Transcription factors and 3D genome conformation in cell-fate decisions.

Exogenous nucleosides modulate expression and activity of transcription factors in Caco-2 cells

Background: cell-free DNA (cfDNA) in plasma primarily originates from hematopoietic cells in healthy individuals, but in cancer patients, it includes circulating tumor DNA from dead tumor cells. cfDNA mutation analysis is already used in clinical practice for biomarker research and treatment decisions. Recent studies have shown that cfDNA fragmentation patterns reflect tumor tissue chromatin status. Inferring transcription factor (TF) activity from cfDNA TF binding site (TFBS) coverage patterns offer a minimally invasive assay to understand cancer biology with limited sequencing depth. However, the accuracy of TF activity inference has only been examined for a few well-known TFs such as AR and ESR1. We conducted a comparative analysis of 377 TF activities between cfDNA whole genome sequence (WGS) and tumor Assay for Transposase-Accessible Chromatin using sequencing (ATAC-sequencing). Methods: Two liver cancer cell lines, HepG2 with a gain-of-function CTNNB1 mutation and HuH7 with wild-type CTNNB1, were used to generate xenograft models. ATAC-sequencing/RNA-sequencing and WGS were performed on the tumors and pooled plasma-derived cfDNA from each model. TCF/LEF family TF activities, downstream targets of CTNNB1, were compared between models with different CTNNB1 mutation statuses in both tumor ATAC-sequencing and cfDNA WGS. For 377 TFs with at least 10, 000 TFBS, the correlation of TF activities between tumor ATAC-sequencing and cfDNA WGS was examined in each model. Furthermore, tumor model-specific TFs were identified based on ATAC-sequencing, followed by comparison of cfDNA TFBS coverage of these TFs between the two models. Results: In pilot analysis with TCF/LEF family TFs, both tumor ATAC- sequencing and cfDNA WGS from HepG2 xenograft model showed higher TCF7 and TCF7L2 activities compared to HuH7. In an expanded analysis of 377 TFs, we found a significant and strong correlation between tumor and cfDNA TF activities (Spearman’s rank correlation coefficients for HepG2 and HuH7: -0.90 and -0.86, respectively). For tumor model-specific TFs, “HepG2-HIGH” and “HuH7-HIGH” which are composed of 29 and 18 TFs with the highest variance between models, cfDNA TFBS coverage of these two groups of TFs also showed significant differences between tumors (p<0.05). Conclusion: Our results indicate that cfDNA can accurately estimate the activity of over 300 TFs in tumor. To assess the potential utility of cfDNA TFBS analysis in clinical samples, further studies are warranted. Citation Format: Ryuji Tamaki, Koji Sagane, Shuyu Dan Li, Taisuke Hoshi. Comparative analysis of transcription factor activities derived from low-pass whole genome sequencing of cell-free DNA and from ATAC-sequencing of tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5899.

Single-cell multiomic technologies enable holistic reconstruction of cell states and hold the potential to identify drivers of tumor evolution and drug resistance. However, the data sparsity of single-cell assays can preclude accurate estimation of transcription factor activity. In addition, gene expression alone cannot capture transcription factor activity, especially in the context of drug treatment which alters transcription factor function without suppressing gene expression. To circumvent these challenges, we have developed epiregulon, a R package that constructs gene regulatory networks and infers transcription factor (TF) activity in single cells by integrating single cell gene expression, chromatin accessibility and bulk TF occupancy data. Epiregulon applies tests of independence to identify likely transcription factor - regulatory element - target gene relationships occurring at joint probabilities exceeding the expected probabilities of independent events. With epiregulon, we are able to detect lineage factor activity at enhanced sensitivity and predict perturbations more accurately than gene expression could. We further applied this tool to understand transcription factor activity to enzalutamide treatment and identify potential drivers of enzalutamide resistance. Finally, we generated a ground truth dataset using reprogram-seq, a technology that captures multi-omic profiles of single cells upon expression of defined transcription factors. Epiregulon correctly predicts NKX2-1 targets and demonstrates that NKX2-1 expression reprograms the epigenome towards a neuroendocrine state in LNCaP cells. Epiregulon is a useful tool that constructs gene regulatory networks to model the underlying gene regulation hierarchies that drive gene expression and cell states. Citation Format: Tomasz Wlodarczyk, Jenille Tan, Kerstin Seidel, Diana Wu, Shang-Yang Chen, Aleksander Chlebowski, Timothy Keyes, Yu Guo, Aaron Lun, Christopher Siebel, Shiqi Xie, Xiaosai Yao. Epiregulon infers single-cell transcription factor activity to dissect mechanisms of lineage plasticity and drug response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3141.

Transcription factors bind to specific DNA sequences in the vicinity of genes and are the major regulators of gene expression. The activity of these factors is controlled by the various signal transduction mechanisms and second messenger systems used by the cell to coordinate and respond to signals from the extracellular environment, including hormones and the local tissue environment. Because of this, transcription factors can be used to indirectly monitor the activity of many cellular components, such as the status of membrane receptor-hormone interactions, the biochemical transformations accompanying the relay of this binding event to a cytoplasmic signal, or the multitude of protein targets used to coordinate this signal to various cellular compartments. By monitoring the activity of several transcription factors, one can gain an overall impression of the state of various signal transduction systems and other targets. Therefore, monitoring transcription factors’ activities is useful for determining the influence that a particular herb or herbal component may have on a cell. The easiest way to detect changes in transcription factor activity is through luciferase reporter gene technology. These plasmid constructs can contain either the complex promoter region of a gene or binding sites for a particular transcription factor. Comparing activity profiles of herbs or herbal components to specific transcription factors can provide an index of specificity, in addition to determining potential mechanisms of action. The particular transcription factor binding sites comprising the battery are chosen based on the type of activity an herb exhibits and/or upon a well-established therapeutic target of the disorder. The same criteria determine the cell type or types used for the assays. Results and examples of the utilization of this technology over the past several years at the National Center for the Development of Natural Products (NCNPR) will be presented from the therapeutic areas of inflammation and immunomodulation.

A high proportion of oncogenes and tumor suppressor genes encode transcription factors. Deregulated expression or activation and inactivation of transcription factors as well as mutations and translocations play critical roles in tumorigenesis. Furthermore, the majority of oncogenic signaling pathways converge on sets of transcription factors that ultimately control gene expression patterns resulting in tumor formation and progression as well as metastasis. Under normal physiological conditions whole sets of genes with similar functions are regulated by highly specific, tightly regulated upstream transcriptional regulators, whereas in cancer aberrant activation of these transcription factors leads to deregulated expression of multiple gene sets associated with tumor development and progression. The activity of these transcription factors can be modulated by multiple mechanisms including posttranslational modifications. Activation or inactivation of transcription factors promote cancer development, cell survival and proliferation and induce tumor angiogenesis. Since many of these transcription factors are inactive under normal physiological conditions and their expression and activities are tightly regulated, these transcription factors represent highly desirable and logical points of therapeutical interference in cancer development and progression. Three major families of transcription factors have emerged as important players in human cancer and are validated targets in drug discovery for cancer therapy: 1) the NF-kappaB and AP-1 families of transcription factors, 2) the STAT family members and 3) the steroids receptors. This review aims to elucidate the divergent molecular mechanisms involved in the deregulated activation of transcription factor signaling in malignant transformation, although additional transcription factor families such as the Ets factors, ATF family members, basic helix-loop-helix transcription factors etc. are additional critical transcriptional regulators in human cancer. We explore new approaches to specifically inhibit these transcription factors in cancer in order to validate them as a drug targets. Efforts to develop novel viral vectors for therapeutic applications are also discussed.

The reticulocyte-type 15-lipoxygenase (15-LO-I) has been implicated in atherogenesis because of its capability of oxidizing low density lipoprotein. Therefore, we investigated the expression of the 15-LO-I gene in human umbilical vein endothelial cells (HUVEC). Nonactivated HUVEC did not exhibit detectable 15-LO-I mRNA. However, exposure of the cells to interleukin 4 (IL-4) induced the transcription of the 15-LO-I gene in a time- and concentration-dependent manner. Interestingly, this induction was not paralleled by a concomitant production of the functional 15-LO-I enzyme, as indicated by activity assays and immunoblotting. To gain more information about the mechanism of the induction process, we investigated IL-4-dependent activation of nuclear transcription factors for which binding sites were previously identified in the 5′-flanking region of the human 15-LO-I gene. Electrophoretic mobility shift assays revealed that IL-4 can activate signal transducer and activator of transcription 6, activator protein 2, GATA motif-binding transcription factor 1, nuclear factor 1, and SP-1 in HUVEC in a time- and concentration-dependent manner. Activation of these transcription factors was observed as early as 30 min after cytokine exposure.These data indicate that IL-4 upregulates the transcription of the 15-LO-I gene in human vascular endothelial cells, and this process may involve the activation of several nuclear transcription factors. The lack of active 15-LO-I protein in the presence of functional 15-LO-I mRNA suggests additional regulatory elements of 15-LO expression at post-transcriptional levels.

BackgroundStem cell transplantation as therapy for hematological disorders is often hampered by severe graft-versus-host-disease. This may be reduced by umbilical cord blood transplantation, an effect that has been attributed to qualitative differences between neonatal and adult T cells. We compared levels of secreted proteins and cytokine mRNA induced in cord blood leukocytes (CBL) and adult blood leukocytes (ABL) by various stimuli.ResultsWhile interleukin-2 (IL-2) levels were similar in CBL and ABL, there was less induction of the Th1 cytokine interferon-γ in CBL. Production of the Th2 cytokines IL-4, IL-5, and IL-13 and the hematopoietic cytokine IL-3 was much lower in CBL versus ABL after T-cell receptor-mediated stimulation, whereas production of GM-CSF was comparable in the 2 cell types. The lower levels of Th1 and Th2 cytokines were maintained in CBL during a 4-day time-course study, while after 12 hours IL-3 and GM-CSF reached in CBL levels similar to those in ABL. For all cytokines except IFNγ, the IC50 values for inhibition by cyclosporin A were similar in ABL and CBL. In contrast, there was less expression and activation of transcription factors in CBL. Activation of NF-κB by TPA/ionomycin was detected in ABL but not CBL. Furthermore, there was less expression of the Th subset-specific transcription factors T-bet and c-maf in CBL versus ABL, whereas GATA-3 expression was similar. Expression of T-bet and c-maf correlated with expression of the Th1 and Th2 cytokines, respectively. Time course experiments revealed that T-bet expression was stimulated in both cell types, whereas c-maf and GATA-3 were induced only in ABL.ConclusionThe diminished capability of CBL to synthesize cytokines is probably due to decreased activation of NF-κB, whereas differences in Th subsets are due to differences in regulation of Th lineage-specific transcriptions factors. We propose that the reduced incidence and severity of GvHD after allogeneic transplantation of umbilical CB cells is due to lesser activation of specific transcription factors and a subsequent reduction in production of certain cytokines.

BackgroundThe endoplasmic-reticulum (ER) responds to the burden of unfolded proteins in its lumen by activating intracellular signal transduction pathways, also known as the unfolded protein response (UPR). Many signal transduction events and transcription factors have been demonstrated to be associated with ER stress. The process in which ER stress affects or interacts with other pathways is still a progressing topic that is not completely understood. Identifying new transcription factors associated with ER stress pathways provides a platform to comprehensively characterize mechanism and functionality of ER.MethodsWe utilized a transcription factor (TF) activation plate array to profile the TF activities which were affected by ER stress induced by pharmacological agents, thapsigargin (TG) and tunicamycin (TM) at 1 h, 4 h, 8 h and 16 h respectively, in MiaPACA2 cells. The altered activity patterns were analyzed and validated using gel shift assays and cell-based luciferase reporter assay.ResultsThe study has not only confirmed previous findings, which the TFs including ATF4, ATF6, XBP, NFkB, CHOP and AP1, were activated by ER stress, but also found four newly discovered TFs, NFAT, TCF/LEF were activated, and PXR was repressed in response of ER stress. Different patterns of TF activities in MiaPaCa2 were demonstrated upon TM or TG treatment in the time course experiments. The altered activities of TFs were confirmed using gel shift assays and luciferase reporter vectors.ConclusionThis study utilized a TF activation array technology to identify four new TFs, HIF, NFAT, TCF/LEF and PXR that were changed in their activity as a result of ER stress induced by TG and TM. The TF activity patterns were demonstrated to be diverse in response to the duration of TG or TM treatment. These new findings will facilitate further unveiling the complex mechanisms of the ER stress process and associated diseases.

Thermal proteome profiling with label-free quantitation using ion-mobility-enhanced LC-MS offers versatile data sets, providing information on protein differential expression, thermal stability, and the activities of transcription factors. We developed a multidimensional data analysis workflow for label-free quantitative thermal proteome profiling (TPP) experiments that incorporates the aspects of gene set enrichment analysis, differential protein expression analysis, and inference of transcription factor activities from LC-MS data. We applied it to study the signaling processes downstream of melanocortin 3 receptor (MC3R) activation by endogenous agonists derived from the proopiomelanocortin prohormone: ACTH, α-MSH, and γ-MSH. The obtained information was used to map signaling pathways downstream of MC3R and to deduce transcription factors responsible for cellular response to ligand treatment. Using our workflow, we identified differentially expressed proteins and investigated their thermal stability. We found in total 298 proteins with altered thermal stability, resulting from MC3R activation. Out of these, several proteins were transcription factors, indicating them as being downstream target regulators that take part in the MC3R signaling cascade. We found transcription factors CCAR2, DDX21, HMGB2, SRSF7, and TET2 to have altered thermal stability. These apparent target transcription factors within the MC3R signaling cascade play important roles in immune responses. Additionally, we inferred the activities of the transcription factors identified in our data set. This was done with Bayesian statistics using the differential expression data we obtained with label-free quantitative LC-MS. The inferred transcription factor activities were validated in our bioinformatic pipeline by the phosphorylated peptide abundances that we observed, highlighting the importance of post-translational modifications in transcription factor regulation. Our multidimensional data analysis workflow allows for a comprehensive characterization of the signaling processes downstream of MC3R activation. It provides insights into protein differential expression, thermal stability, and activities of key transcription factors. All proteomic data generated in this study are publicly available at DOI: 10.6019/PXD039945.

Phosphorylation of CREB transcription factor and expression of the immediate early gene c-fos in the brains of vertebrates play a key role in the molecular genetic mechanisms of long-term neuronal plasticity. The present study identifies the conditions for immunohistochemical detection of pCREB and c-Fos in the nervous system of the mollusk Helix lucorum (Pulmonata: Stylommatophora); activation of these transcription factors was demonstrated after administration of the convulsive agent pentylenetetrazole. Basal pCREB and c-Fos levels in the central nervous system of intact animals were low. Injection of pentylenetetrazole at a dose of 600 mg/kg evoked characteristic stereotypical motor responses, along with sharp reductions in the phosphorylation of CREB1 and expression of the immediate early gene c-fos, this also occurring in identified neurons. Double immunofluorescent labeling of pCREB and c-Fos showed that expression of c-Fos transcription factor was seen only in pCREB-immunoreactive neurons. These data provide evidence that activation of pCREB and c-Fos transcription factors can be used as molecular markers for mapping the processes of neuronal plasticity in the nervous systems of mollusks.

Astrocytoma is the most frequently occurring nervous system cancer in humans. Glioblastoma (GBM) is the most severe form of astrocytoma ([WHO] grade IV), and GBM patients rarely live beyond 5 years. Understanding how transcription factor regulatory networks contribute to the severity of astrocytoma will help identify novel therapeutic strategies for GBM. Nuclear Respiratory Factor 1 (NRF1) is a transcription factor (TF) that we have previously demonstrated as being active in cancer and having the ability to participate in crosstalk with other TFs such as the E2Fs and MYC. We have previously demonstrated that NRF1 gene networks are associated with increased NRF1 gene expression and GBM patient survival outcomes. Here, we examined the TF activity landscape that is differentially expressed in GBM compared to non-tumor tissue and in lower grade (I, II, and III) astrocytoma and their role in disease severity. Activities of 247 TFs including NRF1 were examined using patient mRNA expression data from four independent public datasets including the TCGA. The R package ‘limma' with ‘voom' was used to calculate the differential expression of each gene in the microarray and RNA-Seq datasets included in the present study, and then LRPath was employed to determine TF activity. NRF1 transcription factor (TF) activity is upregulated in glioma, correlates with disease aggressiveness, and is associated with cancer hallmarks and key functional pathways known to be dysregulated in cancer. Further analysis of data from all four patient cohorts including TCGA showed that NRF1 TF activity positively correlates with NFE2L2, E2F1, and RFX1, and negatively correlates with MEF2. Differential activity of these TFs is associated with astrocytoma disease severity. Protein-protein interaction networks (PPIN) were then generated for the TF combinations separately for male and female patients. Downstream gene ontology pathway analysis of the TF target genes revealed that DNA repair (GO:0006281), histone modification (GO:0016570), mitotic cell cycle (GO:0000278), and regulation of neurotransmitter level (GO: 0001505) genes regulated by NFE2L2, E2F1, and RFX1 are upregulated corresponding to increased NRF1 activity, while tissue development (GO: 0009888) genes regulated by MEF2 are down-regulated corresponding to increased NRF1 activity. These findings suggest that aberrant transcription factor activity of NRF1, NFE2L2, E2F1, MEF2, and RFX1 may be involved in the pathogenesis and severity of astrocytoma. Further analysis of these TF-regulated gene signatures combined with patient clinical outcomes will help pave the way for next generation targeted therapies and drug combination strategies for GBM patients. Citation Format: Kaumudi Bhawe, Quentin Felty, Changwon Yoo, Deodutta Roy. Aberrant transcription factor activity of NRF1, NFE2L2, E2F1, RFX1, and MEF2 associated with severity of astrocytoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4391.

MotivationAccurately inferring transcription factor (TF) activity from single-cell RNA sequencing (scRNA-seq) data remains a fundamental challenge in computational biology. While existing methods rely on statistical models, motif enrichment, or prior-based inference, they often depend on deterministic assumptions about regulatory relationships and rely on static regulatory databases. Few approaches effectively integrate prior biological knowledge with data-driven inference to capture novel, dynamic, and context-specific regulatory interactions.ResultsTo address these limitations, we develop scRegulate, a generative deep-learning framework leveraging variational inference to estimate TF activities guided by experimental TF-target gene relationships and progressively adapted based on the input scRNA-seq data. By integrating structured biological constraints with a probabilistic latent space model, scRegulate offers a scalable and biologically grounded estimation of TF activity and gene regulatory network (GRN). Comprehensively benchmarking on public experimental and synthetic datasets demonstrates scRegulate’s superior ability. Further, scRegulate accurately recapitulates experimentally validated TF knockdown effects on a Perturb-seq dataset for key TFs. Applied to experimental human PBMC scRNA-seq data, scRegulate infers cell-type-specific GRNs and identifies differentially active TFs aligned with known regulatory pathways. scRegulate’s TF activity representations capture transcriptional heterogeneity, enabling accurate clustering of cell types. scRegulate is highly efficient, frequently an order of magnitude faster than common baselines. Collectively, our results establish scRegulate as a powerful, interpretable, and scalable framework for inferring TF activities and GRNs from single-cell transcriptomics.Availability and implementationResults and scripts available at github.com/YDaiLab/scRegulate.

Live-cell assays to measure cellular function performed within 3D cultures have the potential to elucidate the underlying processes behind disease progression and tissue formation. Cells cultured in 3D interact and remodel their microenvironment and can develop into complex structures. We have developed a transcription factor (TF) activity array that uses bioluminescence imaging (BLI) of lentiviral delivered luminescent reporter constructs that allows for the non-invasive imaging of TF activity in both 2D and 3D culture. Imaging can be applied repeatedly throughout culture to capture dynamic TF activity, though appropriate normalization is necessary. We investigated in-well normalization using Gaussia or Renilla luciferase, and external well normalization using firefly luciferase. Gaussia and Renilla luciferase were each unable to provide consistent normalization for long-term measurement of TF activity. However, external well normalization provided low variability and accounted for changes in cellular dynamics. Using external normalization, dynamic TF activities were quantified for five TFs. The array captured expected changes in TF activity to stimuli, however the array also provided dynamic profiles within 2D and 3D that have not been previously characterized. The development of the technology to dynamically track TF activity within cells cultured in both 2D and 3D can provide greater understanding of complex cellular processes.

A Highly Robust Model for Predicting Outcome of Multiple Myeloma Patients By Inferring Patient-Specific Transcription Factor Activity

Simple SummaryCellCall is an R package tool that is used to analyze cell–cell communication based on transcription factor (TF) activities calculated by cell-type specificity of target genes and thus cannot directly handle two-condition comparisons. We developed CellCallEXT to complement CellCall. CellCallEXT can directly identify ligand–receptor (L–R) interactions that alter the expression profiles of downstream genes between two conditions, such as tumor and healthy tissue. Scoring in CellCallEXT quantitatively integrates expression of ligands, receptors, TFs, and target genes (TGs). The pathway enrichment analysis and visualization modules allow biologists to investigate how disease alters cell–cell communication. Furthermore, Reactome pathways were added into CellCallEXT to expand the L–R–TF database.(1) Background: Single-cell RNA sequencing (scRNA-seq) data are useful for decoding cell–cell communication. CellCall is a tool that is used to infer inter- and intracellular communication pathways by integrating paired ligand–receptor (L–R) and transcription factor (TF) activities from steady-state data and thus cannot directly handle two-condition comparisons. For tumor and healthy status, it can only individually analyze cells from tumor or healthy tissue and examine L–R pairs only identified in either tumor or healthy controls, but not both together. Furthermore, CellCall is highly affected by gene expression specificity in tissues. (2) Methods: CellCallEXT is an extension of CellCall that deconvolutes intercellular communication and related internal regulatory signals based on scRNA-seq. Information on Reactome was retrieved and integrated with prior knowledge of L–R–TF signaling and gene regulation datasets of CellCall. (3) Results: CellCallEXT was successfully applied to examine tumors and immune cell microenvironments and to identify the altered L–R pairs and downstream gene regulatory networks among immune cells. Application of CellCallEXT to scRNA-seq data from patients with deficiency of adenosine deaminase 2 demonstrated its ability to impute dysfunctional intercellular communication and related transcriptional factor activities. (4) Conclusions: CellCallEXT provides a practical tool to examine intercellular communication in disease based on scRNA-seq data.