Biology subjects

Osmanbeyoglu, H. U.

Publications and source records attributed to Osmanbeyoglu, H. U..

15 recordsLinked to original sources

bioRxiv2025

Loss of CFIm activates YAP/TAZ and connects mRNA cleavage and polyadenylation inhibition to BRCAness

The YAP/TAZ transcription program plays a crucial role in development, regeneration, and cancer. CFIm is a master regulator of mRNA alternative polyadenylation (APA). Loss of CFIm function was shown in human cancer but its impact on the gene expression program is not well characterized. Here we report the discovery that loss of CFIm in cancer cells activates YAP/TAZ and promotes therapeutic resistance. We identified a CFIm-NEDD4L-LATS1 regulatory axis that suppressed YAP/TAZ activation. Furthermore, we found that CFIm loss in the presence of mRNA cleavage and polyadenylation (CPA) inhibition repressed key DNA repair genes in the Fanconi anemia (FA) and homology-directed repair (HDR) pathways, which induced the BRCAness phenotype and aggravated DNA damage. Our study reveals a hidden link between mRNA 3' end processing and the YAP/TAZ transcription program in addition to illustrating how CFIm function impacts the selection of therapeutic agents in cancer.

molecular biology↗

bioRxiv2025

A Multi-Layered Atlas of Spatial Regulatory Programs and Therapeutic Vulnerabilities in Glioblastoma

Glioblastoma (GBM) is marked by profound spatial heterogeneity. This complexity fuels tumor progression and therapy resistance. Although recent spatial transcriptomics (ST) studies have defined transcriptional niches, or metaprograms, the systems level regulatory logic linking upstream signaling, transcriptional programs, and therapeutic vulnerabilities remains unresolved. Here, we introduce an integrated, multi layered framework that unifies transcription factor and pathway activity inference (STAN and SPAN) with ligand/receptor and drug target mapping, thereby constructing a systems-level regulatory atlas of GBM across 26 tumors. This atlas uncovers spatial niches organized around transcriptional hubs and signaling programs, including hypoxic mesenchymal regions coordinated by HIF1A and SOX2 with mTORC1 signaling, macrophage rich areas driven by STAT3 mediated immune modulation, and astrocyte-like states shaped by lipid metabolic regulators such as SREBF2. By coupling this regulatory atlas to drug target predictions, we identify niche specific therapeutic vulnerabilities, such as VEGF blockade in HIF1A high regions and MAO-B inhibition in mesenchymal states. Finally, we provide an open, interactive web resource to make these data broadly accessible. This work provides a generalizable blueprint for linking spatial regulation to therapeutic hypotheses across diverse human diseases.

bioinformatics↗

bioRxiv2025

DGAT: A Dual-Graph Attention Network for Inferring Spatial Protein Landscapes from Transcriptomics

Spatial transcriptomics (ST) technologies provide genome-wide transcriptomic profiles in tissue context but lack direct protein-level measurements, which are critical for interpreting cellular function and microenvironmental organization. We present DGAT (Dual-Graph Attention Network), a deep learning framework that imputes spatial protein expression from transcriptomics-only ST data by learning RNA-protein relationships from spatially resolved transcriptomic and proteomic datasets. The model constructs heterogeneous graphs integrating transcriptomic, proteomic, and spatial information, encoded using graph attention networks. Task-specific decoders reconstruct mRNA and predict protein abundance from a shared latent representation. Benchmarking across public and in-house datasets demonstrates that DGAT outperforms existing methods in protein imputation accuracy. Applied to ST datasets lacking protein measurements, the framework reveals spatially distinct cell states, immune phenotypes, and tissue architectures not evident from transcriptomics alone. Here, we show that this framework accurately reconstructs spatial protein landscapes, reveals biologically meaningful tissue organization, and enables protein-level interpretation from transcriptomics-only spatial data.

bioinformatics↗

bioRxiv2025

Role of the NuRD complex and altered proteostasis in cancer cell quiescence

Cytotoxic chemotherapy remains the primary treatment for ovarian cancer (OvCa). Development of chemoresistance typically results in patient death within two years. As such, understanding chemoresistance is critical. One underexplored mechanism of chemotherapy resistance is quiescence. Quiescent cells, which have reversibly exited the cell cycle, are refractory to most chemotherapies which primarily target rapidly proliferating cells. Here, we report that CHD4 and MBD3, components of the nucleosome remodeling and deacetylase (NuRD) complex, are downregulated in quiescent OvCa cells (qOvCa). Indicating a direct role for NuRD complex downregulation in the induction of quiescence, either CHD4 or MBD3 knockdown or histone deacetylase inhibitors (HDACi), such as vorinostat, induce quiescence in OvCa cells. RNA-Seq analysis of HDACi-treated cells confirmed expression changes consistent with induction of quiescence. We also find that both primary qOvCa and vorinostat-induced qOvCa demonstrate altered proteostasis, including increased proteasome activity and autophagy, and combination therapy of HDACi and proteasome inhibitors or autophagy inhibitors demonstrated profound synergistic death of OvCa cells. Finally, we overlapped RNA-Seq signatures from quiescent ovarian cancer cells with genes essential for quiescence in yeast to identify a "quiescent cell core signature." This core quiescent cell signature appeared to be conserved across multiple cancer types, suggesting new therapeutic targets.

cancer biology↗

bioRxiv2024

STAN, a computational framework for inferring spatially informed transcription factor activity across cellular contexts

Transcription factors (TFs) drive significant cellular changes in response to environmental cues and intercellular signaling. Neighboring cells influence TF activity and, consequently, cellular fate and function. Spatial transcriptomics (ST) captures mRNA expression patterns across tissue samples, enabling characterization of the local microenvironment. However, these datasets have not been fully leveraged to systematically estimate TF activity governing cell identity. Here, we present STAN (Spatially informed Transcription factor Activity Network), a linear mixed-effects computational method that predicts spot-specific, spatially informed TF activities by integrating curated TF-target gene priors, mRNA expression, spatial coordinates, and morphological features from corresponding imaging data. We tested STAN using lymph node, breast cancer, and glioblastoma ST datasets to demonstrate its applicability by identifying TFs associated with specific cell types, spatial domains, pathological regions, and ligand-receptor pairs. STAN augments the utility of STs to reveal the intricate interplay between TFs and spatial organization across a spectrum of cellular contexts.

bioinformatics↗

bioRxiv2024

Cancer-cell derived S100A11 promotes macrophage recruitment in ER+ breast cancer

Macrophages are pivotal in driving breast tumor development, progression, and resistance to treatment, particularly in estrogen receptor-positive (ER+) tumors, where they infiltrate the tumor microenvironment (TME) influenced by cancer cell-secreted factors. By analyzing single-cell RNA-sequencing data from 25 ER+ tumors, we elucidated interactions between cancer cells and macrophages, correlating macrophage density with epithelial cancer cell density. We identified that S100A11, a previously unexplored factor in macrophage-cancer crosstalk, predicts high macrophage density and poor outcomes in ER+ tumors. We found that recombinant S100A11 enhances macrophage infiltration and migration in a dose-dependent manner. Additionally, in 3D models, we showed that S100A11 expression levels in ER+ cancer cells predict macrophage infiltration patterns. Neutralizing S100A11 decreased macrophage recruitment, both in cancer cell lines and in a clinically relevant patient-derived organoid model, underscoring its role as a paracrine regulator of cancer-macrophage interactions in the pro-tumorigenic TME. This study offers novel insights into the interplay between macrophages and cancer cells in ER+ breast tumors, highlighting S100A11 as a potential therapeutic target to modulate the macrophage-rich tumor microenvironment.

systems biology↗

bioRxiv2023

Spatial landscape of malignant pleural and peritoneal mesothelioma tumor immune microenvironment

Immunotherapies have shown modest clinical benefit thus far for malignant mesothelioma (MM). A deeper understanding of immune cell spatial distribution within the tumor immune microenvironment (TIME) is needed to identify interactions between tumor and different immune cell types that might impact the effectiveness of potential immunotherapies. We performed multiplex immunofluorescence (mIF) using tissue microarrays (TMAs, n=3) of samples from patients with malignant peritoneal (n=25) and pleural (n=88) mesothelioma (MPeM and MPM, respectively) to elucidate the spatial distributions of major immune cell populations and their association with LAG3, BAP1, NF2, and MTAP expression, the latter as a proxy for CDKN2A/B. We also analyzed the relationship between the spatial distribution of major immune cell types with MM patient prognosis and clinical features. The distribution of immune cells within the TIME is similar between MPM and MPeM. However, there is a higher level of interaction between immune cells and tumor cells in MPM than MPeM. Within MPM tumors, there is increased amount of interaction between tumor cells and CD8+ T cells in BAP1-low than in BAP1-high expressing tumors. The cell-cell interactions identified in this investigation have potential implications for the immune response against MM tumors and could be a factor in the different behaviors of MPM and MPeM. Our findings provide a valuable resource for the MM cancer research community and exemplifies the utility of spatial resolution within single-cell analyses. Our mesothelioma spatial atlas mIF dataset is available at https://mesotheliomaspatialatlas.streamlit.app/.

bioinformatics↗

bioRxiv2023

Single-cell analysis reveals the stromal dynamics and tumor-specific characteristics in the microenvironment of ovarian cancer

High-grade serous ovarian carcinoma (HGSOC) is a heterogeneous disease, and a high stromal/desmoplastic tumor microenvironment (TME) is associated with a poor outcome. Stromal cell subtypes, including fibroblasts, myofibroblasts, and cancer-associated mesenchymal stem cells, establish a complex network of paracrine signaling pathways with tumor-infiltrating immune cells that drive effector cell tumor immune exclusion and inhibit the antitumor immune response. Single-cell transcriptomics of the HGSOC TME from public and in-house datasets revealed a distinct transcriptomic landscape for immune and non-immune cells in high-stromal vs. low-stromal tumors. High-stromal tumors had a lower fraction of certain T cells, natural killer (NK) cells, and macrophages and increased expression of CXCL12 in epithelial cancer cells and cancer-associated mesenchymal stem cells (CA-MSCs). Analysis of cell-cell communication indicated that epithelial cancer cells and CA-MSCs secreted CXCL12 that interacted with the CXCR4 receptor, which was overexpressed on NK and CD8+ T cells. CXCL12 and/or CXCR4 antibodies confirmed the immunosuppressive role of CXCL12-CXCR4 in high-stromal tumors.

cancer biology↗

bioRxiv2022

COVID-19db linkage maps of cell surface proteins and transcription factors in immune cells

The highly contagious SARS-CoV-2 and its associated disease (COVID-19) are a threat to global public health and economies. To develop effective treatments for COVID-19, we must understand the host cell types, cell states and regulators associated with infection and pathogenesis such as dysregulated transcription factors (TFs) and surface proteins, including signalling receptors. To link cell surface proteins with TFs, we recently developed SPaRTAN (Single-cell Proteomic and RNA-based Transcription factor Activity Network) by integrating parallel single-cell proteomic and transcriptomic data based on Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and gene cis-regulatory information. We apply SPaRTAN to CITE-seq datasets from patients with varying degrees of COVID-19 severity and healthy controls to identify the associations between surface proteins and TFs in host immune cells. Here, we present COVID-19db of Immune Cell States (https://covid19db.streamlit.app/), a web server containing cell surface protein expression, SPaRTAN-inferred TF activities, and their associations with major host immune cell types. The data include four high-quality COVID-19 CITE-seq datasets with a toolset for userfriendly data analysis and visualization. We provide interactive surface protein and TF visualizations across major immune cell types for each dataset, allowing comparison between various patient severity groups for the discovery of potential therapeutic targets and diagnostic biomarkers.

bioinformatics↗

bioRxiv2022

Isolated BAP1 loss in malignant pleural mesothelioma predicts immunogenicity with implications for immunotherapeutic response

Malignant pleural mesothelioma (MPM), an aggressive cancer of the mesothelial cells lining the pleural cavity, lacks effective treatments. Multiple somatic mutations and copy number losses in tumor suppressor genes (TSGs) BAP1, CDKN2A/B, and NF2 are associated with MPM. The impact of single versus multiple losses of TSG on MPM biology, the immune tumor microenvironment, clinical outcomes, and treatment responses are unknown. Tumors with alterations in BAP1 alone were associated with a longer overall patient survival rate compared to tumors with CDKN2A/B and/or NF2 alterations with or without BAP1 and formed a distinct immunogenic subtype with altered transcription factor and pathway activity patterns. CDKN2A/B loss consistently contributed to an adverse clinical outcome. Since the loss of only BAP1 was associated with the PD-1 therapy response signature and higher LAG3 and VISTA gene expression, it is a candidate for immune checkpoint blockade therapy. Our results on the impact of TSG genotypes on MPM and the correlations between TSG alterations and molecular pathways provide a foundation for developing MPM therapies.

bioinformatics↗

bioRxiv2022

Chromatin accessibility and transcription factors in invasive lobular and ductal breast cancers

BackgroundInvasive lobular breast carcinoma (ILC), the second most prevalent histological subtype of breast cancer, exhibits unique molecular features compared with the more common invasive ductal carcinoma (IDC). While genomic and transcriptomic features of ILC and IDC have been characterized, genome-wide chromatin accessibility pattern differences between ILC and IDC remain largely unexplored. MethodsHere, we characterized tumor-intrinsic chromatin accessibility differences between ILC and IDC using primary tumors from The Cancer Genome Atlas (TCGA) breast cancer assay for transposase-accessible chromatin with sequencing (ATAC-seq) dataset. ResultsWe identified distinct patterns of genome-wide chromatin accessibility in ILC and IDC. Inferred patient-specific transcription factor (TF) motif activities revealed regulatory differences between and within ILC and IDC tumors. EGR1, RUNX3, TP63, STAT6, SOX family, and TEAD family TFs were higher in ILC, while ATF4, PBX3, SPDEF, PITX family, and FOX family TFs were higher in IDC. ConclusionsThis study reveals the distinct epigenomic features of ILC and IDC and the active TFs driving cancer progression that may provide valuable information on patient prognosis.

cancer biology↗

bioRxiv2022

ATR kinase inhibition induces thymineless death in proliferating CD8+ T cells

ATR kinase is a central regulator of the DNA damage response (DDR) and cell cycle checkpoints. ATR kinase inhibitors (ATRis) combine with radiation to generate CD8+ T cell-dependent responses in mouse models of cancer. We show that ATRis induce CDK1-dependent origin firing across active replicons in CD8+ T cells activated ex vivo while simultaneously decreasing the activity of rate-limiting enzymes for nucleotide biosynthesis. These pleiotropic effects of ATRi induce deoxyuridine (dU) contamination in genomic DNA, R loops, RNA-DNA polymerase collisions, and type-1 interferons (IFN-1). Remarkably, thymidine rescues ATRi-induced dU contamination, cell death, and IFN-1 expression in proliferating CD8+ T cells. Thymidine also rescues ATRi-induced cancer cell death. We propose that ATRi-induced dU contamination contributes to dose-limiting leukocytopenia and inflammation in the clinic and CD8+ T cell dependent anti-tumor responses in mouse models. We conclude that ATR is essential to limit dU contamination in genomic DNA and IFN-1 expression.

cancer biology↗

bioRxiv2021

Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers

Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Interpreting somatic alterations within context-specific transcriptional programs will facilitate personalized therapeutic decisions but is a monumental task. Toward this goal, we develop a partially interpretable neural network model called Chromatin-informed Inference of Transcriptional Regulators Using Self-attention mechanism (CITRUS). CITRUS models the impact of somatic alterations on transcription factors and downstream transcriptional programs. Our approach employs a self-attention mechanism to model the contextual impact of somatic alterations. Furthermore, CITRUS uses a layer of hidden nodes to explicitly represent the state of transcription factors (TFs) to learn the relationships between TFs and their target genes based on TF binding motifs in the open chromatin regions of tumor samples. We apply CITRUS to genomic, transcriptomic, and epigenomic data from 17 cancer types profiled by The Cancer Genome Atlas. CITRUS predicts patient-specific TF activities and reveals transcriptional program variations between and within tumor types. We show that CITRUS yields biological insights into delineating TFs associated with somatic alterations in individual tumors. Thus, CITRUS is a promising tool for precision oncology.

bioinformatics↗

bioRxiv2020

SPaRTAN, a computational framework for linking cell-surface receptors to transcriptional regulators

The identity and functions of specialized cell types are dependent on the complex interplay between signaling and transcriptional networks. We present SPaRTAN (Single-cell Proteomic and RNA based Transcription factor Activity Network), a computational method to link cell-surface receptors to transcription factors (TFs) by exploiting cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) datasets with cis-regulatory information. SPaRTAN is applied to immune cell types in the blood to predict the coupling of signaling receptors with cell context-specific TFs. The predictions are validated by prior knowledge and flow cytometry analyses. SPaRTAN is then used to predict the signaling coupled TF states of tumor infiltrating CD8+ T cells in malignant peritoneal and pleural mesotheliomas. SPaRTAN greatly enhances the utility of CITE-seq datasets to uncover TF and cell-surface receptor relationships in diverse cellular states. SignificanceRecently single-cell technologies such as CITE-seq have been developed that enable simultaneous quantitative analysis of cell-surface receptor expression with transcriptional states. To date, these datasets have not been used to systematically develop cell-context-specific maps of the interface between signaling and transcriptional regulators orchestrating cellular identity and function. We developed a computational framework, SPaRTAN (Single-cell Proteomic and RNA based Transcription factor Activity Network) that integrates single-cell proteomic and transcriptomic data based on CITE-seq with cis-regulatory information. We applied our method to publicly available (PBMCs) and new CITE-seq datasets (mesothelioma). Our predicted TF activity and cell-surface receptor relationships are validated by prior knowledge as well as experimental testing. SPaRTAN reveals many previously unidentified surface-receptor associated TFs.

bioinformatics↗

bioRxiv2018

Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers

Epigenomic data on transcription factor occupancy and chromatin accessibility can elucidate the developmental origin of cancer cells and reveal the enhancer landscape of key oncogenic transcriptional regulators. However, in many cancers, epigenomic analyses have been limited, and computational methods to infer regulatory networks in tumors typically use expression data alone, or rely on transcription factor (TF) motifs in annotated promoter regions. Here, we develop a novel machine learning strategy called PSIONIC (patient-specific inference of networks informed by chromatin) to combine cell line chromatin accessibility data with large tumor expression data sets and model the effect of enhancers on transcriptional programs in multiple cancers. We generated a new ATAC-seq data set profiling chromatin accessibility in gynecologic and basal breast cancer cell lines and applied PSIONIC to 723 RNA-seq experiments from ovarian, uterine, and basal breast tumors as well as 96 cell line RNA-seq profiles. Our computational framework enables us to share information across tumors to learn patient-specific inferred TF activities, revealing regulatory differences between and within tumor types. Many of the identified TF regulators were significantly associated with survival outcome in basal breast, uterine serous and endometrioid carcinomas. Moreover, PSIONIC-predicted activity for MTF1 in cell line models correlated with sensitivity to MTF1 inhibition. Therefore computationally dissecting the role of TFs in gynecologic cancers may ultimately advance personalized therapy.

bioinformatics↗