Jae Won Cho

Bio: Jae Won Cho is an academic researcher from Yonsei University. The author has contributed to research in topics: Computer science & Transcriptome. The author has an hindex of 6, co-authored 10 publications receiving 661 citations.

TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions.

Abstract: Transcription factors (TFs) are major trans-acting factors in transcriptional regulation. Therefore, elucidating TF-target interactions is a key step toward understanding the regulatory circuitry underlying complex traits such as human diseases. We previously published a reference TF-target interaction database for humans-TRRUST (Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining)-which was constructed using sentence-based text mining, followed by manual curation. Here, we present TRRUST v2 (www.grnpedia.org/trrust) with a significant improvement from the previous version, including a significantly increased size of the database consisting of 8444 regulatory interactions for 800 TFs in humans. More importantly, TRRUST v2 also contains a database for TF-target interactions in mice, including 6552 TF-target interactions for 828 mouse TFs. TRRUST v2 is also substantially more comprehensive and less biased than other TF-target interaction databases. We also improved the web interface, which now enables prioritization of key TFs for a physiological condition depicted by a set of user-input transcriptional responsive genes. With the significant expansion in the database size and inclusion of the new web tool for TF prioritization, we believe that TRRUST v2 will be a versatile database for the study of the transcriptional regulation involved in human diseases.

Single-cell transcriptome analysis reveals TOX as a promoting factor for T cell exhaustion and a predictor for anti-PD-1 responses in human cancer

Abstract: T cells exhibit heterogeneous functional states in the tumor microenvironment. Immune checkpoint inhibitors (ICIs) can reinvigorate only the stem cell-like progenitor exhausted T cells, which suggests that inhibiting the exhaustion progress will improve the efficacy of immunotherapy. Thus, regulatory factors promoting T cell exhaustion could serve as potential targets for delaying the process and improving ICI efficacy. We analyzed the single-cell transcriptome data derived from human melanoma and non-small cell lung cancer (NSCLC) samples and classified the tumor-infiltrating (TI) CD8+ T cell population based on PDCD1 (PD-1) levels, i.e., PDCD1-high and PDCD1-low cells. Additionally, we identified differentially expressed genes as candidate factors regulating intra-tumoral T cell exhaustion. The co-expression of candidate genes with immune checkpoint (IC) molecules in the TI CD8+ T cells was confirmed by single-cell trajectory and flow cytometry analyses. The loss-of-function effect of the candidate regulator was examined by a cell-based knockdown assay. The clinical effect of the candidate regulator was evaluated based on the overall survival and anti-PD-1 responses. We retrieved many known factors for regulating T cell exhaustion among the differentially expressed genes between PDCD1-high and PDCD1-low subsets of the TI CD8+ T cells in human melanoma and NSCLC. TOX was the only transcription factor (TF) predicted in both tumor types. TOX levels tend to increase as CD8+ T cells become more exhausted. Flow cytometry analysis revealed a correlation between TOX expression and severity of intra-tumoral T cell exhaustion. TOX knockdown in the human TI CD8+ T cells resulted in downregulation of PD-1, TIM-3, TIGIT, and CTLA-4, which suggests that TOX promotes intra-tumoral T cell exhaustion by upregulating IC proteins in cancer. Finally, the TOX level in the TI T cells was found to be highly predictive of overall survival and anti-PD-1 efficacy in melanoma and NSCLC. We predicted the regulatory factors involved in T cell exhaustion using single-cell transcriptome profiles of human TI lymphocytes. TOX promoted intra-tumoral CD8+ T cell exhaustion via upregulation of IC molecules. This suggested that TOX inhibition can potentially impede T cell exhaustion and improve ICI efficacy. Additionally, TOX expression in the TI T cells can be used for patient stratification during anti-tumor treatments, including anti-PD-1 immunotherapy.

Genome-wide identification of differentially methylated promoters and enhancers associated with response to anti-PD-1 therapy in non-small cell lung cancer.

Abstract: Although approved programmed cell death protein (PD)-1 inhibitors show durable responses, clinical benefits to these agents are only seen in one-third of patients in most cancer types. Therefore, strategies for improving the response to PD-1 inhibitor for treating various cancers including non-small cell lung cancer (NSCLC) are urgently needed. Compared with genome and transcriptome, tumor DNA methylome in anti-PD-1 response was relatively unexplored. We compared the pre-treatment methylation status of cis-regulatory elements between responders and non-responders to treatment with nivolumab or pembrolizumab using the Infinium Methylation EPIC Array, which can profile ~850,000 CpG sites, including ~350,000 CpG sites located in enhancer regions. Then, we analyzed differentially methylated regions overlapping promoters (pDMRs) or enhancers (eDMRs) between responders and non-responders to PD-1 inhibitors. We identified 1007 pDMRs and 607 eDMRs associated with the anti-PD-1 response. We also identified 1109 and 1173 target genes putatively regulated by these pDMRs and eDMRs, respectively. We found that eDMRs contribute to the epigenetic regulation of the anti-PD-1 response more than pDMRs. Hypomethylated pDMRs of Cytohesin 1 Interacting Protein (CYTIP) and TNF superfamily member 8 (TNFSF8) were more predictive than programmed cell death protein ligand 1 (PD-L1) expression for anti-PD-1 response and progression-free survival (PFS) and overall survival (OS) in a validation cohort, suggesting their potential as predictive biomarkers for anti-PD-1 immunotherapy. The catalog of promoters and enhancers differentially methylated between responders and non-responders to PD-1 inhibitors presented herein will guide the development of biomarkers and therapeutic strategies for improving anti-PD-1 immunotherapy in NSCLC. A study into natural regulatory DNA modifications that influence patient responses could guide strategies to help the roughly two-thirds of patients who respond poorly to treatment with anticancer drugs called PD-1 inhibitors. Researchers at Yonsei University in South Korea, led by Hye Ryun Kim and Insuk Lee, investigated the significance in lung cancer patients of pre-treatment levels of DNA methylation, in which methyl (CH3) groups are added to DNA. They identified > 1000 regions of DNA in which varying methylation levels were associated with differing responses to PD-1 inhibitors. These differences affected regions of DNA called enhancers and promoters, which have been implicated in controlling the activity of more than 1000 identified genes. The research could help predict response outcomes to potential treatments and suggest possibilities for new interventions that might improve responses.

Dysregulation of TFH-B-TRM lymphocyte cooperation is associated with unfavorable anti-PD-1 responses in EGFR-mutant lung cancer.

Abstract: Patients with non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutations exhibit an unfavorable response to PD-1 inhibitor through unclear mechanisms. Hypothesizing that EGFR mutations alter tumor-immune interactions, we compare tumor-infiltrating lymphocytes between EGFR mutant (EGFR-MT) and wild type (EGFR-WT) tumors through single-cell transcriptomic analysis. We find that B cells, CXCL13-producing follicular helper CD4+ T (TFH)-like cells, and tissue-resident memory CD8+ T (TRM)-like cells decreased in EGFR-MT tumors. The NOTCH-RBPJ regulatory network, which is vital for persistence of TRM state, is perturbed, and the interactions between TFH and B cells through the CXCL13-CXCR5 axis disappear in EGFR-MT tumors. Notably, the proportion of TRM-like cells is predictive for anti-PD-1 response in NSCLC. Our findings suggest that the impairment of TFH-B-TRM cooperation in tertiary lymphoid structure formation, accompanied by the dysregulation of TRM homeostasis and the loss of TFH-B crosstalk, underlies unfavorable anti-PD-1 response in EGFR-MT lung tumors.

Tumor-Infiltrating Regulatory T Cell Accumulation in the Tumor Microenvironment is Mediated by IL33/ST2 Signaling

Abstract: Regulatory T cells (Treg) are enriched in the tumor microenvironment (TME) and suppress antitumor immunity; however, the molecular mechanism underlying the accumulation of Tregs in the TME is poorly understood. In various tumor models, tumor-infiltrating Tregs were highly enriched in the TME and had significantly higher expression of immune checkpoint molecules. To characterize tumor-infiltrating Tregs, we performed bulk RNA sequencing (RNA-seq) and found that proliferation-related genes, immune suppression-related genes, and cytokine/chemokine receptor genes were upregulated in tumor-infiltrating Tregs compared with tumor-infiltrating CD4+Foxp3- conventional T cells or splenic Tregs from the same tumor-bearing mice. Single-cell RNA-seq and T-cell receptor sequencing also revealed active proliferation of tumor infiltrating Tregs by clonal expansion. One of these genes, ST2, an IL33 receptor, was identified as a potential factor driving Treg accumulation in the TME. Indeed, IL33-directed ST2 signaling induced the preferential proliferation of tumor-infiltrating Tregs and enhanced tumor progression, whereas genetic deletion of ST2 in Tregs limited their TME accumulation and delayed tumor growth. These data demonstrated the IL33/ST2 axis in Tregs as one of the critical pathways for the preferential accumulation of Tregs in the TME and suggests that the IL33/ST2 axis may be a potential therapeutic target for cancer immunotherapy.

Expanded Theory of H- and J-Molecular Aggregates: The Effects of Vibronic Coupling and Intermolecular Charge Transfer.

Abstract: The electronic excited states of molecular aggregates and their photophysical signatures have long fascinated spectroscopists and theoreticians alike since the advent of Frenkel exciton theory almost 90 years ago. The influence of molecular packing on basic optical probes like absorption and photoluminescence was originally worked out by Kasha for aggregates dominated by Coulombic intermolecular interactions, eventually leading to the classification of J- and H-aggregates. This review outlines advances made in understanding the relationship between aggregate structure and photophysics when vibronic coupling and intermolecular charge transfer are incorporated. An assortment of packing geometries is considered from the humble molecular dimer to more exotic structures including linear and bent aggregates, two-dimensional herringbone and “HJ” aggregates, and chiral aggregates. The interplay between long-range Coulomb coupling and short-range charge-transfer-mediated coupling strongly depends on the aggregate ...

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq

Abstract: Single-cell expression profiles of melanoma Tumors harbor multiple cell types that are thought to play a role in the development of resistance to drug treatments. Tirosh et al. used single-cell sequencing to investigate the distribution of these differing genetic profiles within melanomas. Many cells harbored heterogeneous genetic programs that reflected two different states of genetic expression, one of which was linked to resistance development. Following drug treatment, the resistance-linked expression state was found at a much higher level. Furthermore, the environment of the melanoma cells affected their gene expression programs. Science, this issue p. 189 Melanoma cells show transcriptional heterogeneity. To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.

iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data

Abstract: RNA-seq is widely used for transcriptomic profiling, but the bioinformatics analysis of resultant data can be time-consuming and challenging, especially for biologists. We aim to streamline the bioinformatic analyses of gene-level data by developing a user-friendly, interactive web application for exploratory data analysis, differential expression, and pathway analysis. iDEP (integrated Differential Expression and Pathway analysis) seamlessly connects 63 R/Bioconductor packages, 2 web services, and comprehensive annotation and pathway databases for 220 plant and animal species. The workflow can be reproduced by downloading customized R code and related pathway files. As an example, we analyzed an RNA-Seq dataset of lung fibroblasts with Hoxa1 knockdown and revealed the possible roles of SP1 and E2F1 and their target genes, including microRNAs, in blocking G1/S transition. In another example, our analysis shows that in mouse B cells without functional p53, ionizing radiation activates the MYC pathway and its downstream genes involved in cell proliferation, ribosome biogenesis, and non-coding RNA metabolism. In wildtype B cells, radiation induces p53-mediated apoptosis and DNA repair while suppressing the target genes of MYC and E2F1, and leads to growth and cell cycle arrest. iDEP helps unveil the multifaceted functions of p53 and the possible involvement of several microRNAs such as miR-92a, miR-504, and miR-30a. In both examples, we validated known molecular pathways and generated novel, testable hypotheses. Combining comprehensive analytic functionalities with massive annotation databases, iDEP ( http://ge-lab.org/idep/ ) enables biologists to easily translate transcriptomic and proteomic data into actionable insights.

Benchmark and integration of resources for the estimation of human transcription factor activities.

Abstract: The prediction of transcription factor (TF) activities from the gene expression of their targets (i.e., TF regulon) is becoming a widely used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and data sets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are (1) manually curated repositories, (2) interactions derived from ChIP-seq binding data, (3) in silico prediction of TF binding on gene promoters, and (4) reverse-engineered regulons from large gene expression data sets. However, it is not known how these different sources of regulons affect the TF activity estimations and, thereby, downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark data sets. We assembled a collection of TF-target interactions for 1541 human TFs and evaluated how different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities, or mode of interaction with the chromatin, affect the predictions of TF activity. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies, with confidence scores, as a resource for enhanced prediction of TF activities.

ChEA3: transcription factor enrichment analysis by orthogonal omics integration.

Abstract: Identifying the transcription factors (TFs) responsible for observed changes in gene expression is an important step in understanding gene regulatory networks. ChIP-X Enrichment Analysis 3 (ChEA3) is a transcription factor enrichment analysis tool that ranks TFs associated with user-submitted gene sets. The ChEA3 background database contains a collection of gene set libraries generated from multiple sources including TF-gene co-expression from RNA-seq studies, TF-target associations from ChIP-seq experiments, and TF-gene co-occurrence computed from crowd-submitted gene lists. Enrichment results from these distinct sources are integrated to generate a composite rank that improves the prediction of the correct upstream TF compared to ranks produced by individual libraries. We compare ChEA3 with existing TF prediction tools and show that ChEA3 performs better. By integrating the ChEA3 libraries, we illuminate general transcription factor properties such as whether the TF behaves as an activator or a repressor. The ChEA3 web-server is available from https://amp.pharm.mssm.edu/ChEA3.