Biology subjects

Guney, E.

Publications and source records attributed to Guney, E..

10 recordsLinked to original sources

bioRxiv2026

Local Tumor Microenvironment Niches Correlate With Survival And Immunotherapy Response In Human Glioblastoma

Background: Glioblastoma (GBM) is an aggressive form of primary brain cancer. Recent efforts to characterize GBM using single-cell or spatial transcriptomics have revealed tremendous intra-tumoral heterogeneity among malignant cells and across different tumor areas. However, most studies have focused on malignant cells, and the spatial and cellular heterogeneity of the tumor microenvironment (TME) remains poorly understood. Consequently, it is unclear how TME compositions and organizations influence clinical outcomes for patients. Results: Spatial transcriptomics on 25 tumors, integrated with single-cell RNA sequencing and histological analyses, enabled the estimation of cellular composition within the TME across 46,000 spots (55-m wide). This analysis identified spatial associations between mesenchymal-like cancer cells and monocyte-derived macrophages and revealed six unique niches (N1-N6) characterized by distinct cellular composition and biological pathway activations. Deconvolution of large-scale bulk RNA-seq datasets using a spatial transcriptomics reference revealed that tumor niche composition significantly associated with patient survival and response to immunotherapy. Specifically, the N1 niche, characterized by the enrichment of mesenchymal-like cancer cells and monocyte-derived macrophages alongside hypoxic signature, was associated with lower overall survival. In contrast, the N5 niche, characterized by the enrichment of oligodendrocyte progenitor-like cancer cells and microglia-derived tumor-associated macrophages, was associated with longer patients survival. Analysis of data from patients treated with immunotherapy identified that niches N1 and N3, characterized by mixed mesenchymal-like and astrocyte-like cells, were associated with poor response to anti-PD-1 immune checkpoint inhibitors. Conclusions: Our results demonstrate that GBM exhibits significant spatial heterogeneity within the TMEs, comprising distinct niche classes, and that the specific niche compositions are associated with overall survival and immunotherapy responsiveness. Our results suggest incorporation of TME niche categories as biomarkers for risk stratification and therapeutic decisions for GBM patients.

cancer biology↗

bioRxiv2025

Human stem cell models for group 3 medulloblastoma uncover JARID1B as a regulator of the chromatin landscape.

Medulloblastoma (MB) is one of the most prevalent malignant brain tumors in children, with tremendous cognitive and neuroendocrine disability among survivors. Group 3 MB (G3MB) has poor overall survival at <50%, high frequencies of metastases, and no targeted therapies. Amplification of MYC and activation of TGF{beta} signaling occur frequently in G3MB. Many tumors have no reported mutations, suggesting epigenetic drivers. We here describe novel humanized models for G3MB from human induced pluripotent stem cells (hiPSC). By transducing hiPSC-derived neuroepithelial stem cells (NESC), we determined that: 1) both MYC and TGF{beta} effectors drove tumors in vivo; 2) MYC/TGF{beta}R1 in combination led to more aggressive tumors and resistance to clinical inhibitors of TGF{beta}, and 3) NESC-derived tumors clustered with human G3MB. To decipher mechanisms, we integrated RNA-sequencing with CUT&RUN (for MYC genomic localization and post-translational modification of histones). MYC-bound neural developmental genes were repressed in MYC/TGF{beta}R1 co-driven lines. Gene signatures associated with the Polycomb Repressive Complex (PRC) demarcated with H3K27me3; the histone mark directly regulated by PRC. We identified JARID1B, a MYC binding partner and H3K4me3 demethylase, as a regulator of repressed neural genes. Primary G3MB also showed increased levels of H3K27me3 concurrent with higher expression of JARID1B. Knockdown of JARID1B in human G3MB cell lines reduced growth, supporting potential as a therapeutic target. We conclude that a MYC-TGF{beta}-JARID1B axis represses target genes to drive G3MB and present new humanized models for G3MB to understand epigenetic dysregulation in G3MB.

cancer biology↗

bioRxiv2025

Inferring and Evaluating Network Medicine-Based Disease Modules with Nextflow

Most human diseases result from complex molecular interactions of genes and proteins. Various network-based computational methods characterize these mechanisms by expanding seed genes into disease-associated subnetworks, or disease modules. Evaluating these diverse methods is tedious due to unique installation and data preparation requirements. Moreover, the underlying algorithmic strategies differ, making it difficult to determine which of the created modules are most useful or biologically plausible. To address this challenge, we developed an all-in-one Nextflow pipeline that enables automated and reproducible analyses. It handles installation, input preparation, execution, and systematic evaluation of six widely used module detection tools, considering module topology, functional coherence, robustness, and the capacity to recover seeds. In addition, it annotates the resulting disease modules with biological context information, prioritizes potential drug candidates, and generates visualizations and a comprehensive summary report. To showcase the value of our pipeline and offer guidance to potential users, we performed a comprehensive evaluation across 50 different disease-network combinations, revealing substantial variability among the derived disease modules. We show that this variability is driven by differences in modeling approach, input network, and seed composition. While most methods are robust to minor perturbations, they struggle to recover omitted seeds, and none consistently outperforms others, underscoring the need for careful method selection. Our work enables the research community to systematically compare approaches for disease module discovery, promoting reproducible network medicine research. Integrated into the nf-core project (https://nf-co.re/diseasemodulediscovery), it is intended as an extendable, long-term resource for tracking progress in the field.

bioinformatics↗

bioRxiv2025

NOVA: a novel R-package enabling multi-parameter analysis and visualization of neural activity in MEA recordings

Multielectrode array (MEA) technology enables simultaneous recording of electrical signals from neuronal networks, producing complex datasets. Current analytical approaches typically examine a limited number of metrics such as mean firing rate and synchronicity, leaving much of the data underutilized. To address this gap, we created NOVA (Neural Output Visualization and Analysis), an accessible R-based computational tool for comprehensive MEA data interpretation and visualization. NOVA integrates dimensionality reduction through principal component analysis, hierarchical clustering with heatmap generation, and temporal trajectory mapping of network activity patterns. Our code offers both a userfriendly pipeline requiring minimal coding background as well as customizable advanced plotting modules for experienced users. Validation experiments using primary cortical neurons during development and pharmacological manipulation demonstrated NOVAs capacity to detect subtle activity shifts overlooked by conventional methods. Notably, our unbiased approach identified network burst duration as a stronger contributor to activity variance than commonly reported firing rate metrics, exemplifying NOVAs utility for discovering meaningful patterns and generating data-driven hypotheses.

neuroscience↗

bioRxiv2025

NGSTroubleFinder: A tool for detection and quantification of contamination and kinship across human NGS data

SummaryQuality control is a fundamental but often neglected step in any NGS pipeline. Detecting issues like cross-sample contamination and sample swaps is essential to control the data integrity. Here, we present NGSTroubleFinder, a novel python tool to detect cross-sample contamination in human Whole-Genome and Whole-Transcriptome Sequencing data, sample swaps and mismatches between the reported and the inferred genetic and transcriptomic sexes. NGSTroubleFinder is implemented in Python and incorporates a custom-built parallelized pileup engine written in C. The tool reports extensive information on the samples both in textual and HTML format including key plots for easy interpretation of the results. Availability and ImplementationNGSTroubleFinder is written in Python and C, and it can be easily installed with pip. The tool source code and the models are freely available on github (https://github.com/STALICLA-RnD/NGSTroubleFinder) and a containerized version is available on dockerhub (https://hub.docker.com/r/staliclarnd/ngstroublefinder).

bioinformatics↗

bioRxiv2021

The human hepatocyte TXG-MAPr: WGCNA transcriptomic modules to support mechanism-based risk assessment

Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of comprehensive and mechanisms-revealing data, but analysis tools to interpret mechanisms of toxicity and specific for the testing systems (e.g. hepatocytes) are lacking. In this study we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/), an R-Shiny-based implementation of weighted gene co-expression networks (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. Gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, are perturbed by specific stressors and show preserved in rat systems (rat primary hepatocytes and rat in vivo liver), highlighting stress responses that translate across species/testing systems. The TXG-MAPr tool was successfully applied to investigate the mechanism of toxicity of TG-GATEs compounds and using external datasets obtained from different hepatocyte cells and microarray platforms. Additionally, we suggest that module responses can be calculated from targeted RNA-seq data therefore imputing biological responses from a limited gene. By analyzing 50 different PHH donors responses to a common stressor, tunicamycin, we were able to suggest modules associated with donors traits, e.g. pre-existing disease state, therefore connected to donors variability. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.

pharmacology and toxicology↗

bioRxiv2020

High resolution 3D imaging of liver reveals a central role for subcellular architectural organization in metabolism

Cells display complex intracellular organization through compartmentalization of metabolic processes into organelles, yet neither the resolution of these structures in the native tissue context nor its functional consequences are well understood. Here, we resolved the 3-dimensional organelle structural organization in large (>2.8x105m3) volumes of intact liver tissue (15 partial or full hepatocytes per condition) in high resolution (8nm isotropic pixel size) by utilizing enhanced Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) imaging, followed by deep-learning-based image segmentation and 3D reconstruction. We also performed a comparative analysis of subcellular structures in liver tissue of lean and obese animals and found marked alterations particularly in hepatic endoplasmic reticulum (ER), which undergoes massive structural re-organization in obesity characterized by marked disorganization of stacks of ER sheets and predominance of ER tubules. Finally, we demonstrated the functional importance of these structural changes upon experimental recovery of the subcellular organization and its marked impact on cellular and systemic metabolism. We conclude that hepatic subcellular organization and ERs architecture is highly dynamic, integrated with the metabolic state, and critical for adaptive homeostasis and tissue health.

cell biology↗

bioRxiv2020

Aberrant Ca2+ homeostasis in adipocytes links inflammation to metabolic dysregulation in obesity

Chronic metabolic inflammation is a key feature of obesity, insulin resistance and diabetes, although the initiation and propagation mechanisms of metaflammation are not fully established, particularly in the adipose tissue. Here we show that in adipocytes, altered regulation of the Ca2+ channel inositol triphosphate receptor (IP3Rs) is a key, adipocyte-intrinsic, event involved in the emergence and propagation of inflammatory signaling and the resulting insulin resistance. Inflammation, either induced by cytokine exposure in vitro or by obesity in vivo lead to increased expression and activity of IP3Rs in adipocytes in a JNK-dependent manner. This results in increased cytosolic Ca2+ and impaired insulin action. In mice, adipocyte-specific loss of IP3R1/2 protected against adipose tissue inflammation and insulin resistance despite significant diet-induced weight gain. Thus, this work reveals that IP3R over-activation and the resulting increase in cytosolic Ca2+ is a key link between obesity, inflammation and insulin resistance, and suggests that approaches to target adipocyte Ca2+ homeostasis may offer new therapeutic opportunities against metabolic diseases, especially since GWAS studies also implicate this locus in human obesity.

physiology↗

bioRxiv2018

Targeting comorbid diseases via network endopharmacology

The traditional drug discovery paradigm has shaped around the idea of \"one target, one disease\". Recently, it has become clear that not only it is hard to achieve single target specificity but also it is often more desirable to tinker the complex cellular network by targeting multiple proteins, causing a paradigm shift towards polypharmacology (multiple targets, one disease). Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is targeting common biological pathways involved in diseases, giving rise to \"endopharmacology\" (multiple targets, multiple diseases). In this study, leveraging powerful network medicine tools, we describe a recipe for first, identifying common pathways pertaining to diseases and then, prioritizing drugs that target these pathways towards endopharmacology. We present proximal pathway enrichment analysis (PxEA) that uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs for their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. As a proof of principle, we focus on nine autoimmune disorders and using PxEA, we show that many drugs indicated for these conditions are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide the high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimers disease, two phenotypes that have recently gained attention due to the increased comorbidity among patients.

systems biology↗

bioRxiv2016

Investigating side effect modules in the interactome and their use in drug adverse effect discovery

One of the biggest challenges in drug development is increasing costs of bringing new drugs to the market. Many candidate drugs fail during phase II and III trials due to unexpected side effects and experimental methods remain cost ineffective for large scale discovery of adverse effects. Alternatively, computational methods are used to characterize drug side effects, but they often rely on training predictors based on drug and side effect similarity. Moreover, these methods are typically tailored to the underlying data set and provide little mechanistic insights on the predicted associations. In this study, we investigate the role of network topology in explaining observed side effects of drugs. We find that drug targets are closer in the interactome to the proteins inducing the known side effects of the drug compared to the proteins associated with the rest of the side effects. We show that the interactome based proximity can be used to identify side effects and we highlight a use case in which interactome-based side effect prediction can give insights on drug side effects observed in the clinic.

systems biology↗