Biology subjects

Medvedovic, M.

Publications and source records attributed to Medvedovic, M..

15 recordsLinked to original sources

bioRxiv2024

Pathophysiology of hypereosinophilia-associated heart disease

BackgroundCardiac complications in patients with hypereosinophilia cause significant morbidity and mortality. However, mechanisms of how eosinophilic inflammation causes heart damage are poorly understood. MethodsWe developed a model of hypereosinophilia-associated heart disease by challenging hypereosinophilic mice with peptide from the cardiac myosin heavy chain. Disease outcomes were measured by histology, immunohistochemistry, flow cytometry, and measurement of cells and biomarkers in peripheral blood. Eosinophil dependence was determined by using eosinophil-deficient mice ({Delta}dblGATA). Single cells from heart were subjected to single cell RNA sequencing to assess cell composition, subtypes and expression profiles. ResultsMice challenged with myocarditic and control peptide had peripheral blood leukocytosis, but only those challenged with myocarditic peptide had heart inflammation. Heart tissue was infiltrated by eosinophil-rich inflammatory infiltrates associated with cardiomyocyte damage. Disease penetrance and severity were dependent on the presence of eosinophils. Single cell RNA sequencing showed enrichment of myeloid cells, T-cells and granulocytes (neutrophils and eosinophils) in the myocarditic mice. Macrophages were M2 skewed, and eosinophils had an activated phenotype. Gene enrichment analysis identified several pathways potentially involved in pathophysiology of disease. ConclusionEosinophils are required for heart damage in hypereosinophilia-associated heart disease. Additionally, myeloid cells, granulocytes and T-cell cooperatively or independently participate in the pathogenesis of hypereosinophilia-associated heart disease.

immunology↗

bioRxiv2024

CPI203, a BET inhibitor, down-regulates a consistent set of DNA synthesis genes across a wide array of glioblastoma lines.

IntroductionGlioblastomas utilize malignant gene expression pathways to drive growth. Many of these gene pathways are not directly accessible with molecularly targeted pharmacological agents. Chromatin-modifying compounds can alter gene expression and target glioblastoma growth pathways. In this study, we utilize a systematic screen of chromatin-modifying compounds on a panel of patient-derived glioblastoma lines to identify promising compounds and their associated gene targets. MethodsFive glioblastoma cell lines were subjected to a drug screen of 106 chromatin-modifying compounds representing 36 unique drug classes to determine the twelve most promising drug classes and the best candidate inhibitors in each class. These twelve drugs were then tested with a panel of twelve patient-derived gliomasphere lines to identify growth inhibition and corresponding gene expression patterns. Overlap analysis and weighted co-expression network analysis (WCGNA) were utilized to determine potential target genes and gene pathways. ResultsThe initial drug screen identified twelve candidate pharmacologic agents for further testing. Drug sensitivity testing indicated an overall high degree of variability between gliomasphere lines. However, CPI203 was the most consistently effective compound, and the BET inhibitor class was the most consistently effective class of compounds across the gliomasphere panel. Correspondingly, most of the compounds tested had highly variable effects on gene expression between gliomasphere lines. CPI203 stood out as the only compound to induce a consistent effect on gene expression across different gliomasphere lines, specifically down-regulation of DNA-synthesis genes. Amongst the twelve tested cell lines, high expression of CDKN2A and CDKN2B distinguished more drug sensitive from more drug resistant lines. WCGNA identified two oncogenic gene modules (FBXO5 and MELK) that were effectively downregulated by CPI203 (FBXO5) and ML228 (FBXO5 and MELK). ConclusionsThe bromodomain inhibitor CPI203 induced relatively consistent effects on gene expression and growth across a variety of glioblastoma lines, specifically down-regulating genes associated with DNA replication. We propose that clinically effective BET inhibitors have the potential to induce consistent beneficial effects across a spectrum of glioblastomas.

cancer biology↗

bioRxiv2024

Crosstalk of the MAP3K1 and EGFR pathways mediates gene-environment interactions that disrupt developmental tissue closure

Aberrant signal transduction pathways can adversely derail developmental processes. One such process is embryonic eyelid closure that requires MAP3K1. Map3k1 knockout mice have defective eyelid closure and an autosomal recessive eye-open at birth phenotype. In utero exposure to dioxin, a persistent environmental toxicant, causes the same eye defect in Map3k1+/- hemizygous but not wild type pups. Here we explore the mechanisms of Map3k1 (gene) and dioxin (environment) interactions (GxE) in the tissue closure defect. We show that, acting through the AHR, dioxin activates EGFR signaling, which in turn depresses MAP3K1-dependent JNK activity. This effect of dioxin is exacerbated by Map3k1 heterozygosity. Therefore, dioxin exposed Map3k1+/- embryonic eyelids have a marked reduction of JNK activity, accelerated differentiation and impeded polarization in the epithelial cells. Knocking out Ahr or Egfr in eyelid epithelium attenuates the open-eye defects in dioxin-treated Map3k1+/- pups, whereas knockout of Jnk1 and S1pr, encoding the S1P receptors upstream of the MAP3K1-JNK pathway, potentiates dioxin toxicity. Our novel findings suggest that dioxin and genes of the AHR, EGFR and S1P-MAP3K1-JNK pathways constitute a multifactorial mechanism underlying tissue closure abnormalities. Summary statementThe crosstalk between a global environmental pollutant and the pre-existing genetic conditions is mediated through interactive signaling pathways, resulting in anatomical tissue closure abnormalities in development.

developmental biology↗

bioRxiv2023

GABA(A) receptor activation drives GABARAP-Nix mediated autophagy to radiation-sensitize primary and brain-metastatic lung adenocarcinoma tumors

In non-small cell lung cancer (NSCLC) treatment, targeted therapies benefit only a subset of NSCLC, while radiotherapy responses are not durable and toxicity limits therapy. We find that a GABA(A) receptor activator, AM-101, impairs viability and clonogenicity of NSCLC primary and brain metastatic cells. Employing an ex vivo chip, AM-101 is as efficacious as the chemotherapeutic docetaxel, which is used with radiotherapy for advanced-stage NSCLC. In vivo, AM-101 potentiates radiation, including conferring a survival benefit to mice bearing NSCLC intracranial tumors. GABA(A) receptor activation stimulates a selective-autophagic response via multimerization of GABA(A) Receptor-Associated Protein (GABARAP), stabilization of mitochondrial receptor Nix, and utilization of ubiquitin-binding protein p62. A targeted-peptide disrupting Nix binding to GABARAP inhibits AM-101 cytotoxicity. This supports a model of GABA(A) receptor activation driving a GABARAP-Nix multimerization axis triggering autophagy. In patients receiving radiotherapy, GABA(A) receptor activation may improve tumor control while allowing radiation dose de-intensification to reduce toxicity. HighlightsO_LIActivating GABA(A) receptors intrinsic to lung primary and metastatic brain cancer cells triggers a cytotoxic response. C_LIO_LIGABA(A) receptor activation works as well as chemotherapeutic docetaxel in impairing lung cancer viability ex vivo. C_LIO_LIGABA(A) receptor activation increases survival of mice bearing lung metastatic brain tumors. C_LIO_LIA selective-autophagic response is stimulated by GABA(A) receptor activation that includes multimerization of GABARAP and Nix. C_LIO_LIEmploying a new nanomolar affinity peptide that abrogates autophagosome formation inhibits cytotoxicity elicited by GABA(A) receptor activation. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/569295v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1df2f4eorg.highwire.dtl.DTLVardef@19ac26forg.highwire.dtl.DTLVardef@1533986org.highwire.dtl.DTLVardef@e59653_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2023

The Role of MAP3K1 in the Development of the Female Reproductive Tract

Mitogen-Activated Protein 3 Kinase 1 (MAP3K1) is a dynamic signaling molecule with a plethora of cell-type specific functions, most of which are yet to be understood. Here we describe a role for MAP3K1 in the development of female reproductive tract (FRT). MAP3K1 kinase domain-deficient (Map3k1{Delta}KD) females exhibit imperforate vagina, labor failure, and infertility. These defects correspond to a shunted Mullerian duct (MD), the principle precursor of the FRT, in embryos, while they manifest as a contorted caudal vagina with abrogated vaginal-urogenital sinus fusion in neonates. In epithelial cells, MAP3K1 acts through JNK and ERK to activate WNT, yet in vivo MAP3K1 is crucial for WNT activity in mesenchyme associated with the caudal MD. Expression of Wnt7b is high in wild type, but low in Map3k1 knockout MD epithelium and MAP3K1-deficient keratinocytes. Correspondingly, conditioned media derived from MAP3K1-competent epithelial cells activate TCF/Lef-luciferase reporter in fibroblasts, suggesting that MAP3K1-induced factors released from epithelial cells trans-activate WNT signaling in fibroblasts. Our results reveal a temporal-spatial and paracrine MAP3K1-WNT crosstalk contributing to MD caudal elongation and FRT development. HighlightsO_LIMAP3K1 deficient female mice exhibit imperforate vagina and infertility C_LIO_LILoss of MAP3K1 kinase activity impedes Mullerian duct (MD) caudal elongation and fusion with urogenital sinus (UGS) in embryogenesis C_LIO_LIThe MAP3K1-MAPK pathway up-regulates WNT signaling in epithelial cells C_LIO_LIMAP3K1 deficiency down-regulates Wnt7b expression in the MD epithelium and prevents WNT activity in mesenchyme of the caudal MD C_LI

developmental biology↗

bioRxiv2022

Integrative signatures of signaling pathway response increase robustness and accuracy of pathway predictions

MotivationAberrant cell signaling is known to drive progression of cancer and many other diseases. The study of signaling pathways within cells is central to identifying drugs that seek to modulate these pathways. Expression of pathway genes (i.e. genes that code for pathway proteins) correlates poorly with signaling pathway activity, making prediction of signaling pathway activity changes based on transcriptional disease signatures a challenging problem. Pathway architecture and response also varies across cell lines, which reflects how drug response varies across a patient population. ResultsHere, we present a transcriptional footprinting framework for predicting changes in activity of signaling pathway by integrating transcriptional signatures of genetic perturbations of pathway genes over a diverse set of cell lines into a integrative Pathway Activity Signature (iPAS). We use an unsupervised multi-task learning approach to create pathway signatures across 12 cell lines using genetic loss of function data from the LINCS project. We also use supervised learning to construct an optimal predictor based on the ensemble of 12 cell line signatures. Our methods achieve a sizeable increase in performance, as measured by prediction of pathways targeted by LINCS chemical perturbagens. AvailabilityOpen source R package iPAS is available at https://github.com/uc-bd2k/iPAS. Contactmedvedm@ucmail.uc.edu Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2022

MAP1LC3C regulates lysosomal exocytosis and induces zinc reprogramming in renal cancer cells

MAP1LC3C (LC3C) is a member of the microtubule associated family of proteins that are essential in the formation of autophagosomes and lysosomal degradation of cargo. LC3C has tumor suppressing activity and its expression is dependent on kidney cancer tumor suppressors, such as VHL and FLCN. Recently we demonstrated that LC3C autophagy is regulated by noncannonical upstream regulatory complexes and targets for degradation postdivision midbody rings associated with cancer cells stemness. Here we show that loss of LC3C leads to peripheral positioning of the lysosomes and lysosomal exocytosis (LE) in a subset of cells. This process is independent of the autophagic activity of LC3C. Analysis of isogenic cells with low and high LE shows substantial transcriptomic reprogramming with altered expression of Zn-related genes and activity of Polycomb Repressor Complex 2 (PRC2), accompanied by a robust decrease in intracellular Zn. Metabolomic analysis revealed alterations in amino acid steady-state levels. Cells with augmented LE show tumor initiation properties and form aggressive tumors in xenograft models. Immunocytochemistry identified high levels of LAMP1 on the plasma membrane of cancer cells in human ccRCC and reduced levels of Zn, an indication that LE is a frequent event in ccRCC, potentially contributing to the loss of Zn. Overall, these data indicate that an important tumor suppressing activity of LC3C is contributing to the reprogramming of lysosomal activity and Zn metabolism with implication for epigenetic remodeling in a subpopulation of tumor propagating properties of cancer cells.

cell biology↗

bioRxiv2021

ALOHA: Aggregated local extrema splines for high-throughput dose-response analysis

Computational methods for genomic dose-response integrate dose-response modeling with bioinformatics tools to evaluate changes in molecular and cellular functions related to pathogenic processes. These methods use parametric models to describe each genes dose-response, but such models may not adequately capture expression changes. Additionally, current approaches do not consider gene co-expression networks. When assessing co-expression networks, one typically does not consider the dose-response relationship, resulting in co-regulated gene sets containing genes having different dose-response patterns. To avoid these limitations, we develop an analysis pipeline called Aggregated Local Extrema Splines for High-throughput Analysis (ALOHA), which computes individual genomic dose-response functions using a flexible class Bayesian shape constrained splines and clusters gene co-regulation based upon these fits. Using splines, we reduce information loss due to parametric lack-of-fit issues, and because we cluster on dose-response relationships, we better identify co-regulation clusters for genes that have co-expressed dose-response patterns from chemical exposure. The clustered pathways can then be used to estimate a dose associated with a pre-specified biological response, i.e., the benchmark dose (BMD), and approximate a point of departure dose corresponding to minimal adverse response in the whole tissue/organism. We compare our approach to current parametric methods and our biologically enriched gene sets to cluster on normalized expression data. Using this methodology, we can more effectively extract the underlying structure leading to more cohesive estimates of gene set potency.

bioinformatics↗

bioRxiv2020

Accelerating Drug Discovery and Repurposingby Combining Transcriptional Signature Connectivity with Docking

The development of targeted treatment options for precision medicine is hampered by a slow and costly process of drug screening. While small molecule docking simulations are often applied in conjunction with cheminformatic methods to reduce the number of candidate molecules to be tested experimentally, the current approaches suffer from high false positive rates and are computationally expensive. Here, we present a novel in silico approach for drug discovery and repurposing, dubbed connectivity enhanced Structure Activity Relationship (ceSAR) that improves on current methods by combining docking and virtual screening approaches with pharmacogenomics and transcriptional signature connectivity analysis. ceSAR builds on the landmark LINCS library of transcriptional signatures of over 20,000 drug-like molecules and ~5,000 gene knock-downs (KDs) to connect small molecules and their potential targets. For a set of candidate molecules and specific target gene, candidate molecules are first ranked by chemical similarity to their concordant LINCS analogs that share signature similarity with a knock-down of the target gene. An efficient method for chemical similarity search, optimized for sparse binary fingerprints of chemical moieties, is used to enable fast searches for large libraries of small molecules. A small subset of candidate compounds identified in the first step is then re-scored by combining signature connectivity with docking simulations. On a set of 20 DUD-E benchmark targets with LINCS KDs, the consensus approach reduces significantly false positive rates, improving the median precision 3-fold over docking methods at the extreme library reduction. We conclude that signature connectivity and docking provide complementary signals, offering an avenue to improve the accuracy of virtual screening while reducing run times by multiple orders of magnitude.

bioinformatics↗

bioRxiv2020

Connectivity analysis of single cell RNA-sequencing derived transcriptional signature of lymphangioleiomyomatosis

Lymphangioleiomyomatosis (LAM) is a rare pulmonary disease affecting women of childbearing age that is characterized by the aberrant proliferation of smooth-muscle (SM)-like cells and emphysema-like lung remodeling. In LAM, mutations in TSC1 or TSC2 genes results in the activation of the mechanistic target of rapamycin complex 1 (mTORC1) and thus sirolimus, an mTORC1 inhibitor, has been approved by FDA to treat LAM patients. Sirolimus stabilizes lung function and improves symptoms. However, the disease recurs with discontinuation of the drug, potentially because of the sirolimus-induced refractoriness of the LAM cells. Therefore, there is a critical need to identify remission inducing cytocidal treatments for LAM. Recently released Library of Integrated Network-based Cellular Signatures (LINCS) L1000 transcriptional signatures of chemical perturbations has opened new avenues to study cellular responses to existing drugs and new bioactive compounds. Connecting transcriptional signature of a disease to these chemical perturbation signatures to identify bioactive chemicals that can "revert" the disease signatures can lead to novel drug discovery. We developed methods for constructing disease transcriptional signatures and performing connectivity analysis using single cell RNA-seq data. The methods were applied in the analysis of scRNA-seq data of naive and sirolimus-treated LAM cells. The single cell connectivity analyses implicated mTORC1 inhibitors as capable of reverting the LAM transcriptional signatures while the corresponding standard bulk analysis did not. This indicates the importance of using single cell analysis in constructing disease signatures. The analysis also implicated other classes of drugs, CDK, MEK/MAPK and EGFR/JAK inhibitors, as potential therapeutic agents for LAM.

systems biology↗

bioRxiv2019

Connecting omics signatures of diseases, drugs, and mechanisms of actions with iLINCS

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Among these, iLINCS is unique in scope and versatility of the data provided and the analytics facilitated. iLINCS (http://ilincs.org) is an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates analysis of user-submitted omics signatures of diseases and cellular perturbations in the context of a large compendium of pre-computed signatures (>200,000), as well as mining and re-analysis of the large collection of omics datasets (>12,000), pre-computed signatures, and their connections. Analytics workflows driven by user-friendly interfaces enable users with only conceptual understanding of the analysis strategy to execute sophisticated analyses of omics signatures, such as systems biology analyses and interpretation of signatures, mechanism of action analysis, and signature-driven drug repositioning. In summary, iLINCS workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures.

bioinformatics↗

bioRxiv2019

Predicting mechanism of action of cellular perturbations with pathway activity signatures

MotivationMisregulation of signaling pathway activity is etiologic for many human diseases, and modulating activity of signaling pathways is often the preferred therapeutic strategy. Understanding the mechanism of action (MOA) of bioactive chemicals in terms of targeted signaling pathways is the essential first step in evaluating their therapeutic potential. Changes in signaling pathway activity are often not reflected in changes in expression of pathway genes which makes MOA inferences from transcriptional signatures a difficult problem. ResultsWe developed a new computational method for implicating pathway targets of bioactive chemicals and other cellular perturbations by integrated analysis of pathway network topology, the LINCS transcriptional signatures of genetic perturbations of pathway genes and the transcriptional signature of the perturbation. Our methodology accurately predicts signaling pathways targeted by the perturbation when current pathway analysis approaches utilizing only a transcriptional signature of the perturbation fail. Availability and ImplementationOpen source R package paslincs is available at https://github.com/uc-bd2k/paslincs.

systems biology↗

bioRxiv2019

piNET: a versatile web platform for downstream analysis and visualization of proteomics data

Large proteomics data, including those generated by mass spectrometry, are being generated to characterize biological systems at the protein level. Computational methods and tools to identify and quantify peptides, proteins and post-translational modifications (PTMs) that are captured in modern mass spectrometers have matured over the years. On the other hand, tools for downstream analysis, interpretation and visualization of proteomics data sets, in particular those involving PTMs, require further improvement and integration to accelerate scientific discovery and maximize the impact of proteomics studies by connecting them better with biological knowledge across not only proteomics, but also other Omics domains. With the goal of addressing these challenges, the piNET server has been developed as a versatile web platform to facilitate mapping, annotation, analysis and visualization of peptide, PTM, and protein level quantitative data generated by either targeted, shotgun or other proteomics approaches. Building on our experience with large scale analysis of gene and protein expression profiles as part of the Library of Integrated Network Cellular Signatures (LINCS) project, piNET has been designed as a fast, versatile and easy to use web-based tool with three modules that provide mapping from peptides (with PTMs) to proteins, from PTM sites to modifying enzymes that target those sites, and finally from proteins (with PTMs) to pathways, and for further mechanistic insights to LINCS signatures of chemical and genetic perturbations. piNET is freely available at http://www.pinet-server.org.

bioinformatics↗

bioRxiv2018

Cheminformatics tools for analyzing and designing optimized small molecule libraries

Libraries of highly annotated small molecules have many uses in chemical genetics, drug discovery and drug repurposing. Many such libraries have become available, but few data-driven approaches exist to compare these libraries and design new ones. In this paper, we describe such an approach that makes use of data on binding selectivity, target coverage and induced cellular phenotypes as well as chemical structure and stage of clinical development. We implement the approach as R software and a Web-accessible tool (http://www.smallmoleculesuite.org) that uses incomplete and often confounded public data in combination with user preferences to score and create libraries. Analysis of six kinase inhibitor libraries using our approach reveals dramatic differences among them, leading us to design a new LSP-OptimalKinase library that outperforms all previous collections in terms of target coverage and compact size. We also assemble a mechanism of action library that optimally covers 1852 targets of the liganded genome. Using our tools, individual research groups and companies can quickly analyze private compound collections and public libraries can be progressively improved using the latest data.

systems biology↗

bioRxiv2018

GREIN: An interactive web platform for re-analyzing GEO RNA-seq data

The vast amount of RNA-seq data deposited in Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) is still a grossly underutilized resource for biomedical research. To remove technical roadblocks for reusing these data, we have developed a web-application GREIN (GEO RNA-seq Experiments Interactive Navigator) which provides user-friendly interfaces to manipulate and analyze GEO RNA-seq data. GREIN is powered by the back-end computational pipeline for uniform processing of RNA-seq data and the large number (>6,500) of already processed datasets. The front-end user interfaces provide a wealth of user-analytics options including sub-setting and downloading processed data, interactive visualization, statistical power analyses, construction of differential gene expression signatures and their comprehensive functional characterization, and connectivity analysis with LINCS L1000 data. The combination of the massive amount of back-end data and front-end analytics options driven by user-friendly interfaces makes GREIN a unique open-source resource for re-using GEO RNA-seq data. GREIN is accessible at: https://shiny.ilincs.org/grein, the source code at: https://github.com/uc-bd2k/grein, and the Docker container at: https://hub.docker.com/r/ucbd2k/grein.

bioinformatics↗