Naturally occurring combinations of receptors from single cell transcriptomics in endothelial cells

VEGF inhibitor drugs are part of standard care in oncology and ophthalmology, but not all patients respond to them. Combinations of drugs are likely to be needed for more effective therapies of angiogenesis-related diseases. In this paper we describe naturally occurring combinations of receptors in endothelial cells that might help to understand how cells communicate and to identify targets for drug combinations. We also develop and share a new software tool called DECNEO to identify them. Single-cell gene expression data are used to identify a set of co-expressed endothelial cell receptors, conserved among species (mice and humans) and enriched, within a network, of connections to up-regulated genes. This set includes several receptors previously shown to play a role in angiogenesis. Multiple statistical tests from large datasets, including an independent validation set, support the reproducibility, evolutionary conservation and role in angiogenesis of these naturally occurring combinations of receptors. We also show tissue-specific combinations and, in the case of choroid endothelial cells, consistency with both well-established and recent experimental findings, presented in a separate paper. The results and methods presented here advance the understanding of signaling to endothelial cells. The methods are generally applicable to the decoding of intercellular combinations of signals.

LIF, a mitogen for choroidal endothelial cells, protects the choriocapillaris: implications for prevention of geographic atrophy

In the course of our studies aiming to discover vascular bed-specific endothelial cell (EC) mitogens, we identified leukemia inhibitory factor (LIF) as a mitogen for bovine choroidal EC (BCE), although LIF has been mainly characterized as an EC growth inhibitor and an anti-angiogenic molecule. LIF stimulated growth of BCE while it inhibited, as previously reported, bovine aortic EC (BAE) growth. The JAK-STAT3 pathway mediated LIF actions in both BCE and BAE cells, but a caspase-independent proapoptotic signal mediated by cathepsins was triggered in BAE but not in BCE. LIF administration directly promoted activation of STAT3 and increased blood vessel density in mouse eyes. LIF also had protective effects on the choriocapillaris in a model of oxidative retinal injury. Analysis of available single-cell transcriptomic datasets shows strong expression of the specific LIF receptor in mouse and human choroidal EC. Our data suggest that LIF administration may be an innovative approach to prevent atrophy associated with AMD, through protection of the choriocapillaris.

Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics

MOTIVATION

Analysis of singe cell RNA sequencing (scRNA-seq) typically consists of different steps including quality control, batch correction, clustering, cell identification and characterization, and visualization. The amount of scRNA-seq data is growing extremely fast, and novel algorithmic approaches improving these steps are key to extract more biological information. Here, we introduce: (i) two methods for automatic cell type identification (i.e., without expert curator) based on a voting algorithm and a Hopfield classifier, (ii) a method for cell anomaly quantification based on isolation forest, and (iii) a tool for the visualization of cell phenotypic landscapes based on Hopfield energy-like functions. These new approaches are integrated in a software platform that includes many other state-of-the-art methodologies and provides a self-contained toolkit for scRNA-seq analysis.

RESULTS

We present a suite of software elements for the analysis of scRNA-seq data. This Python-based open source software, Digital Cell Sorter (DCS), consists in an extensive toolkit of methods for scRNA-seq analysis. We illustrate the capability of the software using data from large datasets of peripheral blood mononuclear cells (PBMC), as well as plasma cells of bone marrow samples from healthy donors and multiple myeloma patients. We test the novel algorithms by evaluating their ability to deconvolve cell mixtures and detect small numbers of anomalous cells in PBMC data.

AVAILABILITY

The DCS toolkit is available for download and installation through the Python Package Index (PyPI). The software can be deployed using the Python import function following installation. Source code is also available for download on Zenodo: DOI 10.5281/zenodo.2533377.

SUPPLEMENTARY INFORMATION

Supplemental Materials are available at PeerJ online.

Abstract A14: Modeling drug combination sensitivity with Hopfield networks and transcriptomics data

The Hopfield neural network model is one of the simplest models able to mathematically implement Waddington’s interpretation of normal and anomalous cell phenotypes as dynamical attractors of epigenetic landscapes. Here, we propose a computational approach based on Hopfield’s associative memories that integrate gene expression data and gene interactome networks in (1) a model representing the dynamics and control of disease progression in multiple myeloma (MM), and (2) a model describing the control of angiogenesis. The MM model is built using single-cell RNA-seq data from bone marrow aspirates of MM patients as well as patients diagnosed with monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), two medical conditions that often progress to full MM. We identify different clusters of MGUS, SMM, and MM cells, map them to Hopfield associative memory patterns, and model the dynamics of transition between the different patterns. The model is then used to identify combinations of genes whose simultaneous inhibition is associated with delayed disease progression. In the angiogenesis control model, we use single-cell RNA-seq data to predict specific combinations of targets for inhibition that could induce a cellular transition from tip-like to stalk-like endothelial cells, inhibiting the formation of capillary sprouts in blood vessels. The model generates novel hypotheses for combinations that could complement standard VEGF inhibitors and lead to a more efficient control of angiogenesis in cancer.

Citation Format: Carlo Piermarocchi, Sergii Domanskyi, Alex Hakansson, Giovanni Paternostro. Modeling drug combination sensitivity with Hopfield networks and transcriptomics data [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr A14.

Modeling disease progression in Multiple Myeloma with Hopfield networks and single-cell RNA-seq

Associative memories in Hopfield's neural networks are mapped to gene expression pattern to model different paths of disease progression towards Multiple Myeloma (MM). The model is built using single cell RNA-seq data from bone marrow aspirates of MM patients as well as patients diagnosed with Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM), two medical conditions that often progress to full MM.

RESULTS

We identify different clusters of MGUS, SMM, and MM cells, map them to Hopfield associative memory patterns, and model the dynamics of transition between the different patterns. The model is then used to identify genes that are differentialy expressed across different MM stages and whose simultaneous inhibition is associated to a delayed disease progression.

Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters

BACKGROUND

Single cell RNA sequencing (scRNA-seq) brings unprecedented opportunities for mapping the heterogeneity of complex cellular environments such as bone marrow, and provides insight into many cellular processes. Single cell RNA-seq has a far larger fraction of missing data reported as zeros (dropouts) than traditional bulk RNA-seq, and unsupervised clustering combined with Principal Component Analysis (PCA) can be used to overcome this limitation. After clustering, however, one has to interpret the average expression of markers on each cluster to identify the corresponding cell types, and this is normally done by hand by an expert curator.

RESULTS

We present a computational tool for processing single cell RNA-seq data that uses a voting algorithm to automatically identify cells based on approval votes received by known molecular markers. Using a stochastic procedure that accounts for imbalances in the number of known molecular signatures for different cell types, the method computes the statistical significance of the final approval score and automatically assigns a cell type to clusters without an expert curator. We demonstrate the utility of the tool in the analysis of eight samples of bone marrow from the Human Cell Atlas. The tool provides a systematic identification of cell types in bone marrow based on a list of markers of immune cell types, and incorporates a suite of visualization tools that can be overlaid on a t-SNE representation. The software is freely available as a Python package at https://github.com/sdomanskyi/DigitalCellSorter .

CONCLUSIONS

This methodology assures that extensive marker to cell type matching information is taken into account in a systematic way when assigning cell clusters to cell types. Moreover, the method allows for a high throughput processing of multiple scRNA-seq datasets, since it does not involve an expert curator, and it can be applied recursively to obtain cell sub-types. The software is designed to allow the user to substitute the marker to cell type matching information and apply the methodology to different cellular environments.

Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

Modern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases AURKB, NEK1, TTK, and WEE1 causes simulated HeLa cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model.

Cell cycle—the process in which a parent cell replicates its DNA and divides into two daughter cells—is an upregulated process in many forms of cancer. Identifying gene inhibition targets to regulate cell cycle is important to the development of effective therapies. Although modern high throughput techniques offer unprecedented resolution of the molecular details of biological processes like cell cycle, analyzing the vast quantities of the resulting experimental data and extracting actionable information remains a formidable task. Here, we create a dynamical model of the process of cell cycle using the Hopfield model (a type of recurrent neural network) and gene expression data from human cervical cancer cells and yeast cells. We find that the model recreates the oscillations observed in experimental data. Tuning the level of noise (representing the inherent randomness in gene expression and regulation) to the “edge of chaos” is crucial for the proper behavior of the system. We then use this model to identify potential gene targets for disrupting the process of cell cycle. This method could be applied to other time series data sets and used to predict the effects of untested targeted perturbations.

High-throughput screening and bioinformatic analysis to ascertain compounds that prevent saturated fatty acid-induced β-cell apoptosis

Pancreatic β-cell lipotoxicity is a central feature of the pathogenesis of type 2 diabetes. To study the mechanism by which fatty acids cause β-cell death and develop novel approaches to prevent it, a high-throughput screen on the β-cell line INS1 was carried out. The cells were exposed to palmitate to induce cell death and compounds that reversed palmitate-induced cytotoxicity were ascertained. Hits from the screen were analyzed by an increasingly more stringent testing funnel, ending with studies on primary human islets treated with palmitate. MAP4K4 inhibitors, which were not part of the screening libraries but were ascertained by a bioinformatics analysis, and the endocannabinoid anandamide were effective at inhibiting palmitate-induced apoptosis in INS1 cells as well as primary rat and human islets. These targets could serve as the starting point for the development of therapeutics for type 2 diabetes.

Evolutionary and Topological Properties of Genes and Community Structures in Human Gene Regulatory Networks

The diverse, specialized genes present in today's lifeforms evolved from a common core of ancient, elementary genes. However, these genes did not evolve individually: gene expression is controlled by a complex network of interactions, and alterations in one gene may drive reciprocal changes in its proteins' binding partners. Like many complex networks, these gene regulatory networks (GRNs) are composed of communities, or clusters of genes with relatively high connectivity. A deep understanding of the relationship between the evolutionary history of single genes and the topological properties of the underlying GRN is integral to evolutionary genetics. Here, we show that the topological properties of an acute myeloid leukemia GRN and a general human GRN are strongly coupled with its genes' evolutionary properties. Slowly evolving ("cold"), old genes tend to interact with each other, as do rapidly evolving ("hot"), young genes. This naturally causes genes to segregate into community structures with relatively homogeneous evolutionary histories. We argue that gene duplication placed old, cold genes and communities at the center of the networks, and young, hot genes and communities at the periphery. We demonstrate this with single-node centrality measures and two new measures of efficiency, the set efficiency and the interset efficiency. We conclude that these methods for studying the relationships between a GRN's community structures and its genes' evolutionary properties provide new perspectives for understanding evolutionary genetics.

A scalable method for molecular network reconstruction identifies properties of targets and mutations in acute myeloid leukemia

A key aim of systems biology is the reconstruction of molecular networks. We do not yet, however, have networks that integrate information from all datasets available for a particular clinical condition. This is in part due to the limited scalability, in terms of required computational time and power, of existing algorithms. Network reconstruction methods should also be scalable in the sense of allowing scientists from different backgrounds to efficiently integrate additional data. We present a network model of acute myeloid leukemia (AML). In the current version (AML 2.1), we have used gene expression data (both microarray and RNA-seq) from 5 different studies comprising a total of 771 AML samples and a protein-protein interactions dataset. Our scalable network reconstruction method is in part based on the well-known property of gene expression correlation among interacting molecules. The difficulty of distinguishing between direct and indirect interactions is addressed by optimizing the coefficient of variation of gene expression, using a validated gold-standard dataset of direct interactions. Computational time is much reduced compared to other network reconstruction methods. A key feature is the study of the reproducibility of interactions found in independent clinical datasets. An analysis of the most significant clusters, and of the network properties (intraset efficiency, degree, betweenness centrality, and PageRank) of common AML mutations demonstrated the biological significance of the network. A statistical analysis of the response of blast cells from 11 AML patients to a library of kinase inhibitors provided an experimental validation of the network. A combination of network and experimental data identified CDK1, CDK2, CDK4, and CDK6 and other kinases as potential therapeutic targets in AML.

Abstract B2-40: Many-attractor models of signaling dynamics and heterogeneity in acute myeloid leukemia gene regulatory networks

Introduction and purpose of the study: Significant progress has been made in biological network reconstruction methods in recent years, with much emphasis placed on investigating the topology of gene regulatory networks (GRNs). While studying a network's topology can provide useful biological information, a dynamical model for how genes and proteins exchange information is needed in order to understand and predict a cell's response to stimuli such as drugs which inhibit the activity of a protein.

Two thirds of patients with acute myeloid leukemia (AML) have an unfavorable prognosis. This is in part due to the high tumor cell heterogeneity in AML, which is often the ultimate cause of drug resistance and relapse in patients. Mathematical models of signaling dynamics in AML which account for the heterogeneity of clonally derived cells in a tumor could be valuable new tools for designing effective, original therapies. Heterogeneity can be described in signaling by defining nonlinear models with multiple attractor states.

Novel computational methods: We present two mathematical signaling models that encode real gene expression measurements as attractors in a directed AML GRN. Gene expression profiles obtained from RNA-seq in normal progenitor and AML cells data are used to define a set of robust gene expression profiles corresponding to different clonal states. The first model is Boolean, and is based on an asymmetric Hopfield model with multiple memory patterns (Szedlak et al. 2014). The second model uses continuous expression values in which the elements of the GRN interact like oscillators characterized by multi-stability. The equations of motion for the oscillators are defined in such a way that the expression profiles for the RNA-seq data match the multiple steady states of the signaling network.

Summary of new data: We examine the sensitivity of normal and cancer attractors to single gene perturbations and to real gene-inhibiting drugs in both models. For the network topology we use a recently developed network that is specific for AML, specifically AML 2.3 (Ong et al. 2014). Attractors are defined using RNA-seq data from AML cells and hematopoietic controls (Macrae et al. 2013). According to the Hopfield model, we found that ELAVL1, GATA1, IRX5, MYOG, RXRA, and TFEB are genes in AML which, when inhibited, strongly destabilize the cancer attractor.

In the continuum model, we explicitly included interactions between drugs currently in AML clinical trials and their targets. Focusing on known targets of lenalidomide and sorafenib, we found that FLT1, KDR, and PDGFRB are associated with the strongest sensitivity to perturbations. In addition, we found that besides the direct targets of these drugs (BRAF, RAF1, FLT4, KDR, FLT3, PDGFRB, KIT, FGFR1, RET, FLT1 for sorafenib, and TNF, TNFSF11, CDH5, PTGS2, CRBN for lenalidomide), RPS18, RPS11, RPS3, and TPT1 are also strongly indirectly down-regulated.

Conclusions: We developed two novel methods of network signaling that encode gene expression patterns of cells under varying conditions as attractor states. The methods were applied to AML to identify a set of genes whose perturbation is associated with strong deviations from cancer conditions.

Citation Format: Anthony D. Szedlak, Giovanni Paternostro, Carlo Piermarocchi. Many-attractor models of signaling dynamics and heterogeneity in acute myeloid leukemia gene regulatory networks. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-40.

Control of asymmetric Hopfield networks and application to cancer attractors

The asymmetric Hopfield model is used to simulate signaling dynamics in gene regulatory networks. The model allows for a direct mapping of a gene expression pattern into attractor states. We analyze different control strategies aimed at disrupting attractor patterns using selective local fields representing therapeutic interventions. The control strategies are based on the identification of signaling bottlenecks, which are single nodes or strongly connected clusters of nodes that have a large impact on the signaling. We provide a theorem with bounds on the minimum number of nodes that guarantee control of bottlenecks consisting of strongly connected components. The control strategies are applied to the identification of sets of proteins that, when inhibited, selectively disrupt the signaling of cancer cells while preserving the signaling of normal cells. We use an experimentally validated non-specific and an algorithmically-assembled specific B cell gene regulatory network reconstructed from gene expression data to model cancer signaling in lung and B cells, respectively. Among the potential targets identified here are TP53, FOXM1, BCL6 and SRC. This model could help in the rational design of novel robust therapeutic interventions based on our increasing knowledge of complex gene signaling networks.

Identification of drug combinations containing imatinib for treatment of BCR-ABL+ leukemias

The BCR-ABL translocation is found in chronic myeloid leukemia (CML) and in Ph+ acute lymphoblastic leukemia (ALL) patients. Although imatinib and its analogues have been used as front-line therapy to target this mutation and control the disease for over a decade, resistance to the therapy is still observed and most patients are not cured but need to continue the therapy indefinitely. It is therefore of great importance to find new therapies, possibly as drug combinations, which can overcome drug resistance. In this study, we identified eleven candidate anti-leukemic drugs that might be combined with imatinib, using three approaches: a kinase inhibitor library screen, a gene expression correlation analysis, and literature analysis. We then used an experimental search algorithm to efficiently explore the large space of possible drug and dose combinations and identified drug combinations that selectively kill a BCR-ABL+ leukemic cell line (K562) over a normal fibroblast cell line (IMR-90). Only six iterations of the algorithm were needed to identify very selective drug combinations. The efficacy of the top forty-nine combinations was further confirmed using Ph+ and Ph- ALL patient cells, including imatinib-resistant cells. Collectively, the drug combinations and methods we describe might be a first step towards more effective interventions for leukemia patients, especially those with the BCR-ABL translocation.

Prediction of kinase inhibitor response using activity profiling, in vitro screening, and elastic net regression

Background

Many kinase inhibitors have been approved as cancer therapies. Recently, libraries of kinase inhibitors have been extensively profiled, thus providing a map of the strength of action of each compound on a large number of its targets. These profiled libraries define drug-kinase networks that can predict the effectiveness of untested drugs and elucidate the roles of specific kinases in different cellular systems. Predictions of drug effectiveness based on a comprehensive network model of cellular signalling are difficult, due to our partial knowledge of the complex biological processes downstream of the targeted kinases.

Results

We have developed the Kinase Inhibitors Elastic Net (KIEN) method, which integrates information contained in drug-kinase networks with in vitro screening. The method uses the in vitro cell response of single drugs and drug pair combinations as a training set to build linear and nonlinear regression models. Besides predicting the effectiveness of untested drugs, the KIEN method identifies sets of kinases that are statistically associated to drug sensitivity in a given cell line. We compared different versions of the method, which is based on a regression technique known as elastic net. Data from two-drug combinations led to predictive models, and we found that predictivity can be improved by applying logarithmic transformation to the data. The method was applied to the A549 lung cancer cell line, and we identified specific kinases known to have an important role in this type of cancer (TGFBR2, EGFR, PHKG1 and CDK4). A pathway enrichment analysis of the set of kinases identified by the method showed that axon guidance, activation of Rac, and semaphorin interactions pathways are associated to a selective response to therapeutic intervention in this cell line.

Conclusions

We have proposed an integrated experimental and computational methodology, called KIEN, that identifies the role of specific kinases in the drug response of a given cell line. The method will facilitate the design of new kinase inhibitors and the development of therapeutic interventions with combinations of many inhibitors.

Metabolomics of the tumor microenvironment in pediatric acute lymphoblastic leukemia

The tumor microenvironment is emerging as an important therapeutic target. Most studies, however, are focused on the protein components, and relatively little is known of how the microenvironmental metabolome might influence tumor survival. In this study, we examined the metabolic profiles of paired bone marrow (BM) and peripheral blood (PB) samples from 10 children with acute lymphoblastic leukemia (ALL). BM and PB samples from the same patient were collected at the time of diagnosis and after 29 days of induction therapy, at which point all patients were in remission. We employed two analytical platforms, high-resolution magnetic resonance spectroscopy and gas chromatography-mass spectrometry, to identify and quantify 102 metabolites in the BM and PB. Standard ALL therapy, which includes l-asparaginase, completely removed circulating asparagine, but not glutamine. Statistical analyses of metabolite correlations and network reconstructions showed that the untreated BM microenvironment was characterized by a significant network-level signature: a cluster of highly correlated lipids and metabolites involved in lipid metabolism (p<0.006). In contrast, the strongest correlations in the BM upon remission were observed among amino acid metabolites and derivatives (p<9.2 × 10(-10)). This study provides evidence that metabolic characterization of the cancer niche could generate new hypotheses for the development of cancer therapies.

Statistical properties and robustness of biological controller-target networks

Cells are regulated by networks of controllers having many targets, and targets affected by many controllers, in a “many-to-many” control structure. Here we study several of these bipartite (two-layer) networks. We analyze both naturally occurring biological networks (composed of transcription factors controlling genes, microRNAs controlling mRNA transcripts, and protein kinases controlling protein substrates) and a drug-target network composed of kinase inhibitors and of their kinase targets. Certain statistical properties of these biological bipartite structures seem universal across systems and species, suggesting the existence of common control strategies in biology. The number of controllers is ∼8% of targets and the density of links is 2.5%±1.2%. Links per node are predominantly exponentially distributed. We explain the conservation of the mean number of incoming links per target using a mathematical model of control networks, which also indicates that the “many-to-many” structure of biological control has properties of efficient robustness. The drug-target network has many statistical properties similar to the biological networks and we show that drug-target networks with biomimetic features can be obtained. These findings suggest a completely new approach to pharmacological control of biological systems. Molecular tools, such as kinase inhibitors, are now available to test if therapeutic combinations may benefit from being designed with biomimetic properties, such as “many-to-many” targeting, very wide coverage of the target set, and redundancy of incoming links per target.

Metabolomic high-content nuclear magnetic resonance-based drug screening of a kinase inhibitor library

Metabolism is altered in many highly prevalent diseases and is controlled by a complex network of intracellular regulators. Monitoring cell metabolism during treatment is extremely valuable to investigate cellular response and treatment efficacy. Here we describe a nuclear magnetic resonance-based method for screening of the metabolomic response of drug-treated mammalian cells in a 96-well format. We validate the method using drugs having well-characterized targets and report the results of a screen of a kinase inhibitor library. Four hits are validated from their action on an important clinical parameter, the lactate to pyruvate ratio. An eEF-2 kinase inhibitor and an NF-kB activation inhibitor increased lactate/pyruvate ratio, whereas an MK2 inhibitor and an inhibitor of PKA, PKC and PKG induced a decrease. The method is validated in cell lines and in primary cancer cells, and may have potential applications in both drug development and personalized therapy.

Systems approaches and algorithms for discovery of combinatorial therapies

Effective therapy of complex diseases requires control of highly nonlinear complex networks that remain incompletely characterized. In particular, drug intervention can be seen as control of cellular network activity. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to the incomplete knowledge of the systems biology of cells. In this review paper, we describe the main current and proposed approaches to the design of combinatorial therapies, including the heuristic methods used now by clinicians and alternative approaches suggested recently by several authors. New approaches for designing combinations arising from systems biology are described. We discuss in special detail the design of algorithms that identify optimal control parameters in cellular networks based on a quantitative characterization of control landscapes, maximizing utilization of incomplete knowledge of the state and structure of intracellular networks. The use of new technology for high-throughput measurements is key to these new approaches to combination therapy and essential for the characterization of control landscapes and implementation of the algorithms. Combinatorial optimization in medical therapy is also compared with the combinatorial optimization of engineering and materials science and similarities and differences are delineated.

Metabolism as means for hypoxia adaptation: metabolic profiling and flux balance analysis

Background

Cellular hypoxia is a component of many diseases, but mechanisms of global hypoxic adaptation and resistance are not completely understood. Previously, a population of Drosophila flies was experimentally selected over several generations to survive a chronically hypoxic environment. NMR-based metabolomics, combined with flux-balance simulations of genome-scale metabolic networks, can generate specific hypotheses for global reaction fluxes within the cell. We applied these techniques to compare metabolic activity during acute hypoxia in muscle tissue of adapted versus "naïve" control flies.

Results

Metabolic profiles were gathered for adapted and control flies after exposure to acute hypoxia using ¹H NMR spectroscopy. Principal Component Analysis suggested that the adapted flies are tuned to survive a specific oxygen level. Adapted flies better tolerate acute hypoxic stress, and we explored the mechanisms of this tolerance using a flux-balance model of central metabolism. In the model, adapted flies produced more ATP per glucose and created fewer protons than control flies, had lower pyruvate carboxylase flux, and had greater usage of Complex I over Complex II.

Conclusion

We suggest a network-level hypothesis of metabolic regulation in hypoxia-adapted flies, in which lower baseline rates of biosynthesis in adapted flies draws less anaplerotic flux, resulting in lower rates of glycolysis, less acidosis, and more efficient use of substrate during acute hypoxic stress. In addition we suggest new specific hypothesis, which were found to be consistent with existing data.

Search algorithms as a framework for the optimization of drug combinations

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms -- originally developed for digital communication -- modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs using only one-third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.

Metabolomic and flux-balance analysis of age-related decline of hypoxia tolerance in Drosophila muscle tissue

The fruitfly Drosophila melanogaster is increasingly used as a model organism for studying acute hypoxia tolerance and for studying aging, but the interactions between these two factors are not well known. Here we show that hypoxia tolerance degrades with age in post-hypoxic recovery of whole-body movement, heart rate and ATP content. We previously used (1)H NMR metabolomics and a constraint-based model of ATP-generating metabolism to discover the end products of hypoxic metabolism in flies and generate hypotheses for the biological mechanisms. We expand the reactions in the model using tissue- and age-specific microarray data from the literature, and then examine metabolomic profiles of thoraxes after 4 h at 0.5% O(2) and after 5 min of recovery in 40- versus 3-day-old flies. Model simulations were constrained to fluxes calculated from these data. Simulations suggest that the decreased ATP production during reoxygenation seen in aging flies can be attributed to reduced recovery of mitochondrial respiration pathways and concomitant overdependence on the acetate production pathway as an energy source.

Integrating metabolomics and phenomics with systems models of cardiac hypoxia

Hypoxia is the major cause of necrotic cell death in myocardial infarction. Cellular energy supply and demand under hypoxic conditions is regulated by many interacting signaling and transcriptional networks, which complicates studies on individual proteins and pathways. We apply an integrated systems approach to understand the metabolic and functional response to hypoxia in muscle cells of the fruit fly Drosophila melanogaster. In addition to its utility as a hypoxia-tolerant model organism, Drosophila also offers advantages due to its small size, fecundity, and short life cycle. These traits, along with a large library of single-gene mutations, motivated us to develop new, computer-automated technology for gathering in vivo measurements of heart function under hypoxia for a large number of mutant strains. Phenotype data can be integrated with in silico cellular networks, metabolomic data, and microarrays to form qualitative and quantitative network models for prediction and hypothesis generation. Here we present a framework for a systems approach to hypoxia in the cardiac myocyte, starting from nuclear magnetic resonance (NMR) metabolomics, a constraint-based metabolic model, and phenotypic profiles.

Selective control of the apoptosis signaling network in heterogeneous cell populations

BACKGROUND

Selective control in a population is the ability to control a member of the population while leaving the other members relatively unaffected. The concept of selective control is developed using cell death or apoptosis in heterogeneous cell populations as an example. Control of apoptosis is essential in a variety of therapeutic environments, including cancer where cancer cell death is a desired outcome and Alzheimer's disease where neuron survival is the desired outcome. However, in both cases these responses must occur with minimal response in other cells exposed to treatment; that is, the response must be selective.

METHODOLOGY AND PRINCIPAL FINDINGS

Apoptosis signaling in heterogeneous cells is described by an ensemble of gene networks with identical topology but different link strengths. Selective control depends on the statistics of signaling in the ensemble of networks, and we analyze the effects of superposition, non-linearity and feedback on these statistics. Parallel pathways promote normal statistics while series pathways promote skew distributions, which in the most extreme cases become log-normal. We also show that feedback and non-linearity can produce bimodal signaling statistics, as can discreteness and non-linearity. Two methods for optimizing selective control are presented. The first is an exhaustive search method and the second is a linear programming based approach. Though control of a single gene in the signaling network yields little selectivity, control of a few genes typically yields higher levels of selectivity. The statistics of gene combinations susceptible to selective control in heterogeneous apoptosis networks is studied and is used to identify general control strategies.

CONCLUSIONS AND SIGNIFICANCE

We have explored two methods for the study of selectivity in cell populations. The first is an exhaustive search method limited to three node perturbations. The second is an effective linear model, based on interpolation of single node sensitivity, in which the selective combinations can be found by linear programming optimization. We found that selectivity is promoted by acting on the least sensitive nodes in the case of weak populations, while selective control of robust populations is optimized through perturbations of more sensitive nodes. High throughput experiments with heterogeneous cell lines could be designed in an analogous manner, with the further possibility of incorporating the selectivity optimization process into a closed-loop control system.

Flexibility in energy metabolism supports hypoxia tolerance in Drosophila flight muscle: metabolomic and computational systems analysis

The fruitfly Drosophila melanogaster offers promise as a genetically tractable model for studying adaptation to hypoxia at the cellular level, but the metabolic basis for extreme hypoxia tolerance in flies is not well known. Using ¹H NMR spectroscopy, metabolomic profiles were collected under hypoxia. Accumulation of lactate, alanine, and acetate suggested that these are the major end products of anaerobic metabolism in the fly. A constraint-based model of ATP-producing pathways was built using the annotated genome, existing models, and the literature. Multiple redundant pathways for producing acetate and alanine were added and simulations were run in order to find a single optimal strategy for producing each end product. System-wide adaptation to hypoxia was then investigated in silico using the refined model. Simulations supported the hypothesis that the ability to flexibly convert pyruvate to these three by-products might convey hypoxia tolerance by improving the ATP/H⁺ ratio and efficiency of glucose utilization.

The nitric oxide scavenger cobinamide profoundly improves survival in a Drosophila melanogaster model of bacterial sepsis

Septic shock has an extremely high mortality rate, with approximately 200,000 people dying from sepsis annually in the U.S. The high mortality results in part from severe hypotension secondary to high serum NO concentrations. Reducing NO levels should be beneficial in sepsis, but NOS inhibitors have had a checkered history in animal models, and one such agent increased mortality in a clinical trial. An alternative approach to reduce NO levels in sepsis is to use an NO scavenger, which should leave sufficient free NO for normal physiological functions. Using a well-established model of bacterial sepsis in Drosophila melanogaster, we found that cobinamide, a B(12) analog and an effective NO scavenger in vitro, dramatically improved fly survival. Cobinamide augmented the effect of an antibiotic and was beneficial even in immune-deficient flies. Cobinamide's mechanism of action appeared to be from reducing NO levels and improving cardiac function.

Cardiac systems biology

As more detailed molecular information accumulates on the biology of the heart and other complex systems in health and disease, the need for new integrative analyses and tools is growing. Systems biology and bioengineering seek to use high-throughput technologies and integrative computational analysis to construct networks of the interactions between molecular components in the system, to develop systems models of their functionally integrated biological properties, and to incorporate these systems models into structurally integrated multi-scale models for predicting clinical phenotypes. This review gives examples of recent applications using these approaches to elucidate the electromechanical function of the heart in aging and disease.

A more efficient search strategy for aging genes based on connectivity

MOTIVATION

Many aging genes have been found from unbiased screens in model organisms. Genetic interventions promoting longevity are usually quantitative, while in many other biological fields (e.g. development) null mutations alone have been very informative. Therefore, in the case of aging the task is larger and the need for a more efficient genetic search strategy is especially strong.

RESULTS

The topology of genetic and metabolic networks is organized according to a scale-free distribution, in which hubs with large numbers of links are present. We have developed a computational model of aging genes as the hubs of biological networks. The computational model shows that, after generalized damage, the function of a network with scale-free topology can be significantly restored by a limited intervention on the hubs. Analyses of data on aging genes and biological networks support the applicability of the model to biological aging. The model also might explain several of the properties of aging genes, including the high degree of conservation across different species. The model suggests that aging genes tend to have a higher number of connections and therefore supports a strategy, based on connectivity, for prioritizing what might otherwise be a random search for aging genes.

Age-Associated Cardiac Dysfunction in Drosophila melanogaster

Abstract —The fruit fly, Drosophila melanogaster , has served as a valuable model/organism for the study of aging and was the first organism possessing a circulatory system to have its genome completely sequenced. However, little is known about the function of the heartlike organ of flies during the aging process. We have developed methods for studying cardiac function in vivo in adult flies. Using 2 different cardiovascular stress methods (elevated ambient temperature and external electrical pacing), we found that maximal heart rate is significantly and reproducibly reduced with aging in Drosophila , analogous to observations in elderly humans. We also describe for the first time several other aspects of the cardiac physiology of young adult and aging Drosophila , including an age-associated increase in rhythm disturbances. These observations suggest that the study of declining cardiac function in aging flies may serve as a genetically tractable model for genome-wide mutational screening for genes that participate in or protect against cardiac aging and disease.

Cardiomyopathy in duchenne, becker, and sarcoglycanopathies: a role for coronary dysfunction?

Dilated cardiomyopathy is a feature of Duchenne and Becker muscular dystrophies and occasionally of sarcoglycanopathies. Its pathogenesis is unknown. Patients with myotonic dystrophy have an impairment of coronary smooth muscle and this could contribute to their cardiomyopathy. We used positron emission tomography (PET) to study myocardial blood flow and coronary vasodilator reserve at baseline and during hyperemia in 7 Duchenne, 8 Becker, and 5 sarcoglycanopathy patients. The study was normal in all Becker patients. In contrast, baseline myocardial blood flow was increased and coronary vasodilator reserve blunted in Duchenne and sarcoglycanopathy patients despite normal hyperemic myocardial blood flow. The reduction of coronary vasodilator reserve was due to an increased baseline myocardial blood flow. In Duchenne dystrophy, but not in sarcoglycanopathies, correction for cardiac workload normalized the coronary vasodilator reserve. In the latter patients, abnormal baseline myocardial blood flow could be due to vascular smooth muscle dysfunction.

Insulin resistance in patients with cardiac hypertrophy

OBJECTIVE

Animal studies suggest that left ventricular hypertrophy might be associated with insulin resistance and alterations in glucose transporters. We have previously demonstrated myocardial insulin resistance in patients with post-ischemic heart failure. The aim was to investigate whether myocardial insulin resistance could be demonstrated in human cardiac hypertrophy in the absence of hypertension, diabetes and coronary artery disease.

METHODS

Eleven normotensive nondiabetic patients with cardiac hypertrophy due to aortic stenosis and angiographically normal coronary arteries were compared to 11 normal volunteers. Myocardial glucose uptake (MGU) was measured with positron emission tomography and [18F]2-fluoro-2-deoxy-D-glucose during fasting (low insulinemia) or during euglycemic-hyperinsulinemic clamp (physiologic hyperinsulinemia). Myocardial biopsies were obtained in order to investigate changes in insulin-independent (GLUT-1) and insulin-dependent (GLUT-4) glucose transporters.

RESULTS

During fasting, plasma insulin (7 +/- 1 vs. 6 +/- 1 mU/l) and MGU (0.12 +/- 0.05 vs. 0.11 +/- 0.04 mumol/min/g) were comparable in patients and controls. By contrast, during clamp, MGU was markedly reduced in patients (0.48 +/- 0.02 vs. 0.70 +/- 0.03 mumol/min/g, p < 0.01) despite similar plasma insulin levels (95 +/- 6 vs. 79 +/- 6 mU/l). A decreased GLUT-4/GLUT-1 ratio was shown by Western blot analysis in patients.

CONCLUSIONS

Insulin resistance seems to be a feature of the hypertrophied heart even in the absence of hypertension, coronary artery disease and diabetes and may be explained, at least in part, by abnormalities in glucose transporters.

Cardiac and skeletal muscle insulin resistance in patients with coronary heart disease. A study with positron emission tomography

Patients with coronary artery disease or heart failure have been shown to be insulin resistant. Whether in these patients heart muscle participates in the insulin resistance, and whether reduced blood flow is a mechanism for such resistance is not known. We measured heart and skeletal muscle blood flow and glucose uptake during euglycemic hyperinsulinemia (insulin clamp) in 15 male patients with angiographically proven coronary artery disease and chronic regional wall motion abnormalities. Six age- and weight-matched healthy subjects served as controls. Regional glucose uptake was measured by positron emission tomography using [18F]2-fluoro-2-deoxy-D-glucose (FDG), blood flow was measured by the H2(15)O method. Myocardial glucose utilization was measured in regions with normal perfusion and wall motion as assessed by radionuclide ventriculography. Whole-body glucose uptake was 37+/-4 micromol x min(-1) x kg(-1) in controls and 14+/-2 mciromol x min(-1) x kg(-1) in patients (P = 0.001). Myocardial blood flow (1.09+/-0.06 vs. 0.97+/-0.04 ml x min(-1) x g(-1), controls vs. patients) and skeletal muscle (arm) blood flow (0.046+/-0.012 vs. 0.043+/-0.006 ml x min(-1) x g(-1)) were similar in the two groups (P = NS for both). In contrast, in patients both myocardial (0.38+/-0.03 vs. 0.70+/-0.03 micromol x min(-1) x g(-1), P = 0.0005) and muscle glucose uptake (0.026+/-0.004 vs. 0.056+/-0.006 micromol x min(-1) x g(-1), P = 0.005) were markedly reduced in comparison with controls. In the whole dataset, a direct relationship existed between insulin-stimulated glucose uptake in heart and skeletal muscle. Patients with a history of myocardial infarction and a low ejection fraction are insulin resistant. This insulin resistance affects both the myocardium and skeletal muscle and is independent of blood flow.

Decreased GLUT-4 mRNA content and insulin-sensitive deoxyglucose uptake show insulin resistance in the hypertensive rat heart

OBJECTIVES

Insulin resistance in skeletal muscle and adipose tissue often accompanies hypertension; however, it has not been shown that heart muscle is similarly affected. The aims of this study were to determine whether basal and insulin-stimulated glucose transport and glucose transporter mRNA content are altered in the spontaneously hypertensive rat (SHR) heart.

METHODS

Hearts from 16-18-month-old SHRs were compared to their normotensive (WKY) controls. The accumulation of 2-deoxyglucose-6-phosphate (2DG6P), detected using 31P nuclear magnetic resonance spectroscopy, was used to assess glucose uptake before and during insulin stimulation in the isolated perfused heart. The mRNA levels of both the insulin-sensitive glucose transporter (GLUT-4) and the transporter responsible for basal glucose uptake (GLUT-1) were quantified by Northern blot analysis.

RESULTS

The hypertensive rat hearts exhibited hypertrophy in that the heart/body weight ratio was increased by 59%. In these hearts, the basal rate of glucose uptake was 3-fold greater and hexokinase activity was 1.6 fold greater than that of the control rat hearts. On exposure to insulin, accumulation of 2DG6P increased 5-fold in the control hearts, but only 1.4-fold in the SHR hearts. Thus, in the presence of insulin, the rate of glucose uptake by the hypertensive rat heart was significantly (P < 0.05) reduced, being 82% of control. GLUT-4 mRNA content was decreased was no significant difference in the GLUT-1 mRNA content.

CONCLUSION

We have demonstrated insulin resistance in the hypertrophied heart of the hypertensive rat that may have a molecular basis in a lower GLUT-4 content.