The tool, called Spatial ChIP, or ChIPSP, is an R package developed by Tianyi Zhou, Kevin Song, Hui Huang, and Qin Feng of the Center for Nuclear Receptors and Cell Signaling and the Department of Biology and Biochemistry at the University of Houston, together with Ning Lyu of the Division of Pharmacoepidemiology and Pharmacoeconomics and the Harvard-MIT Center for Regulatory Science at Harvard Medical School. Described in the journal BMC Genomics, ChIPSP integrates standard ChIP-seq data with chromatin loop interactions derived from Hi-C, a genome-wide chromosome conformation capture assay that detects which DNA segments physically come into contact inside the nucleus. By overlaying transcription factor binding peaks onto Hi-C maps, the package prioritizes candidate transcription factor-associated genes that lie within the same three-dimensional regulatory neighborhoods, even when they are far apart in linear genomic distance.

The technical logic behind ChIPSP is straightforward but powerful. Hi-C experiments, often performed at resolutions ranging from tens of kilobases down to a few kilobases, yield millions of pairwise chromatin interactions that can be filtered into significant loop calls. ChIPSP takes these loop anchors and asks which of them overlap transcription factor binding sites identified by ChIP-seq, and which of the opposing anchors contain gene promoters. A gene becomes a candidate target when its promoter participates in a loop whose other anchor is occupied by the transcription factor of interest. This spatial framework captures enhancer-promoter communication that crosses chromatin loop boundaries, situations in which the nearest-gene heuristic would either pick the wrong gene or miss the true regulatory relationship entirely. The result is a ranked table of candidate genes accompanied by the supporting loop and peak evidence, allowing researchers to inspect the underlying interactions for any locus of interest.

To evaluate the package, the team applied it to one of the most clinically consequential transcription factors in oncology: the androgen receptor, or AR, the nuclear hormone receptor that drives prostate cancer growth and is the principal target of modern androgen receptor pathway inhibitors such as darolutamide. Using ChIP-seq data from LNCaP prostate cancer cells, a widely used model of androgen-dependent prostate cancer, ChIPSP identified 1,499 candidate AR-associated genes. Strikingly, 658 of these genes, more than forty percent of the candidate list, were missed entirely by conventional linear annotation methods. In other words, more than four hundred additional potential AR targets emerged simply by asking how the genome folds rather than how it reads in a straight line.

The biological plausibility of these newly detected candidates was tested using RNA-seq. Many of the 658 spatially linked genes proved to be androgen-responsive, changing their expression in response to androgen signaling, which is exactly what would be expected of genuine AR targets. Pathway analysis of the expanded candidate set revealed enrichment in developmental transcriptional programs that were distinct from the pathways captured by standard ChIP-seq annotation alone. This suggests that conventional approaches have not merely been undercounting targets but may have been systematically skewing the perceived biology of androgen receptor signaling toward a subset of programs that happen to sit near AR binding sites in linear space.

Among the individual loci highlighted by the analysis, the genes KRT8, which encodes the keratin 8 intermediate filament protein, and MAF, which encodes a transcription factor implicated in cancer biology, stood out as consistent with candidate long-range AR-associated regulation across chromatin loop boundaries. At these loci, AR-bound distal regions are brought into contact with the gene promoters by looping, providing a structural explanation for how the receptor could control genes that appear distant or unrelated in linear genomic coordinates. Supplementary analyses further supported the spatial linkages, showing androgen receptor occupancy and H3K27ac enhancer marks, a histone modification associated with active regulatory elements, at the relevant genomic regions, and connecting distal AR-bound sites to additional targets including NDRG1 and ERRFI1.

To demonstrate that the approach is not specific to a single receptor or cell type, the researchers applied ChIPSP to the glucocorticoid receptor, or GR, another nuclear hormone receptor, using ChIP-seq data from A549 lung cancer cells. In this second system, the package nominated candidate gene targets including IRS2, UBL3, and FOXO1. The team then showed, using RT-qPCR validation experiments, that these genes were responsive to dexamethasone, a synthetic glucocorticoid that activates the receptor. The confirmation that spatially nominated candidates respond experimentally to receptor activation provides a second, independent nuclear-receptor example and strengthens the case that the method captures genuine regulatory relationships rather than genomic coincidences.

The implications for cancer research are considerable. Nuclear receptors such as AR and GR are among the most heavily drugged classes of transcription factors, and identifying their full repertoire of target genes is essential for understanding both therapeutic response and resistance. Genes that regulate cell identity, survival, and lineage plasticity often sit far from the binding sites that control them, and a nearest-gene annotation strategy can leave entire regulatory programs unexamined. By bridging protein-DNA binding data with chromatin interaction maps, ChIPSP extends ChIP-seq annotation into a spatial framework and complements rather than replaces conventional peak-to-gene annotation. Because it is distributed as an R package, the tool is accessible to the same bioinformatics community that already processes ChIP-seq data, requiring only ChIP-seq peak calls and publicly available Hi-C loop datasets as input.

The work also arrives at a moment when three-dimensional genome organization has moved from a specialist interest to a mainstream concern in genomics. Hi-C and related chromatin conformation capture technologies have matured, and public repositories now hold chromatin loop maps for many of the same cancer cell lines routinely used in ChIP-seq studies. Tools like ChIPSP make it practical to combine these data layers routinely rather than treating 3D context as an afterthought reserved for detailed single-locus case studies. The authors note that Hi-C loop detection is resolution-dependent, an important technical caveat for users, since loops called at coarse resolution may merge distinct regulatory contacts, while very high resolution data may be needed to resolve fine-scale enhancer-promoter architecture.

For researchers studying transcription factors in cancer and beyond, ChIPSP offers a practical route to regulatory targets that are invisible to conventional approaches. The package’s developers, who received no external funding for the research, have made the tool available as an open-access contribution, with the underlying study published in BMC Genomics and accompanied by ranked candidate gene lists, pathway analyses across KEGG, Gene Ontology, Hallmark, and Reactome databases, and experimental validation data. As spatially aware annotation becomes part of the standard ChIP-seq analysis pipeline, the field may find that the genomes of cancer cells have been holding far more regulatory information in their folds than linear analysis ever revealed.

Subject of Research: An R package integrating ChIP-seq with Hi-C chromatin loop data to characterize spatial transcription factor gene regulation in three-dimensional genomic context.

Article Title: Spatial ChIP (ChIPSP), an R package for characterizing spatial gene regulation

Article References: Spatial ChIP (ChIPSP), an R package for characterizing spatial gene regulation. (n.d.). https://doi.org/10.1186/s12864-026-13371-w

Image Credits: AI Generated

DOI: 10.1186/s12864-026-13371-w

Keywords: ChIPSP, ChIP-seq, Hi-C, three-dimensional genome organization, chromatin looping, transcription factor, androgen receptor, glucocorticoid receptor, prostate cancer, enhancer-promoter interactions, R package, gene regulation

The research, conducted by an Argentine team working with cell and molecular models of AML, focused on the glucocorticoid receptor, a nuclear receptor that, upon binding its ligand, translocates to the nucleus and modulates gene expression both directly, through binding to specific DNA sequences called glucocorticoid response elements, and indirectly, through interactions with other transcription factors. Glucocorticoids regulate nearly twenty percent of genome activity, and their transcriptional output is shaped by a web of cross-talking signaling pathways. The laboratory had previously shown that histamine signaling through the H1 receptor can modulate glucocorticoid receptor activity through a dual mechanism, potentiating the receptor via G-protein beta-gamma subunits while inhibiting it through the G-alpha-q pathway. The new work asked whether the H2 receptor, a related G-protein-coupled receptor, exerts similar control.

To answer this question, the researchers used the H2 receptor agonist amthamine alongside dexamethasone, a synthetic glucocorticoid, in engineered cell systems. When cells co-expressing the glucocorticoid receptor and the H2 receptor were treated with amthamine before dexamethasone, the maximal transcriptional response driven by the steroid increased by roughly fifty percent, measured with a luciferase reporter built from tandem glucocorticoid response elements. Notably, the potency of dexamethasone was essentially unchanged, indicating that amthamine amplified the ceiling of glucocorticoid-driven transcription rather than making the receptor more sensitive to its ligand. The effect was traced to signaling cascades downstream of the receptor rather than any direct interaction between histaminergic ligands and the glucocorticoid receptor itself, since inhibitors of those cascades abolished the potentiation.

Unpacking the mechanism revealed a strikingly composite picture. Activation of the H2 receptor canonically splits the G-alpha-s subunit from the G-protein beta-gamma dimer, raising intracellular cAMP. Paradoxically, this cAMP arm worked against the glucocorticoid receptor: directly stimulating adenylyl cyclase with forskolin reduced dexamethasone-induced receptor activity, and that inhibition was reversed by H89, a protein kinase A inhibitor, identifying PKA as the mediator of cAMP’s negative influence. The beta-gamma arm, by contrast, pushed in the opposite direction. Amthamine increased ERK phosphorylation while dampening PI3K-Akt and mTOR signaling, and blocking G-beta-gamma with the inhibitor gallein eliminated the potentiation. A MEK inhibitor completely abolished the amthamine effect, whereas inhibiting PI3K or mTOR on their own actually boosted glucocorticoid receptor activity, confirming that these pathways normally restrain the receptor. The net result of H2 receptor activation is therefore a contest between inhibitory cAMP signaling and stimulatory beta-gamma signaling, with the stimulatory side prevailing.

Intriguingly, the team found that clinically used H2 receptor inverse agonists, including cimetidine, famotidine and ranitidine, the familiar heartburn drugs, also enhanced dexamethasone-driven reporter activity. These ligands behave as ERK-biased antagonists, decreasing cAMP while increasing ERK phosphorylation, which tilts the same signaling balance toward potentiation. The finding raises the provocative possibility that widely available antihistamines could, in principle, modulate glucocorticoid signaling, although the authors stress that their study was conducted in cell models and that such a repurposing remains speculative.

The critical question was whether the artificial reporter results would hold for real genes in leukemic cells. In U937 cells, a human AML model, dexamethasone induced the expression of three endogenous glucocorticoid receptor target genes, GILZ, MKP1 and ANXA1, and amthamine co-treatment enhanced all three. But when the experiments were repeated in a U937 clone engineered to overexpress the H2 receptor, the picture became gene-specific: amthamine’s enhancement persisted for ANXA1, vanished for GILZ, and flipped to inhibition for MKP1. The researchers attribute this heterogeneity to differences in promoter architecture. GILZ is driven by tandem high-affinity glucocorticoid response elements, MKP1 by a single chromatin-remodeling element dependent on the coactivator p300, and ANXA1 by a tethering mechanism that does not require direct receptor binding to DNA. Each architecture confers different sensitivity to competition for limiting coactivators such as CBP/p300, and the relative stoichiometry of receptors, G-proteins and the glucocorticoid receptor itself determines the transcriptional outcome for each gene.

The functional consequences for leukemic cell behavior proved equally nuanced. Dexamethasone displayed a biphasic effect on U937 proliferation: low concentrations, from 0.1 to 10 nanomolar, actually increased cell growth, while higher concentrations suppressed it. Amthamine pretreatment dampened the pro-proliferative effect of low-dose dexamethasone without altering the antiproliferative action of high doses. Mechanistically, low-dose dexamethasone increased phosphorylation of S6K, a readout of mTOR pathway activity, and amthamine blocked this increase. Pharmacological mimicry supported the model: forskolin, the PI3K inhibitor wortmannin and rapamycin all hindered the proliferative effect of low-dose dexamethasone, and only rapamycin additionally boosted the antiproliferative effect of high doses. Consistent with these changes, low-dose dexamethasone reduced expression of the differentiation marker CD14, while high doses increased it, alongside parallel changes in the proliferation-related genes GADD45-beta and CDKN1A.

The most clinically resonant experiments involved cytarabine resistance. The team generated a U937-derived clone, U937-640R, that tolerates cytarabine concentrations more than two hundred times higher than the roughly 1.5 nanomolar IC50 that kills parental cells. In these resistant cells, dexamethasone alone re-sensitized the population to cytarabine, pulling the IC50 down from an effectively unmeasurable level to 3.7 nanomolar at a 10 nanomolar dexamethasone dose and to 20 nanomolar at 1 micromolar. Amthamine then produced a paradoxical, dose-dependent modulation: combined with low-dose dexamethasone, it shifted the cytarabine IC50 even lower, to 1.1 nanomolar, but with high-dose dexamethasone it pushed the IC50 back up to 170 nanomolar. In other words, the H2 agonist amplified chemosensitization at steroid doses low enough to minimize side effects but undermined it at high doses. In the sensitive parental cells, by contrast, neither drug alone nor in combination significantly shifted the cytarabine response curve, underscoring that the combination strategy is specifically relevant to the resistant state.

The authors propose a working model centered on REDD1, a canonical glucocorticoid receptor target gene that represses mTOR. In their framework, low glucocorticoid receptor occupancy fails to induce enough REDD1 to counter a constitutive mTOR-activating pathway, so cells proliferate; at higher occupancy, REDD1 induction overrides that pathway and proliferation halts. H2 receptor signaling may intervene at two points, both by lowering mTOR activity independently of the glucocorticoid receptor and by potentiating receptor activity enough to lower the occupancy threshold for REDD1 induction, thereby converting AML’s biphasic glucocorticoid response into a monotonically antiproliferative one. The model also connects to a broader re-evaluation of glucocorticoid resistance in AML: dexamethasone added to intensive chemotherapy has been associated with reduced relapse and improved survival in hyperleukocytic AML, an effect enriched in NPM1-mutated disease, and the recent DEXAML-02 Phase II trial has provided a first prospective clinical signal in older patients. Histamine itself already holds an approved niche in the disease, since histamine dihydrochloride combined with interleukin-2 has been used as maintenance therapy to prevent AML relapse by protecting antitumor lymphocytes and natural killer cells from oxidative damage.

The study’s practical implication is that pairing an H2 receptor agonist with low-dose dexamethasone could allow lower glucocorticoid and chemotherapy exposures while maintaining or enhancing antileukemic efficacy, a strategy that would matter greatly given the substantial adverse effects of chronic steroid use. The authors are careful to note the limitations: all results derive from a single cell line, and validation across additional models and primary AML samples is essential before any clinical translation. Even so, the demonstration that a histamine receptor can rewire glucocorticoid receptor transcription in a gene- and context-dependent manner, and that this rewiring can resensitize chemoresistant leukemic cells at low steroid doses, adds a compelling new dimension to the pharmacology of an old drug class and offers a fresh lead in the search for less toxic AML combinations.

Subject of Research: Cross-talk between the histamine H2 receptor and the glucocorticoid receptor in acute myeloid leukemia treatment

Article Title: Cross‐Talk Between Histamine H2 Receptor and Glucocorticoid Receptor: Potential Implications in Acute Myeloid Leukemia Treatment

Article References: Torralba‐Agu, V., Fernández, N., Shayo, C., Davio, C., Zappia, C. D., & Monczor, F. (2026). Cross‐Talk Between Histamine H 2 Receptor and Glucocorticoid Receptor: Potential Implications in Acute Myeloid Leukemia Treatment. Pharmacology Research & Perspectives, 14(5), Article e70314. https://doi.org/10.1002/prp2.70314

Image Credits: AI Generated

DOI: 10.1002/prp2.70314

Keywords: acute myeloid leukemia, glucocorticoid receptor, histamine H2 receptor, dexamethasone, cytarabine resistance, amthamine, mTOR signaling, G-protein-coupled receptor, REDD1, chemosensitization, U937 cells, leukemia therapy