Theses supervised by Prof. Dr. Şefika Kutlu Ülgen
11 theses · Boğaziçi University
Communication of tumor-immune cells in 3d bioprinted lymph node environment
Melanoma is a very aggressive type of skin cancer that develops when melanocytes undergo malignant transformation. The aim of this thesis is to examine how gold nanoparticles (AuNPs), the mTOR inhibitor temsirolimus, and selected phytochemicals affect human A375 melanoma cells, with a particular focus on how immune cell-tumor communication shapes treatment outcomes. Experiments are carried out in both standard 2D cultures and advanced 3D models, including hydrogels and bioprinted scaffolds, to better reflect the complex tumor microenvironment. The 3D systems incorporated extracellular matrix (ECM)-like materials and, in some cases, THP-1-derived M0 macrophages to simulate the immune component of tumors. In 2D monoculture systems, A375-GFP cells seem more vulnerable to AuNP treatment, most likely due in part to fluorescence quenching, while A375-WT tolerates higher doses of AuNPs in both 2D and 3D systems. Temsirolimus shows a more potent effect in 3D than in 2D cultures, though its effects are delayed, reflecting the slower penetration and altered uptake in ECM-rich conditions. The combination of AuNP and temsirolimus reduces A375-WT viability more than either single treatment, acting synergistically. Curcumin induces viability reduction in both 2D and 3D rapidly, which can be linked to inhibition of NK-kB, PI3K/Akt, and MAPK pathways. The other phytochemicals (EGCG, botulin, thymoquinone) cause dose- and time-dependent cell death. Stronger effects are shown when delivered with AuNPs, likely driven by elevated ROS and loss of mitochondrial membrane potential. On the other hand, M0 macrophages responded differently to treatments. AuNPs alone are well-tolerated, but high-dose temsirolimus (500 nM) and curcumin (40 uM) reduced their viability, especially after bioprinting, which creates mechanical stress. Co-culture experiments revealed that the presence of macrophages could alter drug responses mainly by accelerating but sometimes slowing cell death, underscoring the importance of cell-cell interactions. By integrating these in vitro findings with physiologically-based pharmacokinetic (PBPK) modeling of curcumin, this work bridges experimental results with potential in vivo outcomes. Overall, the study highlights the value of combining immune-tumor cocultures with 3D bioprinted models for more predictive drug testing and points toward the promise of nano-particle-drug-phytochemical combinations in melanoma therapy.
Reconstruction of metabolic brain model with the aid of physiologically based pharmacokinetic modelling in presence of autism spectrum disorder
Autism spectrum disorder (ASD) is a neurodevelopmental disorder with only symptomatic medical treatment with increasing prevalence. This study aims to reconstruct a genome-scale metabolic brain model that is expanded via genes and reactions related to ASD and observe changes in dopamine D2 receptors in the presence of antipsychotics. For this aim, iMS570, iNL403, and MODEL1608180000 are merged. After standardization, duplicate reaction removals, deletion of zero flux reactions, and expanding model with ASD-related genes and reactions, the final model consists of 1638 reactions, 1358 metabolites, and 756 genes. For further investigations of the autism-specific brain model, transcriptome gene expression data GSE28475 is prepared and integrated. Autism-specific brain model shows mitochondrial and glutaminergic dysfunctions. Final 18360 unique genes are assigned as up-or down-regulated based on threshold determined by downregulated SHANK3 expression and 30% of averages of gene expression data. Additionally, the physiology-based pharmacokinetic (PBPK) model of risperidone and its metabolite paliperidone is simulated. Both drugs are antipsychotics that are used in ASD symptoms. First, simulations are performed to observe population density, CYP2D6 subtypes, ethnicity, total hepatic clearance, and P-gp concentration changes for 2 mg risperidone. PBPK simulation results of experimental articles were in the range of literature findings. Receptor occupancy simulations with chronic dosing underestimate plasma concentrations, but results are still in the range. With down-regulation of the SLC6A3 dopamine transporter gene, which is used to mimic DRD2 gene, proportional to receptor occupancy findings, the effect of risperidone on autism-specific brain model is investigated. Glutaminergic neurotransmission is decreased below healthy brain level, and mitochondrial dysfunction was relieved but still not in the healthy range. Reconstruction of ASD-specific brain model with PBPK modeling enhancement is promising work to better under disease mechanism.
Investigation of medulloblastoma metabolism by tissue-specific genome-scale brain metabolic model and identification of therapeutic targets
Medulloblastoma (MB) is the most prevalent pediatric brain tumor arising in the cerebellum. Since conventional therapies decrease life quality and cause deleterious effects on children, computer models are urgently required to simulate cancer phenotypes and determine potential therapeutic targets with minimum side effects on healthy cells. In the present study, metabolic alterations specific to MB were reflected on the brain genome-scale metabolic model by employing transcriptome data. Moreover, the relation between metastasis and the Warburg effects and the pathways utilized by MB without carbon source were investigated. Flux sampling analysis was also performed to detect statistically different reactions in healthy and MB cases. Regulation, flux coupling, and essentiality analyses were conducted as well to find therapeutic targets for MB. Additionally, the antimetabolites which might lessen the use of substrates in cells by causing competitive inhibition were identified by using similarity scores and conducting FBA. To investigate sphingolipid metabolism in depth, 79 reactions were newly included in the MB model. Consequently, the MB model captured metabolic characteristics of MB successfully as confirmed by experimental studies. It was found that targeting proteins/enzymes related to fatty acid synthesis, mevalonate pathway in cholesterol synthesis and inhibition of cardiolipin production, and tumor-inducing sphingolipid metabolites might be beneficial therapeutic strategies for MB. Furthermore, the suppression of GABA catalyzing and succinate-producing enzymes simultaneously might be a potential solution for metastatic MB. Using oleic acid as an antimetabolite owing to its structural similarity to linoleate and its downregulation in MB might be also a promising approach for this life-threatening disease.
Genome-scale metabolic reconstruction of freshwater organism daphnia pulex
The aim of this study is the reconstruction of the genome-scale model of an ecological organism and investigation of its cellular functionality towards toxicity by using computational systems biology approaches. Daphnia pulex is one of the keystone species of the toxicity tests for the environment, and hence it is here chosen as a model organism. The study on D. pulex genome was first published in February 2011 and contributed to numerous scientific researches. In the present study, manually curated genome-scale metabolic model of D. pulex is reconstructed by using KEGG and UniProt databases along with the literature search. The completed model includes 1051 reactions (985 internal, 66 exchange) and 774 metabolites (222 mitochondrial, 485 in the cytosol, 67 externals) related to 697 genes. Based on the constraint-based modeling, biomass is chosen as the objective function to inspect the cellular fluxes through the growth of D. pulex. The robustness analysis is performed on selected compounds such as L-isoleucine, selenite and sulfate so as to monitor their effects on biomass. L-isoleucine has a positive impact on the growth of D. pulex. The applicability of the reconstructed model of D. pulex to toxicity studies is tested by investigating the relation between selenate and sulfate uptakes, where these fluxes are estimated by flux balance analysis. The computational results demonstrate that when sulfate uptake is getting close to zero flux, selenate uptake begins. This observation on selenate and sulfate uptake mechanism indicates that predicted results are in agreement with the literature. Consequently, the reconstructed metabolic model of D. pulex is able to predict the cellular behavior and can be used for metabolic toxicity studies.
A droplet-based platform for high throughput screening of drug administered cells
Droplet-based microfluidic systems move forward since 2000s having benefits compared to the conventional methods like improved sensitivity, automated and high throughput operation. This thesis involves a combined approach of the engineering and biological principles and analyses within the droplet-based microfluidic system. The performances of different microfluidic bioreactor designs were tested, and the surfactant concentration against the toxicity to cells and the flow rates were optimized. Diploid BY4743 strain with GFP tagged Rpl5 protein and haploid EY0987 strain with Nop56 protein were treated with hydroxyurea (HU) and temsirolimus drugs to understand the functions of these proteins in ribosome biogenesis. The responses of the cells were measured based on the cell area and the amount of protein expressions. SOM analysis was performed, and several clustering approaches gather the over- and underexpressed genes together. GO analysis was applied to these clusters to understand the cumulative functions of the genes in the clusters. The experimental and computational results on the response of the cells were interpreted within the context of ribosome biogenesis. HU inhibits cell growth, decreases cell dimension and protein expression of Nop56 and Rpl5, which function in ribosome biogenesis. Temsirolimus, inhibits EY0987 yeast cell growth, decreases cell dimension and protein expression of Nop56, whereas the drug effects on diploid BY4743 strain and Rpl5 protein are unclear. SOM analysis reveals that the clusters, in which NOP56, NOP58 and RPL5 are involved, are related to ribosome biogenesis, rRNA processing and nucleolus, and the genes in these clusters are suppressed after 10 to 14 minutes of rapamycin treatment. This study elucidates the role of nucleolar components, snoRNP proteins, in ribosome biogenesis and confidently lead to novel therapeutic strategies for ribosomal protein related diseases.
Understanding the attention deficit hyperactivity disorder-gut axis by metabolic network analysis
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder diagnosed with hyperactivity, impulsivity, and a lack of attention inconsistent with the patient's development level. People with ADHD frequently experience gastrointestinal (GI) dysfunction, suggesting a possible role of the gut microbiome. The present research aims to determine a set of biomarkers for ADHD by reconstructing a model of the gut-microbial community and understanding the effects of metabolites produced by gut microbiota on the human brain. Genome-scale metabolic models (GEM), considering the relationship between gene-protein-reaction associations, are used to simulate metabolic activities in gut organisms. The production rates of dopamine and serotonin precursors and the key short-chain fatty acids which affect the health status are determined under three diets (Western, Atkins', Vegan) and compared with healthy subjects. Elasticities are calculated to understand the sensitivity of exchange fluxes to changes in diet and bacterial abundance at the species level. A pre-prepared human brain model with 812 metabolites, 994 reactions, 671 genes, and 71 metabolic pathways is used as the healthy brain model. Genes NOS1 and SLC6A3 are deleted from the healthy model to simulate an ADHD brain. In order to achieve an integrated gut-brain model, a three-compartment model (gut, blood, and brain) is curated. The presence of Bacillota, Actinobacteria, Bacteroidetes, and Bacteroidota may be possible gut microbiota indicators of ADHD. This type of modeling approach, taking microbial genome-environment interactions into account and how the metabolites they produced interact with other organs in the human body, aids in understanding gastrointestinal mechanisms behind ADHD and improving the quality of life for patients.
Reconstruction of human tissue-specific genome-scale metabolic models for therapeutic applications
The objective of this study is the investigation of different human tissues including brain, liver and pancreas by using genome-scale metabolic models (GEMs) and their therapeutic applications. Brain cell-specific GEMs were reconstructed based on single-cell transcriptome data and the intercellular reactions between neurons, astrocytes, microglia, and oligodendrocytes were added to generate a combined brain model, iHumanBrain2690. These brain networks were used in the prediction of metabolic alterations in glucose, ketone bodies and oxygen change and reporter metabolites. Glucose supplementation increased the subsystems' activities in glycolysis and ketone bodies elevated those in the TCA cycle and oxidative phosphorylation. Reporter metabolite analysis identified L-carnitine and arachidonate as the top reporter metabolites in grey and white matter microglia in multiple sclerosis, respectively. The effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on liver metabolism was investigated using integrated GEMs of human hepatocytes and SARS-CoV-2. Computational approaches unraveled possible virus-induced hepatocellular reprogramming in fatty acid, glycerophospholipid, sphingolipid, cholesterol, and folate metabolisms, bile acid biosynthesis, and carnitine shuttle among others. Reaction knockout analysis provided critical reactions in glycolysis, oxidative phosphorylation, purine metabolism, and reactive oxygen species detoxification subsystems. The single-cell transcriptome data was used to generate context-specific GEMs for alpha, beta, delta and pancreatic polypeptide cells for non-diabetic and type 2 diabetes (T2D) states at single-cell resolution. The in silico analysis was performed for the investigation of metabolic activities in non-diabetic and T2D pancreatic cells. By altering glucose and oxygen uptakes to the metabolic networks, hypoglycemia, hyperglycemia and hypoxia led to changes in metabolic activities in various cellular subsystems. Taken together, single and integrated GEMs help us elucidate complex metabolism in various human tissues in health and disease.
Metabolic modeling of daphnia for environmental toxicity studies
Daphnia, a genus of small planktonic freshwater crustaceans commonly referred to as "water fleas," plays a critical role in aquatic ecosystems and has long served as a model organism in ecotoxicological studies. Owing to its sensitivity to pollutants, short life cycle, ease of laboratory cultivation, and transparent body, Daphnia pulex has become a widely adopted test species for acute and chronic toxicity assessments. As a heterotrophic organism, Daphnia's growth and survival are highly dependent on food availability and quality, particularly essential nutrients such as phosphorus, polyunsaturated fatty acids (PUFAs), and sterols. Among various feed sources, Chlorella vulgaris has demonstrated considerable nutritional value for Daphnia cultivation, exhibiting high protein content and the ability to effectively remove selenium from wastewater. In this study, a genome-scale metabolic model of Daphnia pulex was constructed using the COBRA Toolbox within MATLAB, enabling simulation of its metabolic behavior under different nutrient conditions. The model was employed to evaluate the organism's growth response to various dietary inputs and to identify key metabolic pathways involved in nutrient assimilation. In the final stage, toxicological assessments were carried out by integrating metal concentration data from Lake Beyşehir with the metabolic model. Heavy metal compounds commonly found in freshwater environments were analyzed in terms of their potential impact on Daphnia's metabolic performance. The study presents a comprehensive framework that combines metabolic modeling with toxicological evaluation, offering novel insights into the metabolic adaptability of Daphnia pulex under both nutrient-driven and chemically stressed conditions. The results support the potential use of this integrated modeling approach for predicting the ecological consequences of pollutant exposure and assessing algal-based feed viability in contaminated water systems.
Otizm spektrum bozukluğunun mekanizmalarını anlamaya yönelik beyin-bağırsak-mikrobiyota ağı
ASD is a complex neurodevelopmental disorder associated with gastrointestinal issues and gut–brain axis disruptions. To examine ASD-related metabolic alterations and assess personalized interventions, a systems-level modeling approach was developed connecting host and microbial metabolism. Prefrontal cortex (PFC)-specific metabolic models were reconstructed to capture ASD-related alterations in a key executive region. Flux sampling revealed disrupted energy metabolism and elevated inflammatory and oxidative pathways. Sphingolipid metabolism was also affected, suggesting a promising direction for investigation. Colon models were also reconstructed to assess their capacity to handle neuroactive and microbially derived metabolites. At the microbiome level, personalized gut microbiota models were built using species-level metagenomic profiles and inferred diets from ASD, neurotypical, and microbiota transfer therapy treated children. ASD models showed reduced short-chain fatty acid secretion, redox imbalance, and impaired neurotransmitter production. Personalized whole-body models were created by adapting infant and adult models with individual microbiome, diet data, and age-specific physiology. Integration with PFC and microbial models enabled simulation of PFC-targeted, microbiota-modulated metabolic profiles. Among interventions, plant-based and fiber-rich diets most consistently reduced deviations from neurotypical PFC fluxes, while supplements like coenzyme Q10, iron, and calcium improved individual profiles. This systems approach reveals ASD-related metabolic disruptions and offers a framework for evaluating personalized nutritional and microbiome interventions. Interindividual variability emphasizes the need for personalized models that capture unique metabolic profiles for more precise and effective interventions.
Synthesis and analysis of gold nanoparticles incorporating phytochemicals for targeted cancer therapy
The clinical application of therapeutic compounds is often constrained by poor water solubility, low bioavailability, instability, and non-specific distribution. Drug delivery systems utilizing nanotechnology, especially metallic nanoparticles like gold nanoparticles offer a critical solution by providing highly customizable carriers capable of overcoming these inherent physicochemical limitations. This research addresses a key gap in literature, the lack of a standardized methodology for comparing the efficacy of gold nanoparticle-based drug delivery systems, utilizing widely varied therapeutic agents. The study aimed to create several gold nanoparticle drug delivery systems from a diverse panel of agents (EGCG, Curcumin, Thymoquinone, Betulin, Ginsenoside, and Temsirolimus), which exhibit significant differences in functional groups, lipophilicity, and complexity. Two primary synthetic approaches were explored: direct reduction/mediation (successful for polyphenols like EGCG, thymoquinone and curcumin), often optimized via initial deprotonation under alkaline conditions and post-synthesis conjugation to pegylated gold nanoparticles (effective for challenging structures like Betulin and Ginsenoside). In depth characterization confirmed that these methodologies were successful in the creation of gold nanoparticle systems that are comparable in size (± 20 nm), structure (spherical), concentration, dispersity (mono dispersed or semi-mono dispersed), and therapeutic agent retention (30 ± 10% of total mass). The results validate that these standardized synthetic methodologies enable the creation of a wide variety of comparable systems, paving the way for a novel comparative and competitive framework. This approach is expected to be essential for rapidly evaluating the effectiveness of drug delivery across different therapeutic compounds, thereby significantly accelerating the speed of preclinical drug discovery and helping to identify the most effective nanocarrier system.
Investigation of bacterial infection strategies through bacteria-human protein-protein interactions by bioinformatics approaches
Infectious diseases, which have been a serious threat for human life in ancient times, is still leading the causes of death and disability worldwide. Pathogens (viruses, bacteria, protozoa, fungi, etc.) express a wide range of molecules that bind to host cell targets to manipulate human mechanisms for their own advantage, and result in infection in the host organism. Well characterization of these interspecies (pathogen-human) interactions is required for a complete understanding of pathogenesis. The key point of fighting with infectious diseases is the identification and characterization of the strategies used by these pathogens to interact with the host, as these strategies are usually unique to specific pathogens or conserved across several different species. Within the framework of this project, bacterial infection mechanisms are investigated through bacteria-human protein-protein interactions. The targeted human proteins with specific properties different from the non-targeted ones are analyzed to enlighten several infection mechanisms. Gene Ontology enrichments of targeted human proteins are investigated; that is, all three GO terms are examined to identify the terms having significant association with each human protein set studied. In addition to GO terms, Kegg pathways, transcription factor targets and pathway commons of the targeted human proteins are identified. A thorough bioinformatic based analysis of bacteria-human protein interaction network is still missing in the literature. This study is the first attempt to comprehensively investigate bacterial infection strategies through bacteria-human protein-protein interactions. Common and specific infection strategies of different types of bacteria are identified, contributing to the novelty of the present research work.