Dual mechanism of inflammation sensing by the hematopoietic progenitor genome

Abstract

Genomes adapt dynamically to alterations in the signaling milieu, including inflammation that transiently or permanently disrupts genome function. Here, we elucidate how a progenitor cell genome senses and responds to inflammation when the developmental and transcriptional regulator GATA2 is limiting, which causes bone marrow failure in humans and mice and predisposes to leukemia in humans. GATA2^low murine progenitors are hypersensitive to inflammatory mediators. We discovered that the hematopoietic transcription factor PU.1 conferred transcriptional activation in GATA2^low progenitors in response to Interferon-γ and Toll-Like Receptor 1/2 agonists. In a locus-specific manner, inflammation reconfigured genome activity by promoting PU.1 recruitment to chromatin or tuning activity of PU.1-preoccupied chromatin. The recruitment mechanism disproportionately required IKKβ activity. Inflammation-activated genes were enriched in motifs for RUNX factors that cooperate with GATA factors. Contrasting with the GATA2-RUNX1 cooperativity paradigm, GATA2 suppressed and RUNX1 promoted PU.1 mechanisms to endow the progenitor genome with inflammation-sensing capacity.

Fig. 1.. Myd88 ablation does not normalize the disproportionately high monocytic to granulocytic progenitor ratio of GATA2^low mouse embryos.

(A) Strategy for attenuating TLR signaling. Embryonic day 14.5 (E14.5) fetal livers from two timed mating schemes (−77^+/− and −77^+/−;Myd88^−/−) were harvested, and lineage-depleted progenitors were isolated and treated with IFN-γ (1 ng/ml), Pam₃CSK₄ (100 ng/ml), or both for 4 hours. In parallel, fetal livers were harvested for flow cytometry to quantify the cellular composition and GP versus MP ratios. (B) Representative E14.5 embryos obtained from the mating schemes in (A). (C) Total cellularity of E14.5 fetal livers. (D) The expression of select genes was quantitated using RT-qPCR. (E) The responsiveness of Tnf and Cxcl10 to IFN-γ and Pam₃CSK₄ was quantitated using RT-qPCR. Statistics in (C) and (D) were one-way analysis of variance (ANOVA) with Tukey’s multiple comparisons test and in (E) were multiple unpaired t tests. (F) Quantitation of MP and GP frequency within the Ly6C⁺ GMP population in E13.5 to E14.5 fetal liver obtained from eight litters. (G) Quantitation of progenitor populations in E14.5 fetal livers obtained from seven litters. Error bars for all plots represent means ± SD. *P < 0.05; **P < 0.01; ***P < 0.001; Welch’s unequal variance t tests. (H) Model depicting Irf8, but not Myd88, ablation reverses the GP:MP imbalance resulting from Gata2 −77 enhancer deletion. Although the levels of GPs and MPs were reduced by Myd88 ablation, the ratio was not altered.

Fig. 2.. TLR signaling promotes hematopoietic progenitor granulopoietic activity in GATA2^low mouse embryos.

Quantitation of colonies from sorted CMP (A) and GMP (B) (five litters) plated in M3434 methylcellulose media at 500 cells per plate. Colonies were enumerated for erythroid, granulocyte, and monocyte content as CFU-GM, CFU-G, CFU-M, or CFU-GEMM. Error bars for all plots represent means ± SD. Statistics: Welch’s unequal variance t tests. *P < 0.05; **P < 0.01; ***P < 0.001.

Fig. 3.. Genome sensing of inflammatory stimuli in GATA2^low progenitors.

(A) Strategy for gene expression analysis in ER-HOXB8–immortalized (hi) fetal liver myeloid progenitors. Wild-type (hi-77^+/+), GATA2^low (hi-77^−/−), and GATA2/PU.1^low (hi-77^−/−;Spi1URE^−/−) progenitors were treated with vehicle, IFN-γ (1 ng/ml), Pam₃CSK₄ (100 ng/ml), or both for 4 hours (n = 4 biological replicates). Total RNA was isolated for RNA-seq. (B) RT-qPCR analysis of Gata2 and Spi1 expression (n = 4 to 6 biological replicates). (C) Left: Representative Western blot of GATA2 and PU.1 expression. β-Actin was used as a loading control. Right: Densitometric analysis of band intensities normalized to β-Actin (n = 4). M_r is the apparent molecular mass in kDa. Statistics in (B) and (C): One-way ANOVA with Tukey’s multiple comparisons test. *P < 0.05; **P < 0.01; ***P < 0.001. (D) Left: Overlap of activated differentially expressed genes (DEGs) in hi-77^−/− and hi-77^−/−;Spi1URE^−/− cells in response to Pam₃CSK₄. A DEG had |log₂(fold change)| ≥ 1, adjusted P-value <0.05, and transcripts per million (TPM) ≥ 1 in all replicates in at least one of the two conditions compared. Right: Heatmap depicting expression of activated DEGs in response to Pam₃CSK₄ and presented as z-scores of TPMs. Representative genes are indicated. (E) Left: Overlap of activated DEGs in hi-77^−/− and hi-77^−/−;Spi1URE^−/− cells in response to IFN-γ. Right: Heatmap depicting expression of activated DEG in response to IFN-γ and presented as z-scores of TPMs. (F) Left: Overlap of activated DEGs in hi-77^−/− and hi-77^−/−;Spi1URE^−/− cells in response to IFN-γ and Pam₃CSK₄. Right: Heatmap depicting expression of activated DEGs upon combinatorial signaling and presented as z-scores of TPMs. Each section of the heatmaps in (D) and (E) corresponds to each section of Venn diagrams in the same order. (G) TLR gene (Tlr1 and Tlr2) and IFN-γ receptor subunit (Ifngr1 and Ifngr2) expression in GATA2^low and GATA2/PU.1^low progenitors.

Fig. 4.. Molecular determinants of inflammation-activated transcriptional responses.

(A) GATA2-repressed genes that were activated synergistically in hi-77^−/− cells by IFN-γ and Pam₃CSK₄ were parsed into PU.1-activated, PU.1-repressed, and PU.1-insensitive cohorts. The synergistic genes were defined as inflammation-activated DEGs with a larger |log₂(fold change)| in combination versus vehicle than that of IFN-γ versus vehicle and Pam₃CSK₄ versus vehicle. (B) Top: Models depicting three different modes of regulation of signal-dependent genes by GATA2 and PU.1. Bottom: RT-qPCR analysis of representative synergistic genes with different GATA2/PU.1 regulatory modes was quantitated with RT-qPCR (n = 4). (C) The responsiveness of prioritized synergistic genes in primary lineage-depleted progenitors isolated from E14.5 fetal livers of −77^+/+ (n = 6) and −77^−/− (n = 4) embryos (pooled from three litters). Statistics in (B) and (C): Multiple unpaired t tests. *P < 0.05; **P < 0.01; ***P < 0.001.

Fig. 5.. Locus-specific mechanisms of inflammation-sensing in GATA2^low progenitors.

(A) Schematic depicting the chromatin occupancy analysis. hi-77^+/+, hi-77^−/−, and hi-77^−/−;Spi1URE^−/− cells were stimulated with vehicle or IFN-γ (1 ng/ml) and Pam₃CSK₄ (100 ng/ml) for 4 hours. Cells were cross-linked with 0.1% formaldehyde for 2 min before CUT&Tag analysis with antibodies against GATA2 (n = 4), PU.1 (n = 4), or H3K4me3 (n = 2). IgG with hi-77^+/+ served as a negative control. (B) Comparison of median PU.1 (left) and GATA2 (right) CUT&Tag signals at 217 DEGs in untreated versus inflammation-activated hi-77^−/− and hi-77^+/+ cells, respectively. DEGs were colored by their cluster identities. The median signal is the median of signals from 1 kb upstream to 1 kb downstream of the transcription start site (TSS) of each DEG (n = 4). (C) Heatmaps depicting PU.1 CUT&Tag signals in untreated versus inflammation-treated hi-77^+/+ (WT), hi-77^−/− (G^low), and hi-77^−/−;Spi1URE^−/− (GP^low) progenitors. A total of 217 DEGs were clustered as inflammation-activated and inflammation-independent and ranked by their log₂(fold change) on the basis of RNA-seq data of hi-77^−/− (inflammation) versus hi-77^−/− (vehicle). FC, fold change. (D) Range of signals for three categories of DEGs. The range is defined by the average signals of the four replicates from the same condition and presented as lines in the figure. Average signals are calculated over (TSS − 1 kb, TSS + 1 kb) with a bin width of 100 bp. (E) Representative CUT&Tag profiles (replicate 2) for genes with inflammation-induced GATA2 and PU.1 occupancy (Cd40 and Gbp5) and genes prebound by GATA2 and PU.1 (Ccl3 and Cd69). (F) Model of signal-dependent GATA2 and PU.1 occupancy at loci in GATA2^low progenitors. (G) Model depicting inflammation-independent GATA2 and PU.1 occupancy at inflammation-activated genes.

Fig. 6.. Distinct molecular hallmarks of inflammation-activated genes with inflammation-induced or inflammation-independent PU.1 occupancy.

(A) ATAC-seq profiles depicting chromatin accessibility of the top 10 genes with inflammation-induced (left) and inflammation-independent PU.1 occupancy (right). ATAC-seq profiles in hi-77^−/− cells without inflammation were mined from published data (GSE201968) at loci harboring PU.1 occupancy (CUT&Tag replicate 2) in vehicle-treated (−) and inflammation-treated (+) hi-77^−/− cells. (B) Comparison of average ATAC-seq signals from four biological replicates between genes with inflammation-induced PU.1 occupancy and genes with inflammation-independent PU.1 occupancy in (A). (C) Dose-response curve of three representative genes from inflammation-induced PU.1 occupancy and inflammation-independent PU.1 occupancy to IKK inhibitor BMS-345541. hi-77^−/− progenitors were pretreated with increasing concentrations of BMS-345541 for 1 hour and treated with both IFN-γ (1 ng/ml) and Pam₃CSK₄ (100 ng/ml) for 4 hours (n = 6 biological replicates). hi-77^+/+ treated with or without both agents served as negative controls. Median inhibitory concentration (IC₅₀) for each gene was calculated using nonlinear regression. Cd69 IC₅₀ was uncalculated. (D) The IC₅₀ of genes with inflammation-induced PU.1 occupancy was compared to those of genes with inflammation-independent occupancy. Individual values in (B) and (D) and means ± SEM in (B) to (D) were shown. Unpaired t test in (B) and (D) and multiple unpaired t test in (C). *P < 0.05; **P < 0.01; ***P < 0.001.

Fig. 7.. Inflammation imparts opposing GATA2 and RUNX1 mechanisms.

(A) Schematic of promoters and introns used for motif enrichment analysis of inflammation-activated genes. The promoter was defined as 1 kb upstream and 100 bp downstream of the TSS. (B) Heatmap showing motif enrichment, by MEME analysis, at promoters (top) and introns (bottom) of IFN-γ– and TLR-activated genes in hi-77^+/+, hi-77^−/−, and hi-77^−/−;Spi1URE^−/− cells. Motifs binding canonical inflammation-activated transcription factors (STAT, IRF, and NF-κB) are presented as positive controls. Motifs enriched in hi-77^−/− but not hi-77^+/+ or hi-77^−/−;Spi1URE^−/− progenitors were ranked on the basis of −log₁₀(P value) and a P value cutoff of 0.005. Motifs with adjusted P value > 0.05 were set to 1 Complete heatmaps of all enriched motifs were presented in fig. S8 (A and B). (C) Schematic depicting positions of guide RNA for CRISPR-Cas9 gene editing tool to delete Runx1 gene in hi-77^−/− progenitors. (D) PCR-based genotyping assay for Runx1^−/− allele in hi-77^−/− progenitors. Two hi-77^−/−;Runx1^−/− clonal lines are denoted as C1 and C2. (E) Runx1, Gata2, and Spi1 expression in hi-77^+/+, hi-77^−/−, and hi-77^−/−; Runx1^−/− progenitors (n = 4). (F) Representative Western blot analysis of RUNX1 in hi-77^+/+, hi-77^−/−, and hi-77^−/−;Runx1^−/− progenitors with β-Actin as a control. M_r is the apparent molecular mass in kDa. (G) The responsiveness of genes to IFN-γ and Pam₃CSK₄ was quantified using RT-qPCR. hi-77^+/+, hi-77^−/−, and hi-77^−/−;Runx1^−/− progenitors were treated with or without IFN-γ (1 ng/ml), Pam₃CSK₄ (100 ng/ml), or both for 4 hours (n = 4 to 6). Statistics in (E) and (F): One-way ANOVA with Tukey’s multiple comparisons test. *P < 0.05; **P < 0.01; ***P < 0.001.

Fig. 8.. Differential GATA2 and RUNX1 functions in genome sensing of qualitatively distinct inflammatory signals.

(A) PCR-based genotyping assay for Runx1^−/− allele in hi-77^+/+ progenitors. (B) Runx1, Gata2, and Spi1 expression was quantified using RT-qPCR. (C) The responsiveness of select genes to TLR1/2 agonist (Pam₃CSK₄) and TLR4 agonist (LPS) were quantitated using RT-qPCR. hi-77^+/+, hi-77^−/−, and hi-Runx1^−/− progenitors were treated with vehicle, Pam₃CSK₄ (100 ng/ml), or LPS (100 ng/ml) for 4 hours (n = 6 biological replicates). (D) Tlr1, Tlr2, and Tlr4 expression in hi-77^+/+, hi-77^−/−, and hi-Runx1^−/− progenitors was quantified using RT-qPCR. Statistics: One-way ANOVA with Tukey’s multiple comparisons test. *P < 0.05; **P < 0.01; ***P < 0.001.

Fig. 9.. Model for hematopoietic progenitor genome sensing of inflammation.

The hematopoietic progenitor genome uses a dual PU.1-dependent mechanism to sense and respond to inflammation. A gene cohort has inaccessible chromatin in the steady state, and inflammation increases chromatin accessibility and occupancy of the hematopoietic transcription factors GATA2 and PU.1 to activate transcription via an IKKβ-dependent mechanism. At another inflammation-activated gene cohort, GATA2 and PU.1 occupancy precede inflammation, and this mechanism is not compromised by IKKβ inhibition. RUNX1 co-occupies chromatin with GATA2 and PU.1, yet GATA2 and RUNX1 differentially control inflammation-activated transcription, and transcriptional responses involving distinct TLR signaling pathways activated by unique pathogen-associated molecular patterns (82).