Biology subjects

Tuncbag, N.

Publications and source records attributed to Tuncbag, N..

13 recordsLinked to original sources

bioRxiv2025

Extracellular Matrix-Guided Tumor Stratification and Network Models Reveal Clinical Molecular Grades

The extracellular matrix (ECM) critically shapes tumor fate and treatment outcome, serving as a potent prognostic factor. Yet, its compositional heterogeneity across tumors makes it difficult to assess its impact on tumor dynamics. To address this, we introduce an ECM-guided patient stratification pipeline through integration of multi-omic data in lung cancer patients. We obtained four patient groups, representing ECM-grades that showed distinct clinical features, mutation profiles, and cellular heterogeneity. Investigation of patient-specific ECM-induced intracellular signaling via network modeling revealed strong enrichment of pathways and transcriptional regulators related to epithelial-mesenchymal transition (EMT) and cancer stemness in higher ECM-grades. Drug proximity analysis on ECM-grade specific networks predicted olaparib as an ECM-grade dependent therapeutic while erlotinib to be ECM-insensitive which were validated experimentally on lung tumor cells with distinct mutational profiles in response to differing ECM microenvironments. Overall, our ECM-mediated stratification approach is a robust system for capturing ECM heterogeneity and identifying patient groups that can be selectively targeted by distinct therapeutic strategies.

systems biology↗

bioRxiv2024

Co-occurring Mutations in Different Genes Can Fuel Oncogenic Signaling and Serve as Metastatic Tumor Markers

Interrogation of big genomic data and integration with large-scale protein-protein interaction networks and pathways, can provide deep patterns that are rare- yet can prompt dramatic phenotypic alterations and serve as clinical signatures. Mapping cancer-specific co-occurring mutation-pair signatures, in primary and metastatic tumors, is indispensable in precision oncology. The additivity of co-occurring driver mutations in different genes (in trans) can lead to powerful proliferation signals. Co-occurring rare in trans combinations can serve as metastasis markers; excluded combinations may indicate candidates for oncogene-induced senescence (OIS), a tumor-suppressive mechanism. Our statistical framework of the pan-cancer mutation profiles of [~]60,000 tumor sequences from the TCGA and AACR GENIE databases, identified 3424 statistically significant different double mutations in non-redundant pathways, that is, have different downstream targets that may promote specific cancers through single or multiple pathways. Our analysis indicates that they are mostly in primary tumors. We list actionable in trans mutations for 2385 metastatic tumors and provide co-occurrence trees of metastatic breast- cancer markers. This innovative work clarifies the mechanistic conceptual basis and establishes the first of its kind tool for identifying and predicting metastasis. Crucially, when coupled with their proliferative functions and pathways, and linked with drugs, it could provide an invaluable metastasis-targeting resource.

systems biology↗

bioRxiv2024

Comparative Analysis of Structural Alignment Algorithms for Protein-Protein Interfaces in Template-Based Docking Studies

Protein-protein interactions are pivotal for various functions within living organisms. Understanding their underlying mechanisms holds significant potential for unraveling cellular processes. There are several methods to identify protein-protein interactions, including but not limited to template-based docking. The power of template docking lies in the template library selection and the quality of structural alignment. Within the scope of our investigation, we specifically delve into the performance of four structural alignment algorithms on one protein interface and four protein structure benchmark sets. This study places particular emphasis on assessing these tools on protein interfaces, composed of non-continuous structure segments, as these interfaces play a crucial role in protein interactions, especially in the context of template-based docking. Notably, our findings indicate that TM-align, despite not being explicitly designed for sequence-order independent alignment, exhibits comparable performance to tools tailored for this purpose while executing in a considerably shorter time frame. Therefore, TM-align emerges as a promising candidate for the crucial structural alignment step in template-docking pipelines.

bioinformatics↗

bioRxiv2023

Extracellular matrix sulfation in the tumor microenvironment stimulates cancer stemness and invasiveness

Tumor extracellular matrices (ECM) exhibit aberrant changes in composition and mechanics compared to normal tissues. Proteoglycans (PG) are vital regulators of cellular signaling in the ECM with ability to modulate receptor tyrosine kinase (RTK) activation via their sulfated glycosaminoglycan (sGAG) side chains. However, their role on tumor cell behavior is controversial. Here, we demonstrate that PGs are heavily expressed in lung adenocarcinoma patients in correlation with invasive phenotype and poor prognosis. We developed a bioengineered human lung tumor model which recapitulates the increase of sGAGs in tumors in an organotypic matrix with independent control of stiffness, viscoelasticity, ligand density and porosity. Our model reveals that increased sulfation stimulates extensive proliferation, epithelial-mesenchymal transition and stemness in cancer cells. We identified the FAK-PI3K-mTOR signaling axis as a mediator of sulfation-induced molecular changes in cells upon activation of a distinct set of RTKs within tumor-mimetic hydrogels. We demonstrate that the transcriptomic landscape of tumor cells in response to increased sulfation resembles native PG-rich patient tumors through employing integrative omics and network modeling approaches.

bioengineering↗

bioRxiv2023

Structural Coverage of the Human Interactome

Complex biological processes in cells are embedded in the interactome, representing the complete set of protein-protein interactions. Mapping and analyzing the protein structures are essential to fully comprehending these processes molecular details. Therefore, knowing the structural coverage of the interactome is essential to show the current limitations. Structural modeling of protein-protein interactions requires accurate protein structures. In this study, we mapped all experimental structures to the reference human proteome. Later, we found the enrichment in structural coverage when complementary methods such as homology modeling and deep learning (AlphaFold) are included. We then collected the interactions from the literature and databases to form the reference human interactome resulting in 117,897 non-redundant interactions. When we analyzed the structural coverage of the interactome, we found that the number of experimentally determined protein complex structures is scarce, corresponding to 3.95% of all binary interactions. We also analyzed known and modeled structures to potentially construct the structural interactome with a docking method. Our analysis showed that 12.97% of the interactions from HuRI, 73.62%, and 32.94% from the filtered versions of STRING and HIPPIE could potentially be modeled with a high structural coverage or accuracy, respectively. Overall, this paper provides an overview of the current state of structural coverage of the human proteome and interactome. Significance StatementWe gathered binary protein-protein interactions from three prominent interactome databases to create a comprehensive human reference interactome. We quantified the structural coverage of the human interactome using already available structural data from four different sources. We further evaluate the percentage of interactions that can be accurately predicted using docking methods.

bioinformatics↗

bioRxiv2023

Unveiling Hidden Connections in Omics Data via pyPARAGON: an Integrative Hybrid Approach for Disease Network Construction

Omics technologies are powerful tools for detecting dysregulated and altered signaling components in various contexts, encompassing disease states, patients, and drug-perturbations. Network inference or reconstruction algorithms play an integral role in the successful analysis and identification of causal relationships between omics hits. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. By leveraging network motifs instead of pairwise connections among proteins, pyPARAGON offers improved accuracy and reduces the inclusion of nonspecific interactions in signaling networks. Through comprehensive evaluations on benchmark cancer signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome, leading to the discovery of tumor-specific signaling pathways. Overall, the development and evaluation of pyPARAGON significantly contributes to the field as an effective tool for the analysis and integration of multi-omic data in the context of signaling networks. pyPARAGON is available at https://github.com/metunetlab/pyPARAGON.

systems biology↗

bioRxiv2023

Neurodevelopmental disorders and cancer networks share pathways; but differ in mechanisms, signaling strength, and outcome

Neurodevelopmental disorders (NDDs) and cancer are connected, with immunity as their common factor. Their clinical presentations differ; however, individuals with NDDs are more likely to acquire cancer. Schizophrenia patients have [~]50% increased risk; autistic individuals also face an increased cancer likelihood. NDDs are associated with specific brain cell types at specific locations, emerging at certain developmental time windows during brain evolution. Their related mutations are germline; cancer mutations are sporadic, emerging during life. At the same time, NDDs and cancer share proteins, pathways, and mutations. Here we ask exactly which features they share, and how despite their commonality, they differ in outcomes. Our pioneering bioinformatics exploration of the mutations, reconstructed disease-specific networks, pathways, and transcriptome profiles of autism spectrum disorder (ASD) and cancers, points to elevated signal strength in pathways related to proliferation in cancer, and differentiation in ASD. Signaling strength, not the activating mutation, is the key factor in deciding cancer versus NDDs.

systems biology↗

bioRxiv2022

Epigenetic-focused CRISPR/Cas9 screen identifies ASH2L as a regulator of glioblastoma cell survival

Glioblastoma is the most common and aggressive primary brain tumor with poor prognosis, highlighting an urgent need for novel treatment strategies. In this study, we investigated epigenetic regulators of glioblastoma cell survival through CRISPR/Cas9 based genetic ablation screens using a customized sgRNA library EpiDoKOL, which targets critical functional domains of chromatin modifiers. Screens conducted in multiple cell lines revealed ASH2L, a histone lysine methyltransferase complex subunit, as a major regulator of glioblastoma cell viability. ASH2L depletion led to cell cycle arrest and apoptosis. RNA sequencing and greenCUT&RUN together identified a set of cell cycle regulatory genes, such as TRA2B, BARD1, KIF20B, ARID4A and SMARCC1 that were downregulated upon ASH2L depletion. Mass spectrometry analysis revealed the interaction partners of ASH2L in glioblastoma cell lines as SET1/MLL family members including SETD1A, SETD1B, MLL1 and MLL2. We further showed that glioblastoma cells had a differential dependency on expression of SET1/MLL family members for survival. The growth of ASH2L-depleted glioblastoma cells was markedly slower than controls in orthotopic in vivo models. TCGA analysis showed high ASH2L expression in glioblastoma compared to low grade gliomas and immunohistochemical analysis revealed significant ASH2L expression in glioblastoma tissues, attesting to its clinical relevance. Therefore, high throughput, robust and affordable screens with focused libraries, such as EpiDoKOL, holds great promise to enable rapid discovery of novel epigenetic regulators of cancer cell survival, such as ASH2L. Together, we suggest that targeting ASH2L could serve as a new therapeutic opportunity for glioblastoma.

cancer biology↗

bioRxiv2021

Discovery of Latent Drivers from Double Mutations in Pan-Cancer Data Reveal their Clinical Impact

Background Transforming patient-specific molecular data into clinical decisions is fundamental to personalized medicine. Despite massive advancements in cancer genomics, to date driver mutations whose frequencies are low, and their observable transformation potential is minor have escaped identification. Yet, when paired with other mutations in cis, such latent driver mutations can drive cancer. Here, we discover potential latent driver double mutations. Method We applied a statistical approach to identify significantly co-occurring mutations in the pan-cancer data of mutation profiles of [~]80,000 tumor sequences from the TCGA and AACR GENIE databases. The components of same gene doublets were assessed as potential latent drivers. We merged the analysis of the significant double mutations with drug response data of cell lines and patient derived xenografts (PDXs). This allowed us to link the potential impact of double mutations to clinical information and discover signatures for some cancer types. Results Our comprehensive statistical analysis identified 228 same gene double mutations of which 113 mutations are cataloged as latent drivers. Oncogenic activation of a protein can be through either single or multiple independent mechanisms of action. Combinations of a driver mutation with either a driver, a weak driver, or a strong latent driver have the potential of a single gene leading to a fully activated state and high drug response rate. Tumor suppressors require higher mutational load to coincide with double mutations compared to oncogenes which implies their relative robustness to losing their functions. Evaluation of the response of cell lines and patient-derived xenograft data to drug treatment indicate that in certain genes double mutations can increase oncogenic activity, hence a better drug response (e.g. in PIK3CA), or they can promote resistance to the drugs (e.g. in EGFR). Conclusion Our comprehensive analysis of same allele double mutations in cancer genome landscapes emphasizes that interrogation of big genomic data and integration with the results of large-scale small-molecule sensitivity data can provide deep patterns that are rare; but can still result in dramatic phenotypic alterations, and provide clinical signatures for some cancer types.

systems biology↗

bioRxiv2021

Context dependent isoform specific PI3K inhibition confers drugresistance in Hepatocellular carcinoma cells

BackgroundTargeted therapies for Primary liver cancer (HCC) is limited to the multi-kinase inhibitors, and not fully effective due to the resistance to these agents because of the heterogeneous molecular nature of HCC developed during chronic liver disease stages and cirrhosis. Although combinatorial therapy can increase the efficiency of targeted therapies through synergistic activities, isoform specific effects of the inhibitors are usually ignored. This study concentrated on PI3K/Akt/mTOR pathway and the differential combinatory bioactivities of isoform specific PI3K- inhibitor (PIK-75) or PI3K-{beta} inhibitor (TGX-221) with Sorafenib dependent on PTEN context. MethodsThe bioactivities of inhibitors on PTEN adequate Huh7 and deficient Mahlavu cells were investigated with real time cell growth, cell cycle and cell migration assays. Differentially expressed genes from RNA-Seq were identified by edgeR tool. Systems level network analysis of treatment specific pathways were performed with Prize Collecting Steiner Tree (PCST) on human interactome and enriched networks were visualized with Cytoscape platform. ResultsOur data from combinatory treatment of Sorafenib and PIK-75 and TGX-221 showed opposite effects; while PIK-75 displays synergistic effects on Huh7 cells leading to apoptotic cell death, Sorafenib with TGX-221 display antagonistic effects and significantly promotes cell growth in PTEN deficient Mahlavu cells. Transcriptomic states of combinatory treatments with PIK-75 and TGX-221 inhibitors were identified in PTEN deficient and adequate cells. Molecular interactions and cell signaling pathways were reconstructed and analyzed in-depth to understand mechanism of differential synergistic or antagonistic effects of PI3K- (PIK-75) and PI3K-{beta} (TGX-221) inhibitors with Sorafenib. ConclusionsSimultaneously constructed and analyzed differentially expressed cellular networks presented in this study, revealed distinct consequences of isoform specific PI3K inhibition in PTEN adequate and deficient liver cancer cells. We demonstrated the importance of context dependent and isoform specific PI3K/Akt/mTOR signaling inhibition in drug resistance during combination therapies. (https://github.com/cansyl/Isoform-spesific-PI3K-inhibitor-analysis).

cancer biology↗

bioRxiv2021

Quantitative proteomics identifies secreted diagnostic biomarkers as well as tumor-dependent prognostic targets for clear cell Renal Cell Carcinoma

Clear cell Renal Cell Carcinoma (ccRCC) is the third most common and most malignant urological cancer, with a 5-year survival rate of 10% for patients with advanced tumors. Here, we identified 10,160 unique proteins by in-depth quantitative proteomics, of which 955 proteins were significantly regulated between tumor and normal adjacent tissues. We verified 4 putatively secreted biomarker candidates, namely PLOD2, FERMT3, SPARC and SIRP, as highly expressed proteins that are not affected by intra- and inter-tumor heterogeneity. Moreover, SPARC displayed a significant increase in urine samples of ccRCC patients, making it a promising marker for clinical screening assays. Furthermore, based on molecular expression profiles, we propose a biomarker panel for the robust classification of ccRCC tumors into two main clusters, which significantly differed in patient outcome with an almost three times higher risk of death for cluster 1 tumors compared to cluster 2 tumors. Moreover, among the most significant clustering proteins, 13 were targets of repurposed inhibitory FDA-approved drugs. Our rigorous proteomics approach identified promising diagnostic and tumor-discriminative biomarker candidates which can serve as therapeutic targets for the treatment of ccRCC.

cancer biology↗

bioRxiv2019

3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients

Mutation profiles of Glioblastoma (GBM) tumors are very heterogeneous which is the main challenge in the interpretation of the effects of mutations in disease. Additionally, the impact of the mutations is not uniform across the proteins and protein-protein interactions. The pathway level representation of the mutations is very limited. In this work, we approach these challenges through a systems level perspective in which we analyze how the mutations in GBM tumors are distributed in protein structures/interfaces and how they are organized at the network level. Our results show that out of 14644 mutations, 4392 have structural information and ~13% of them form spatial patches. Despite a small portion of all mutations, 3D patches partially decrease the heterogeneity across the patients. Hub proteins adapt multiple patches of mutations usually with a very large one and connects mutations in multiple binding sites through the core of the protein. We reconstructed patient specific networks for 290 GBM tumors. Network-guided analysis of mutations completes the interaction components that mutated proteins potentially affect, and groups the patients according to the reconstructed networks. As a result, we found 4 tumor clusters that overcome the heterogeneity in mutation profiles, and reveal predominant pathways in each group. Additionally, the network-based similarity analysis shows that each group of patients carries a set of signature 3D mutation patches. We believe that this study provides another perspective to the analysis of mutation effects and a good training towards the network-guided precision medicine.\n\nAuthor SummaryGlioblastoma (GBM) is the most aggressive brain tumor type with a 15 months of survival on average. The mutation distribution of the GBM patients is very heterogeneous and standard treatments fail to consider the inter-tumor heterogeneity. In our study, we follow a systems level approach that integrates mutation profiles with protein-protein interaction networks. We hypothesized that although the mutations are heterogeneous, the mutations that are close in 3D of the same protein may affect the protein function similarly and this information can be used to get meaningful relation between disease state and mutations. Therefore, we spatially grouped these mutations as \"patches\" and reconstructed patient specific protein interaction networks. When we cluster these networks based on their pathway similarities, we found four patient groups in GBM. Then, the comparison of groups revealed overrepresentation of 3D patches. This finding can be used for patient specific therapy.

systems biology↗

bioRxiv2016

Functional constraints on replacing an essential gene with its ancient and modern homologs

The complexity hypothesis posits that network connectivity and protein function are two important determinants of how a gene adapts to and functions in a foreign genome. Genes encoding proteins that carry out essential informational tasks in the cell, in particular where multiple interaction partners are involved, are less likely to be transferable to a foreign organism. Here we investigated the constraints on transfer of a gene encoding a highly conserved informational protein, translation elongation factor Tu (EF-Tu), by systematically replacing the endogenous tufA gene in the Escherichia coli genome with its extant and ancestral homologs. The extant homologs represented tuf variants from both near and distant homologous organisms. The ancestral homologs represented phylogenetically resurrected tuf sequences dating from 0.7 to 3.6 bya. Our results demonstrate that all of the foreign tuf genes are transferable to the E. coli genome, provided that an additional copy of the EF-Tu gene, tufB, remains present in the E. coli genome. However, when the tufB gene was removed, only the variants obtained from the {gamma}-proteobacterial family (extant and ancestral), supported growth. This demonstrates the limited functional interchangability of E. coli tuf with its homologs. Our data show a linear correlation between relative bacterial fitness and the evolutionary distance of the extant tuf homologs inserted into the E. coli genome. Our data and analysis also suggest that the functional conservation of protein activity, and its network interactivity, act to constrain the successful transfer of this essential gene into foreign bacteria.

evolutionary biology↗