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Implementation of <em>In Vitro</em> Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Although targeted therapies are initially effective, resistance inevitably emerges. Several methods, such as genetic analysis of resistant clinical ...

H3 Biomedicine

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.

A more efficient search strategy for aging genes based on connectivity.

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 (1)H 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.

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

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.

Integrating metabolomics and phenomics with systems models of cardiac hypoxia.

Necrosis and apoptosis during acute myocardial infarction result in part from the inability of hypoxic cardiac myocytes to match ATP supply and demand. In contrast, hypoxia-tolerant organisms, such as Drosophila, can rapidly regulate cellular metabolism to survive large oxygen fluctuations. A genetic screen of fly heart function during acute hypoxia can be an unbiased way to discover essential enzymes and novel signaling proteins involved in this response. We have developed a prototype to show proof of concept for a genome-scale screen, using computer automation to rapidly gather in vivo hypoxic heart data in adult Drosophila. Our system automatically anesthetizes flies, deposits them on a microscope slide, and locates the heart organ of each fly. The system then applies a hypoxia stimulus, acquires time-space (M-mode) images of the heart walls, and analyzes heart rate and rhythm. The prototype can produce highly controlled measurements of up to 55 flies per hour, which we demonstrated by characterizing the effect of temperature, oxygen content, and genetic background on the hypoxia response. We discuss the possible applications of a genome-wide cardiac phenotype data set in systems biology analyses of hypoxic metabolism, using genome-scale interaction networks and constraint-based metabolic models.

Discovering regulators of the Drosophila cardiac hypoxia response using automated phenotyping technology.

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.

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

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.

Search algorithms as a framework for the optimization of drug combinations.

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.

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

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.

Systems approaches and algorithms for discovery of combinatorial therapies.

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.

Statistical properties and robustness of biological controller-target networks.

The rate of traumatic brain injury (TBI) in service members with wartime injuries has risen rapidly in recent years, and complex, variable links have emerged between TBI and long-term neurological disorders. The multifactorial nature of TBI secondary cellular response has confounded attempts to find cellular biomarkers for its diagnosis and prognosis or for guiding therapy for brain injury. One possibility is to apply emerging systems biology strategies to holistically probe and analyze the complex interweaving molecular pathways and networks that mediate the secondary cellular response through computational models that integrate these diverse data sets. Here, we review available systems biology strategies, databases, and tools. In addition, we describe opportunities for applying this methodology to existing TBI data sets to identify new biomarker candidates and gain insights about the underlying molecular mechanisms of TBI response. As an exemplar, we apply network and pathway analysis to a manually compiled list of 32 protein biomarker candidates from the literature, recover known TBI-related mechanisms, and generate hypothetical new biomarker candidates.

Systems biology approaches for discovering biomarkers for traumatic brain injury.

The multifactorial nature of traumatic brain injury (TBI), especially the complex secondary tissue injury involving intertwined networks of molecular pathways that mediate cellular behavior, has confounded attempts to elucidate the pathology underlying the progression of TBI. Here, systems biology strategies are exploited to identify novel molecular mechanisms and protein indicators of brain injury. To this end, we performed a meta-analysis of four distinct high-throughput gene expression studies involving different animal models of TBI. By using canonical pathways and a large human protein-interaction network as a scaffold, we separately overlaid the gene expression data from each study to identify molecular signatures that were conserved across the different studies. At 24 hr after injury, the significantly activated molecular signatures were nonspecific to TBI, whereas the significantly suppressed molecular signatures were specific to the nervous system. In particular, we identified a suppressed subnetwork consisting of 58 highly interacting, coregulated proteins associated with synaptic function. We selected three proteins from this subnetwork, postsynaptic density protein 95, nitric oxide synthase 1, and disrupted in schizophrenia 1, and hypothesized that their abundance would be significantly reduced after TBI. In a penetrating ballistic-like brain injury rat model of severe TBI, Western blot analysis confirmed our hypothesis. In addition, our analysis recovered 12 previously identified protein biomarkers of TBI. The results suggest that systems biology may provide an efficient, high-yield approach to generate testable hypotheses that can be experimentally validated to identify novel mechanisms of action and molecular indicators of TBI.

A systems biology strategy to identify molecular mechanisms of action and protein indicators of traumatic brain injury.

Recurrent mutations in the spliceosome are observed in several human cancers, but their functional and therapeutic significance remains elusive. SF3B1, the most frequently mutated component of the spliceosome in cancer, is involved in the recognition of the branch point sequence (BPS) during selection of the 3? splice site (ss) in RNA splicing. Here, we report that common and tumor-specific splicing aberrations are induced by SF3B1 mutations and establish aberrant 3? ss selection as the most frequent splicing defect. Strikingly, mutant SF3B1 utilizes a BPS that differs from that used by wild-type SF3B1 and requires the canonical 3? ss to enable aberrant splicing during the second step. Approximately 50% of the aberrantly spliced mRNAs are subjected to nonsense-mediated decay resulting in downregulation of gene and protein expression. These findings ascribe functional significance to the consequences of SF3B1 mutations in cancer.

Jacob Feala

H3 Biomedicine

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms