Biology subjects

Uyar, B.

Publications and source records attributed to Uyar, B..

14 recordsLinked to original sources

bioRxiv2026

An atlas-scale generative model for unified representation learning of bulk RNA-seq data

Public bulk RNA-seq repositories contain hundreds of thousands of samples, creating opportunities for large-scale representation learning, but integration across studies remains challenging because of heterogeneous annotations, experimental protocols, and technical variation. While pre-trained foundation models are now widely available for single-cell RNA-seq, comparable resources for bulk RNA-seq remain scarce, motivating a model that learns a unified, tissue-aware representation directly from bulk data. We trained a supervised variational autoencoder (VAE) on a compendium of 118,263 bulk RNA-seq samples that we assembled from TCGA, GTEx, and ARCHS4 and mapped to 42 tissue categories. The model classifies tissue of origin at 94.9% balanced accuracy (weighted F1 96.2%) and compresses 16,115 genes into a 121-dimensional latent space. Tissue identity is the primary organizing axis of the latent space, while source effects remain secondary. To assess the impact of data volume, we constructed training sets at three different scales (38K, 75K, and 118K samples). Our results demonstrated that reconstruction fidelity improved incrementally with each expansion of the dataset, but with diminishing returns. We validated the model on an independent cohort of 734 paediatric tumour samples from TARGET, achieving 84.6% agreement with the expected tissue of origin. The trained model and code are available at GitHub (https://github.com/BIMSBbioinfo/flexynesis_tissue_vae_manuscript) with an interactive web application.

bioinformatics↗

bioRxiv2024

Flexynesis: A deep learning framework for bulk multi-omics data integration for precision oncology and beyond

Accurate decision making in precision oncology depends on integration of multimodal molecular information, such as the genetic data, gene expression, protein abundance, and epigenetic measurements. Deep learning methods facilitate integration of heterogeneous datasets. However, almost all published deep learning-based bulk multi-omics integration methods have constrained usability. They suffer from lack of transparency, modularity, deployability, and are applicable exclusively to narrow tasks. To address these limitations, we introduce Flexynesis, a versatile tool designed with usability, and adaptability in mind. Flexynesis streamlines data processing, enforces structured data splitting, and ensures rigorous model evaluation. It offers unsupervised feature selection, different omics layer fusion options, and hyperparameter tuning. Users can choose from distinct architectures - fully connected networks, variational autoencoders, multi-triplet networks, graph neural networks, and cross-modality encoding networks. Each model is complemented with a straightforward input interface and standardized training, evaluation, and feature importance quantification methods, enabling easy incorporation into data integration pipelines. For improved user experience, Flexynesis supports features such as on-the-fly task determination and compatibility with regression, classification, and survival modeling. It accommodates multi-task prediction of a mixture of numerical/categorical outcome variables with a tolerance for missing labels. We also developed an extensive benchmarking pipeline, showcasing the tools capability across diverse real-life datasets. This toolset should make deep-learning based bulk multi-omics data integration in the context of clinical/pre-clinical data analysis and marker discovery more accessible to a wider audience with or without experience in deep-learning development. Flexynesis is available at https://github.com/BIMSBbioinfo/flexynesis and can be installed from https://pypi.org/project/flexynesis/. O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/603606v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@17eda85org.highwire.dtl.DTLVardef@13c89ecorg.highwire.dtl.DTLVardef@182f706org.highwire.dtl.DTLVardef@127d9d9_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO Summary of the Flexynesis data integration and analysis workflow. C_FIG

bioinformatics↗

bioRxiv2024

Oncogene inactivation-induced senescence facilitates tumor relapse

Oncogene-directed therapies can induce profound tumor regression in oncogene-addicted cancers such as BRAF-mutant melanoma and KRAS-driven pancreatic cancer, but their long-term benefit is often limited by resistance and early relapse. The mechanisms that allow residual cells to adapt, persist in a dormant state, and eventually fuel recurrence remain poorly understood. Here, we show that oncogene inactivation rapidly induces hallmark features of senescence together with a pro-inflammatory senescence-associated secretory phenotype (SASP). In vivo, oncogene inactivation-induced senescence (OIIS) predisposed tumors to relapse, characterized by polyploidy, chromosomal instability, and acquisition of alternative oncogenic pathways such as Mdm2 upregulation. Tumor microenvironment profiling by spectral flow cytometry revealed that relapse was associated with neovascularization and a shift from an immune-activated to an immunosuppressive milieu, indicating that senescent cells remodel their niche to promote regrowth. Importantly, OIIS features were also observed in human BRAFV600E melanoma cells treated with vemurafenib, confirming the clinical relevance of our findings. Together, our findings establish OIIS as a double-edged process: it initially restrains tumor growth but simultaneously creates conditions that favor recurrence. By defining the genetic, metabolic, cytogenetic, and microenvironmental hallmarks of OIIS, our study highlights adaptations to oncogene deprivation that limit the durability of targeted therapies.

cancer biology↗

bioRxiv2023

Pathogenic mutations of human phosphorylation sites affect protein-protein interactions

Despite their lack of a defined 3D structure, intrinsically disordered regions (IDRs) of proteins play important biological roles. Many IDRs contain short linear motifs (SLiMs) that mediate protein-protein interactions (PPIs), which can be regulated by post-translational modifications like phosphorylation. 20% of pathogenic missense mutations are found in IDRs, and understanding how such mutations affect PPIs is essential for unraveling disease mechanisms. Here, we employed peptide-based interaction proteomics to investigate 36 disease-causing mutations affecting phosphorylation sites. Our results unveiled significant differences in interactomes between phosphorylated and non-phosphorylated peptides, often due to disrupted phosphorylation-dependent SLiMs. We focused on a mutation of a serine phosphorylation site in the transcription factor GATAD1, which causes dilated cardiomyopathy. We found that this phosphorylation site mediates interaction with 14-3-3 family proteins. Follow-up experiments revealed the structural basis of this interaction and suggest that 14-3-3 binding affects GATAD1 nucleocytoplasmic transport by masking a nuclear localisation signal. Our results demonstrate that pathogenic mutations of human phosphorylation sites can significantly impact protein-protein interactions, offering fresh insights into potential molecular mechanisms underlying pathogenesis.

systems biology↗

bioRxiv2022

Multi-omics alleviates the limitations of panel-sequencing for cancer drug response prediction

Comprehensive genomic profiling using cancer gene panels has been shown to improve treatment options for a variety of cancer types. However, genomic aberrations detected via such gene panels dont necessarily serve as strong predictors of drug sensitivity. In this study, using pharmacogenomics datasets of cell lines, patient-derived xenografts, and ex-vivo treated fresh tumor specimens, we demonstrate that utilizing the transcriptome on top of gene panel features substantially improves drug response prediction performance in cancer.

bioinformatics↗

bioRxiv2021

Identifying tumor cells at the single cell level

Tumors are highly complex tissues composed of cancerous cells, surrounded by a heterogeneous cellular microenvironment. Tumor response to treatments is governed by an interaction of cancer cell intrinsic factors with external influences of the tumor microenvironment. Disentangling the heterogeneity within a tumor is a crucial step in developing and utilization of effective cancer therapies. The single cell sequencing technology enables an effective molecular characterization of single cells within the tumor. This technology can help deconvolute heterogeneous tumor samples and thus revolutionize personalized medicine. However, a governing challenge in cancer single cell analysis is cell annotation, the assignment of a particular cell type or a cell state to each sequenced cell. One of the critical cell type annotation challenges is identification of tumor cells within single cell or spatial sequencing experiments.This is a critical limiting step for a multitude of research, clinical, and commercial applications. A reliable method addressing that challenge is a prerequisite for automatic annotation of histopathological data, profiled using multichannel immunofluorescence or spatial sequencing. Here, we propose Ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single cell level. We have tested ikarus on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.

cancer biology↗

bioRxiv2021

Multi-omics and deep learning provide a multifaceted view of cancer

Cancer is a complex disease with a large financial and healthcare burden on society. One hallmark of the disease is the uncontrolled growth and proliferation of malignant cells. Unlike Mendelian diseases which may be explained by a few genomic loci, a deeper molecular and mechanistic understanding of the development of cancer is needed. Such an endeavor requires the integration of tens of thousands of molecular features across multiple layers of information encoded in the cells. In practical terms, this implies integration of multi omics information from the genome, transcriptome, epigenome, proteome, metabolome, and even micro-environmental factors such as the microbiome. Finding mechanistic insights and biomarkers in such a high dimensional space is a challenging task. Therefore, efficient machine learning techniques are needed to reduce the dimensionality of the data while simultaneously discovering complex but meaningful biomarkers. These markers then can lead to testable hypotheses in research and clinical applications. In this study, we applied advanced deep learning methods to uncover multi-omic fingerprints that are associated with a wide range of clinical and molecular features of tumor samples. Using these fingerprints, we can accurately classify different cancer types, and their subtypes. Non-linear multi-omic fingerprints can uncover clinical features associated with patient survival and response to treatment, ranging from chemotherapy to immunotherapy. In addition, multi-omic fingerprints may be deconvoluted into a meaningful subset of genes and genomic alterations to support clinically relevant decisions. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=123 SRC="FIGDIR/small/462364v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@4b53d0org.highwire.dtl.DTLVardef@16b9a96org.highwire.dtl.DTLVardef@1720212org.highwire.dtl.DTLVardef@1e5f096_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Parallel genetics of regulatory sequences in vivo

Understanding how regulatory sequences control gene expression is fundamental to explain how phenotypes arise in health and disease. Traditional reporter assays inform about function of individual regulatory elements, typically in isolation. However, regulatory elements must ultimately be understood by perturbing them within their genomic environment and developmental- or tissue-specific contexts. This is technically challenging; therefore, few regulatory elements have been characterized in vivo. Here, we used inducible Cas9 and multiplexed guide RNAs to create hundreds of mutations in enhancers/promoters and 3' UTRs of 16 genes in C. elegans. To quantify the consequences of mutations on expression, we developed a targeted RNA sequencing strategy across hundreds of mutant animals. We were also able to systematically and quantitatively assign fitness cost to mutations. Finally, we identified and characterized sequence elements that strongly regulate phenotypic traits. Our approach enables highly parallelized, functional analysis of regulatory sequences in vivo.

genetics↗

bioRxiv2019

A novel IKK- and proteasome-independent mechanism of RelA activation triggers senescence associated secretome via transcriptional repression of NFKBIA

The I{kappa}B kinase (IKK) - NF-{kappa}B pathway is activated as part of the DNA damage response and controls both resistance to apoptosis and inflammation. How these different functions are achieved remained unknown. We demonstrate here that DNA double strand breaks elicit two subsequent phases of NF-{kappa}B activation in vivo and in vitro, which are mechanistically and functionally distinct. RNA-sequencing reveals that the first phase controls anti-apoptotic gene expression, while the second drives expression of senescence-associated secretory phenotype (SASP) genes. The first, rapidly activated phase is driven by the ATM-PARP1-TRAF6-IKK cascade, which triggers proteasomal destruction of I{kappa}B and is terminated through I{kappa}B (NFKBIA) re-expression. The second phase is activated days later in senescent cells but is independent of IKK and the proteasome. An altered phosphorylation status of p65, in part driven by GSK3{beta}, results in transcriptional silencing of NFKBIA and IKK-independent, constitutive activation of NF-{kappa}B in senescence. Collectively, our study reveals a novel physiological mechanism of NF-{kappa}B activation with important implications for genotoxic cancer treatment.

cell biology↗

bioRxiv2019

The conserved histone chaperone LIN-53 links lifespan and healthspan regulation in C. elegans

Whether extension of lifespan provides an extended time without health deteriorations is an important issue for human aging. However, to which degree lifespan and healthspan regulation might be linked is not well understood. Chromatin factors could be involved in linking both aging aspects, as epigenetic mechanisms bridge regulation of different biological processes. The epigenetic factor LIN-53 (RBBP4/7) is required for safeguarding cell identities in Caenorhabditis elegans as well as mammals and for preventing memory loss and premature aging in humans. LIN-53 is a histone chaperone that associates with different chromatin-regulating complexes. We show that LIN-53 interacts with the Nucleosome remodeling and deacteylase (NuRD)-complex in C. elegans muscles to promote healthy locomotion during aging. While mutants for other NuRD members show a normal lifespan, animals lacking LIN-53 die early because LIN-53 depletion affects also the Histone deacetylase complex Sin3, which is required for a normal lifespan. To determine why lin-53 and sin-3 mutants die early, we performed transcriptome and metabolome analysis and found that levels of the disaccharide Trehalose are significantly decreased in both mutants. As Trehalose is required for normal lifespan in C. elegans, lin-53 and sin-3 mutants could be rescued by either feeding with Trehalose or increasing Trehalose levels via the Insulin/IGF1 signaling pathway. Overall, our findings suggest that LIN-53 is required for maintaining lifespan and promoting healthspan through discrete chromatin regulatory mechanisms. Since both LIN-53 and its mammalian homologs safeguard cell identities, it is conceivable that its implication in lifespan and healthspan regulation is also evolutionarily conserved.

cell biology↗

bioRxiv2018

Reproducible genomics analysis pipelines with GNU Guix

In bioinformatics, as well as other computationally-intensive research fields, there is a need for workflows that can reliably produce consistent output, independent of the software environment or configuration settings of the machine on which they are executed. Indeed, this is essential for controlled comparison between different observations or for the wider dissemination of workflows. Providing this type of reproducibility, however, is often complicated by the need to accommodate the myriad dependencies included in a larger body of software, each of which generally come in various versions. Moreover, in many fields (bioinformatics being a prime example), these versions are subject to continual change due to rapidly evolving technologies, further complicating problems related to reproducibility. Here, we propose a principled approach for building analysis pipelines and managing their dependencies. As a case study to demonstrate the utility of our approach, we present a set of highly reproducible pipelines for the analysis of RNA-seq, ChIP-seq, Bisulfite-seq, and single-cell RNA-seq. All pipelines process raw experimental data, and generate reports containing publication-ready plots and figures, with interactive report elements and standard observables. Users may install these highly reproducible packages and apply them to their own datasets without any special computational expertise beyond the use of the command line. We hope such a toolkit will provide immediate benefit to laboratory workers wishing to process their own data sets or bioinformaticians seeking to automate all, or parts of, their analyses. In the long term, we hope our approach to reproducibility will serve as a blueprint for reproducible workflows in other areas. Our pipelines, along with their corresponding documentation and sample reports, are available at http://bioinformatics.mdc-berlin.de/pigx

bioinformatics↗

bioRxiv2017

FACT sets a barrier for cell fate reprogramming in C. elegans and Human

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/185116_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (59K):\norg.highwire.dtl.DTLVardef@15ae7aaorg.highwire.dtl.DTLVardef@11f5c08org.highwire.dtl.DTLVardef@1d32442org.highwire.dtl.DTLVardef@f1808f_HPS_FORMAT_FIGEXP M_FIG C_FIG The chromatin regulator FACT (Facilitates Chromatin Transcription) is essential for ensuring stable gene expression by promoting transcription. In a genetic screen using C. elegans we identified that FACT maintains cell identities and acts as a barrier for transcription factor-mediated cell fate reprogramming. Strikingly, FACTs role as a reprogramming barrier is conserved in humans as we show that FACT depletion enhances reprogramming of fibroblasts into stem cells and neurons. Such activity of FACT is unexpected since known reprogramming barriers typically repress gene expression by silencing chromatin. In contrast, FACT is a positive regulator of gene expression suggesting an unprecedented link of cell fate maintenance with counteracting alternative cell identities. This notion is supported by ATAC-seq analysis showing that FACT depletion results in decreased but also increased chromatin accessibility for transcription factors. Our findings identify FACT as a cellular reprogramming barrier in C. elegans and humans, revealing an evolutionarily conserved mechanism for cell fate protection.

developmental biology↗

bioRxiv2017

Mutations In Disordered Regions Cause Disease By Creating Endocytosis Motifs

Mutations in intrinsically disordered regions (IDRs) of proteins can cause a wide spectrum of diseases. Since IDRs lack a fixed three-dimensional structure, the mechanism by which such mutations cause disease is often unknown. Here, we employ a proteomic screen to investigate the impact of mutations in IDRs on protein-protein interactions. We find that mutations in disordered cytosolic regions of three transmembrane proteins (GLUT1, ITPR1 and CACNA1H) lead to an increased binding of clathrins. In all three cases, the mutation creates a dileucine motif known to mediate clathrin-dependent trafficking. Follow-up experiments on GLUT1 (SLC2A1), a glucose transporter involved in GLUT1 deficiency syndrome, revealed that the mutated protein mislocalizes to intracellular compartments. A systematic analysis of other known disease-causing variants revealed a significant and specific overrepresentation of gained dileucine motifs in cytosolic tails of transmembrane proteins. Dileucine motif gains thus appear to be a recurrent cause of disease.

biochemistry↗

bioRxiv2017

HOT or not: Examining the basis of high-occupancy target regions

High-occupancy target (HOT) regions are the segments of the genome with unusually high number of transcription factor binding sites. These regions are observed in multiple species and thought to have biological importance due to high transcription factor occupancy. Furthermore, they coincide with house-keeping gene promoters and the associated genes are stably expressed across multiple cell types. Despite these features, HOT regions are solemnly defined using ChIP-seq experiments and shown to lack canonical motifs for transcription factors that are thought to be bound there. Although, ChIP-seq experiments are the golden standard for finding genome-wide binding sites of a protein, they are not noise free. Here, we show that HOT regions are likely to be ChIP-seq artifacts and they are similar to previously proposed \"hyper-ChIPable\" regions. Using ChIP-seq data sets for knocked-out transcription factors, we demonstrate presence of false positive signals on HOT regions. We observe sequence characteristics and genomic features that are discriminatory of HOT regions, such as GC/CpG-rich k-mers and enrichment of RNA-DNA hybrids (R-loops) and DNA tertiary structures (G-quadruplex DNA). The artificial ChIP-seq enrichment on HOT regions could be associated to these discriminatory features. Furthermore, we propose strategies to deal with such artifacts for the future ChIP-seq studies.

genomics↗