Biology subjects

Erdem, C.

Publications and source records attributed to Erdem, C..

11 recordsLinked to original sources

bioRxiv2026

Bacterial Stress Responses Lower mRNA-Protein Level Correlations

Diverse bacterial pathogens have evolved complex regulatory mechanisms to adapt to various environmental stresses during infection. The uncertainty in mRNA-protein levels in response to environmental stressors complicates our understanding of bacterial physiology and their adaptation to stressful environments. To examine this issue, we have integrated transcriptomics and proteomics data on three human bacterial pathogens Salmonella enterica Typhimurium, Yersinia pseudotuberculosis, and Staphylococcus aureus under ten infection-relevant stress conditions. We observed positive correlations between mRNA and protein levels, which were decreased under different stress conditions. Essential genes exhibited higher expression levels with lower variation across the conditions and stronger mRNA-protein correlations compared to non-essential genes, highlighting their critical role in bacterial adaptability and survival. Moreover, we identified a substantial number of genes with stress-induced non-correlating mRNA-protein levels, especially under conditions triggering strong stress responses. Particularly this level was dramatically lowered for osmotic stress specific genes affected by impaired translational activity under osmotic stress. Our findings highlight the prevalence of non-correlating mRNA-protein levels and the potential role of post-translational modifications in modulating protein levels in response to environmental stressors during infection. This study provides a comprehensive framework for integrating transcriptomics and proteomics data and identifies potential gene products that might significantly impact the ability of diverse bacterial pathogens to adapt to hostile infection environments. Significance StatementUnderstanding how bacteria adapt to host environments is crucial for combating infections. We employed an integrative transcriptomics and proteomics approach to investigate mRNA-protein correlations in three clinically relevant pathogens under ten infection-relevant stress conditions. We identified genes whose mRNA-protein relationships are significantly disrupted by specific stressors. This study provides a deeper understanding of mRNA-protein level uncertainty, coupling it to stress responses known to trigger critical post-transcriptional and post-translational regulations. These findings help reveal novel mechanisms of bacterial adaptation and pathogenesis. By providing a comprehensive, multi-species dataset, this study serves as a foundational resource for infection biology, driving future advancements in understanding complex regulatory networks and identifying potential antimicrobial targets.

microbiology↗

bioRxiv2025

Gene regulatory networks linked to GABA signalling emerge as relevant for glioblastoma pathogenesis

Glioblastoma (GB) is the most common and aggressive adult brain tumor. Recent evidence shows that GB cells form glutamatergic synapses with neurons, promoting tumor growth and invasion, yet the gene regulatory networks (GRNs) underlying this interaction remain underexplored. GRNs coordinate gene expression through interactions among transcription factors (TFs), enhancers, and promoters, and are often disrupted in cancer due to epigenetic and chromatin architecture changes. To identify GRNs involved in GB pathogenesis, we applied the machine learning-based MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) pipeline to integrate multi-omics data. GABA-signalling emerged as a previously unrecognized contributor to GB, involving relevant TFs as ARX, GSX2 and DLX family, key regulators of GABAergic interneuron development. Co-culture assays further demonstrated that GABAergic input promotes GB proliferation through non-synaptic mechanisms likely involving metabolic or paracrine interactions. Our findings reveal novel GRNs in GB, positioning GABA signalling as a potential therapeutic target to disrupt neuron-glioma interactions. SignificanceThis study uncovers novel gene regulatory networks driving glioblastoma progression through integration of multi-omics data with machine-learning tools. By identifying GABAergic signalling as a previously unrecognized contributor to tumour growth, it reveals new transcriptional regulators, providing valuable insights into neuron-glioma interactions. These findings open new therapeutic avenues for brain-cancer treatment. GRAPHICAL ABSTRAC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=61 SRC="FIGDIR/small/651564v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@ed95e1org.highwire.dtl.DTLVardef@b1d9forg.highwire.dtl.DTLVardef@1c5bc00org.highwire.dtl.DTLVardef@b9d833_HPS_FORMAT_FIGEXP M_FIG C_FIG Integration of multi-omics data from glioblastoma (GB) patients identified networks related to GABA signalling as relevant for GB pathogenesis. In co-culture assays, GABAergic interneurons increase the proliferation of GB cells.

genomics↗

bioRxiv2025

Seven blood biomarkers are associated with TGF-β and VHL-HIF signaling in patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is an aggressive kidney cancer subtype frequently associated with poor prognosis. Most ccRCC cases are asymptomatic in early stages and symptomatic mostly in advanced stages. Furthermore, the heterogeneity of ccRCC presents a challenge to design new treatments. In this study, using proximity extension assay (PEA), we analyzed blood samples from 134 patients with ccRCC and from 111 age- and gender-matched healthy donors. We identified a panel of seven proteins (ANXA1, ESM1, FGFBP1, MDK, METAP2, SDC1, and TFPI2) that are associated with clinicopathological parameters and patient survival. These biomarkers can differentiate patients with ccRCC from the control individuals with high diagnostic sensitivity and specificity. Moreover, by studying protein expression in solid tumors from the same ccRCC patients, we revealed associations between the panel biomarkers and proteins in the TGF-{beta} and VHL-HIF signaling pathways. We found that most tumor promoting biomarkers were positively associated with TGF-{beta} signaling and HIF-2, and negatively associated with pVHL and HIF-1. We also found that most tumor suppressing biomarkers were positively associated with pVHL and HIF-1 and negatively associated with TGF-{beta} signaling and HIF-2. For ccRCC patients, the blood protein biomarkers that were connected to poor prognosis and TGF-{beta}/HIF-2 signaling, as identified in this study, are potentially important assets in personalized medicine.

cancer biology↗

bioRxiv2024

Mechanistic modeling of cell viability assays with in silico lineage tracing

Data from cell viability assays, which measure cumulative division and death events in a population and reflect substantial cellular heterogeneity, are widely available. However, interpreting such data with mechanistic computational models is hindered because direct model/data comparison is often muddled. We developed an algorithm that tracks simulated division and death events in mechanistically-detailed single-cell lineages to enable such a model/data comparison and suggest causes of cell-cell drug response variability. Using our previously developed model of mammalian single-cell proliferation and death signaling, we simulated drug dose response experiments for four targeted anti-cancer drugs (alpelisib, neratinib, trametinib and palbociclib) and compared them to experimental data. Simulations are consistent with data for strong growth inhibition by trametinib (MEK inhibitor) and overall lack of efficacy for alpelisib (PI-3K inhibitor), but are inconsistent with data for palbociclib (CDK4/6 inhibitor) and neratinib (EGFR inhibitor). Model/data inconsistencies suggest that (i) the importance of CDK4/6 for driving the cell cycle may be overestimated, and (ii) the cellular balance between basal (tonic) and ligand-induced signaling is a critical determinant of receptor inhibitor response. Simulations show subpopulations of rapidly and slowly dividing cells in both control and drug-treated conditions. Variations in mother cells prior to drug treatment impinging on ERK pathway activity are associated with the rapidly dividing phenotype and trametinib resistance. This work lays a foundation for the application of mechanistic modeling to large-scale cell viability datasets and better understanding determinants of cellular heterogeneity in drug response. Author SummaryThere is a large amount of data in the public domain for how a variety of cancer cell types respond to a multitude of anti-cancer drugs. However, interpreting such data mechanistically is hindered because of a lack of precise computational tools for model/data comparison. We developed an algorithm that tracks simulated division and death events in mechanistically detailed single-cell lineages to enable such a model/data comparison and suggest causes of drug response variability. We applied this tool to understand four targeted anti-cancer drugs better. Simulations are consistent with data for strong growth inhibition by trametinib and overall lack of efficacy for alpelisib, but are inconsistent with data for palbociclib and neratinib. Model/data inconsistencies suggest where biological understanding may be incomplete. Simulations show subpopulations of rapidly and slowly dividing cells in both control and drug-treated conditions, and analysis thereof suggest mechanisms potentially involved with acute drug resistance. This work lays a foundation for using large-scale datasets to better understand drug response.

systems biology↗

bioRxiv2022

Expanding large-scale mechanistic models with machine learned associations and big datasets

Computational models that can explain and predict complex sub-cellular, cellular, and tissue level drug response mechanisms could speed drug discovery and prioritize patient-specific treatments (i.e., precision medicine). Some models are mechanistic: detailed equations describing known (or supposed) physicochemical processes, while some models are statistical/machine learning-based: descriptive correlations that explain datasets but have no mechanistic or causal guarantees. These two types of modeling are rarely combined, missing the opportunity to explore possibly causal but data-driven new knowledge while explaining what is already known. Here, we explore a combination of machine learning with mechanistic modeling methods to develop computational models that could more fully represent cell-line-specific drug responses. In this proposed framework, machine learning/statistical models built using omics datasets provide high confidence predictions for new interactions between genes and proteins where there is physicochemical uncertainty. These possibly new interactions are used as new connections (edges) in a large-scale mechanistic model (called SPARCED) to better recapitulate the recently released NIH LINCS Consortium large-scale MCF10A dataset. As a test case, we focused on incorporating novel IFN{gamma}/PD-L1 related associations into the SPARCED model to enable description of the cellular response to checkpoint inhibitor immunotherapies. This work is a template for combining big data, machine-learning-inferred interactions with mechanistic models, which could be more broadly applicable towards building multi-scale precision medicine and whole cell models.

systems biology↗

bioRxiv2022

Computational Speed-Up of Large-Scale, Single-Cell Model Simulations Via a Fully-Integrated SBML-Based Format

SummaryLarge-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed up simulation by ~4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations. Availability and ImplementationSource code is freely available at https://github.com/birtwistlelab/SPARCED/releases/tag/v1.1.0 implemented in python, and supported on Linux, Windows, and MacOS (via Docker). ContactMarc Birtwistle mbirtwi@clemson.edu Supplementary informationN/A

systems biology↗

bioRxiv2022

Multi-Omics Binary Integration via Lasso Ensembles (MOBILE) for identification of context-specific networks and new regulatory mechanisms

Cell phenotypes are dictated by both extra- and intra-cellular contexts, and robust identification of context-specific network features that control phenotypes remains challenging. Here, we developed a multi-omics data integration strategy called MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) to nominate molecular features associated with specific cellular phenotypes. We applied this method to chromatin accessibility, mRNA, protein, and phospho-protein time course datasets and focus on two illustrative use cases after we show MOBILE could recover known biology. First, MOBILE nominated new mechanisms of interferon-{gamma} (IFN{gamma}) regulated PD-L1 expression, where analyses suggested, and literature supported that IFN{gamma}-controlled PD-L1 expression involves BST2, CLIC2, FAM83D, ACSL5, and HIST2H2AA3 genes. Second, we explored differences between the highly similar transforming growth factor-beta 1 (TGF{beta}1) and bone morphogenetic protein 2 (BMP2) and showed that differential cell size and clustering properties induced by TGF{beta}1, but not BMP2, were related to the laminin/collagen pathway activity. Given the ever-growing availability of multi-omics datasets, we envision that MOBILE will be broadly applicable to identify context-specific molecular features associated with cellular phenotypes. Graphical Summary O_FIG O_LINKSMALLFIG WIDTH=162 HEIGHT=200 SRC="FIGDIR/small/501297v1_ufig1.gif" ALT="Figure 1"> View larger version (76K): org.highwire.dtl.DTLVardef@12fa3a5org.highwire.dtl.DTLVardef@a0b6a8org.highwire.dtl.DTLVardef@137efb7org.highwire.dtl.DTLVardef@15aec85_HPS_FORMAT_FIGEXP M_FIG C_FIG Multi-Omics Binary Integration via Lasso Ensembles (MOBILE) pipeline yields statistically robust, context-specific association networksThe MOBILE pipeline integrates omics datasets in a data-driven, biologically-structured manner. The pipeline outputs are gene-level, contextspecific association networks. These association networks nominate differentially enriched pathways, subnetworks, and new connections. Broadly applicable to find condition specific networks using multi-omics datasets.

systems biology↗

bioRxiv2021

Mesowestern Blot: Simultaneous Analysis of Hundreds of Sub-Microliter Lysates

Western blotting is a widely-used technique for molecular-weight-resolved analysis of proteins and their post-translational modifications, but has been refractory to affordable scale-up. Here, we report the Mesowestern blot, which uses a 3D-printable gel-casting mold to enable affordable, high-throughput Western blotting with standard sample preparation and small (<1 uL) sample sizes. The casted polyacrylamide gel contains 336, 0.5 uL micropipette-loadable sample wells arranged within a standard microplate footprint. Polyacrylamide % can be altered to change molecular weight resolution range. Proof-of-concept experiments using both infrared-fluorescent molecular weight protein ladder as well as cell lysate (RIPA buffer) demonstrate protein loaded in Mesowestern gels is amenable to the standard Western blotting steps. The main difference between Mesowestern and traditional Western is that semi-dry horizontal instead of immersed vertical gel electrophoresis is used. The linear range of detection is approximately 2 orders of magnitude, with a limit of detection (for {beta}-actin) of around 30 ng of total protein from mammalian cell lysates (~30-3000 cells). Because the gel mold is 3D-printable, users have significant design freedom for custom layouts, and there are few barriers to adoption by the typical cell and molecular biology laboratory already performing Western blots.

biochemistry↗

bioRxiv2021

A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes.

systems biology↗

bioRxiv2020

A Simple and Efficient Pipeline for Construction, Merging, Expansion, and Simulation of Large-Scale, Single-Cell Mechanistic Models

Mechanistic models of how single cells respond to different perturbagens can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Our lab previously constructed one of the largest mechanistic models for single mammalian cell regulation of proliferation and death (774 species, 141 genes, 8 ligands, 2400 reactions). However, this, as many other large-scale models, was written using licensed software (MATLAB) with intricate programming structure, impeding alteration, expansion, and sharing. Here, we generated a new foundation for this model, which includes a python-based creation and simulation pipeline converting a few structured text files into an SBML-compatible format. This new open-source model (named SPARCED) is high-performance- and cloud-computing compatible and enables the study of virtual cell population responses at the single-cell level. We applied this new model to a subset of the LINCS MCF10A Data Cube, which observed that IFN{gamma} acts as an anti-proliferative factor, but the reasons why were unknown. After expanding the SPARCED model with an IFN{gamma} signaling module (to 950 species, 150 genes, 9 ligands, 2500 reactions), we ran stochastic single-cell simulations for two different putative crosstalk mechanisms and looked at the number of cycling cells in each case. Our model-based analysis suggested, and experiments support that these observations are better explained by IFN{gamma}-induced SOCS1 expression sequestering activated EGF receptors, thereby downregulating AKT activity, as opposed to direct IFN{gamma}-induced upregulation of p21 expression. This work forms a foundation for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically predictive mechanistic models.

systems biology↗

bioRxiv2020

Computational response modeling reveals context dependent Akt activity in luminal breast cancer cells

Aberrant signaling through insulin (Ins) and insulin-like growth factor I (IGF1) receptors contributes to the risk and advancement of many cancer types by activating cell survival cascades. Mechanistic computational modeling of such pathways provides insights into each components role in the cell response. In previous computational models, the two receptors were treated as indistinguishable, missing the opportunity to delineate their distinct roles in cancer progression. Here, a dual receptor (IGF1R & InsR) computational model elucidated new experimental hypotheses on how differential early responses emerge. Complementary to our previous findings, the model suggested that the regulation of insulin receptor substrate (IRS) is critical in inducing differential MAPK and Akt activation. As predicted, perturbing ribosomal protein S6 kinase (RPS6K) kinase activity led to an increased Akt activation with insulin stimulation compared to IGF1 stimulation. Being able to discern differential downstream signaling, we can explore improved anti-IGF1R cancer therapies by eliminating emergence of compensation mechanisms, without disrupting InsR signaling. ImplicationsThe study shows, both experimentally and through computational models, that IGF1 and insulin receptor signaling pathways respond differently to RPS6K inhibition.

systems biology↗