Biology subjects

Bendtsen, C.

Publications and source records attributed to Bendtsen, C..

6 recordsLinked to original sources

bioRxiv2022

Small gene networks can delineate immune cell states and characterize immunotherapy response in melanoma

BackgroundSingle-cell sequencing studies have elucidated some of the underlying mechanisms responsible for immune checkpoint inhibitor (ICI) response, but are difficult to implement as a general strategy or in a clinical diagnostic setting. In contrast, bulk RNAseq is now routine for both research and clinical applications. Therefore, our analysis extracts small transcription factor-directed co-expression networks (regulons) from single-cell RNA-seq data and uses them to deconvolute immune functional states from bulk RNA-seq data to characterize patient responses. MethodsRegulons were inferred in pre-treatment CD45+ cells from metastatic melanoma samples (n=19) treated with first-line ICI therapy (discovery dataset). A logistic regression-based classifier identified immune cell states associated with response, which were characterized according to differentially active, cell-state specific regulons. The complexity of these regulons was reduced and scored in bulk RNAseq melanoma samples from four independent studies (n=209, validation dataset). Patients were clustered according to their regulon scores, and the associations between cluster assignment, response, and survival were determined. Intercellular communication analysis of cell states was performed, and the resulting effector genes were analyzed by trajectory inference. ResultsRegulons preserved the information of gene expression data and accurately delineated immune cell phenotypes, despite reducing dimensionality by > 100-fold. Four cell states, termed exhausted T cells, monocyte lineage cells, memory T cells, and B cells, were associated with therapeutic responses in the discovery dataset. The cell states were characterized by seven differentially active and specific regulons that showed low specificity in non-immune cells. Four clusters with significantly different response outcomes (P <0.001) were identified in the bulk RNAseq validation cohort. An intercellular link between exhausted T cells and monocyte lineage cells was established, whereby their cell numbers were correlated, and exhausted T cells predicted prognosis as a function of monocyte lineage cell number. Analysis of ligand - receptor expression suggested that monocyte lineage cells drive exhausted T cells into terminal exhaustion through programs that regulate antigen presentation, chronic inflammation, and negative co-stimulation. ConclusionsRegulon-based characterization of cell states provides robust and functionally informative markers that can deconvolve bulk RNA-seq data to identify ICI responders.

immunology↗

bioRxiv2022

Epigenetic modulators link mitochondrial redox homeostasis to cardiac function

Excessive production of reactive oxygen species (ROS) is characteristic of numerous diseases, but most studies in this area have not considered the impact of endogenous antioxidative defenses. Here, utilizing multi-omics, we demonstrate that in cardiomyocytes mitochondrial isocitrate dehydrogenase (IDH2) constitutes a major antioxidant defense. In both male and female mice and humans the paradoxical reduction in expression of IDH2 associated with heart failure is compensated for by an increase in the enzymes activity. We describe extensive mutual regulation of the antioxidant activities of IDH2 and NRF2 by a network involving 2-oxoglutarate and L2-hydroxyglutarate and mediated in part through unconventional hydroxymethylation of cytosine residues present in introns. Conditional targeting of ROS in a murine model of heart failure improves cardiac function. Together, these insights may explain why previous attempts to treat heart failure with antioxidants have been unsuccessful and open new approaches to personalizing and, thereby, improving such treatment. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/485908v1_ufig1.gif" ALT="Figure 1"> View larger version (66K): org.highwire.dtl.DTLVardef@1032ebeorg.highwire.dtl.DTLVardef@1f1c7bforg.highwire.dtl.DTLVardef@70f6a0org.highwire.dtl.DTLVardef@4fafbc_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIParadoxical downregulation of mitochondrial isocitrate dehydrogenase (IDH2) in response to oxidative stress leads to the discovery of a robust antioxidative defense in the heart. C_LIO_LIAn antioxidative loop involving IDH2 coordinates other antioxidative defenses, such as NRF2. C_LIO_LIThis loop produces epigenetic modifications that link oxidative stress to mitochondrial function. C_LIO_LIThe conclusion that enhancing antioxidative capacity improves cardiac function only when the endogenous capacity is insufficient opens new approaches to individualized treatment of patients with heart failure. C_LI

molecular biology↗

bioRxiv2021

Epithelial-mesenchymal transition is the main driver of intrinsic metabolism in cancer cell lines

A fundamental feature of cancer cells is genomic heterogeneity. It is a main driver of phenotypic differences, including the response to drugs, and therefore a key factor in therapy selection. Motivated by the increasing role attributed to metabolic reprogramming in tumor development, we wondered how genomic heterogeneity affects metabolic phenotype. To this end, we profiled the intracellular metabolome of 180 cancer cell lines grown in similar conditions to exclude environmental factors. For each cell line, we estimate activity for 49 pathways across the whole metabolic network. Upon clustering of activity data, we found a convergence into only two major metabolic types. These were further characterized by 13C-flux analysis, lipidomics, and analysis of sensitivity to perturbations. These experiments revealed differences in lipid, mitochondrial, and carbohydrate metabolism between the two major types. Finally, a thorough integration of our metabolic data with multiple omics data revealed a strong association with markers of epithelial-mesenchymal transition (EMT). Our analysis indicates that in absence of variations imposed by the microenvironment, the metabolism of cancer cell lines falls into only two major classes despite genetic heterogeneity.

systems biology↗

bioRxiv2021

Heterogeneity of the Cancer Cell Line Metabolic Landscape

The unravelling of the complexity of cellular metabolism is in its infancy. Cancer-associated genetic alterations may result in changes to cellular metabolism that aid in understanding phenotypic changes, reveal detectable metabolic signatures, or elucidate vulnerabilities to particular drugs. To understand cancer-associated metabolic transformation we performed untargeted metabolite analysis of 173 different cancer cell lines from 11 different tissues under constant conditions for 1099 different species using liquid chromatography-mass spectrometry (LC-MS). We correlate known cancer-associated mutations and gene expression programs with metabolic signatures, generating novel associations of known metabolic pathways with known cancer drivers. We show that metabolic activity correlates with drug sensitivity and use metabolic activity to predict drug response and synergy. Finally, we study the metabolic heterogeneity of cancer mutations across tissues, and find that genes exhibit a range of context specific, and more general metabolic control.

cancer biology↗

bioRxiv2018

A Toolbox for Discrete Modelling of Cell Signalling Dynamics

In an age where the volume of data regarding biological systems exceeds our ability to analyse it, many researchers are looking towards systems biology and computational modelling to help unravel the complexities of gene and protein regulatory networks. In particular, the use of discrete modelling allows generation of signalling networks in the absence of full quantitative descriptions of systems, which are necessary for ordinary differential equation (ODE) models. In order to make such techniques more accessible to mainstream researchers, tools such as the BioModelAnalyzer (BMA) have been developed to provide a user-friendly graphical interface for discrete modelling of biological systems. Here we use the BMA to build a library of discrete target functions of known canonical molecular interactions, translated from ordinary differential equations (ODEs). We then show that these BMA target functions can be used to reconstruct complex networks, which can correctly predict many known genetic perturbations. This new library supports the accessibility ethos behind the creation of BMA, providing a toolbox for the construction of complex cell signalling models without the need for extensive experience in computer programming or mathematical modelling, and allows for construction and simulation of complex biological systems with only small amounts of quantitative data.\n\nAUTHOR SUMMARYOrdinary differential equation (ODE) based models are a popular approach for modelling biological networks. A limitation of ODE models is that they require complete networks and detailed kinetic parameterisation. An alternative is the use of discrete, executable models, in which nodes are assigned discrete value ranges, and the relationship between them defined with simple mathematical operations. One tool for constructing such models is the BioModelAnalyzer (BMA), an open source and publicly available (https://www.biomodelanalyzer.org) software, aimed to be fully usable by researchers without extensive computational or mathematical experience. A fundamental question for executable models is whether the high level of abstraction substantially reduces expressivity relative to continuous approaches. Here, we present a canonical library of biological signalling motifs, initially defined by Tyson et al (2003), translated for the first time into the BMA. We show that; 1) these motifs are easily and fully translatable from continuous to discrete models, 2) Combining these motifs in a computationally naive way generates a fully functional and predictive model of the yeast cell cycle.

systems biology↗

bioRxiv2017

Rational cell culture optimization enhances experimental reproducibility in cancer cells.

Optimization of experimental conditions is critical in ensuring robust experimental reproducibility. Through detailed metabolomic analysis we found that cell culture conditions significantly impacted on glutaminase (GLS1) sensitivity resulting in variable sensitivity and irreproducibility in data.\n\nBaseline metabolite profiling highlighted that untreated cells underwent significant changes in metabolic status. Both the extracellular levels of glutamine and lactate and the intracellular levels of multiple metabolites changed drastically during the assay. We show that these changes compromise the robustness of the assay and make it difficult to reproduce.\n\nWe then devised metabolically rationalized standard assay conditions, in which glutaminase-1 inhibition reduced glutamine metabolism differently in both cell lines assayed, and decreased the proliferation of one of them. The adoption of optimized conditions such as the ones described here should lead to an improvement in reproducibility and help eliminate false negatives as well as false positives in these assays.

cell biology↗