About

Chongzhi Zang is an Associate Professor in the Department of Genome Sciences at the University of Virginia School of Medicine. He holds a BS in Physics from Peking University and a PhD in Physics from George Washington University. He completed postdoctoral training in Biostatistics and Computational Biology at Harvard University. His research focuses on bioinformatics methodology development, epigenetics and chromatin biology, transcriptional regulation, cancer genomics and epigenomics, statistical methods for biomedical data integration, advanced machine learning, and theoretical and computational biophysics. The Zang Lab investigates how gene expression is regulated within chromatin, a fundamental question in molecular biology. The lab emphasizes understanding the transcription program, which is a major determinant of cell identity and involved in many biological processes and human diseases. Utilizing high-throughput genomics technologies, including sequencing, single-cell, and spatial omics, the lab aims to analyze massive datasets that measure the dynamic patterns of factors affecting chromatin states and gene regulation. The research involves developing quantitative models and computational methods for analyzing these data, and applying data science, computational, and functional genomics approaches to study chromatin biology, epigenetics, and transcriptional regulation in mammalian systems and human diseases such as cancer.

Research topics

• Biology
• Genetics
• Computational biology
• Cell biology
• Cancer research
• Molecular biology
• Bioinformatics
• Immunology
• Pathology
• Evolutionary biology
• Medicine
• Internal medicine
• Chemistry

Selected publications

• Abstract TU198: Exercise-induced Elevation of Fibroblast growth factor-1 Observed in MoTrPAC Data Promotes Skeletal Muscle Insulin Sensitivity in Mice.

  Circulation · 2026-03-24

  article

  Exercise is well-known to improve metabolic function and reduce the risk of development of cardiometabolic diseases, however, the molecular mechanisms underlying the exercise benefits remain incompletely understood. Fibroblast growth factor-1 (Fgf1) is secreted from brown adipose tissue and skeletal muscle upon acute exercise in rats. We observed increased expression of Fgf1 in skeletal muscle after single bout of exercise in mice. Recent study shows that recombinant FGF1 (rFGF1) injection lowers blood glucose levels in diet-induced obese mice to a healthy range. However, the role the of endogenous FGF1 induced by endurance exercise training in whole-body insulin sensitivity are unknown. To address this question, we have generated loss-of-function Fgf1- knock-in (KI) mice by using CRISPR/Cas9-mediated gene editing. Three-month-old WT and KI (male and female) mice were subjected to voluntary wheel running for 4 weeks with sedentary controls followed by measurements for whole-body metabolism (energy expenditure, locomotor activity and food intake by CLAMS) body composition (Echo MRI), exercise capacity (metabolic treadmill), cardiac function, cognitive function, as well as whole-body and skeletal muscle insulin signaling (GTT, ITT, HOMA-IR and serum insulin levels during GTT). Our data shows that Fgf1KI mice failed to improve glucose tolerance after 4-week training. We therefore, for the first time, demonstrate that Fgf1 plays a critical role in improving insulin sensitivity in response to endurance exercise training in mice. Altogether, our findings support an important role of Fgf1 in promoting insulin sensitivity in response to endurance exercise training, which has improved our understanding of molecular mechanisms underlying exercise-associated health benefits.

  PublisherDOI

• Abstract 5514: BART-spatial: Predicting biologically significant transcriptional regulators from spatial omics data.

  Cancer Research · 2026-04-03

  articleSenior author

  Abstract Transcription regulators (TRs), including transcription factors and chromatin regulators, are essential for maintaining cell identity and directing cell fate decisions by activating or repressing lineage-specific gene expression program and integrating environmental signals with intrinsic regulatory networks. Identifying active TRs is critical for understanding transcriptional regulation in both normal physiology and diseases like cancer. Emerging spatial omics technologies, such as 10x Visium and Visium HD and spatial ATAC-seq, enable simultaneous profiling of genomic information and spatial location at near-single-cell resolution, providing unprecedented opportunities to study transcription activities in the tissue microenvironment. However, inferring functional TRs from spatial omics data remains challenging due to data sparsity, high dimensionality, and the complex nature of transcriptional regulation. Here we present BART-spatial (Binding Analysis for Regulation of Transcription for spatial omics data), a computational method for identifying functional TRs from spatially resolved transcriptomics or epigenomics data. BART-spatial integrates spatial variability and pseudo-temporal dynamics of molecular profiles to generate biologically informed predictions of TR activity. It leverages public TR binding profiles to enhance prediction accuracy, without relying on TR expression levels. Applied to multiple real spatial transcriptomics datasets across different biological systems and platforms, BART-spatial successfully identifies TRs with region- or stage-specific activities, outperforming existing tools. Moreover, BART-spatial also works for other spatial omics data such as spatial ATAC-seq, enabling cross-validation between transcriptomic and epigenomic layers. Implemented as an open-source package, BART-spatial provides a useful computational tool for decoding spatial omics data and offers new insights into transcriptional regulation in various biological systems. Citation Format: Jingyi Wang, Hongpan Zhang, Zhenjia Wang, Chongzhi Zang. BART-spatial: Predicting biologically significant transcriptional regulators from spatial omics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5514.

  PublisherDOI

• BART-spatial unravels biologically significant transcriptional regulators from spatial omics data

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-08

  articleOpen accessSenior authorCorresponding

  Transcriptional regulators (TRs) are crucial regulators of cell fate decisions by activating or repressing lineage-specific genes and integrating environmental signals with intrinsic networks. Identifying functional TRs is essential for understanding development, tissue organization, and disease. Emerging spatial transcriptomics and epigenomics technologies now provide near-single-cell resolution mapping of genomic features while preserving information of each cell's physical location and microenvironment which influence TR activity. Despite these advances, identifying active TRs in spatial data remains challenging due to low TR expression and the fact that TR activity often does not correlate directly with mRNA levels. Moreover, existing tools mainly designed for non-spatial single-cell data overlook spatial heterogeneity. To bridge this gap, we developed BART-spatial (Binding Analysis for Regulation of Prediction for spatial omics), an innovative computational method to infer functional TRs from spatial omics data. BART-spatial integrates spatial variability and pseudo-temporal information with publicly available TR binding profiles. Applied to multiple spatial datasets from diverse platforms, including 10X Visium, Visium HD, Atera, and spatial RNA-ATAC-seq, BART-spatial consistently outperforms existing methods, identifying stage-specific TRs and revealing regulators undetectable by expression alone. Its compatibility with spatial epigenomics data further strengthens its utility and enables cross-validation. Overall, BART-spatial provides a powerful and robust tool for decoding spatially resolved gene regulatory programs.

  PublisherDOI

• BART2 data library

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-03

  datasetOpen accessSenior author

  This is the data library used for BART2: https://github.com/zang-lab/bart2.

  PublisherDOI

• BART2 data library

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-03

  datasetOpen accessSenior author

  This is the data library used for BART2: https://github.com/zang-lab/bart2.

  PublisherDOI

• Temporal single-cell analysis reveals age-associated delay in immune resolution after respiratory viral infection

  eLife · 2026-03-12

  articleOpen access

  Aging is a major risk factor for increased morbidity and mortality following acute respiratory virus infections. To elucidate the immune determinants underlying viral pathogenesis and delayed lung repair in the aged lung, a comprehensive time-course study was conducted. Single-cell RNA sequencing (scRNAseq) and high-dimensional flow cytometry were utilized to compare lungs from young and aged mice infected with influenza A virus (IAV). Aged hosts displayed diminished alveolar macrophage (AM) and dendritic cell (DC) but elevated monocyte-derived macrophage (MoM) and interstitial macrophage (IM) presence following infection. Additionally, enhanced accumulation of adaptive immune cells, including CD4+ tissue-resident helper (TRH) cells, CD8+ tissue-resident memory (TRM) cells, and a B cell subset resembling age-associated B cells, was observed in the memory phase. Pathway analysis revealed that elevated type I and II interferon (IFNα/γ) signaling, especially in MoM/IM subsets, distinguished the aged hosts from the young. Inhibition of IFNα/γ signaling after viral clearance improved long-term respiratory outcomes and reduced both IM and TRH populations in aged mice. These findings highlight the pivotal role of IFNα/γ signaling, likely within MoM/IM subsets, in driving the exuberant persistence of adaptive immune cells and chronic immunopathology in the aged lung following acute viral infection.

  PublisherOA PDFDOI

• Data associated with the publication: TEAD1 condensates are transcriptionally inactive storage sites on the pericentromeric heterochromatin in cancer cells

  Research Data Repository, Duke University · 2026-04-01

  datasetOpen access

  This dataset contain image data of microscopic experiments reported in the paper titled “TEAD1 condensates are transcriptionally inactive storage sites on the pericentromeric heterochromatin in cancer cells”. <br> <b>Abstract</b>: TEA domain transcription factor 1 (TEAD1), a Hippo pathway transcription factor important in cellular homeostasis and development, is increasingly implicated in cancer biology. Here, we reveal a novel role for TEAD1 in organizing nuclear condensates, independent of active transcription. Using high-resolution imaging, ChIP-seq, RNA-seq and proximity-based proteomics, we demonstrate that in patient-derived renal cell carcinoma cells, TEAD1 forms micron-sized condensates by binding to the heterochromatic pericentromeric regions using its DNA-binding domain. TEAD-specific MCAT motifs selectively enrich and cluster in the pericentromeric region, specifically seeding TEAD1 condensates. TEAD1 condensates do not activate transcription but instead serve as depots for excess TEAD1, and disrupting TEAD1 condensates leads to increases in YAP/TEAD target gene expression. This organization of TEAD1 contrasts with that observed in other genomic regions of both RCC and normal kidney cells, in which TEAD1 associates with markers of active transcription. Our findings provide a mechanistic framework for TEAD1’s dual regulatory roles, offering new insights into its contribution to transcriptional dysregulation and tumor progression. <br> <b>Associated preprint</b>: <a href ="https://www.biorxiv.org/content/10.1101/2025.05.02.651992v2">https://www.biorxiv.org/content/10.1101/2025.05.02.651992v2,</a>

  PublisherDOI

• BARTsc identifies key transcriptional regulators from single-cell omics data

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-02-25

  articleOpen accessSenior authorCorresponding

  Inference of transcriptional regulatory mechanisms from single-cell (sc) omics data, such as scRNA-seq, scATAC-seq, and scMultiome, remains an important problem in single-cell biology and functional genomics. Most existing methods for predicting functional transcriptional regulators (TRs) from single-cell data rely on co-expression between regulator and target genes and/or sequence motif enrichment, holding inherent limitations. Here, we present BARTsc, a computational method that accurately predicts functional TRs from clustered single-cell omics data by leveraging a large collection of public ChIP-seq profiles. BARTsc implements a novel framework to infer a cis-regulatory profile from differential genomic features from either unimodal (RNA or ATAC) or bimodal (scMultiome) single-cell profiling data and identify TRs whose binding profiles most associate with the cis-regulatory profile. BARTsc can quantify TR activity across cell clusters and predict key regulators for each cell cluster. We demonstrate that BARTsc can successfully identify active TRs in each cell type and cell-type-defining key regulators across diverse biological systems, including mouse cortex, human peripheral blood mononuclear cells (PBMCs), and human pancreatic ductal adenocarcinoma (PDAC). Using a generative-AI-assisted, literature-supported collection of cell-type key regulators as benchmarks, we show that BARTsc consistently outperforms existing state-of-the-art methods. We apply BARTsc to identify critical regulators in PDAC, including NEFLA, a novel PDAC key regulator, and validate its function in pancreatic tumor proliferation by experiments. As a robust and versatile computational method, BARTsc provides deeper insights into cell-type-specific regulatory programs, facilitating the discovery of key regulators across diverse biological systems.

  PublisherOA PDFDOI

• CCAAT‐enhancer binding protein delta functions as a tumor suppressor gene in acute myeloid leukemia

  Neoplasia · 2026-03-16 · 1 citations

  articleOpen access

  There is a continued need for identification of novel disease drivers of acute myeloid leukemia (AML) as many patients experience relapse and have poor clinical outcomes. Using genomic analyses of a study dataset of paired diagnosis and relapse specimens (n = 59), we identified recurrent downregulation of CCAAT-enhancer binding protein delta (CEBPD) expression at relapse and inferred CEBPD as one of the key regulators of gene transcription in a subset of relapse patients. Three independent public datasets validated downregulation of CEBPD expression at relapse and predicted it as a candidate tumor suppressor gene in AML. To evaluate CEBPD's tumor suppressor function, we performed complementary loss- and gain-of-function experiments in human AML cell lines OCI-AML2 and OCI-AML5. Consistent with the prediction, knockdown of CEBPD expression led to activation of MAPK signaling and upregulation of downstream effectors cyclin D1 and TNFα expression with concomitant increase in leukemic growth, while CEBPD overexpression resulted in induction of myeloid differentiation marker CD14 expression in the cell lines. Consistent with prior reports, our integrative genomic analyses and azacytidine treatment experiments further suggest a role for DNA methylation in downregulation of CEBPD expression during AML progression. Collectively, our results provide direct functional evidence for a tumor suppressor function of CEBPD in human cell lines and support prior studies implicating its epigenetic silencing in human AML.

  PublisherDOI

• Abstract 1480: Transcriptional condensates at super-enhancers mediate pH-dependent transcriptional control in innate immunity

  Cancer Research · 2026-04-03

  articleSenior author

  Abstract Tissue acidification is a common feature of hypoxia, inflammation and solid tumor. Acidic pH regulates innate immune response in macrophages by weakening BRD4-containing transcriptional condensates. Yet how disruption of transcriptional condensates leads to gene-specific regulation of immune programs remain unclear. Here, we integrated ATAC-seq, ChIP-seq, and RNA-seq of primary murine macrophages and performed integrative epigenomics analyses to identify transcriptional regulators (TRs) with pH-sensitive regulatory potential and association to BRD4-dependent transcriptional condensates. We determined pH-dependent super-enhancers (SEs) based on dynamic extended profiles of BRD4 binding and H3K27ac marks under pH perturbation. We found RELA, IRF family, and STAT family as candidate TRs enriched at BRD4-associated, pH-sensitive SE regions, particularly in response to LPS stimulation in macrophages. RELA and IRF3 preferentially occupied BRD4-associated and pH-sensitive SEs, and displayed markedly reduced binding under acidic conditions, aligning with BRD4 occupancy change. Correspondingly, immune-response genes within BRD4-associated, pH-sensitive SE regions, including Ch25h, Il20rb, Slc2a6, and Ifit family, were significantly higher expressed at pH 7.4 than at pH 6.5. Additionally, analysis of TCGA data for colorectal cancer revealed that chromatin accessibility at potential RELA or IRF binding sites within SEs had significantly elevated association with patient survival, indicating the clinical relevance of TR binding at transcriptional condensates in human cancer. Together, these results reveal a set of TRs involved in BRD4-associated, pH-sensitive transcriptional condensates that coordinate macrophage gene activation under physiological conditions, providing mechanistic insight into how acidic stress modulates transcriptional condensates in immune responses and tumor microenvironment. Citation Format: Shengyuan Wang, Zhongyang Wu, Zhe Zhong, Zhenjia Wang, Xu Zhou, Chongzhi Zang. Transcriptional condensates at super-enhancers mediate pH-dependent transcriptional control in innate immunity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1480.

  PublisherDOI

Recent grants

• Integrative computational models for functional epigenomics and transcriptional regulation

  NIH · $2.9M · 2019–2029

Frequent coauthors

• Zhenjia Wang

  University of Virginia

  66 shared

• X. Shirley Liu

  G1 Therapeutics (United States)

  39 shared

• Myles Brown

  Dana-Farber Cancer Institute

  35 shared

• Clifford A. Meyer

  28 shared

• B Bernstein

  Broad Institute

  26 shared

• Soumya Raychaudhuri

  Brigham and Women's Hospital

  25 shared

• Qian Qin

  Harbin Medical University

  23 shared

• Shaun Purcell

  Harvard University

  23 shared

Education

• PhD, Physics

  The George Washington University

  2010

• Bachelor of Science, Physics

  Peking University

  2005