Biology subjects

Yilmaz, A.

Publications and source records attributed to Yilmaz, A..

16 recordsLinked to original sources

bioRxiv2026

MICAL-Inspired Chiral Vanadate Nanoclusters Decelerate Actin Dynamics in Live Cells

Actin turnover is a fundamental cellular process essential for cell dynamics, whose control is critical for both medicine and biotechnology. However, conventional small molecules modifying actin turnover scramble the structure of actin filaments and display high cellular toxicity. MICAL enzymes oxidizing methionine (Met) residues in actin can potentially address this challenge, but their large size and multiple required cofactors make MICALs manufacturing and utilization difficult. Here we show that redox-active chiral decavanadate nanoclusters with tartaric acid are capable of site-selective actin modulation, mimicking MICALs, while requiring no cofactors, displaying high biocompatibility and being membrane permeable. Decavanadate nanoclusters serve as atomically precise "nano-enzymes" oxidizing three Met residues in globular actin, including Met-176; the latter inhibits the opening of the backdoor segment and prevents depolymerization of actin filaments. The structure of actin filaments formed after nanocluster treatment revealed no structural disturbances as confirmed by cryo-electron microscopy. The biocompatibility and bioactivity of chiral decavanadate nanoclusters was demonstrated by modulation of actin in living NG108-15 cells. Taking advantage of atomically precise structure of the nanoclusters, we show that their docking into actin can be predicted computationally, indicating the possibility of programmable actin modulation using the tools of nanochemistry.

biochemistry↗

bioRxiv2026

Reconstituting Mouse Embryogenesis Ex Utero from Gastrulation to Fetal Development Reveals Maternally Independent Metabolic Programs

Mammalian development takes place inside the maternal uterus, creating technological constraints that make difficult the study of embryogenesis in live developing embryos. A central challenge for understanding the role of metabolism in mammalian development is discriminating placental and uterine-regulated signals from embryo-intrinsic processes independent of maternal influence, a process that until now has remained inseparable during gastrulation and organogenesis1-3. Ex utero culture systems allowing continuous growth of embryos during pre-gastrulation to organogenesis4,5 offer a promising solution to this challenge. Here, we present optimized ex utero culture platforms that support faithful development of mouse embryos from gastrulation (embryonic day 6.5/7.5) through the fetal period (embryonic day [~]12.5) and harnessed these platforms for dissecting metabolic transitions in vivo during embryogenesis independently of uterus and placenta. We characterized the metabolome of in utero and ex utero whole embryos, fetal organs and culture medium between embryonic days E6.5 and E12.5 by liquid chromatography mass-spectrometry (LC-MS) metabolomics, isotope tracing, and single cell transcriptomics. These datasets present a comprehensive overview of the dynamic embryonic metabolism during gastrulation and organogenesis in utero and ex utero. This analysis revealed that the midgestational metabolic switch occurring at E10.5-E11.5 is faithfully recapitulated ex utero, indicating that this transition is intrinsically programmed in embryonic tissues and does not require direct maternal or placental cues. Notably, oxygen availability modulated the extent of this transition, but elevated oxygen was insufficient to induce it prematurely, demonstrating that the switch is developmentally timed and only partially environmental-responsive. We further harnessed the ex utero platform for identifying and perturbing a mitochondrial redox shift at E7.5-E8.5 that is critical for developmental progress after gastrulation. These findings uncover the remarkable metabolic plasticity of the mammalian embryo, demonstrating its capacity to sustain growth independently of maternal inputs from the establishment of the body plan through the onset of the fetal period. Moreover, they highlight the use of long-term ex utero culture as a unique framework for dissecting the mechanisms that shape embryogenesis under physiological and experimentally perturbed conditions, while functionally uncoupling embryonic programs from maternal and placental influences.

developmental biology↗

bioRxiv2025

Distinct Metabolic Signatures Linked to High-Resolution Computed Tomography Radiographic Phenotypes in Stable and Progressive Fibrotic Lung Disease

Rationale and ObjectivesThis study aimed to identify distinct metabolic signatures associated with disease progression by integrating high-resolution computed tomography (HRCT) visual scoring with comprehensive metabolomic profiling. Materials and MethodsThis single-center, cross-sectional study enrolled 60 idiopathic pulmonary fibrosis/interstitial lung disease (IPF/ILD) patients from December 2021 to October 2022. Participants underwent standardized pulmonary function testing, HRCT imaging, and peripheral blood collection for metabolomic analysis using one-dimensional hydrogen nuclear magnetic resonance (1H NMR) spectroscopy and ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS). A comprehensive HRCT scoring system quantified total HRCT composite scores. Linear regression analysis integrated radiographic scores with metabolomic profiles, adjusted for multiple covariates ResultsStable IPF/ILD exhibited moderate negative correlations between the six most significant metabolites and HRCT scores (r = -0.27 to -0.51), along with a high abundance of specific phospholipids (triacylglycerol, monoacylglycerol, phosphatidylglycerol, phosphatidylethanolamine, diacylglycerol), sphingomyelin, ceramide, and acylcarnitine. In contrast, progressive disease showed weak positive correlations between the six most significant metabolites and HRCT scores (r = 0.19-0.26), and moderate negative correlation between specific triacylglycerol species and HRCT scores (r = -0.37-0.4). Furthermore, metabolomic analysis in individuals with progressive disease revealed both high and low abundances of specific phospholipid species (including high and low triacylglycerol species, as well as low levels of phosphatidylglycerol, phosphatidylethanolamine, phosphatidylcholine, phosphatidylserine, and phosphatidylinositol), along with high levels of certain sphingomyelin, ceramide, taurine, and purine bases, and low levels of xanthine and lactic acid observed. ConclusionIntegration of systematic HRCT visual scoring with metabolomic profiling successfully differentiated stable from progressive IPF/ILD through distinct molecular-radiographic signatures. FundingSupporting Effective Educator Development (SEED) grant

cell biology↗

bioRxiv2025

The CXCL10/CXCR3 axis is essential for sustaining immunological dormancy in breast cancer

Immune surveillance plays a pivotal role in controlling tumor emergence, dormancy and progression, including in breast cancer. Despite its potential clinical relevance, the mechanisms governing dormancy initiation, maintenance and escape, as well as the molecular mediators involved, remain poorly understood. Here, we identify the interferon-inducible chemokine CXCL10 and its receptor CXCR3 as key regulators of immunological dormancy in breast cancer. By transcriptomic profiling we observed high expression of Cxcl10 in dormant cells in two different orthotopic, syngeneic models of breast cancer dormancy (D2.0R and 4T1-MR20). Genetic silencing of Cxcl10 in dormant cells or pharmacological blockade of CXCR3 in vivo, led to early tumor onset and rapid growth in immunocompetent mice. In contrast dormant cells effectively formed tumors in immune-deficient mice independently of Cxcl10 status, demonstrating that the CXCL10/CXCR3 axis-mediated dormancy requires a functional immune system. Further analysis confirmed that Cxcl10 silencing altered the local immune microenvironment, reducing CD4+ and CD8+ T cell infiltration while increasing the presence of granulocytic Myeloid Derived Suppressor Cells and Natural Killer cells. Moreover, Cxcl10 silencing significantly increased the burden of tumor cells disseminated to the lung. Leveraging on these findings, we identified a CXCL10-mediated dormancy signature that predicts improved overall survival in triple-negative breast cancer (TNBC) patients. Our findings have identified a new mechanism modulating breast cancer dormancy with important clinical implications: the CXCL10/CXCR3 axis as a potential therapeutic target for improving survival of patients with TNBC, and the CXCL10-dependent dormancy signature as a tool for identifying these patients.

cancer biology↗

bioRxiv2025

A Benchmarking Platform for Assessing Protein Language Models on Function-related Prediction Tasks

Proteins play a crucial role in almost all biological processes, serving as the building blocks of life and mediating various cellular functions, from enzymatic reactions to immune responses. Accurate annotation of protein functions is essential for advancing our understanding of biological systems and developing innovative biotechnological applications and therapeutic strategies. To predict protein function, researchers primarily rely on classical homology-based methods, which use evolutionary relationships, and increasingly on machine learning (ML) approaches. Lately, protein language models (PLMs) have gained prominence; these models leverage specialised deep learning architectures to effectively capture intricate relationships between sequence, structure, and function. We recently conducted a comprehensive benchmarking study to evaluate diverse protein representations (i.e., classical approaches and PLMs) and discuss their trade-offs. The current work introduces the Protein Representation Benchmark - PROBE tool, a benchmarking framework designed to evaluate protein representations on function-related prediction tasks. Here, we provide a detailed protocol for running the framework via the GitHub repository and accessing our newly developed user-friendly web service. PROBE encompasses four core tasks: semantic similarity inference, ontology-based function prediction, drug target family classification, and protein-protein binding affinity estimation. We demonstrate PROBEs usage through a new use case evaluating ESM2 and three recent multimodal PLMs--ESM3, ProstT5, and SaProt--highlighting their ability to integrate diverse data types, including sequence and structural information. This study underscores the potential of protein language models in advancing protein function prediction and serves as a valuable tool for both PLM developers and users.

bioinformatics↗

bioRxiv2025

Delineating transcriptomic signatures of in vitro human skeletal musclemodels in comparison to in vivo references

A pivotal question at the heart of stem cell research is how faithful cellular models recapitulate the biology of human tissues. Skeletal muscle is the largest tissue in the human body and has been extensively modelled by various in vitro systems. Here, we sought to delineate the state-of-the-art of in vitro human skeletal muscle models by performing a large-scale analysis of transcriptome datasets, covering more than 400 samples across 39 studies, including bulk and single cell RNA sequencing of 2D and 3D models and their in vivo counterparts. Our analyses highlight common discrepancies between a wide range of cellular models and human skeletal muscle with respect to their myogenic identity, transcription factors, epigenetic complexes, metabolic processes and signaling pathways. Our analyses reveal cellular processes that can be modulated to improve the in vitro models of skeletal muscle, while also paving the way for similar studies for other cell types.

cell biology↗

bioRxiv2024

Interlaboratory comparison of standardised metabolomics and lipidomics analyses in human and rodent blood using the MxP(R) Quant 500 kit

Metabolomics and lipidomics are pivotal in understanding phenotypic variations beyond genomics. However, quantification and comparability of mass spectrometry (MS)-derived data are challenging. Standardised assays can enhance data comparability, enabling applications in multi-center epidemiological and clinical studies. Here we evaluated the performance and reproducibility of the MxP(R) Quant 500 kit across 14 laboratories. The kit allows quantification of 634 different metabolites from 26 compound classes using triple quadrupole MS. Each laboratory analysed twelve samples, including human plasma and serum, lipaemic plasma, NIST SRM 1950, and mouse and rat plasma, in triplicates. 505 out of the 634 metabolites were measurable above the limit of detection in all laboratories, while eight metabolites were undetectable in our study. Out of the 505 metabolites, 412 were observed in both human and rodent samples. Overall, the kit exhibited high reproducibility with a median coefficient of variation (CV) of 14.3 %. CVs in NIST SRM 1950 reference plasma were below 25 % and 10 % for 494 and 138 metabolites, respectively. To facilitate further inspection of reproducibility for any compound, we provide detailed results from the in-depth evaluation of reproducibility across concentration ranges using Deming regression. Interlaboratory reproducibility was similar across sample types, with some species-, matrix-, and phenotype-specific differences due to variations in concentration ranges. Comparisons with previous studies on the performance of MS-based kits (including the AbsoluteIDQ p180 and the Lipidyzer) revealed good concordance of reproducibility results and measured absolute concentrations in NIST SRM 1950 for most metabolites, making the MxP(R) Quant 500 kit a relevant tool to apply metabolomics and lipidomics in multi-center studies.

biochemistry↗

bioRxiv2024

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) elicited dose-dependent shifts in the murine urinary metabolome associated with hepatic AHR-mediated differential gene expression

Epidemiological evidence suggests an association between dioxin and dioxin-like compound (DLC) exposure and human liver disease. The prototypical DLC, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), has been shown to induce the progression of reversible hepatic steatosis to steatohepatitis with periportal fibrosis and biliary hyperplasia in mice. Although the effects of TCDD toxicity are mediated by aryl hydrocarbon receptor (AHR) activation, the underlying mechanisms of TCDD-induced hepatotoxicity are unresolved. In the present study, male C57BL/6NCrl mice were gavaged every 4 days for 28 days with 0.03 - 30 g/kg TCDD and evaluated for liver histopathology and gene expression as well as complementary 1-dimensional proton magnetic resonance (1D-1H NMR) urinary metabolic profiling. Urinary trimethylamine (TMA), trimethylamine N-oxide (TMAO), and 1-methylnicotinamide (1MN) levels were altered by TCDD at doses [≤] 3 g/kg; other urinary metabolites, like glycolate, urocanate, and 3-hydroxyisovalerate, were only altered at doses that induced moderate to severe steatohepatitis. Bulk liver RNA-seq data suggested altered urinary metabolites correlated with hepatic differential gene expression corresponding to specific metabolic pathways. In addition to evaluating whether altered urinary metabolites were liver-dependent, published single-nuclear RNA-seq (snRNA-seq), AHR ChIP-seq, and AHR knockout gene expression datasets provide further support for hepatic cell-type and AHR-regulated dependency, respectively. Overall, TCDD-induced liver effects were preceded by and occurred with changes in urinary metabolite levels due to AHR-mediated changes in hepatic gene expression.

molecular biology↗

bioRxiv2024

DeepKin: Predicting relatedness from low-coverage genomes and paleogenomes with convolutional neural networks

DeepKin is a novel tool designed to predict relatedness from genomic data using convolutional neural networks (CNNs). Traditional methods for estimating relatedness often struggle when genomic data is limited, as with paleogenomes and degraded forensic samples. DeepKin addresses this challenge by leveraging two CNN models trained on simulated genomic data to classify relatedness up to the third-degree and to identify parent-offspring and sibling pairs. Our benchmarking shows DeepKin performs comparably or better than the widely used tool READv2. We validated DeepKin on empirical paleogenomes from two paleological sites, demonstrating its robustness and adaptability across different genetic backgrounds, with accuracy >90% above 10K shared SNPs. By capturing information across genomic segments, DeepKin offers a new methodological path for relatedness estimation in settings with highly degraded samples, with applications in ancient DNA, as well as forensic and conservation genetics.

bioinformatics↗

bioRxiv2024

Changes in Cerebrospinal Fluid Proteins across the Spectrum of Untreated and Treated Chronic HIV-1 Infection

Using the Olink Explore 1536 platform, we measured 1,463 unique proteins in 303 cerebrospinal fluid (CSF) specimens from four clinical centers that included uninfected controls and 12 groups of people living with HIV-1 infection representing the spectrum of progressive untreated and treated chronic infection. We present three initial analyses of these measurements: an overview of the CSF protein features of the sample; correlations of the CSF proteins with CSF HIV-1 RNA and neurofilament light chain protein (NfL) concentrations; and comparison of the CSF proteins in HIV-associated dementia (HAD) and neurosymptomatic CSF escape (NSE). These reveal a complex but coherent picture of CSF protein changes that includes highest concentrations of many proteins during CNS injury in the HAD and NSE groups and variable protein changes across the course of neuroasymptomatic systemic HIV-1 progression, including two common patterns, designated as lymphoid and myeloid patterns, related to the principal involvement of their underlying inflammatory cell lineages. Antiretroviral therapy reduced CSF protein perturbations, though not always to control levels. The dataset of these CSF protein measurements, along with background clinical information, is posted online. Extended studies of this unique dataset will provide more detailed characterization of the dynamic impact of HIV-1 infection on the CSF proteome across the spectrum of HIV-1 infection, and further the mechanistic understanding of HIV-1-related CNS pathobiology.

microbiology↗

bioRxiv2023

Pancreatic islet cell stress induced by insulin-degrading enzyme deficiency promotes islet regeneration and protection from autoimmune diabetes

Appropriate tuning of protein homeostasis through mobilization of the unfolded protein response (UPR) is key to the capacity of pancreatic beta cells to cope with highly variable demand for insulin synthesis. An efficient UPR ensures a sufficient beta cell mass and secretory output but can also affect beta cell resilience to autoimmune aggression. The factors regulating protein homeostasis in the face of metabolic and immune challenges are insufficiently understood. We examined beta cell adaptation to stress in mice deficient for insulin-degrading enzyme (IDE), a ubiquitous protease with high affinity for insulin and genetic association with type 2 diabetes. IDE deficiency induced a low-level UPR in both C57BL/6 and autoimmune non-obese diabetic (NOD) mice, associated with rapamycin-sensitive beta cell proliferation strongly enhanced by proteotoxic stress. Moreover, in NOD mice, IDE deficiency protected from spontaneous diabetes and triggered an additional independent pathway, conditional on the presence of islet inflammation but inhibited by proteotoxic stress, highlighted by strong upregulation of regenerating islet-derived protein 2, a protein attenuating autoimmune inflammation. Our findings establish a key role of IDE in islet cell protein homeostasis, identify a link between low-level UPR and proliferation, and reveal an UPR-independent anti-inflammatory islet cell response uncovered in the absence of IDE of potential interest in autoimmune diabetes.

cell biology↗

bioRxiv2023

An Image-Guided Microfluidic System for Single-Cell Lineage Tracking

Cell lineage tracking is a long-standing and unresolved problem in biology. Microfluidic technologies have the potential to address this problem, by virtue of their ability to manipulate and process single-cells in a rapid, controllable and efficient manner. Indeed, when coupled with traditional imaging approaches, microfluidic systems allow the experimentalist to follow single-cell divisions over time. Herein, we present a valve-based microfluidic system able to probe the decision-making processes of single-cells, by tracking their lineage over multiple generations. The system operates by trapping single-cells within growth chambers, allowing the trapped cells to grow and divide, isolating sister cells after a user-defined number of divisions and finally extracting them for downstream transcriptome analysis. The platform incorporates multiple cell manipulation operations, image processing-based automation for cell loading and growth monitoring, reagent addition and device washing. To demonstrate the efficacy of the microfluidic workflow, 6C2 (chicken erythroleukemia) and T2EC (primary chicken erythrocytic progenitors) cells are tracked inside the microfluidic device over two generations, with a cell viability rate in excess of 90%. Sister cells are successfully isolated after division and extracted within a 500 nL volume, which is compatible with downstream single-cell RNA sequencing analysis.

cell biology↗

bioRxiv2022

Cryogenic X-ray crystallographic studies of biomacromolecules at Turkish Light Source: Turkish DeLight

X-ray crystallography is a robust and powerful structural biology technique that provides high-resolution atomic structures of biomacromolecules. Scientists use this technique to unravel mechanistic and structural details of biological macromolecules (e.g. proteins, nucleic acids, protein complexes, protein-nucleic acid complexes, or large biological compartments). Since its inception, single-crystal cryo-crystallography has never been performed in Turkiye due to the lack of a single-crystal X-ray diffractometer. The X-ray diffraction facility recently established at the University of Health Sciences, Istanbul, Turkiye will enable Turkish and international researchers to easily perform high-resolution structural analysis of biomacromolecules from single crystals. Here, we describe the technical and practical outlook of a state-of-the-art home-source X-ray, using lysozyme as a model protein. The methods and practice described in this article can be applied to any biological sample for structural studies. Therefore, this article will be a valuable practical guide from sample preparation to data analysis.

biophysics↗

bioRxiv2021

Effects of the COVID-19 pandemic on publication landscape in chimeric antigen receptor-modified immune cell research

Chimeric antigen receptors (CARs) are artificial receptors introduced mainly into T cells. CAR-induced immune cell (CARi) products have achieved impressive success rates in treating some difficult-to-treat hematological malignancies. Here, we describe effects of the global COVID-19 pandemic on CARi publication landscape. Due to the pandemic, the total number of publications decreased in 2020 compared to 2019 in all fields of cancer immunotherapy except CARi. Nearly exponential increases in the number of CARi publications slowed-down in 2020 for the first time in the past 11 years. There were more CARi than coronavirus publications until 2020 when coronavirus publications increased over 5,000% compared to 2019 (575 publications in 2019 vs. 30,390 in 2020). Unlike cancer immunotherapy where the majority of the publications consist of conference abstracts and review articles, majority of the coronavirus publications are original research articles. There are more coronavirus publications in Pubmed than Embase. The opposite is true for CARi publications. Our analysis of the data from the FDA Adverse Event Reporting System (FAERS) show significantly higher death rate in patients treated with Kymirah than Yescarta (28.14% vs. 16.02%). Kymirah and Yescarta are the two main CAR T cell products for treatment of DLBCL and/or B-ALL. However, despite being highly significant, this result is not easily interpretable due to multiple confounding variables in the FAERS data. Our analysis additionally suggest that the significant effects of co-stimulatory domains (4-1BB vs. CD28) consistently reported in preclinical studies do not translate into clinical results. Our manual curation of the CARi publications in PubMed shows that only 5.2% of the publications report results from CARi clinical trials, although we found 663 clinical trials listed on ClinicalTrials.gov database. In conclusion, publication landscape in CARi as well as other fields of cancer immunotherapy has changed due to the global COVID-19 pandemic. This trend will likely continue in the near future. CARi research is now in need of increased measures by publishers to reduce repetitive and/or duplicate publications and more stringent criteria for data entry into public databases including PubMed, Embase, ClinicalTrials.gov, and FAERS to advance this important field of medical research.

cancer biology↗

bioRxiv2020

Network based multifactorial modelling of miRNA-target interactions

Competing endogenous RNA (ceRNA) regulations and crosstalk between various types of non-coding RNA in human is an important and under-explored subject. Several studies have pointed out that an alteration in miRNA:target interaction can result in unexpected changes due to indirect and complex interactions. In this paper, we defined a new network-based model that incorporates miRNA:ceRNA interactions with expression values and then calculates network-wide effects after perturbation in expression level of element(s) while utilizing miRNA interaction factors such as seed type, binding energy. We have carried out analysis of large scale miRNA:target networks from breast cancer patients. Highly perturbing genes identified by our approach coincide with breast cancer associated genes and miRNAs. Our network-based approach helps unveiling the crosstalk between node elements in miRNA:target network where abundance of targets leading to sponge effect is taken into account. The model has potential to reveal unforeseen and unpredicted regulations which are only evident when considered in network context. Our tool is scalable and can be plugged in with emerging miRNA effectors such as circRNAs, lncRNAs and available as R package ceRNAnet-sim https://www.bioconductor.org/packages/release/bioc/html/ceRNAnetsim.html.

bioinformatics↗

bioRxiv2020

Gut microbiome dysbiosis is associated with elevated toxic bile acids in Parkinson's disease

The gut microbiome can impact brain health and is altered in Parkinsons disease (PD) patients. Here, we investigate changes in the functional microbiome in the appendix of PD patients relative to controls by metatranscriptomic analysis. We find microbial dysbiosis affecting lipid metabolism, particularly an upregulation of bacteria responsible for secondary bile acid synthesis. Proteomic and transcript analysis corroborates a disruption in cholesterol homeostasis and lipid catabolism. Bile acid analysis reveals an increase in microbially-derived, toxic secondary bile acids. Synucleinopathy in mice induces similar microbiome alterations to those of PD patients. The mouse model of synucleinopathy has elevated DCA and LCA. An analysis of blood markers shows evidence of biliary abnormalities early in PD, including elevated alkaline phosphatase and bilirubin. Increased bilirubin levels are also evident before PD diagnosis. In sum, microbially-derived toxic bile acids are heightened in PD and biliary changes may even precede the onset of overt motor symptoms.

neuroscience↗