Obesity is clinically diagnosed by a simple formula based on the weight and height of a person (body mass index), but is associated with a host of other behavioral symptoms that are likely neurological in origin. In recent years, many scientists have asked whether similar behavioral and cognitive changes occur in drug addiction and obesity, lending many to discuss the potential for “food addiction”. Advances in understanding the circuitry underlying both feeding behaviors and drug addiction may allow us to consider this question from the viewpoint of neural circuits, to complement behavioral perspectives. Here, we review advances in understanding of these circuits and use them to consider whether drawing comparisons to drug addiction is helpful for understanding certain forms of obesity.
obesity; addiction; optogenetics; food; feeding; arcuate; striatum
Doublesex (dsx) is a transcription factor in Drosophila that regulates somatic sexual differentiation. Male- and female-specific splicing isoforms of DSX share a novel DNA-binding domain, designated the DM motif. Broadly conserved among metazoan sex-determining factors, the DM domain contains a nonclassical zinc module and binds in the DNA minor groove. Here, we characterize the DM motif by site-directed and random mutagenesis using a yeast one-hybrid (Y1H) system and extend this analysis by chemogenetic complementation in vitro. The Y1H system is based on a sex-specific Drosophila enhancer element and validated through studies of intersexual dsx mutations. We demonstrate that the eight motif-specific histidines and cysteines engaged in zinc coordination are each critical and cannot be interchanged; folding also requires conserved aliphatic side chains in the hydrophobic core. Mutations that impair DNA binding tend to occur at conserved positions, whereas neutral substitutions occur at nonconserved sites. Evidence for a specific salt bridge between a conserved lysine and the DNA backbone is obtained through the synthesis of nonstandard protein and DNA analogs. Together, these results provide molecular links between the structure of the DM domain and its function in the regulation of sexual dimorphism.
Coherence-gated dynamic light scattering captures cellular dynamics through ultra-low-frequency (0.005–5 Hz) speckle fluctuations and Doppler shifts that encode a broad range of cellular and subcellular motions. The dynamic physiological response of tissues to applied drugs is the basis for a new type of phenotypic profiling for drug screening on multicellular tumor spheroids. Volumetrically resolved tissue-response fluctuation spectrograms act as fingerprints that are segmented through feature masks into high-dimensional feature vectors. Drug-response clustering is achieved through multidimensional scaling with simulated annealing to construct phenotypic drug profiles that cluster drugs with similar responses. Hypoxic vs. normoxic tissue responses present two distinct phenotypes with differentiated responses to environmental perturbations and to pharmacological doses.
(090.1995) Digital holography; (170.0170) Medical optics and biotechnology; (170.3880) Medical and biological imaging; (110.1650) Coherence imaging
To better understand off-target effects of widely prescribed psychoactive drugs, we performed a comprehensive series of chemogenomic screens using the budding yeast Saccharomyces cerevisiae as a model system. Because the known human targets of these drugs do not exist in yeast, we could employ the yeast gene deletion collections and parallel fitness profiling to explore potential off-target effects in a genome-wide manner. Among 214 tested, documented psychoactive drugs, we identified 81 compounds that inhibited wild-type yeast growth and were thus selected for genome-wide fitness profiling. Many of these drugs had a propensity to affect multiple cellular functions. The sensitivity profiles of half of the analyzed drugs were enriched for core cellular processes such as secretion, protein folding, RNA processing, and chromatin structure. Interestingly, fluoxetine (Prozac) interfered with establishment of cell polarity, cyproheptadine (Periactin) targeted essential genes with chromatin-remodeling roles, while paroxetine (Paxil) interfered with essential RNA metabolism genes, suggesting potential secondary drug targets. We also found that the more recently developed atypical antipsychotic clozapine (Clozaril) had no fewer off-target effects in yeast than the typical antipsychotics haloperidol (Haldol) and pimozide (Orap). Our results suggest that model organism pharmacogenetic studies provide a rational foundation for understanding the off-target effects of clinically important psychoactive agents and suggest a rational means both for devising compound derivatives with fewer side effects and for tailoring drug treatment to individual patient genotypes.
Neuropsychiatric disorders such as depression and psychosis affect one-quarter of all individuals during their lifetime, and despite efforts to improve the selectivity of psychoactive drugs, all are associated with side effects. Drug efficacy and tolerance are known to be linked to an individual's genetic profile, but little is known about the nature of this correlation due, in part, to the current emphasis on screening compounds against targets in vitro. Here we present a comprehensive, genome-wide effort to understand drug effects on the cellular level using an unbiased genome-wide assay to determine the importance of every yeast gene for tolerance to 81 psychoactive drugs. We found that these medications perturbed many evolutionarily conserved genes and cellular pathways, such as those required for vesicle transport, establishment of cell polarity, and chromosome biology. The 500,000 drug–gene measurements obtained in this study increase our understanding of the mechanism of action of psychoactive drugs. Specifically, this study provides a framework to assess the next generation of psychoactive agents and to guide personalized medicine approaches that associate genotype and phenotype.
Recent progress in reconstructing gene regulatory networks has established a framework for a quantitative description of the dynamics of many important cellular processes. Such a description will require novel experimental techniques that enable the generation of time-series data for the governing regulatory proteins in a large number of individual living cells. Here, we utilize microfabrication to construct a Tesla microchemostat that permits single-cell fluorescence imaging of gene expression over many cellular generations. The device is used to capture and constrain asymmetrically dividing or motile cells within a trapping region and to deliver nutrients and regulate the cellular population within this region. We illustrate the operation of the microchemostat with Saccharomyces cerevisiae and explore the evolution of single-cell gene expression and cycle time as a function of generation. Our findings highlight the importance of novel assays for quantifying the dynamics of gene expression and cellular growth, and establish a methodology for exploring the effects of gene expression on long-term processes such as cellular aging.
gene regulation; microfluidics; microscopy
The macrolide heptaene amphotericin B (AmB) induced concentration-dependent effects on Saccharomyces cerevisiae which were separable into two distinct stages. At low concentrations the drug inhibited the growth of the yeast and reversible changed cell permeability to Na+ and K+. At high levels it was lethal. The intracellular K+ concentration of cells with reversible damage (stage I) could be increased by addition of K+ to the medium, but cells irreversibly damaged (stage II) were not able to retain K+. The addition of K+ to the medium did not influence the growth-inhibitory or killing action of AmB. Addition of Mg2+ to cultures increased S. cerevisiae resistance to the killing effects of AmB. At low concentrations of AmB, growth inhibition was also decreased by extracellular Mg2+, but at higher concentration of AmB, growth inhibition was increased, probably because the prevention by Mg2+ of the lethal effect allowed expression of the inhibitory effect in a greater range. Simultaneous addition of K+ and Mg2+ markedly decreased both the inhibitory and lethal action of AmB at all concentrations. Filipin, a pentaene macrolide, had only lethal effects, which were unaffected when K+ was added to the medium but were diminished when medium was supplemented with Mg2+.
Heat shock protein 90 (Hsp90) is an essential molecular chaperone in eukaryotes that facilitates the conformational maturation and function of a diverse protein clientele, including aberrant and/or over-expressed proteins that are involved in cancer growth and survival. A role for Hsp90 in supporting the protein homeostasis of cancer cells has buoyed interest in the utility of Hsp90 inhibitors as anti-cancer drugs. Despite the fact that all clinically evaluated Hsp90 inhibitors target an identical nucleotide-binding pocket in the N domain of the chaperone, the precise determinants that affect drug binding in the cellular environment remain unclear, and it is possible that chemically distinct inhibitors may not share similar binding preferences. Here we demonstrate that two chemically unrelated Hsp90 inhibitors, the benzoquinone ansamycin geldanamycin and the purine analog PU-H71, select for overlapping but not identical subpopulations of total cellular Hsp90, even though both inhibitors bind to an amino terminal nucleotide pocket and prevent N domain dimerization. Our data also suggest that PU-H71 is able to access a broader range of N domain undimerized Hsp90 conformations than is geldanamycin and is less affected by Hsp90 phosphorylation, consistent with its broader and more potent anti-tumor activity. A more complete understanding of the impact of the cellular milieu on small molecule inhibitor binding to Hsp90 should facilitate their more effective use in the clinic.
Hsp90; posttranslational modification; phosphorylation; drug binding; Hsp90 inhibitor
The conserved target of rapamycin (TOR) kinases regulate many aspects of cellular physiology. They exist in two distinct complexes, termed TOR complex 1 (TORC1) and TOR complex 2 (TORC2), that posses both overlapping and distinct components. TORC1 and TORC2 respond differently to the drug rapamycin and have different cellular functions: whereas the rapamycin-sensitive TORC1 controls many aspects of cell growth and has been characterized in great detail, the TOR complex 2 is less understood and regulates actin polymerization, cell polarity, and ceramide metabolism. How signaling specificity and discrimination between different input signals for the two kinase complexes is achieved is not understood. Here, we show that TORC1 and TORC2 have different localizations in Saccharomyces cerevisiae. TORC1 is localized exclusively to the vacuolar membrane, whereas TORC2 is localized dynamically in a previously unrecognized plasma membrane domain, which we term membrane compartment containing TORC2 (MCT). We find that plasma membrane localization of TORC2 is essential for viability and mediated by lipid binding of the C-terminal domain of the Avo1 subunit. From these data, we suggest that the TOR complexes are spatially separated to determine downstream signaling specificity and their responsiveness to different inputs.
To gain insight on the interrelationships of the cellular environment, the properties of growth, and cell cycle progression, we analyzed the dynamic reactions of individual Saccharomyces cerevisiae cells to changes and manipulations of their surroundings. We used a new flow cytometric approach which allows, in asynchronous growing S. cerevisiae populations, tagging of both the cell age and the cell protein content of cells belonging to the different cell cycle set points. Since the cell protein content is a good estimation of the cell size, it is possible to follow the kinetics of the cell size increase during cell cycle progression. The analysis of the findings obtained indicates that both during a nutritional shift-up (from ethanol to glucose) and following the addition of cyclic AMP (cAMP), two important delays are induced. The preexisting cells that at the moment of the nutritional shift-up were cycling before the Start phase delay their entrance into S phase, while cells that were cycling after Start are delayed in their exit from the cycle. The combined effects of the two delays allow the cellular population that preexisted the shift-up to quickly adjust to the new growth condition. The effects of a nutritional shift-down were also determined.
The many virtues that made the yeast Saccharomyces cerevisiae a dominant model organism for genetics and molecular biology, are now establishing its role in chemical genetics. Its experimental tractability (i.e., rapid doubling time, simple culture conditions) and the availability of powerful tools for drug-target identification, make yeast an ideal organism for high-throughput phenotypic screening. It may be especially applicable for the discovery of chemical probes targeting highly conserved cellular processes, such as metabolism and bioenergetics, because these probes would likely inhibit the same processes in higher eukaryotes (including man). Importantly, changes in normal cellular metabolism are associated with a variety of diseased states (including neurological disorders and cancer), and exploiting these changes for therapeutic purposes has accordingly gained considerable attention. Here, we review progress and challenges associated with forward chemical genetic screening in yeast. We also discuss evidence supporting these screens as a useful strategy for discovery of new chemical probes and new druggable targets related to cellular metabolism.
yeast; forward chemical genetics; chemogenomic profiling; glycolysis; cancer metabolism; Warburg effect; mitochondria; methotrexate; leucovorin
High-throughput screens comparing growth rates of arrays of distinct micro-organism cultures on solid agar are useful, rapid methods of quantifying genetic interactions. Growth rate is an informative phenotype which can be estimated by measuring cell densities at one or more times after inoculation. Precise estimates can be made by inoculating cultures onto agar and capturing cell density frequently by plate-scanning or photography, especially throughout the exponential growth phase, and summarising growth with a simple dynamic model (e.g. the logistic growth model). In order to parametrize such a model, a robust image analysis tool capable of capturing a wide range of cell densities from plate photographs is required.
Colonyzer is a collection of image analysis algorithms for automatic quantification of the size, granularity, colour and location of micro-organism cultures grown on solid agar. Colonyzer is uniquely sensitive to extremely low cell densities photographed after dilute liquid culture inoculation (spotting) due to image segmentation using a mixed Gaussian model for plate-wide thresholding based on pixel intensity. Colonyzer is robust to slight experimental imperfections and corrects for lighting gradients which would otherwise introduce spatial bias to cell density estimates without the need for imaging dummy plates. Colonyzer is general enough to quantify cultures growing in any rectangular array format, either growing after pinning with a dense inoculum or growing with the irregular morphology characteristic of spotted cultures. Colonyzer was developed using the open source packages: Python, RPy and the Python Imaging Library and its source code and documentation are available on SourceForge under GNU General Public License. Colonyzer is adaptable to suit specific requirements: e.g. automatic detection of cultures at irregular locations on streaked plates for robotic picking, or decreasing analysis time by disabling components such as lighting correction or colour measures.
Colonyzer can automatically quantify culture growth from large batches of captured images of microbial cultures grown during genome-wide scans over the wide range of cell densities observable after highly dilute liquid spot inoculation, as well as after more concentrated pinning inoculation. Colonyzer is open-source, allowing users to assess it, adapt it to particular research requirements and to contribute to its development.
In most organisms, storage lipids are packaged into specialized structures called lipid droplets. These contain a core of neutral lipids surrounded by a monolayer of phospholipids, and various proteins which vary depending on the species. Hydrophobic structural proteins stabilize the interface between the lipid core and aqueous cellular environment (perilipin family of proteins, apolipoproteins, oleosins). We developed a genetic approach using heterologous expression in Saccharomyces cerevisiae of the Arabidopsis thaliana lipid droplet oleosin and caleosin proteins AtOle1 and AtClo1. These transformed yeasts overaccumulate lipid droplets, leading to a specific increase in storage lipids. The phenotype of these cells was explored using synchrotron FT-IR microspectroscopy to investigate the dynamics of lipid storage and cellular carbon fluxes reflected as changes in spectral fingerprints. Multivariate statistical analysis of the data showed a clear effect on storage carbohydrates and more specifically, a decrease in glycogen in our modified strains. These observations were confirmed by biochemical quantification of the storage carbohydrates glycogen and trehalose. Our results demonstrate that neutral lipid and storage carbohydrate fluxes are tightly connected and co-regulated.
Characterization of cellular growth is central to understanding living systems. Here, we applied a three-factor design to study the relationship between specific growth rate and genome-wide gene expression in 36 steady-state chemostat cultures of Saccharomyces cerevisiae. The three factors we considered were specific growth rate, nutrient limitation, and oxygen availability.
We identified 268 growth rate dependent genes, independent of nutrient limitation and oxygen availability. The transcriptional response was used to identify key areas in metabolism around which mRNA expression changes are significantly associated. Among key metabolic pathways, this analysis revealed de novo synthesis of pyrimidine ribonucleotides and ATP producing and consuming reactions at fast cellular growth. By scoring the significance of overlap between growth rate dependent genes and known transcription factor target sets, transcription factors that coordinate balanced growth were also identified. Our analysis shows that Fhl1, Rap1, and Sfp1, regulating protein biosynthesis, have significantly enriched target sets for genes up-regulated with increasing growth rate. Cell cycle regulators, such as Ace2 and Swi6, and stress response regulators, such as Yap1, were also shown to have significantly enriched target sets.
Our work, which is the first genome-wide gene expression study to investigate specific growth rate and consider the impact of oxygen availability, provides a more conservative estimate of growth rate dependent genes than previously reported. We also provide a global view of how a small set of transcription factors, 13 in total, contribute to control of cellular growth rate. We anticipate that multi-factorial designs will play an increasing role in elucidating cellular regulation.
This study uses experimentally evolved brewer's yeasts to explore the costs and benefits of different nutrient-switching strategies when energy sources vary or remain constant.
Organisms respond to environmental changes by adapting the expression of key genes. However, such transcriptional reprogramming requires time and energy, and may also leave the organism ill-adapted when the original environment returns. Here, we study the dynamics of transcriptional reprogramming and fitness in the model eukaryote Saccharomyces cerevisiae in response to changing carbon environments. Population and single-cell analyses reveal that some wild yeast strains rapidly and uniformly adapt gene expression and growth to changing carbon sources, whereas other strains respond more slowly, resulting in long periods of slow growth (the so-called “lag phase”) and large differences between individual cells within the population. We exploit this natural heterogeneity to evolve a set of mutants that demonstrate how the frequency and duration of changes in carbon source can favor different carbon catabolite repression strategies. At one end of this spectrum are “specialist” strategies that display high rates of growth in stable environments, with more stringent catabolite repression and slower transcriptional reprogramming. The other mutants display less stringent catabolite repression, resulting in leaky expression of genes that are not required for growth in glucose. This “generalist” strategy reduces fitness in glucose, but allows faster transcriptional reprogramming and shorter lag phases when the cells need to shift to alternative carbon sources. Whole-genome sequencing of these mutants reveals that mutations in key regulatory genes such as HXK2 and STD1 adjust the regulation and transcriptional noise of metabolic genes, with some mutations leading to alternative gene regulatory strategies that allow “stochastic sensing” of the environment. Together, our study unmasks how variable and stable environments favor distinct strategies of transcriptional reprogramming and growth.
When microbes grow in a mixture of different nutrients, they repress the metabolism of nonpreferred nutrients such as complex carbohydrates until preferred nutrients, like glucose, are depleted. While this “catabolite repression” allows cells to use the most efficient nutrients first, it also comes at a cost because the switch to nonpreferred nutrients requires the de-repression of specific genes, and during this transition cells must temporarily stop dividing. Naively, one might expect that cells would activate the genes needed to resume growth in the new environment as quickly as possible. However, we find that the length of the growth lag that occurs when yeast cells are switched from the preferred carbon source glucose to alternative nutrients like maltose, galactose, or ethanol differs between wild yeast strains. By repeatedly alternating a slow-switching strain between glucose and maltose, we obtained mutants that show shortened lag phases. Although these variants can switch rapidly between carbon sources, they show reduced growth rates in environments where glucose is available continuously. Further analysis revealed that mutations in genes like HXK2 cause variations in the degree of catabolite repression, with some mutants showing leaky or stochastic maltose gene expression. Together, these results reveal how different gene regulation strategies can affect fitness in variable or stable environments.
Regulation of polarised cell growth is essential for many cellular processes including spatial coordination of cell morphology changes during the division cycle. We present a mathematical model of the core mechanism responsible for the regulation of polarised growth dynamics during the fission yeast cell cycle. The model is based on the competition of growth zones localised at the cell tips for a common substrate distributed uniformly in the cytosol. We analyse the bifurcations in this model as the cell length increases, and show that the growth activation dynamics provides an explanation for the new-end take-off (NETO) as a saddle-node bifurcation at which the cell sharply switches from monopolar to bipolar growth. We study the parameter sensitivity of the bifurcation diagram and relate qualitative changes of the growth pattern, e.g. delayed or absent NETO, to previously observed mutant phenotypes. We investigate the effects of imperfect asymmetric cell division, and show that this leads to distinct growth patterns that provide experimentally testable predictions for validating the presented competitive growth zone activation model. Finally we discuss extension of the model for describing mutant cells with more than two growth zones.
Drugs currently available for leishmaniasis treatment often show parasite resistance, highly toxic side effects and prohibitive costs commonly incompatible with patients from the tropical endemic countries. In this sense, there is an urgent need for new drugs as a treatment solution for this neglected disease. Here we show the development and implementation of an automated high-throughput viability screening assay for the discovery of new drugs against Leishmania. Assay validation was done with Leishmania promastigote forms, including the screening of 4,000 compounds with known pharmacological properties. In an attempt to find new compounds with leishmanicidal properties, 26,500 structurally diverse chemical compounds were screened. A cut-off of 70% growth inhibition in the primary screening led to the identification of 567 active compounds. Cellular toxicity and selectivity were responsible for the exclusion of 78% of the pre-selected compounds. The activity of the remaining 124 compounds was confirmed against the intramacrophagic amastigote form of the parasite. In vitro microsomal stability and cytochrome P450 (CYP) inhibition of the two most active compounds from this screening effort were assessed to obtain preliminary information on their metabolism in the host. The HTS approach employed here resulted in the discovery of two new antileishmanial compounds, bringing promising candidates to the leishmaniasis drug discovery pipeline.
Every year, more than 2 million people worldwide suffer from leishmaniasis, a neglected tropical disease present in 88 countries. The disease is caused by the single-celled protozoan parasite species of the genus Leishmania, which is transmitted to humans by the bite of the sandfly. The disease manifests itself in a broad range of symptoms, and its most virulent form, named visceral leishmaniasis, is lethal if not treated. Most of the few available treatments for leishmaniasis were developed decades ago and are often toxic, sometimes even leading to the patient's death. Furthermore, the parasite is developing resistance to available drugs, making the discovery and development of new antileishmanials an urgent need. To tackle this problem, the authors of this study employed the use of high-throughput technologies to screen a large library of small, synthetic molecules for their ability to interfere with the viability of Leishmania parasites. This study resulted in the discovery of two novel compounds with leishmanicidal properties and promising drug-like properties, bringing new candidates to the leishmaniasis drug discovery pipeline.
Whether a trade-off exists between robustness and evolvability is an important issue for protein evolution. Although traditional viewpoints have assumed that existing functions must be compromised by the evolution of novel activities, recent research has suggested that existing phenotypes can be robust to the evolution of novel protein functions. Enzymes that are targets of antibiotics that are competitive inhibitors must evolve decreased drug affinity while maintaining their function and sustaining growth. Utilizing a transgenic Saccharomyces cerevisiae model expressing the dihydrofolate reductase (DHFR) enzyme from the malarial parasite Plasmodium falciparum, we examine the robustness of growth rate to drug-resistance mutations. We assay the growth rate and resistance of all 48 combinations of 6 DHFR point mutations associated with increased drug resistance in field isolates of the parasite. We observe no consistent relationship between growth rate and resistance phenotypes among the DHFR alleles. The three evolutionary pathways that dominate DHFR evolution show that mutations with increased resistance can compensate for initial declines in growth rate from previously acquired mutations. In other words, resistance mutations that occur later in evolutionary trajectories can compensate for the fitness consequences of earlier mutations. Our results suggest that high levels of resistance may be selected for without necessarily jeopardizing overall fitness.
drug resistance; malaria; phenotypic robustness; mutational landscapes; compensatory mutations
The protease encoded by the human cytomegalovirus (HCMV) is an attractive target for antiviral drug development because of its essential function in viral replication. We describe here a cellular assay in the yeast Saccharomyces cerevisiae for the identification of small molecule inhibitors of HCMV protease by conditional growth in selective medium. In this system, the protease cleavage sequence is inserted into the N-(5′-phosphoribosyl)anthranilate isomerase (Trp1p), a yeast protein essential for cell proliferation in the absence of tryptophan. Coexpression of HCMV protease with the engineered Trp1p substrate in yeast cells results in site-specific cleavage and functional inactivation of the Trp1p enzyme, thereby leading to an arrest of cell proliferation. This growth arrest can be suppressed by the addition of validated HCMV protease inhibitors. The growth selection system presented here provides the basis for a high-throughput screen to identify HCMV protease inhibitors that are active in eukaryotic cells.
The growth inhibition and the lysis of Saccharomyces cerevisiae caused by 2-deoxy-d-glucose (2-DG) were shown to be a consequence of unbalanced cellular growth and division. The lysis, but not the repression of growth and osmotic fragility of cells, could be suppressed by the addition of mannitol as an osmotic stabilizer. This result, as well as the morphological changes observed in the cells and changes in the chemical composition of the cell walls, showed that S. cerevisiae grown in the presence of 2-DG formed weakened cell walls responsible for the osmotic fragility. Evidence is presented for the first time demonstrating the incorporation of 2-DG into yeast cell wall material. Other data suggest that the inhibition of yeast growth by 2-DG results from an interference of phosphorylated metabolites of 2-DG with metabolic processes of glucose and mannose involved in the synthesis of structural cell wall polysaccharides.
Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing the full potential of multi-target treatments in different disease phenotypes. Our specific focus is on system-level network approaches to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies.
Network pharmacology; computational models; experimental design; anticancer therapies.
Cell-based screening can facilitate rapid identification of compounds inducing complex cellular phenotypes. Advancing a compound toward the clinic, however, generally requires identification of precise mechanisms of action. We previously found that epidermal growth factor receptor (EGFR) inhibitors induce acute myeloid leukemia (AML) differentiation via a non-EGFR mechanism. In this report, we integrated proteomic and RNAi-based strategies to identify their off-target anti-AML mechanism. These orthogonal approaches identified Syk as a target in AML. Genetic and pharmacological inactivation of Syk with a drug in clinical trial for other indications promoted differentiation of AML cells and attenuated leukemia growth in vivo. These results demonstrate the power of integrating diverse chemical, proteomic, and genomic screening approaches to identify therapeutic strategies for cancer.
We describe the results of a systematic search for a class of hitherto-overlooked chemical-genetic interactions in the Saccharomyces cerevisiae genome, which exists between a detrimental genetic mutation and a chemical/drug that can ameliorate, rather than exacerbate, that detriment. We refer to this type of interaction as “chemical suppression.” Our work was driven by the hypothesis that genome instability in a certain class of mutants could be alleviated by mild replication inhibition using chemicals/drugs. We queried a collection of conditionally lethal, i.e., temperature-sensitive, alleles representing 40% of the yeast essential genes for those mutants whose growth defect can be suppressed by hydroxyurea (HU), known as a potent DNA replication inhibitor, at the restrictive temperature. Unexpectedly, we identified a number of mutants defective in diverse cellular pathways other than DNA replication. Here we report that HU suppresses selected mutants defective in the kinetochore-microtubule attachment pathway during mitotic chromosome segregation. HU also suppresses an ero1-1 mutant defective for a thiol oxidase of the endoplasmic reticulum by providing oxidation equivalents. Finally, we report that HU suppresses an erg26-1 mutant defective for a C-3 sterol dehydrogenase through regulating iron homeostasis and in turn impacting ergosterol biosynthesis. We further demonstrate that cells carrying the erg26-1 mutation show an increased rate of mitochondrial DNA loss and delayed G1 to S phase transition. We conclude that systematic gathering of a compendium of “chemical suppression” of yeast mutants by genotoxic drugs will not only enable the identification of novel functions of both chemicals and genes, but also have profound implications in cautionary measures of anticancer intervention in humans.
DNA replication; hydroxyurea; kinetochore-microtubule attachment; endoplasmic reticulum redox; ergosterol biosynthesis
A previously described microbroth kinetic system (J. Meletiadis, J. F. Meis, J. W. Mouton, and P. E. Verweij, J. Clin. Microbiol. 39:478-484, 2001) based on continuous monitoring of changes in the optical density of fungal growth was used to describe turbidimetric growth curves of different filamentous fungi in the presence of increasing concentrations of antifungal drugs. Therefore, 24 clinical mold isolates, including Rhizopus oryzae, Aspergillus fumigatus, Aspergillus flavus, and Scedosporium prolificans, were tested against itraconazole, terbinafine, and amphotericin B according to NCCLS guidelines. Among various parameters of the growth curves, the duration of the lag phase was strongly affected by the presence of antifungal drugs. Exposure to increasing drug concentrations resulted in prolonged lag phases of the turbidimetric growth curves. The lag phases of the growth curves at drug concentrations which resulted in more than 50% growth (for itraconazole and terbinafine) and more than 75% growth (for amphotericin B) after 24 h of incubation for R. oryzae, 48 h for Aspergillus spp., and 72 h for S. prolificans were 4 h longer than the lag phases of the growth curves at the corresponding drug-free growth controls which varied from 4.4 h for R. oryzae, 6.5 h for A. flavus, 7.9 h for A. fumigatus, and 11.6 h for S. prolificans. The duration of the lag phases showed small experimental and interstrain variability, with differences of less than 2 h in most of the cases. Using this system, itraconazole and terbinafine resistance (presence of >50% growth) as well as amphotericin B resistance (presence of >75% growth) was determined within incubation periods of 5.0 to 7.7 h for R. oryzae (for amphotericin B resistance incubation for up to 12 h was required), 8.8 to 11.4 h for A. fumigatus, 6.7 to 8.5 h for A. flavus, and 13 to 15.6 h for S. prolificans while awaiting formal MIC determination by the NCCLS reference method.
A screen of the Saccharomyces cerevisiae genome for fragments conferring a growth-impairment phenotype identified 714 fragments in about 84,000 clones tested.
We have screened the genome of Saccharomyces cerevisiae for fragments that confer a growth-retardation phenotype when overexpressed in a multicopy plasmid with a tetracycline-regulatable (Tet-off) promoter. We selected 714 such fragments with a mean size of 700 base-pairs out of around 84,000 clones tested. These include 493 in-frame open reading frame fragments corresponding to 454 distinct genes (of which 91 are of unknown function), and 162 out-of-frame, antisense and intergenic genomic fragments, representing the largest collection of toxic inserts published so far in yeast.
A phenotypic array method, developed for quantifying cell growth, was applied to the haploid and homozygous diploid yeast deletion strain sets. A growth index was developed to screen for non-additive interacting effects between gene deletion and induced perturbations.
A phenotypic array method, developed for quantifying cell growth, was applied to the haploid and homozygous diploid yeast deletion strain sets. A growth index was developed to screen for non-additive interacting effects between gene deletion and induced perturbations. From a genome screen for hydroxyurea (HU) chemical-genetic interactions, 298 haploid deletion strains were selected for further analysis. The strength of interactions was quantified using a wide range of HU concentrations affecting reference strain growth. The selectivity of interaction was determined by comparison with drugs targeting other cellular processes. Bio-modules were defined as gene clusters with shared strength and selectivity of interaction profiles. The functions and connectivity of modules involved in processes such as DNA repair, protein secretion and metabolic control were inferred from their respective gene composition. The work provides an example of, and a general experimental framework for, quantitative analysis of gene interaction networks that buffer cell growth.