Epigenetic modulators link mitochondrial redox homeostasis to cardiac function in a sex-dependent manner.

While excessive production of reactive oxygen species (ROS) is a characteristic hallmark of numerous diseases, clinical approaches that ameliorate oxidative stress have been unsuccessful. Here, utilizing multi-omics, we demonstrate that in cardiomyocytes, mitochondrial isocitrate dehydrogenase (IDH2) constitutes a major antioxidative defense mechanism. Paradoxically reduced expression of IDH2 associated with ventricular eccentric hypertrophy is counterbalanced by an increase in the enzyme activity. We unveil redox-dependent sex dimorphism, and extensive mutual regulation of the antioxidative activities of IDH2 and NRF2 by a feedforward network that involves 2-oxoglutarate and L-2-hydroxyglutarate and mediated in part through unconventional hydroxy-methylation of cytosine residues present in introns. Consequently, conditional targeting of ROS in a murine model of heart failure improves cardiac function in sex- and phenotype-dependent manners. 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.

Small Gene Networks Delineate Immune Cell States and Characterize Immunotherapy Response in Melanoma.

Single-cell technologies have elucidated mechanisms responsible for immune checkpoint inhibitor (ICI) response, but are not amenable to a clinical diagnostic setting. In contrast, bulk RNA sequencing (RNA-seq) is now routine for research and clinical applications. Our workflow uses transcription factor (TF)-directed coexpression networks (regulons) inferred from single-cell RNA-seq data to deconvolute immune functional states from bulk RNA-seq data. Regulons preserve the phenotypic variation in CD45+ immune cells from metastatic melanoma samples (n = 19, discovery dataset) treated with ICIs, 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 therapy response, and were characterized by differentially active and cell state-specific regulons. Clustering of bulk RNA-seq melanoma samples from four independent studies (n = 209, validation dataset) according to regulon-inferred scores identified four groups with significantly different response outcomes (P < 0.001). An intercellular link was established between exhausted T cells and monocyte lineage cells, whereby their cell numbers were correlated, and exhausted T cells predicted prognosis as a function of monocyte lineage cell number. The ligand-receptor expression analysis suggested that monocyte lineage cells drive exhausted T cells into terminal exhaustion through programs that regulate antigen presentation, chronic inflammation, and negative costimulation. Together, our results demonstrate how regulon-based characterization of cell states provide robust and functionally informative markers that can deconvolve bulk RNA-seq data to identify ICI responders.

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 1,099 different species using mass spectrometry (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.

A functional analysis of 180 cancer cell lines reveals conserved intrinsic metabolic programs.

Cancer cells reprogram their metabolism to support growth and invasion. While previous work has highlighted how single altered reactions and pathways can drive tumorigenesis, it remains unclear how individual changes propagate at the network level and eventually determine global metabolic activity. To characterize the metabolic lifestyle of cancer cells across pathways and genotypes, we profiled the intracellular metabolome of 180 pan-cancer cell lines grown in identical conditions. For each cell line, we estimated activity for 49 pathways spanning the entirety of the metabolic network. Upon clustering, we discovered a convergence into only two major metabolic types. These were functionally confirmed by 13 C-flux analysis, lipidomics, and analysis of sensitivity to perturbations. They revealed that the major differences in cancers are associated with lipid, TCA cycle, and carbohydrate metabolism. Thorough integration of these types with multiomics highlighted little association with genetic alterations but a strong association with markers of epithelial-mesenchymal transition. Our analysis indicates that in absence of variations imposed by the microenvironment, cancer cells adopt distinct metabolic programs which serve as vulnerabilities for therapy.

Selecting the right therapeutic target for kidney disease.

Kidney disease is a complex disease with several different etiologies and underlying associated pathophysiology. This is reflected by the lack of effective treatment therapies in chronic kidney disease (CKD) that stop disease progression. However, novel strategies, recent scientific breakthroughs, and technological advances have revealed new possibilities for finding novel disease drivers in CKD. This review describes some of the latest advances in the field and brings them together in a more holistic framework as applied to identification and validation of disease drivers in CKD. It uses high-resolution 'patient-centric' omics data sets, advanced in silico tools (systems biology, connectivity mapping, and machine learning) and 'state-of-the-art' experimental systems (complex 3D systems in vitro, CRISPR gene editing, and various model biological systems in vivo). Application of such a framework is expected to increase the likelihood of successful identification of novel drug candidates based on strong human target validation and a better scientific understanding of underlying mechanisms.

Monitoring VEGF-Stimulated Calcium Ion Flux in Endothelial Cells.

The endothelial response to vascular endothelial growth factor A (VEGF-A) regulates many aspects of animal physiology in health and disease. Such VEGF-A-regulated phenomena include vasculogenesis, angiogenesis, tumor growth and progression. VEGF-A binding to receptor tyrosine kinases such as vascular endothelial growth factor receptor 2 (VEGFR2 ) activates multiple signal transduction pathways and changes in homeostasis, metabolism, gene expression, cell proliferation, migration, and survival. One such VEGF-A-regulated response is a rapid rise in cytosolic calcium ion levels which modulates different biochemical events and impacts on endothelial-specific responses. Here, we present a series of detailed and robust protocols for evaluating ligand-stimulated cytosolic calcium ion flux in endothelial cells. By monitoring an endogenous endothelial transcription factor (NFATc2 ) which displays calcium-sensitive redistribution, we can assess the relevance of cytosolic calcium to protein function. This protocol can be easily applied to both adherent and non-adherent cultured cells to evaluate calcium ion flux in response to exogenous stimuli such as VEGF-A.

Statistical reproducibility for pairwise t-tests in pharmaceutical research.

This paper investigates statistical reproducibility of the t-test. We formulate reproducibility as a predictive inference problem and apply the nonparametric predictive inference method. Within our research framework, statistical reproducibility provides inference on the probability that the same test outcome would be reached, if the test were repeated under identical conditions. We present an nonparametric predictive inference algorithm to calculate the reproducibility of the t-test and then use simulations to explore the reproducibility both under the null and alternative hypotheses. We then apply nonparametric predictive inference reproducibility to a real-life scenario of a preclinical experiment, which involves multiple pairwise comparisons of test groups, where different groups are given a different concentration of a drug. The aim of the experiment is to decide the concentration of the drug which is most effective. In both simulations and the application scenario, we study the relationship between reproducibility and two test statistics, the Cohen's d and the p-value. We also compare the reproducibility of the t-test with the reproducibility of the Wilcoxon Mann-Whitney test. Finally, we examine reproducibility for the final decision of choosing a particular dose in the multiple pairwise comparisons scenario. This paper presents advances on the topic of test reproducibility with relevance for tests used in pharmaceutical research.

Evaluation of FOXO1 Target Engagement Using a Single-Cell Microfluidic Platform.

The cellular thermal shift assay (CETSA) has been used extensively since its introduction to study drug-target engagement within both live cells and cellular lysate. This has proven to be a useful tool in early stage drug discovery and is used to study a wide range of protein classes. We describe the application of a single-cell CETSA workflow within a microfluidic affinity capture (MAC) chip. This has enabled us to quantitatively determine the active FOXO1 single-molecule count and observe FOXO1 stabilization and destabilization in the presence of three small molecule inhibitors, including demonstrating the determination of EC50. The successful use of the MAC chip for single-cell CETSA paves the way for the study of precious clinical samples owing to the low number of cells needed by the chip. It also provides a useful tool for studying any underlying population heterogeneity that exists within a cellular system, a feature that is usually masked when conducting ensemble measurements.

Signaling Dynamics Regulating Crosstalks between T-Cell Activation and Immune Checkpoints.

Immune checkpoint inhibitors (ICIs) targeting cytotoxic T lymphocyte-associated protein-4 (CTLA-4) and programmed cell death protein-1 (PD-1) have been hailed as major advances in cancer therapeutics; however, in many cancers response rates remain low. Extensive research efforts are underway to improve the efficacy of ICIs. The signaling pathways regulated by immune checkpoints (ICs) may be an important lever as they interfere with T-cell activation when activated by ICIs. Here, we review the current understanding of T-cell receptor signaling and their intersection with IC signaling pathways. As these signaling processes are highly dynamic and controlled by intricate spatiotemporal mechanisms, we focus on aspects of kinetic regulation that are modulated by ICs. Recent advances in computational modeling and experimental methods that can resolve spatiotemporal dynamics provide insights that reveal molecular mechanisms and new potential approaches for improving the design and application of ICIs.

Modeling the Effect of Mucin Binding in the Gut on Drug Delivery.

An important part the absorption, distribution, metabolism and excretion of an oral therapeutic is the flux rate of drug compound crossing the mucus lining of the gut. To understand this part of the absorption process, we develop a mathematical model of advection, diffusion and binding of drug compounds within the mucus layer of the intestines. Analysis of this model yields simple, measurable criteria for the successful mucin layer traversal of drug compound.

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.

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. Baseline 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. We discuss the implications of the cells' metabolic environment when studying the effects of perturbations to cell function by any type of inhibitor. We 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.

Kinase Inhibition Leads to Hormesis in a Dual Phosphorylation-Dephosphorylation Cycle.

Many antimicrobial and anti-tumour drugs elicit hormetic responses characterised by low-dose stimulation and high-dose inhibition. While this can have profound consequences for human health, with low drug concentrations actually stimulating pathogen or tumour growth, the mechanistic understanding behind such responses is still lacking. We propose a novel, simple but general mechanism that could give rise to hormesis in systems where an inhibitor acts on an enzyme. At its core is one of the basic building blocks in intracellular signalling, the dual phosphorylation-dephosphorylation motif, found in diverse regulatory processes including control of cell proliferation and programmed cell death. Our analytically-derived conditions for observing hormesis provide clues as to why this mechanism has not been previously identified. Current mathematical models regularly make simplifying assumptions that lack empirical support but inadvertently preclude the observation of hormesis. In addition, due to the inherent population heterogeneities, the presence of hormesis is likely to be masked in empirical population-level studies. Therefore, examining hormetic responses at single-cell level coupled with improved mathematical models could substantially enhance detection and mechanistic understanding of hormesis.

Bridging the gap between in vitro and in vivo: Dose and schedule predictions for the ATR inhibitor AZD6738.

Understanding the therapeutic effect of drug dose and scheduling is critical to inform the design and implementation of clinical trials. The increasing complexity of both mono, and particularly combination therapies presents a substantial challenge in the clinical stages of drug development for oncology. Using a systems pharmacology approach, we have extended an existing PK-PD model of tumor growth with a mechanistic model of the cell cycle, enabling simulation of mono and combination treatment with the ATR inhibitor AZD6738 and ionizing radiation. Using AZD6738, we have developed multi-parametric cell based assays measuring DNA damage and cell cycle transition, providing quantitative data suitable for model calibration. Our in vitro calibrated cell cycle model is predictive of tumor growth observed in in vivo mouse xenograft studies. The model is being used for phase I clinical trial designs for AZD6738, with the aim of improving patient care through quantitative dose and scheduling prediction.

Visualization and quantitation of GLUT4 translocation in human skeletal muscle following glucose ingestion and exercise.

Insulin- and contraction-stimulated increases in glucose uptake into skeletal muscle occur in part as a result of the translocation of glucose transporter 4 (GLUT4) from intracellular stores to the plasma membrane (PM). This study aimed to use immunofluorescence microscopy in human skeletal muscle to quantify GLUT4 redistribution from intracellular stores to the PM in response to glucose feeding and exercise. Percutaneous muscle biopsy samples were taken from the m. vastus lateralis of ten insulin-sensitive men in the basal state and following 30 min of cycling exercise (65% VO2 max). Muscle biopsy samples were also taken from a second cohort of ten age-, BMI- and VO2 max-matched insulin-sensitive men in the basal state and 30 and 60 min following glucose feeding (75 g glucose). GLUT4 and dystrophin colocalization, measured using the Pearson's correlation coefficient, was increased following 30 min of cycling exercise (baseline r = 0.47 ± 0.01; post exercise r = 0.58 ± 0.02; P < 0.001) and 30 min after glucose ingestion (baseline r = 0.42 ± 0.02; 30 min r = 0.46 ± 0.02; P < 0.05). Large and small GLUT4 clusters were partially depleted following 30 min cycling exercise, but not 30 min after glucose feeding. This study has, for the first time, used immunofluorescence microscopy in human skeletal muscle to quantify increases in GLUT4 and dystrophin colocalization and depletion of GLUT4 from large and smaller clusters as evidence of net GLUT4 translocation to the PM.

The Role of Historical Bioactivity Data in the Deconvolution of Phenotypic Screens.

A substantial challenge in phenotypic drug discovery is the identification of the molecular targets that govern a phenotypic response of interest. Several experimental strategies are available for this, the so-called target deconvolution process. Most of these approaches exploit the affinity between a small-molecule compound and its putative targets or use large-scale genetic manipulations and profiling. Each of these methods has strengths but also limitations such as bias toward high-affinity interactions or risks from genetic compensation. The use of computational methods for target and mechanism of action identification is a complementary approach that can influence each step of a phenotypic screening campaign. Here, we describe how cheminformatics and bioinformatics are embedded in the process from initial selection of a focused compound library from a large set of historical small-molecule screens through the analysis of screening results. We present a deconvolution method based on enrichment analysis and using known bioactivity data of screened compounds to infer putative targets, pathways, and biological processes that are consistent with the observed phenotypic response. As an example, the approach is applied to a cellular screen aiming at identifying inhibitors of tumor necrosis factor-α production in lipopolysaccharide-stimulated THP-1 cells. In summary, we find that the approach can contribute to solving the often very complex target deconvolution task.

Modeling RAS phenotype in colorectal cancer uncovers novel molecular traits of RAS dependency and improves prediction of response to targeted agents in patients.

PURPOSE: KRAS wild-type status is an imperfect predictor of sensitivity to anti-EGF receptor (EGFR) monoclonal antibodies in colorectal cancer, motivating efforts to identify novel molecular aberrations driving RAS. This study aimed to build a quantitative readout of RAS pathway activity to (i) uncover molecular surrogates of RAS activity specific to colorectal cancer, (ii) improve the prediction of cetuximab response in patients, and (iii) suggest new treatment strategies. EXPERIMENTAL DESIGN: A model of RAS pathway activity was trained in a large colorectal cancer dataset and validated in three independent colorectal cancer patient datasets. Novel molecular traits were inferred from The Cancer Genome Atlas colorectal cancer data. The ability of the RAS model to predict resistance to cetuximab was tested in mouse xenografts and three independent patient cohorts. Drug sensitivity correlations between our model and large cell line compendiums were performed. RESULTS: The performance of the RAS model was remarkably robust across three validation datasets. (i) Our model confirmed the heterogeneity of the RAS phenotype in KRAS wild-type patients, and suggests novel molecular traits driving its phenotype (e.g., MED12 loss, FBXW7 mutation, MAP2K4 mutation). (ii) It improved the prediction of response and progression-free survival (HR, 2.0; P < 0.01) to cetuximab compared with KRAS mutation (xenograft and patient cohorts). (iii) Our model consistently predicted sensitivity to MAP-ERK kinase (MEK) inhibitors (P < 0.01) in two cell panel screens. CONCLUSIONS: Modeling the RAS phenotype in colorectal cancer allows for the robust interrogation of RAS pathway activity across cell lines, xenografts, and patient cohorts. It demonstrates clinical utility in predicting response to anti-EGFR agents and MEK inhibitors.

Automated Delineation of Lung Tumors from CT Images Using a Single Click Ensemble Segmentation Approach.

A single click ensemble segmentation (SCES) approach based on an existing "Click&Grow" algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76% respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated.

Interplay between α-, ß-, and γ-secretases determines biphasic amyloid-ß protein level in the presence of a γ-secretase inhibitor.

Amyloid-ß (Aß) is produced by the consecutive cleavage of amyloid precursor protein (APP) first by ß-secretase, generating C99, and then by γ-secretase. APP is also cleaved by α-secretase. It is hypothesized that reducing the production of Aß in the brain may slow the progression of Alzheimer disease. Therefore, different γ-secretase inhibitors have been developed to reduce Aß production. Paradoxically, it has been shown that low to moderate inhibitor concentrations cause a rise in Aß production in different cell lines, in different animal models, and also in humans. A mechanistic understanding of the Aß rise remains elusive. Here, a minimal mathematical model has been developed that quantitatively describes the Aß dynamics in cell lines that exhibit the rise as well as in cell lines that do not. The model includes steps of APP processing through both the so-called amyloidogenic pathway and the so-called non-amyloidogenic pathway. It is shown that the cross-talk between these two pathways accounts for the increase in Aß production in response to inhibitor, i.e. an increase in C99 will inhibit the non-amyloidogenic pathway, redirecting APP to be cleaved by ß-secretase, leading to an additional increase in C99 that overcomes the loss in γ-secretase activity. With a minor extension, the model also describes plasma Aß profiles observed in humans upon dosing with a γ-secretase inhibitor. In conclusion, this mechanistic model rationalizes a series of experimental results that spans from in vitro to in vivo and to humans. This has important implications for the development of drugs targeting Aß production in Alzheimer disease.