteomic comparison between HRS and SLL. Red dots represent significantly differentially expressed proteins. Proteins with largest fold-change are annotated with their names. Statistical significance was determined using two-sided t -test (FDR<0.05, S0=0.1). (E) The scatter plot of the differential enrichment of proteins in the HRS and SLL populations in comparison to GC. Proteins that show significant expression in HRS (orange), SLL (magenta), and both populations (blue) are highlighted. (F) Expression of IRF4 determined by MS analysis. The box plot displays the distribution of the data from the minimum to the maximum intensity. The horizontal line within each box represents the mean value of the intensity. Statistical significance was determined using one-way ANOVA. F-value (2, 9) = 92.55; **** P < 0.0001. * P < 0.05. (G) Representative images illustrating immunofluorescence staining of CD20, CD30 and IRF4 in non-lesional germinal center regions and tumor areas. Arrows indicate HRS cells. (H) Quantification of staining intensity of IRF4 in GC, HRS and SLL (similar as in F). The horizontal line within each box represents the median value of the intensity. Statistical significance was determined using one-way ANOVA. F (2, 41786) = 4132, **** P < 0.0001. (I) Representative images illustrating immunofluorescence staining of CD20, CD30 and LENG1 in non-lesional germinal center regions and tumor areas. Arrows indicate HRS cells. (J) Quantification of staining intensity of LENG1 in GC, HRS and SLL as (H). F (2, 40108) = 3054, **** P < 0.0001. (K) Expression of LENG1 determined by MS analysis (similar as in F). F (2, 9) = 217.7. (L) Representative immunohistochemistry images of LENG1 in human lymph node tissues, including both normal and lymphoma cases, sourced from the HPA database. (M) Kaplan-Meier analysis to pan-cancer in TCGA using UCSC Xena, with a focus on LENG1 (see main text). The p-values reflect comparisons between two groups through univariate analysis using the log-rank test.

Article Snippet: The tissue was then blocked with 5% BSA for 20 minutes at room temperature and incubated overnight at 4°C with primary antibodies, including anti-CD30 clone Ber-H2 (1:50, M0751, Agilent) with anti-SKIL antibody (1:200, HPA008472, Sigma-Aldrich) / anti-IRF4 antibody (1:100, 11247-2-AP, Proteintech) / anti-LENG1 antibody (1:500, HPA043541, Sigma-Aldrich) / Alexa Fluor® 488 Collagen I (1:1000, ab275996, Abcam) / CD3 (1:500, A0452, Agilent).

Techniques: Expressing, Comparison, Immunofluorescence, Staining, Immunohistochemistry

Journal: bioRxiv

Article Title: Deep Visual Proteomics Unveils Precision Medicine Insights in Composite Small Lymphocytic and Classical Hodgkin Lymphoma

doi: 10.1101/2024.06.12.598635

Figure Lengend Snippet: (A) Volcano plot comparing the proteomes of HRS and GC. Orange dots represent significantly differentially expressed proteins, and proteins with largest fold-change are annotated with their names. Statistical significance was determined using two-sided t -test (FDR<0.05, S0=0.1). (B) Interaction between compounds and proteins using STITCH and STRING (see Methods). Interactions are visualized in Cytoscape, representing nodes with gray lines connecting them, where thicker lines signified stronger associations. IRF4 and related proteins appear as circles, while compounds appear as ovals in the visual representation. (C) Volcano plot comparing the proteomes of HRS and GC. Magenta dots represent significantly differentially expressed proteins as in (A). (D) Gene Ontology (GO) enrichment analysis on the upregulated pathways in HRS involving iterative comparisons and functional grouping of these pathways. The plot illustrates -log10 (FDR of term) values, with circle size denoting the percentage of associated upregulated entities in HRS compared to the background entities. (E) Heatmap illustrating entities participating in the cell cycle process pathway identified through GO enrichment analysis. GC, HRS, and SLL are represented with four replicates each, with Z-scores depicted using a gradient color scale. (F) Heatmap of the proteasome subunit proteins. (G) GO enrichment analysis of the upregulated pathways in SLL similar to (D). (H) Interaction between compounds and proteasome proteins.

Article Snippet: The tissue was then blocked with 5% BSA for 20 minutes at room temperature and incubated overnight at 4°C with primary antibodies, including anti-CD30 clone Ber-H2 (1:50, M0751, Agilent) with anti-SKIL antibody (1:200, HPA008472, Sigma-Aldrich) / anti-IRF4 antibody (1:100, 11247-2-AP, Proteintech) / anti-LENG1 antibody (1:500, HPA043541, Sigma-Aldrich) / Alexa Fluor® 488 Collagen I (1:1000, ab275996, Abcam) / CD3 (1:500, A0452, Agilent).

Techniques: Functional Assay

Journal: bioRxiv

Article Title: Unveiling IRF4-steered regulation of context-dependent effector programs in Th17 and Treg cells

doi: 10.1101/2023.09.14.557376

Figure Lengend Snippet: (a) IRF4 expression levels in fully functional Th17 and iTreg cells from WT mice. (b) Schematic overview of the experimental setup for the analysis of in vitro polarized Th17 and iTreg cells. (c) FACS analysis of Th17 and iTreg cells grown in the presence of Th17/Treg-polarizing cytokines. Upon lack of IRF4, Th17 cells cannot develop into fully functional effector cells, showing impaired IL-17 expression while concomitantly displaying elevated FOXP3 levels. FACS analysis reveals similar (to slightly elevated) FOXP3 and CD25 levels in iTreg cells from Irf4 −/− as compared to WT mice. (d) LFQ intensity values from LC-MS based proteome analysis for selected cell-type specific proteins. (e) Heatmap of proteins with significantly different expression levels in Th17 and iTreg cells from WT and Irf4 −/− mice ( p-value (limma) < 0.01, |log 2 (FC Irf4 −/− /WT)| > 1). (f) PCA of protein abundances reveals a stronger separation of Irf4 −/− Th17 cells from WT Th17 cells as for Irf4 −/− iTreg cells from WT iTreg cells. (g) Numbers of differentially regulated proteins between naïve CD4 + T (0h) cells and fully differentiated Th17 and iTreg cells (72h) and Irf4 −/− as well as WT mice. Numbers next to the arrows indicate the overall number of differentially expressed proteins (inclusive up- and downregulated proteins) between the different conditions (see also Supplementary Fig. 3). (h) Reactome pathway analysis of proteins that were upregulated upon differentiation (d3 as compared to d0) in an IRF4 dependent manner and/or in differentiated Th17 and iTreg cells from WT and Irf4 −/− animals. Parts of panel (b) were created with Biorender.com and exported under a paid subscription.

Article Snippet: Polyclonal rabbit anti-IRF4 (P173), horseradish peroxidase-conjugated anti-rabbit IgG antibodies as well as horseradish peroxidase-conjugated anti-mouse IgG for Western blot analysis were obtained from Cell Signaling Technology.

Techniques: Expressing, Functional Assay, In Vitro, Liquid Chromatography with Mass Spectroscopy

Journal: bioRxiv

Article Title: Unveiling IRF4-steered regulation of context-dependent effector programs in Th17 and Treg cells

doi: 10.1101/2023.09.14.557376

Figure Lengend Snippet: (a) Graphical illustration of the optimized protocol for the isolation and MS-based characterization of IRF4 complexes derived from ex vivo differentiated Th17 and iTreg cells. (b) BAC transgene for the generation of the IRF4 Bio mouse. (c) Western blot analyses of full cell lysates and nuclear extracts demonstrate successful in vivo biotinylation of IRF4 (left panel: anti-IRF4 antibody; right panel: horseradish peroxidase-conjugated streptavidin (SA-HPO)) as exemplarily shown for Th17 cells. (d) Th17 cells show strong nuclear expression of IRF4 three days after isolation and ex vivo differentiation (white scale bar: 10 µm). (e) FACS analysis of murine Th17 and iTreg cells. (f) Chemical cross-linking combined with stringent washing conditions improves the quality and specificity of IRF4 pulldown experiments. Pulldown experiments were conducted with splenocytes isolated from IRF4 Bio and control mice. Parts of panel (a) were created with Biorender.com and exported under a paid subscription.

Article Snippet: Polyclonal rabbit anti-IRF4 (P173), horseradish peroxidase-conjugated anti-rabbit IgG antibodies as well as horseradish peroxidase-conjugated anti-mouse IgG for Western blot analysis were obtained from Cell Signaling Technology.

Techniques: Isolation, Derivative Assay, Ex Vivo, Western Blot, In Vivo, Expressing, Control

Journal: bioRxiv

Article Title: Unveiling IRF4-steered regulation of context-dependent effector programs in Th17 and Treg cells

doi: 10.1101/2023.09.14.557376

Figure Lengend Snippet: IRF4 interactome analyses were performed using ex vivo generated Th17 and iTreg cells (72h). Naïve splenic CD4 + T cells from three mice were pooled per experiment prior to ex vivo differentiation. A total of three individual AP-LC-MS experiments per cell type was conducted. (a) STRING-based IRF4 interaction network. Displayed are interactors that have a direct functional and/or physical association with IRF4 (STRING DB default settings). The full IRF4 network is shown in Supplementary Fig. 13. Color scheme indicates enrichment either in Th17 (red) or iTreg cells (blue). (b) ClueGO analysis of IRF4 interactors that were significantly enriched either in Th17 or iTreg cells ( adj. p-value < 0.01, |log 2 (FC Th17/iTreg)| > 0.5) (c) Volcano plot displaying differentially enriched IRF4 interactors in Th17 and iTreg cells ( adj. p-value < 0.01, |log 2 (FC Th17/iTreg)| > 0.5). In total, 440 interactors were identified in the IRF4 complex across both cell types. (d) GO and reactome pathway analysis of IRF4 interactors. Grey: “core” IRF4 interactome, red: Th17, log 2 (FC Th17/iTreg) > 0.5, blue: iTreg log 2 (FC Th17/iTreg) < −0.5). (e) Comparison of log-transformed protein fold-changes at the interactome and at the proteome level between Th17 and iTreg cells. (f) Overlap of the IRF4 interactome with proteins that are differentially expressed in Th17 versus iTreg cells (full cellular proteome).

Article Snippet: Polyclonal rabbit anti-IRF4 (P173), horseradish peroxidase-conjugated anti-rabbit IgG antibodies as well as horseradish peroxidase-conjugated anti-mouse IgG for Western blot analysis were obtained from Cell Signaling Technology.

Techniques: Ex Vivo, Generated, Liquid Chromatography with Mass Spectroscopy, Functional Assay, Comparison, Transformation Assay

Journal: bioRxiv

Article Title: Unveiling IRF4-steered regulation of context-dependent effector programs in Th17 and Treg cells

doi: 10.1101/2023.09.14.557376

Figure Lengend Snippet: Bio-ChIP-seq analysis of IRF4 binding was performed in Th17 and iTreg cells differentiated ex vivo from naïve splenic CD4 + T cells of IRF4 Bio mice. Cells derived from ROSA26 BirA (marked as IRF4 WT ) served as control. (a) ChIP-seq binding tracks for selected loci in Treg and Th17 cells at 72 h. (b) Distribution of identified binding sites in Th17 and iTreg cells. (c) Overlap of identified binding sites with EnhancerDB and SilencerDB knowledge bases. (d,e) GO and Reactome pathway analysis of genes bound by IRF4 in (d) enhancer/promoter and (e) silencer regions.

Article Snippet: Polyclonal rabbit anti-IRF4 (P173), horseradish peroxidase-conjugated anti-rabbit IgG antibodies as well as horseradish peroxidase-conjugated anti-mouse IgG for Western blot analysis were obtained from Cell Signaling Technology.

Techniques: ChIP-sequencing, Binding Assay, Ex Vivo, Derivative Assay, Control

Journal: bioRxiv

Article Title: Unveiling IRF4-steered regulation of context-dependent effector programs in Th17 and Treg cells

doi: 10.1101/2023.09.14.557376

Figure Lengend Snippet: Bio-ChIP-seq analysis of IRF4 binding was performed in Th17 and iTreg cells differentiated ex vivo from naïve splenic CD4 + T cells of IRF4 Bio mice. (a) Overlap of motifs identified by MATCH/TRANSFAC and MEME/Tomtom in Th17 and iTreg cells. (b) Binding motifs of IRF4-interactors were identified and enriched in the IRF4-ChIP-Seq data. (c) Novel unmapped motifs identified in Th17 and iTreg cells. (d) Distribution of combined binding motives across the peaks identified by IRF4-ChIP-Seq (IRF4 and indicated interactor within a proximity of 5 bp). Number of peaks where combined motifs were detected is indicated below the bar plots. (e) Reactome analysis of genes regulated by IRF4 and its interactors in Th17 and iTreg cells.

Article Snippet: Polyclonal rabbit anti-IRF4 (P173), horseradish peroxidase-conjugated anti-rabbit IgG antibodies as well as horseradish peroxidase-conjugated anti-mouse IgG for Western blot analysis were obtained from Cell Signaling Technology.

Techniques: ChIP-sequencing, Binding Assay, Ex Vivo

Journal: bioRxiv

Article Title: Unveiling IRF4-steered regulation of context-dependent effector programs in Th17 and Treg cells

doi: 10.1101/2023.09.14.557376

Figure Lengend Snippet: Number of genes regulated by IRF4 and distinct interactors in (a) Th17 and (b) iTreg cells. Insets show an overlap of regulated genes (filtered for IRF4 binding in ‘promoter + enhancer’ or ‘promoter + silencer’), IRF4-dependent differentially expressed proteins at d0 and d3, as well as differentially expressed proteins in Th17 and iTreg cells of Irf4 −/− and littermate control mice (see also Supplementary Fig. 15) (c) Protein expression patterns of selected candidates regulated by the IRF4-complex that display differential protein levels upon IRF4 knock-out. (d) Genes (proteins) regulated by IRF4 that show the same patterns in Th17 and iTreg cells. (e) ChIP-seq binding tracks for selected loci in iTreg and Th17 cells at 72 h. Red arrows indicate enhancer regions.

Article Snippet: Polyclonal rabbit anti-IRF4 (P173), horseradish peroxidase-conjugated anti-rabbit IgG antibodies as well as horseradish peroxidase-conjugated anti-mouse IgG for Western blot analysis were obtained from Cell Signaling Technology.

Techniques: Binding Assay, Control, Expressing, Knock-Out, ChIP-sequencing