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1.  Design, Synthesis and Characterization of a Highly Effective Inhibitor for Analog-Sensitive (as) Kinases 
PLoS ONE  2011;6(6):e20789.
Highly selective, cell-permeable and fast-acting inhibitors of individual kinases are sought-after as tools for studying the cellular function of kinases in real time. A combination of small molecule synthesis and protein mutagenesis, identified a highly potent inhibitor (1-Isopropyl-3-(phenylethynyl)-1H-pyrazolo[3,4-d]pyrimidin-4-amine) of a rationally engineered Hog1 serine/threonine kinase (Hog1T100G). This inhibitor has been successfully used to study various aspects of Hog1 signaling, including a transient cell cycle arrest and gene expression changes mediated by Hog1 in response to stress. This study also underscores that the general applicability of this approach depends, in part, on the selectivity of the designed the inhibitor with respect to activity versus the engineered and wild type kinases. To explore this specificity in detail, we used a validated chemogenetic assay to assess the effect of this inhibitor on all gene products in yeast in parallel. The results from this screen emphasize the need for caution and for case-by-case assessment when using the Analog-Sensitive Kinase Allele technology to assess the physiological roles of kinases.
PMCID: PMC3117834  PMID: 21698101
2.  Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole 
The authors screen for compounds that show synergistic antifungal activity when combined with the widely-used fungistatic drug fluconazole. Chemogenomic profiling explains the mode of action of synergistic drugs and allows the prediction of additional drug synergies.
The authors screen for compounds that show synergistic antifungal activity when combined with the widely-used fungistatic drug fluconazole. Chemogenomic profiling explains the mode of action of synergistic drugs and allows the prediction of additional drug synergies.
Chemical screens with a library enriched for known drugs identified a diverse set of 148 compounds that potentiated the action of the antifungal drug fluconazole against the fungal pathogens Cryptococcus neoformans, Cryptococcus gattii and Candida albicans, and the model yeast Saccharomyces cerevisiae, often in a species-specific manner.Chemogenomic profiles of six confirmed hits in S. cerevisiae revealed different modes of action and enabled the prediction of additional synergistic combinations; three-way synergistic interactions exhibited even stronger synergies at low doses of fluconazole.The synergistic combination of fluconazole and the antidepressant sertraline was active against fluconazole-resistant clinical fungal isolates and in an in vivo model of Cryptococcal infection.
Rising fungal infection rates, especially among immune-suppressed individuals, represent a serious clinical challenge (Gullo, 2009). Cancer, organ transplant and HIV patients, for example, often succumb to opportunistic fungal pathogens. The limited repertoire of approved antifungal agents and emerging drug resistance in the clinic further complicate the effective treatment of systemic fungal infections. At the molecular level, the paucity of fungal-specific essential targets arises from the conserved nature of cellular functions from yeast to humans, as well as from the fact that many essential yeast genes can confer viability at a fraction of wild-type dosage (Yan et al, 2009). Although only ∼1100 of the ∼6000 genes in yeast are essential, almost all genes become essential in specific genetic backgrounds in which another non-essential gene has been deleted or otherwise attenuated, an effect termed synthetic lethality (Tong et al, 2001). Genome-scale surveys suggest that over 200 000 binary synthetic lethal gene combinations dominate the yeast genetic landscape (Costanzo et al, 2010). The genetic buffering phenomenon is also manifest as a plethora of differential chemical–genetic interactions in the presence of sublethal doses of bioactive compounds (Hillenmeyer et al, 2008). These observations frame the difficulty of interdicting network functions in eukaryotic pathogens with single agent therapeutics. At the same time, however, this genetic network organization suggests that judicious combinations of small molecule inhibitors of both essential and non-essential targets may elicit additive or synergistic effects on cell growth (Sharom et al, 2004; Lehar et al, 2008). Unbiased screens for drugs that synergistically enhance a specific bioactive effect, but which are not themselves individually active—termed a syncretic combination—are one means to substantially elaborate chemical space (Keith et al, 2005). Indeed, compounds that enhance the activity of known agents in model yeast and cancer cell line systems have been identified both by focused small molecule library screens and by computational methods (Borisy et al, 2003; Lehar et al, 2007; Nelander et al, 2008; Jansen et al, 2009; Zinner et al, 2009).
To extend the stratagem of chemical synthetic lethality to clinically relevant fungal pathogens, we screened a bioactive library of known drugs for synergistic enhancers of the widely used fungistatic drug fluconazole against the clinically relevant pathogens C. albicans, C. neoformans and C. gattii, as well as the genetically tractable budding yeast S. cerevisiae. Fluconazole is an azole drug that inhibits lanosterol 14α-demethylase, the gene product of ERG11, an essential cytochrome P450 enzyme in the ergosterol biosynthetic pathway (Groll et al, 1998). We identified 148 drugs that potentiate the antifungal action of fluconazole against the four species. These syncretic compounds had not been previously recognized in the clinic as antifungal agents, and many acted in a species-specific manner, often in a potent fungicidal manner.
To understand the mechanisms of synergism, we interrogated six syncretic drugs—trifluoperazine, tamoxifen, clomiphene, sertraline, suloctidil and L-cycloserine—in genome-wide chemogenomic profiles of the S. cerevisiae deletion strain collection (Giaever et al, 1999). These profiles revealed that membrane, vesicle trafficking and lipid biosynthesis pathways are targeted by five of the synergizers, whereas the sphingolipid biosynthesis pathway is targeted by L-cycloserine. Cell biological assays confirmed the predicted membrane disruption effects of the former group of compounds, which may perturb ergosterol metabolism, impair fluconazole export by drug efflux pumps and/or affect active import of fluconazole (Kuo et al, 2010; Mansfield et al, 2010). Based on the integration of chemical–genetic and genetic interaction space, a signature set of deletion strains that are sensitive to the membrane active synergizers correctly predicted additional drug synergies with fluconazole. Similarly, the L-cycloserine chemogenomic profile correctly predicted a synergistic interaction between fluconazole and myriocin, another inhibitor of sphingolipid biosynthesis. The structure of genetic networks suggests that it should be possible to devise higher order drug combinations with even greater selectivity and potency (Sharom et al, 2004). In an initial test of this concept, we found that the combination of a non-synergistic pair drawn from the membrane active and sphingolipid target classes exhibited potent three-way synergism with a low dose of fluconazole. Finally, the combination of sertraline and fluconazole was active in a G. mellonella model of Cryptococcal infection, and was also efficacious against fluconazole-resistant clinical isolates of C. albicans and C. glabrata.
Collectively, these results demonstrate that the combinatorial redeployment of known drugs defines a powerful antifungal strategy and establish a number of potential lead combinations for future clinical assessment.
Resistance to widely used fungistatic drugs, particularly to the ergosterol biosynthesis inhibitor fluconazole, threatens millions of immunocompromised patients susceptible to invasive fungal infections. The dense network structure of synthetic lethal genetic interactions in yeast suggests that combinatorial network inhibition may afford increased drug efficacy and specificity. We carried out systematic screens with a bioactive library enriched for off-patent drugs to identify compounds that potentiate fluconazole action in pathogenic Candida and Cryptococcus strains and the model yeast Saccharomyces. Many compounds exhibited species- or genus-specific synergism, and often improved fluconazole from fungistatic to fungicidal activity. Mode of action studies revealed two classes of synergistic compound, which either perturbed membrane permeability or inhibited sphingolipid biosynthesis. Synergistic drug interactions were rationalized by global genetic interaction networks and, notably, higher order drug combinations further potentiated the activity of fluconazole. Synergistic combinations were active against fluconazole-resistant clinical isolates and an in vivo model of Cryptococcus infection. The systematic repurposing of approved drugs against a spectrum of pathogens thus identifies network vulnerabilities that may be exploited to increase the activity and repertoire of antifungal agents.
PMCID: PMC3159983  PMID: 21694716
antifungal; combination; pathogen; resistance; synergism
3.  A highly tunable dopaminergic oscillator generates ultradian rhythms of behavioral arousal 
eLife  null;3:e05105.
Ultradian (∼4 hr) rhythms in locomotor activity that do not depend on the master circadian pacemaker in the suprachiasmatic nucleus have been observed across mammalian species, however, the underlying mechanisms driving these rhythms are unknown. We show that disruption of the dopamine transporter gene lengthens the period of ultradian locomotor rhythms in mice. Period lengthening also results from chemogenetic activation of midbrain dopamine neurons and psychostimulant treatment, while the antipsychotic haloperidol has the opposite effect. We further reveal that striatal dopamine levels fluctuate in synchrony with ultradian activity cycles and that dopaminergic tone strongly predicts ultradian period. Our data indicate that an arousal regulating, dopaminergic ultradian oscillator (DUO) operates in the mammalian brain, which normally cycles in harmony with the circadian clock, but can desynchronize when dopamine tone is elevated, thereby producing aberrant patterns of arousal which are strikingly similar to perturbed sleep-wake cycles comorbid with psychopathology.
eLife digest
The sleep-wake cycle of mammals is controlled by a ‘circadian clock’ within the brain, which is synchronized to the day–night cycle. However, other aspects of mammalian physiology including alertness and activity levels, as well as appetite and body temperature—fluctuate in cycles that repeat every few hours. These cycles are known as ultradian rhythms, and they may offer survival benefits by enabling potentially risky behaviors, such as foraging, to be coordinated between members of a group.
Despite their widespread nature and the fact that they appear to be conserved in evolution, virtually nothing is known about the molecular basis of ultradian rhythms. Blum et al. have now identified a second internal clock within the brain, which they name ‘the DUO’, and shown that this clock normally works in concert with the circadian clock to regulate daily patterns of activity and alertness.
Experiments in mice revealed that the DUO uses the brain chemical dopamine to generate bursts of activity roughly every four hours. Moreover, it continues to work when the circadian clock has been destroyed. Measurements of dopamine in freely moving mice showed that levels of the chemical fluctuate in synchrony with the animals' activity levels. Moreover, drugs that flood the brain with dopamine, such as methamphetamine, disrupt the 4-hour cycle by lengthening the period between bursts of activity, whereas drugs that block dopamine receptors have the opposite effect.
As well as revealing a mechanism by which the brain coordinates processes that repeat several times per day, the identification of the DUO could also provide insights into the biological basis of psychiatric disorders. Conditions such as schizophrenia and bipolar disorder are often accompanied by disturbances in patterns of activity and rest. While these have previously been attributed to the disruption of circadian rhythms, there is little direct evidence for this, which raises the possibility that these changes might instead reflect the disruption of ultradian rhythms.
PMCID: PMC4337656  PMID: 25546305
chronobiology; dopamine; arousal; ultradian; oscillator; dopamine transporter; Mouse
4.  Cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action 
Chemogenomic screens were performed in both budding and fission yeasts, allowing for a cross-species comparison of drug–gene interaction networks.Drug–module interactions were more conserved than individual drug–gene interactions.Combination of data from both species can improve drug–module predictions and helps identify a compound's mode of action.
Understanding the molecular effects of chemical compounds in living cells is an important step toward rational therapeutics. Drug discovery aims to find compounds that will target a specific pathway or pathogen with minimal side effects. However, even when an effective drug is found, its mode of action (MoA) is typically not well understood. The lack of knowledge regarding a drug's MoA makes the drug discovery process slow and rational therapeutics incredibly difficult. More recently, different high-throughput methods have been developed that attempt to discern how a compound exerts its effects in cells. One of these methods relies on measuring the growth of cells carrying different mutations in the presence of the compounds of interest, commonly referred to as chemogenomics (Wuster and Babu, 2008). The differential growth of the different mutants provides clues as to what the compounds target in the cell (Figure 2). For example, if a drug inhibits a branch in a vital two-branch pathway, then mutations in the second branch might result in cell death if the mutants are grown in the presence of the drug (Figure 2C). As these compound–mutant functional interactions are expected to be relatively rare, one can assume that the growth rate of a mutant–drug combination should generally be equal to the product of the growth rate of the untreated mutant with the growth rate of the drug-treated wild type. This expectation is defined as the neutral model and deviations from this provide a quantitative score that allow us to make informed predictions regarding a drug's MoA (Figure 2B; Parsons et al, 2006).
The availability of these high-throughput approaches now allows us to perform cross-species studies of functional interactions between compounds and genes. In this study, we have performed a quantitative analysis of compound–gene interactions for two fungal species (budding yeast (S. cerevisiae) and fission yeast (S. pombe)) that diverged from each other approximately 500–700 million years ago. A collection of 2957 compounds from the National Cancer Institute (NCI) were screened in both species for inhibition of wild-type cell growth. A total of 132 were found to be bioactive in both fungi and 9, along with 12 additional well-characterized drugs, were selected for subsequent screening. Mutant libraries of 727 and 438 gene deletions were used for S. cerevisiae and S. pombe, respectively, and these were selected based on availability of genetic interaction data from previous studies (Collins et al, 2007; Roguev et al, 2008; Fiedler et al, 2009) and contain an overlap of 190 one-to-one orthologs that can be directly compared. Deviations from the neutral expectation were quantified as drug–gene interactions scores (D-scores) for the 21 compounds against the deletion libraries. Replicates of both screens showed very high correlations (S. cerevisiae r=0.72, S. pombe r=0.76) and reproduced well previously known compound–gene interactions (Supplementary information). We then compared the D-scores for the 190 one-to-one orthologs present in the data set of both species. Despite the high reproducibility, we observed a very poor conservation of these compound–gene interaction scores across these species (r=0.13, Figure 4A).
Previous work had shown that, across these same species, genetic interactions within protein complexes were much more conserved than average genetic interactions (Roguev et al, 2008). Similarly we observed a higher cross-species conservation of the compound–module (complex or pathway) interactions than the overall compound–gene interactions. Specifically, the data derived from fission yeast were a poor predictor of S. cerevisaie drug–gene interactions, but a good predictor of budding yeast compound–module connections (Figure 4B). Also, a combined score from both species improved the prediction of compound–module interactions, above the accuracy observed with the S. cerevisae information alone, but this improvement was not observed for the prediction of drug–gene interactions (Figure 4B). Data from both species were used to predict drug–module interactions, and one specific interaction (compound NSC-207895 interaction with DNA repair complexes) was experimentally verified by showing that the compound activates the DNA damage repair pathway in three species (S. cerevisiae, S. pombe and H. sapiens).
To understand why the combination of chemogenomic data from two species might improve drug–module interaction predictions, we also analyzed previously published cross-species genetic–interaction data. We observed a significant correlation between the conservation of drug–gene and gene–gene interactions among the one-to-one orthologs (r=0.28, P-value=0.0078). Additionally, the strongest interactions of benomyl (a microtubule inhibitor) were to complexes that also had strong and conserved genetic interactions with microtubules (Figure 4C). We hypothesize that a significant number of the compound–gene interactions obtained from chemogenomic studies are not direct interactions with the physical target of the compounds, but include many indirect interactions that genetically interact with the main target(s). This would explain why the compound interaction networks show similar evolutionary patterns as the genetic interactions networks.
In summary, these results shed some light on the interplay between the evolution of genetic networks and the evolution of drug response. Understanding how genetic variability across different species might result in different sensitivity to drugs should improve our capacity to design treatments. Concretely, we hope that this line of research might one day help us create drugs and drug combinations that specifically affect a pathogen or diseased tissue, but not the host.
We present a cross-species chemogenomic screening platform using libraries of haploid deletion mutants from two yeast species, Saccharomyces cerevisiae and Schizosaccharomyces pombe. We screened a set of compounds of known and unknown mode of action (MoA) and derived quantitative drug scores (or D-scores), identifying mutants that are either sensitive or resistant to particular compounds. We found that compound–functional module relationships are more conserved than individual compound–gene interactions between these two species. Furthermore, we observed that combining data from both species allows for more accurate prediction of MoA. Finally, using this platform, we identified a novel small molecule that acts as a DNA damaging agent and demonstrate that its MoA is conserved in human cells.
PMCID: PMC3018166  PMID: 21179023
chemogenomics; evolution; modularity
5.  Gene Annotation and Drug Target Discovery in Candida albicans with a Tagged Transposon Mutant Collection 
PLoS Pathogens  2010;6(10):e1001140.
Candida albicans is the most common human fungal pathogen, causing infections that can be lethal in immunocompromised patients. Although Saccharomyces cerevisiae has been used as a model for C. albicans, it lacks C. albicans' diverse morphogenic forms and is primarily non-pathogenic. Comprehensive genetic analyses that have been instrumental for determining gene function in S. cerevisiae are hampered in C. albicans, due in part to limited resources to systematically assay phenotypes of loss-of-function alleles. Here, we constructed and screened a library of 3633 tagged heterozygous transposon disruption mutants, using them in a competitive growth assay to examine nutrient- and drug-dependent haploinsufficiency. We identified 269 genes that were haploinsufficient in four growth conditions, the majority of which were condition-specific. These screens identified two new genes necessary for filamentous growth as well as ten genes that function in essential processes. We also screened 57 chemically diverse compounds that more potently inhibited growth of C. albicans versus S. cerevisiae. For four of these compounds, we examined the genetic basis of this differential inhibition. Notably, Sec7p was identified as the target of brefeldin A in C. albicans screens, while S. cerevisiae screens with this compound failed to identify this target. We also uncovered a new C. albicans-specific target, Tfp1p, for the synthetic compound 0136-0228. These results highlight the value of haploinsufficiency screens directly in this pathogen for gene annotation and drug target identification.
Author Summary
Candida albicans is a normal inhabitant in our bodies, yet it can become pathogenic and cause infections that range from the superficial in healthy individuals to deadly in the immunocompromised. Comprehensive genetic analysis of C. albicans to identify mechanisms of virulence and new treatment strategies has been hampered by limited, publically accessible genomic resources. By combining the principles of Saccharomyces cerevisiae strain tagging with transposon mutagenesis to generate individually tagged mutants, we created the first entirely public resource that allows simultaneous measurement of strain fitness of ∼60% of the genome in a wide range of experimental treatments. By identifying genes that confer a fitness or growth defect when reduced in copy number, we uncovered genes whose protein products represent potential antifungal targets. Moreover, screening this strain collection with chemical compounds allowed us to identify anticandidal chemicals while concurrently gaining insight into their cellular mechanism of action. This resource, combined with straightforward screening methodology, provides powerful tools to generate hypotheses for functional annotation of the genome, and our results highlight the value of direct versus model-based pathogen studies.
PMCID: PMC2951378  PMID: 20949076
6.  Automatic Morphological Subtyping Reveals New Roles of Caspases in Mitochondrial Dynamics 
PLoS Computational Biology  2011;7(10):e1002212.
Morphological dynamics of mitochondria is associated with key cellular processes related to aging and neuronal degenerative diseases, but the lack of standard quantification of mitochondrial morphology impedes systematic investigation. This paper presents an automated system for the quantification and classification of mitochondrial morphology. We discovered six morphological subtypes of mitochondria for objective quantification of mitochondrial morphology. These six subtypes are small globules, swollen globules, straight tubules, twisted tubules, branched tubules and loops. The subtyping was derived by applying consensus clustering to a huge collection of more than 200 thousand mitochondrial images extracted from 1422 micrographs of Chinese hamster ovary (CHO) cells treated with different drugs, and was validated by evidence of functional similarity reported in the literature. Quantitative statistics of subtype compositions in cells is useful for correlating drug response and mitochondrial dynamics. Combining the quantitative results with our biochemical studies about the effects of squamocin on CHO cells reveals new roles of Caspases in the regulatory mechanisms of mitochondrial dynamics. This system is not only of value to the mitochondrial field, but also applicable to the investigation of other subcellular organelle morphology.
Author Summary
Mitochondria are “cellular power plants” that synthesize adenosine triphosphate (ATP) from degradation of nutrients, providing chemical energy for cellular activities. In addition, mitochondria are involved in a range of other cellular processes, such as signaling, cell differentiation, cell death, cell cycle and cell growth. Dysfunctional mitochondrial dynamics have been linked to several neurodegenerative diseases, and may play a role in the aging process. Previous studies on the correlation between mitochondrial morphological changes and pathological processes involve mostly manual or semi-automated classification and quantification of morphological features, which introduces biases and inconsistency, and are labor intensive. In this work we have developed an automated quantification system for mitochondrial morphology, which is able to extract and distinguish six representative morphological subtypes within cells. Using this system, we have analyzed 1422 cells and extracted more than 200 thousand individual mitochondrion, and calculated morphological statistics for each cell. From the numerical results we were able to derive new biological conclusions about mitochondrial morphological dynamics. With this new system, investigations of mitochondrial morphology can be scaled up and objectively quantified, allowing standardization of morphological distinctions and replicability between experiments. This system will facilitate future research on the relation between subcellular morphology and various physiological processes.
PMCID: PMC3188504  PMID: 21998575
7.  Structural and Chemical Profiling of the Human Cytosolic Sulfotransferases  
PLoS Biology  2007;5(5):e97.
The human cytosolic sulfotransfases (hSULTs) comprise a family of 12 phase II enzymes involved in the metabolism of drugs and hormones, the bioactivation of carcinogens, and the detoxification of xenobiotics. Knowledge of the structural and mechanistic basis of substrate specificity and activity is crucial for understanding steroid and hormone metabolism, drug sensitivity, pharmacogenomics, and response to environmental toxins. We have determined the crystal structures of five hSULTs for which structural information was lacking, and screened nine of the 12 hSULTs for binding and activity toward a panel of potential substrates and inhibitors, revealing unique “chemical fingerprints” for each protein. The family-wide analysis of the screening and structural data provides a comprehensive, high-level view of the determinants of substrate binding, the mechanisms of inhibition by substrates and environmental toxins, and the functions of the orphan family members SULT1C3 and SULT4A1. Evidence is provided for structural “priming” of the enzyme active site by cofactor binding, which influences the spectrum of small molecules that can bind to each enzyme. The data help explain substrate promiscuity in this family and, at the same time, reveal new similarities between hSULT family members that were previously unrecognized by sequence or structure comparison alone.
Author Summary
We metabolize many hormones, drugs, and bioactive chemicals and toxins from the environment. One family of enzymes that participate in the metabolic process consists of the cytosolic sulfotransferases, or SULTs. SULTs have a variety of mechanisms of action—sometimes they inactivate the biological activity of the chemical (e.g., in the case of estrogen). At other times, the enzymes make the chemical more toxic (e.g., for certain carcinogens). Humans have 12 distinct SULT enzymes. Determining how each of these human enzymes recognizes and distinguishes between the thousands of chemicals we confront each day is essential for understanding hormone regulation, assessing environmental risk, and eventually developing better, more-effective drugs. We have studied the human SULT family of enzymes to profile which small molecules are recognized by each enzyme. We also visualized and compared the detailed structural features that determine which enzyme interacts with which molecule. By studying the entire family, we discovered new ways in which chemicals interact with each enzyme. Furthermore, we identified new inhibitors and inhibitory mechanisms. Finally, we discovered functions for many of the human enzymes that were previously uncharacterized.
Structural genomics and substrate screening provide "chemical fingerprints" and insights into substrate promiscuity for the human family of drug- and hormone-metabolizing cytosolic sulfotransferase enzymes.
PMCID: PMC1847840  PMID: 17425406
8.  Modularity and hormone sensitivity of the Drosophila melanogaster insulin receptor/target of rapamycin interaction proteome 
First systematic analysis of the evolutionary conserved InR/TOR pathway interaction proteome in Drosophila.Quantitative mass spectrometry revealed that 22% of identified protein interactions are regulated by the growth hormone insulin affecting membrane proximal as well as intracellular signaling complexes.Systematic RNA interference linked a significant fraction of network components to the control of dTOR kinase activity.Combined biochemical and genetic data suggest dTTT, a dTOR-containing complex required for cell growth control by dTORC1 and dTORC2 in vivo.
Cellular growth is a fundamental process that requires constant adaptations to changing environmental conditions, like growth factor and nutrient availability, energy levels and more. Over the years, the insulin receptor/target of rapamycin pathway (InR/TOR) emerged as a key signaling system for the control of metazoan cell growth. Genetic screens carried out in the fruit fly Drosophila melanogaster identified key InR/TOR pathway components and their relationships. Phenotypes such as altered cell growth are likely to emerge from perturbed dynamic networks containing InR/TOR pathway components, which stably or transiently interact with other cellular proteins to form complexes and networks thereof. Systematic studies on the topology and dynamics of protein interaction networks become therefore highly relevant to gain systems level understanding of deregulated cell growth. Despite much progress in genetic analysis only few systematic protein interaction studies have been reported for Drosophila, which in most cases lack quantitative information representing the dynamic nature of such networks. Here, we present the first quantitative affinity purification mass spectrometry (AP–MS/MS) analysis on the evolutionary conserved InR/TOR signaling network in Drosophila. Systematic RNAi-based functional analysis of identified network components revealed key components linked to the regulation of the central effector kinase dTOR. This includes also dTTT, a novel dTOR-containing complex required for the control of dTORC1 and dTORC2 in vivo.
For systematic AP–MS analysis, we generated Drosophila Kc167 cell lines inducibly expressing affinity-tagged bait proteins previously linked to InR/TOR signaling. Bait expressing Kc167 cell lines were harvested before and after insulin stimulation for subsequent affinity purification. Following LC–MS/MS analysis and probabilistic data filtering using SAINT (Choi et al, 2010), we generated a quantitative network model from 97 high confidence protein–protein interactions and 58 network components (Figure 2). The presented network displayed a high degree of orthologous interactions conserved also in human cells and identified a number of novel molecular interactions with InR/TOR signaling components for future hypothesis driven analysis.
To measure insulin-induced changes within the InR/TOR interaction proteome, we applied a recently introduced label-free quantitative MS approach (Rinner et al, 2007). The obtained quantitative data suggest that 22% of all interactions in the network are regulated by insulin. Major changes could be observed within the membrane proximal InR/chico/PI3K signaling complexes, and also in 14-3-3 protein containing signaling complexes and dTORC1, a complex that contains besides dTOR all major orthologous proteins found also in human mTORC1 including the two dTORC1 substrates d4E-BP (Thor) and S6 Kinase (S6K). Insulin triggered both, dissociation and association of dTORC1 proteins. Among the proteins that showed enhanced binding to dTORC1 upon insulin stimulation we found Unkempt, a RING-finger protein with a proposed role in ubiquitin-mediated protein degradation (Lores et al, 2010). Besides dTORC1 our systematic AP–MS analysis also revealed the presence of dTORC2, the second major TOR complex in Drosophila. dTORC2 contains the Drosophila orthologous of human mTORC2 proteins, but in contrast to dTORC1 was not affected upon insulin stimulation. Interestingly, we also found a specific set of proteins that were not linked to the canonical TOR complexes TORC1 and TORC2 in dTOR purifications. These include LqfR (liquid facets related), Pontin, Reptin, Spaghetti and the gene product of CG16908. We found the same set of proteins when we used CG16908 as a bait, suggesting complex formation among the identified proteins. None of the dTORC1/2 components besides dTOR was identified in CG16908 purifications, indicating that these proteins form dTOR complexes distinct from dTORC1 and dTORC2. Based on known interaction information from other species and data obtained from this study we refer to this complex as dTTT (Drosophila TOR, TELO2, TTI1) (Horejsi et al, 2010; [18]Hurov et al, 2010; [20]Kaizuka et al, 2010). A directed quantitative MS analysis of dTOR complex components suggests that dTORC1 is the most abundant dTOR complex we identified in Kc167 cells.
We next studied the potential roles of the identified network components for controlling the activity of the dInR/TOR pathway using systematic RNAi depletion and quantitative western blotting to measure the changes in abundance of phosphorylated substrates of dTORC1 (Thor/d4E-BP, dS6K) and dTORC2 (dPKB) in RNAi-treated cells (Figure 5). Overall, we could identify 16 proteins (out of 58) whose depletion caused an at least 50% increase or decrease in the levels of phosphorylated d4E-BP, S6K and/or PKB compared with control GFP RNAi. Besides established pathway components, we found several novel regulators within the dInR/TOR interaction network. For example, RNAi against the novel insulin-regulated dTORC1 component Unkempt resulted in enhanced phosphorylation of the dTORC1 substrate d4E-BP, which suggests a negative role for Unkempt on dTORC1 activity. In contrast, depletion of CG16908 and LqfR caused hypo-phosphorylation of all dTOR substrates similar to dTOR itself, suggesting a positive role for the dTTT complex on dTOR activity. Subsequently, we tested whether dTTT components also plays a role in dTOR-mediated cell growth in vivo. Depletion of both dTTT components, CG16908 and LqfR, in the Drosophila eye resulted in a substantial decrease in eye size. Likewise, FLP-FRT-mediated mitotic recombination resulted in CG16908 and LqfR mutant clones with a similar reduced growth phenotype as observed in dTOR mutant clones. Hence, the combined biochemical and genetic analysis revealed dTTT as a dTOR-containing complex required for the activity of both dTORC1 and dTORC2 and thus plays a critical role in controlling cell growth.
Taken together, these results illustrate how a systematic quantitative AP–MS approach when combined with systematic functional analysis in Drosophila can reveal novel insights into the dynamic organization of regulatory networks for cell growth control in metazoans.
Using quantitative mass spectrometry, this study reports how insulin affects the modularity of the interaction proteome of the Drosophila InR/TOR pathway, an evolutionary conserved signaling system for the control of metazoan cell growth. Systematic functional analysis linked a significant number of identified network components to the control of dTOR activity and revealed dTTT, a dTOR complex required for in vivo cell growth control by dTORC1 and dTORC2.
Genetic analysis in Drosophila melanogaster has been widely used to identify a system of genes that control cell growth in response to insulin and nutrients. Many of these genes encode components of the insulin receptor/target of rapamycin (InR/TOR) pathway. However, the biochemical context of this regulatory system is still poorly characterized in Drosophila. Here, we present the first quantitative study that systematically characterizes the modularity and hormone sensitivity of the interaction proteome underlying growth control by the dInR/TOR pathway. Applying quantitative affinity purification and mass spectrometry, we identified 97 high confidence protein interactions among 58 network components. In all, 22% of the detected interactions were regulated by insulin affecting membrane proximal as well as intracellular signaling complexes. Systematic functional analysis linked a subset of network components to the control of dTORC1 and dTORC2 activity. Furthermore, our data suggest the presence of three distinct dTOR kinase complexes, including the evolutionary conserved dTTT complex (Drosophila TOR, TELO2, TTI1). Subsequent genetic studies in flies suggest a role for dTTT in controlling cell growth via a dTORC1- and dTORC2-dependent mechanism.
PMCID: PMC3261712  PMID: 22068330
cell growth; InR/TOR pathway; interaction proteome; quantitative mass spectrometry; signaling
9.  Chemical combination effects predict connectivity in biological systems 
Chemical synergies can be novel probes of biological systems.Simulated response shapes depend on target connectivity in a pathway.Experiments with yeast and cancer cells confirm simulated effects.Profiles across many combinations yield target location information.
Living organisms are built of interacting components, whose function and dysfunction can be described through dynamic network models (Davidson et al, 2002). Systems Biology involves the iterative construction of such models (Ideker et al, 2001), and may eventually improve the understanding of diseases using in silico simulations. Such simulations may eventually permit drugs to be prioritized for clinical trials, reducing potential risks and increasing the likelihood of successful outcomes. Given the complexity of biological systems, constructing realistic models will require large and diverse sets of connectivity data.
Chemical combinations provide a new window into biological connectivity. Information gleaned from targeted combinations, such as paired mutations (Tong et al, 2004), has proven to be especially useful for revealing functional interactions between components. We have been screening chemical combinations for therapeutic synergies (Borisy et al, 2003; Zimmermann et al, 2007), collecting full-dose matrices where combinations are tested in all possible pairings of serially diluted single agent doses (Figure 1). Such screens yield a variety of response surfaces with distinct shapes for combinations that work through different known mechanisms, suggesting that combination effects may contain information on the nature of functional connections between drug targets.
Simulations of biological pathways predict synergistic responses to inhibitors that depend on target connectivity. We explored theoretical predictions by simulating a metabolic pathway with pairs of inhibitors aimed at different targets with varying doses. We found that the shape of each combination response depended on how the inhibitor pair's targets were connected in the pathway (Figure 2). The predicted response shapes were robust to plausible variations in the simulated pathway that did not affect the network topology (e.g., kinetic assumptions, parameter values, and nonlinear response functions), but were very sensitive to topological alterations in the modelled network (e.g., feedback regulation or changing the type of junction at a branch point). These findings suggest that connectivity of the inhibitor targets has a major influence on combination response morphology.
The predicted shapes were experimentally confirmed in yeast combination experiments. The proliferation experiment used drugs focused on the sterol biosynthesis pathway, which is mostly linear between the targets covered in this study, and is known to be regulated by negative feedback (Gardner et al, 2001). The combinations between sterol inhibitors confirmed expectations from our simulations, showing dose-additive responses for pairs targeting the same enzyme and strong synergies across enzymes of the shape predicted in our simulations for linear pathways under negative feedback. Combinations across pathways showed much more variable responses with a trend towards less synergy on average.
Further experimental support was obtained from human cells. A combination screen of 90 annotated drugs in a human tumour cell line (HCT116) proliferation assay produced strong synergies for combinations within pathways and more variable effects between targeted functions. Synergy profiles (sets of all synergy scores involving each drug) also showed a greater degree of similarity for pairs of drugs with related targets. Finally, the most extreme outliers were dominated by inhibitors of kinases that are especially critical for HCT116 proliferation (Awwad et al, 2003), with effects that are consistent across mechanistic replicates, showing that chemical combinations can highlight biologically relevant cellular processes.
This study demonstrates the potential of chemical combinations for exploring functional connectivity in biological systems. This information complements genetic studies by providing more details through variable dosing, by directly targeting single domains of multi-domain proteins, and by probing cell types that are not amenable to mutagenesis. Responses from large chemical combination screens can be used to identify molecular targets through chemical–genetic profiling (Macdonald et al, 2006), or to directly constrain network models by means of a prediction-validation procedure (Ideker et al, 2001). This initial exploration can be extended to cover a wider range of response shapes and network topologies, as well as to combinations of three or more chemical agents. Moreover, this approach may even be applicable to non-biological systems where responses to targeted perturbations can be measured.
Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured.
PMCID: PMC1828746  PMID: 17332758
chemical genetics; combinations and synergy; metabolic and regulatory networks; simulation and data analysis
10.  Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery 
Chromosome 1 of Vibrio vulnificus tends to contain larger portion of essential or housekeeping genes on the basis of the genomic analysis and gene knockout experiments performed in this study, while its chromosome 2 seems to have originated and evolved from a plasmid.The genome-scale metabolic network model of V. vulnificus was reconstructed based on databases and literature, and was used to identify 193 essential metabolites.Five essential metabolites finally selected after the filtering process are 2-amino-4-hydroxy-6-hydroxymethyl-7,8-dihydropteridine (AHHMP), D-glutamate (DGLU), 2,3-dihydrodipicolinate (DHDP), 1-deoxy-D-xylulose 5-phosphate (DX5P), and 4-aminobenzoate (PABA), which were predicted to be essential in V. vulnificus, absent in human, and are consumed by multiple reactions.Chemical analogs of the five essential metabolites were screened and a hit compound showing the minimal inhibitory concentration (MIC) of 2 μg/ml and the minimal bactericidal concentration (MBC) of 4 μg/ml against V. vulnificus was identified.
Discovering new antimicrobial targets and consequently new antimicrobials is important as drug resistance of pathogenic microorganisms is becoming an increasingly serious problem in human healthcare management (Fischbach and Walsh, 2009). There clearly exists a gap between genomic studies and drug discovery as the accumulation of knowledge on pathogens at genome level has not successfully transformed into the development of effective drugs (Mills, 2006; Payne et al, 2007). In this study, we dissected the genome of a microbial pathogen in detail, and subsequently developed a systems biological strategy of employing genome-scale metabolic modeling and simulation together with metabolite essentiality analysis for effective drug targeting and discovery. This strategy was used for identifying new drug targets in an opportunistic pathogen Vibrio vulnificus CMCP6 as a model.
V. vulnificus is a Gram-negative halophilic bacterium that is found in estuarine waters, brackish ponds, or coastal areas, and its Biotype 1 is an opportunistic human pathogen that can attack immune-compromised patients, and causes primary septicemia, necrotized wound infections, and gastroenteritis. We previously found that many metabolic genes were specifically induced in vivo, suggesting that specific metabolic pathways are essential for in vivo survival and virulence of this pathogen (Kim et al, 2003; Lee et al, 2007). These results motivated us to carry out systems biological analysis of the genome and the metabolic network for new drug target discovery.
V. vulnificus CMCP6 has two chromosomes. We first re-sequenced genomic regions assembled in low quality and low depth, and subsequently re-annotated the whole genome of V. vulnificus. Horizontal gene transfer was suspected to be responsible for the diversification of each chromosome of V. vulnificus, and the presence of metabolic genes was more biased to chromosome 1 than chromosome 2. Further studies on V. vulnificus genome revealed that chromosome 2 is more prone to diversification for better adaptation to the environment than its chromosome 1, while chromosome 1 tends to expand their genetic repertoire while maintaining the core genes at a constant level.
Next, a genome-scale metabolic network VvuMBEL943 was reconstructed based on literature, databases and experiments for systematic studies on the metabolism of this pathogen and prediction of drug targets. The VvuMBEL943 model is composed of 943 reactions and 765 metabolites, and covers 673 genes. The model was validated by comparing its simulated cell growth phenotype obtained by constraints-based flux analysis with the V. vulnificus-specific experimental data previously reported in the literature. In this study, constraints-based flux analysis is an optimization-based simulation method that calculates intracellular fluxes under the specific genetic and environmental condition (Kim et al, 2008). As a result, 17 growth phenotypes were correctly predicted out of 18 cases, which demonstrate the validity of VvuMBEL943.
The main objective of constructing VvuMBEL943 in this study is to predict potential drug targets by system-wide analysis of the metabolic network for the effective treatment of V. vulnificus. To achieve this goal, a set of drug target candidates was predicted by taking a metabolite-centric approach. Metabolite essentiality analysis is a concept recently introduced for the study of cellular robustness to complement conventional reaction or gene-centric approach (Kim et al, 2007b). Metabolite essentiality analysis observes changes in flux distribution by removing each metabolite from the in silico metabolic network. Hence, metabolite essentiality predicts essential metabolites whose absence causes cell death. By selecting essential metabolites, it is possible to directly screen only their structural analogs, which substantially reduces the number of chemical compounds to screen from the chemical compound library. As a result of implementing this approach, 193 metabolites were initially identified to be essential to the cell. These essential metabolites were then further filtered based on the predetermined criteria, mainly organism specificity and multiple connectivity associated with each metabolite, in order to reduce the number of initial target candidates towards identifying the most effective ones.
Five essential metabolites finally selected are 2-amino-4-hydroxy-6-hydroxymethyl-7,8-dihydropteridine (AHHMP), D-glutamate (DGLU), 2,3-dihydrodipicolinate (DHDP), 1-deoxy-D-xylulose 5-phosphate (DX5P), and 4-aminobenzoate (PABA). Enzymes that consume these essential metabolites were experimentally verified to be essential, which indeed demonstrates the essentiality of these five metabolites. On the basis of the structural information of these five essential metabolites, whole-cell screening assay was performed using their analogs for possible antibacterial discovery. We screened 352 chemical analogs of the essential metabolites selected from the chemical compound library, and found a hit compound 24837, which shows the minimal inhibitory concentration (MIC) of 2 μg/ml and minimal bactericidal concentration (MBC) of 4 μg/ml, showing good antibacterial activity without further structural modification. Although this study demonstrates a proof-of-concept, the approaches and their rationale taken here should serve as a general strategy for discovering novel antibiotics and drugs based on systems-level analysis of metabolic networks.
Although the genomes of many microbial pathogens have been studied to help identify effective drug targets and novel drugs, such efforts have not yet reached full fruition. In this study, we report a systems biological approach that efficiently utilizes genomic information for drug targeting and discovery, and apply this approach to the opportunistic pathogen Vibrio vulnificus CMCP6. First, we partially re-sequenced and fully re-annotated the V. vulnificus CMCP6 genome, and accordingly reconstructed its genome-scale metabolic network, VvuMBEL943. The validated network model was employed to systematically predict drug targets using the concept of metabolite essentiality, along with additional filtering criteria. Target genes encoding enzymes that interact with the five essential metabolites finally selected were experimentally validated. These five essential metabolites are critical to the survival of the cell, and hence were used to guide the cost-effective selection of chemical analogs, which were then screened for antimicrobial activity in a whole-cell assay. This approach is expected to help fill the existing gap between genomics and drug discovery.
PMCID: PMC3049409  PMID: 21245845
drug discovery; drug targeting; genome analysis; metabolic network; Vibrio vulnificus
11.  A Phenotypic Profile of the Candida albicans Regulatory Network 
PLoS Genetics  2009;5(12):e1000783.
Candida albicans is a normal resident of the gastrointestinal tract and also the most prevalent fungal pathogen of humans. It last shared a common ancestor with the model yeast Saccharomyces cerevisiae over 300 million years ago. We describe a collection of 143 genetically matched strains of C. albicans, each of which has been deleted for a specific transcriptional regulator. This collection represents a large fraction of the non-essential transcription circuitry. A phenotypic profile for each mutant was developed using a screen of 55 growth conditions. The results identify the biological roles of many individual transcriptional regulators; for many, this work represents the first description of their functions. For example, a quarter of the strains showed altered colony formation, a phenotype reflecting transitions among yeast, pseudohyphal, and hyphal cell forms. These transitions, which have been closely linked to pathogenesis, have been extensively studied, yet our work nearly doubles the number of transcriptional regulators known to influence them. As a second example, nearly a quarter of the knockout strains affected sensitivity to commonly used antifungal drugs; although a few transcriptional regulators have previously been implicated in susceptibility to these drugs, our work indicates many additional mechanisms of sensitivity and resistance. Finally, our results inform how transcriptional networks evolve. Comparison with the existing S. cerevisiae data (supplemented by additional S. cerevisiae experiments reported here) allows the first systematic analysis of phenotypic conservation by orthologous transcriptional regulators over a large evolutionary distance. We find that, despite the many specific wiring changes documented between these species, the general phenotypes of orthologous transcriptional regulator knockouts are largely conserved. These observations support the idea that many wiring changes affect the detailed architecture of the circuit, but not its overall output.
Author Summary
A key goal in the understanding of the biology of an organism is the description of the regulatory networks that control the expression of its genes. Changes in gene expression result in new cellular phenotypes that can be acted upon by evolutionary forces to influence the configuration of these networks. We have developed a phenotypic description of the transcriptional regulatory networks of the major fungal pathogen of humans, Candida albicans, by individually deleting genes that encode transcriptional regulators and observing the resulting phenotypes in a variety of environmental conditions. This approach provides insight into the biological roles of many previously uncharacterized regulators, and allows us to assign groups of regulators to specific biological roles, many of which are relevant to pathogenesis. For example, we identified groups of regulators that influenced sensitivity to antifungal drugs, the ability to acquire iron (a challenge for organisms in a human host), and the ability to form complex multi-cellular colonies. Our results also allow us to analyze how the phenotypes associated with transcriptional regulators change as organisms diverge. A comparison of C. albicans data with that from the well-characterized yeast S. cerevisiae revealed strong phenotypic conservation between related transcriptional regulators, despite the more than 300 million years which separate the species.
PMCID: PMC2790342  PMID: 20041210
12.  Analysis of multiple compound–protein interactions reveals novel bioactive molecules 
The authors use machine learning of compound-protein interactions to explore drug polypharmacology and to efficiently identify bioactive ligands, including novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein coupled receptors and protein kinases.
We have demonstrated that machine learning of multiple compound–protein interactions is useful for efficient ligand screening and for assessing drug polypharmacology.This approach successfully identified novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein-coupled receptors and protein kinases.These bioactive compounds were not detected by existing computational ligand-screening methods in comparative studies.The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. Perturbations of biological systems by chemical probes provide broader applications not only for analysis of complex systems but also for intentional manipulations of these systems. Nevertheless, the lack of well-characterized chemical modulators has limited their use. Recently, chemical genomics has emerged as a promising area of research applicable to the exploration of novel bioactive molecules, and researchers are currently striving toward the identification of all possible ligands for all target protein families (Wang et al, 2009). Chemical genomics studies have shown that patterns of compound–protein interactions (CPIs) are too diverse to be understood as simple one-to-one events. There is an urgent need to develop appropriate data mining methods for characterizing and visualizing the full complexity of interactions between chemical space and biological systems. However, no existing screening approach has so far succeeded in identifying novel bioactive compounds using multiple interactions among compounds and target proteins.
High-throughput screening (HTS) and computational screening have greatly aided in the identification of early lead compounds for drug discovery. However, the large number of assays required for HTS to identify drugs that target multiple proteins render this process very costly and time-consuming. Therefore, interest in using in silico strategies for screening has increased. The most common computational approaches, ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS; Oprea and Matter, 2004; Muegge and Oloff, 2006; McInnes, 2007; Figure 1A), have been used for practical drug development. LBVS aims to identify molecules that are very similar to known active molecules and generally has difficulty identifying compounds with novel structural scaffolds that differ from reference molecules. The other popular strategy, SBVS, is constrained by the number of three-dimensional crystallographic structures available. To circumvent these limitations, we have shown that a new computational screening strategy, chemical genomics-based virtual screening (CGBVS), has the potential to identify novel, scaffold-hopping compounds and assess their polypharmacology by using a machine-learning method to recognize conserved molecular patterns in comprehensive CPI data sets.
The CGBVS strategy used in this study was made up of five steps: CPI data collection, descriptor calculation, representation of interaction vectors, predictive model construction using training data sets, and predictions from test data (Figure 1A). Importantly, step 1, the construction of a data set of chemical structures and protein sequences for known CPIs, did not require the three-dimensional protein structures needed for SBVS. In step 2, compound structures and protein sequences were converted into numerical descriptors. These descriptors were used to construct chemical or biological spaces in which decreasing distance between vectors corresponded to increasing similarity of compound structures or protein sequences. In step 3, we represented multiple CPI patterns by concatenating these chemical and protein descriptors. Using these interaction vectors, we could quantify the similarity of molecular interactions for compound–protein pairs, despite the fact that the ligand and protein similarity maps differed substantially. In step 4, concatenated vectors for CPI pairs (positive samples) and non-interacting pairs (negative samples) were input into an established machine-learning method. In the final step, the classifier constructed using training sets was applied to test data.
To evaluate the predictive value of CGBVS, we first compared its performance with that of LBVS by fivefold cross-validation. CGBVS performed with considerably higher accuracy (91.9%) than did LBVS (84.4%; Figure 1B). We next compared CGBVS and SBVS in a retrospective virtual screening based on the human β2-adrenergic receptor (ADRB2). Figure 1C shows that CGBVS provided higher hit rates than did SBVS. These results suggest that CGBVS is more successful than conventional approaches for prediction of CPIs.
We then evaluated the ability of the CGBVS method to predict the polypharmacology of ADRB2 by attempting to identify novel ADRB2 ligands from a group of G-protein-coupled receptor (GPCR) ligands. We ranked the prediction scores for the interactions of 826 reported GPCR ligands with ADRB2 and then analyzed the 50 highest-ranked compounds in greater detail. Of 21 commercially available compounds, 11 showed ADRB2-binding activity and were not previously reported to be ADRB2 ligands. These compounds included ligands not only for aminergic receptors but also for neuropeptide Y-type 1 receptors (NPY1R), which have low protein homology to ADRB2. Most ligands we identified were not detected by LBVS and SBVS, which suggests that only CGBVS could identify this unexpected cross-reaction for a ligand developed as a target to a peptidergic receptor.
The true value of CGBVS in drug discovery must be tested by assessing whether this method can identify scaffold-hopping lead compounds from a set of compounds that is structurally more diverse. To assess this ability, we analyzed 11 500 commercially available compounds to predict compounds likely to bind to two GPCRs and two protein kinases. Functional assays revealed that nine ADRB2 ligands, three NPY1R ligands, five epidermal growth factor receptor (EGFR) inhibitors, and two cyclin-dependent kinase 2 (CDK2) inhibitors were concentrated in the top-ranked compounds (hit rate=30, 15, 25, and 10%, respectively). We also evaluated the extent of scaffold hopping achieved in the identification of these novel ligands. One ADRB2 ligand, two NPY1R ligands, and one CDK2 inhibitor exhibited scaffold hopping (Figure 4), indicating that CGBVS can use this characteristic to rationally predict novel lead compounds, a crucial and very difficult step in drug discovery. This feature of CGBVS is critically different from existing predictive methods, such as LBVS, which depend on similarities between test and reference ligands, and focus on a single protein or highly homologous proteins. In particular, CGBVS is useful for targets with undefined ligands because this method can use CPIs with target proteins that exhibit lower levels of homology.
In summary, we have demonstrated that data mining of multiple CPIs is of great practical value for exploration of chemical space. As a predictive model, CGBVS could provide an important step in the discovery of such multi-target drugs by identifying the group of proteins targeted by a particular ligand, leading to innovation in pharmaceutical research.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold-hopping compounds. Through a machine-learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G-protein-coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand-screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
PMCID: PMC3094066  PMID: 21364574
chemical genomics; data mining; drug discovery; ligand screening; systems chemical biology
13.  Off-Target Effects of Psychoactive Drugs Revealed by Genome-Wide Assays in Yeast 
PLoS Genetics  2008;4(8):e1000151.
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.
Author Summary
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.
PMCID: PMC2483942  PMID: 18688276
14.  Cell wall staining with Trypan blue enables quantitative analysis of morphological changes in yeast cells 
Yeast cells are protected by a cell wall that plays an important role in the exchange of substances with the environment. The cell wall structure is dynamic and can adapt to different physiological states or environmental conditions. For the investigation of morphological changes, selective staining with fluorescent dyes is a valuable tool. Furthermore, cell wall staining is used to facilitate sub-cellular localization experiments with fluorescently-labeled proteins and the detection of yeast cells in non-fungal host tissues. Here, we report staining of Saccharomyces cerevisiae cell wall with Trypan Blue, which emits strong red fluorescence upon binding to chitin and yeast glucan; thereby, it facilitates cell wall analysis by confocal and super-resolution microscopy. The staining pattern of Trypan Blue was similar to that of the widely used UV-excitable, blue fluorescent cell wall stain Calcofluor White. Trypan Blue staining facilitated quantification of cell size and cell wall volume when utilizing the optical sectioning capacity of a confocal microscope. This enabled the quantification of morphological changes during growth under anaerobic conditions and in the presence of chemicals, demonstrating the potential of this approach for morphological investigations or screening assays.
PMCID: PMC4324143  PMID: 25717323
cell wall staining; Trypan blue; Saccharomyces cerevisiae; Calcofluor white; cell volume; Anaerobiosis; super-resolution microscopy; 3D structured illumination
15.  Orbitofrontal and striatal circuits dynamically encode the shift between goal-directed and habitual actions 
Nature communications  2013;4:2264.
Shifting between goal-directed and habitual actions allows for efficient and flexible decision-making. Here we demonstrate a novel, within-subject instrumental lever-pressing paradigm where mice shift between goal-directed and habitual actions. We identify a role for orbitofrontal cortex (OFC) in actions following outcome-revaluation, and confirm that dorsal medial (DMS) and lateral striatum (DLS) mediate different action strategies. In-vivo simultaneous recordings of OFC, DMS, and DLS neuronal ensembles during shifting reveal that the same neurons display different activity depending on whether presses are goal-directed or habitual, with DMS and OFC becoming more—and DLS less-engaged during goal-directed actions. Importantly, the magnitude of neural activity changes in OFC following changes in outcome value positively correlates with the level of goal-directed behavior. Chemogenetic inhibition of OFC disruptsgoal-directed actions, while optogenetic activation of OFC specifically increases goal-directed pressing. They also reveal a role for OFC in action revaluation, which has implications for understanding compulsive behavior.
PMCID: PMC4026062  PMID: 23921250
16.  Dissecting RNA folding by nucleotide analog interference mapping (NAIM) 
Nature protocols  2008;3(5):811-823.
Nucleotide analog interference mapping (NAIM) is a powerful chemogenetic approach that allows RNA structure and function to be characterized at the atomic level. Random modifications of base or backbone moieties are incorporated into the RNA transcript as nucleotide analog phosphorothioates. The resulting RNA pool is then subjected to a stringent selection step, in which the RNA has to accomplish a specific task, for example, folding. RNA functional groups important for this process can be identified by physical isolation of the functional and the nonfunctional RNA molecules and subsequent mapping of the modified nucleotide positions in both RNA populations by iodine cleavage of the susceptible phosphorothioate linkage. This approach has been used to analyze a variety of aspects of RNA biochemistry, including RNA structure, catalysis and ligand interaction. Here, I describe how to set up a NAIM assay for studying RNA folding. This protocol can be readily adapted to study any RNAs and their properties. The time required to complete the experiment is dependent on the length of the RNA and the number of atomic modifications tested. In general, a single NAIM experiment can be completed in 1–2 weeks, but expect a time frame of several weeks to obtain reliable and statistically meaningful results.
PMCID: PMC2873565  PMID: 18451789
17.  A dynamic model of proteome changes reveals new roles for transcript alteration in yeast 
By characterizing dynamic changes in yeast protein abundance following osmotic shock, this study shows that the correlation between protein and mRNA differs for transcripts that increase versus decrease in abundance, and reveals physiological reasons for these differences.
The correlation between protein and mRNA change is very high at transcripts that increase in abundance, but negligible at reduced transcripts following NaCl shock.Modeling and experimental data suggest that reducing levels of high-abundance transcripts helps to direct translational machinery to newly made transcripts.The transient burst of transcript increase serves to accelerate changes in protein abundance.Post-transcriptional regulation of protein abundance is pervasive, although most of the variance in protein change is explained by changes in mRNA abundance.
Natural microenvironments change rapidly, and living creatures must respond quickly and efficiently to thrive within this flux. At all cellular levels—signaling, transcription, translation, metabolism, cell growth, and division—the response is dynamic and coordinated. Some aspects of this response, such as dynamic changes of the transcriptome, are well understood. But other aspects, like the response of the proteome, have remained obscured primarily because of previous limitations in technology. Without coordinated time-course data, it has remained impossible to correctly characterize the correlations and dependencies between these two essential levels of cell biology.
This work presents an extended picture of the coordinated response of the transcriptome and proteome as cells respond to an abrupt environmental change. To assay proteomic dynamics, we developed a strategy for large-scale, multiplexed quantitation using isobaric tags and high mass accuracy mass spectrometry. This sensitive yet efficient platform allows for the expedient collection of quantitative time-course proteomic data at six time points, sufficiently reproducible to permit meaningful interpretation of variation across biological replicates. Time-course transcriptome data were generated from paired biological samples, allowing us to examine the relationships between changes in mRNA and protein for each gene in terms of direction and intensity, as well as the characteristics of the temporal profiles for each gene.
It was immediately obvious that a single measure of correlation across the entire data set was a meaningless metric. We therefore analyzed relationships between mRNA and protein for different subsets of data. In response to osmotic shock, hundreds of transcripts are highly induced, and their temporal pattern reveals a transient peak of maximal induction, which resolves into a new elevated level as cells acclimate (Figure 2). For this group of genes, there is extremely high correlation between peak mRNA change and protein change (R2∼0.8). But the dynamics of the molecules differ: while mRNA levels transiently overshoot their final levels, proteins gradually rise in abundance toward their new, elevated state. We observed, however, that a measure of efficiency connects the two profiles. The time it takes for a protein to acclimate to its new state correlates with the magnitude of the excess mRNA induction. Thus, the cell imparts an urgency to protein induction by transiently producing excess transcript.
The most surprising result, however, involves transcripts that decrease in abundance. In response to osmotic shock, the cell transiently reduces over 600 transcripts, many of which are among the most highly expressed in unstressed cells. But protein levels for these genes remain, for the most part, almost completely unchanged. The stark absence of protein repression is independent of basal protein abundance, independent of reported protein half-lives, reproducible across biological replicates, and validated by quantitative western blots. Furthermore, since we do detect a handful of proteins whose abundance is significantly reduced, our technology is capable of identifying protein loss. Thus, we conclude that transcript reduction serves another purpose besides reducing protein levels.
To explore alternate interpretations of the consequence of transcriptional repression, we devised a mass-action kinetic model, which describes protein changes based on mRNA dynamics in the context of transient changes in the rates of cell division. The model successfully recapitulated the observed data, allowing us to alter modeling parameters to test various hypotheses.
In response to osmotic shock, overall rates of translation temporarily decrease and cell growth transiently arrests before resuming at a slower rate. We reasoned that mRNA reduction might lower the rate of new protein synthesis, but that retarded production is balanced by reduced cell division. We explored both aspects of this logic with our model.
As expected, removing cell division from our model led to a calculated decrease of protein levels, indicating that reduced growth is necessary for maintaining protein levels. However, when we computationally held mRNA levels stable and calculated protein levels in the absence of mRNA repression, we did not find the expected increase in protein abundance.
We then considered the possibility that one function of the regulated repression of these highly abundant transcripts was to liberate proteins essential for translation, such as ribosomes or translation initiation factors. To explore this, we examined a mutant lacking the Dot6p/Tod6p transcriptional repressors, which fails to properly repress ∼250 genes in response to osmotic shock. In the wild type, the mRNA for a Dot6p/Tod6p target (ARX1) decreased seven-fold, and the remaining transcript was generally unassociated with poly-ribosomes. In the mutant, however, the mRNA levels were reduced only two-fold, while the remaining transcript continued to bind ribosomes. Therefore, failure to reduce transcript levels led to a persistent association with poly-ribosomes, thereby consuming translational machinery.
Our hypothesis is, therefore, that widespread changes in the transcriptome promote efficient translation of new proteins. Transcript increase serves to increase abundance of the encoded proteins, while reduction of some of the most abundant and highly translated mRNAs supports this project by liberating translational capacity. While it is not clear what factors are the limiting elements, it is clear that a full picture of cellular biology requires exploring the dynamics of the cellular response.
The transcriptome and proteome change dynamically as cells respond to environmental stress; however, prior proteomic studies reported poor correlation between mRNA and protein, rendering their relationships unclear. To address this, we combined high mass accuracy mass spectrometry with isobaric tagging to quantify dynamic changes in ∼2500 Saccharomyces cerevisiae proteins, in biological triplicate and with paired mRNA samples, as cells acclimated to high osmolarity. Surprisingly, while transcript induction correlated extremely well with protein increase, transcript reduction produced little to no change in the corresponding proteins. We constructed a mathematical model of dynamic protein changes and propose that the lack of protein reduction is explained by cell-division arrest, while transcript reduction supports redistribution of translational machinery. Furthermore, the transient ‘burst' of mRNA induction after stress serves to accelerate change in the corresponding protein levels. We identified several classes of post-transcriptional regulation, but show that most of the variance in protein changes is explained by mRNA. Our results present a picture of the coordinated physiological responses at the levels of mRNA, protein, protein-synthetic capacity, and cellular growth.
PMCID: PMC3159980  PMID: 21772262
dynamics; modeling; proteomics; stress; transcriptomics
18.  Different Levels of Catabolite Repression Optimize Growth in Stable and Variable Environments 
PLoS Biology  2014;12(1):e1001764.
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.
Author Summary
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.
PMCID: PMC3891604  PMID: 24453942
19.  Dynamic transcriptome analysis measures rates of mRNA synthesis and decay in yeast 
Rates of mRNA synthesis and decay can be measured on a genome-wide scale in yeast by dynamic transcriptome analysis (DTA), which combines non-perturbing metabolic RNA labeling with dynamic kinetic modeling.DTA reveals that most mRNA synthesis rates are around several transcripts per cell and cell cycle, and most mRNA half-lives range around a median of 11 min.DTA realistically monitors the cellular response to osmotic stress with higher sensitivity and temporal resolution than transcriptomics, and can be used to follow changes in RNA metabolism in gene regulatory systems.
Nascent transcriptome analysis reveals dynamics of mRNA synthesis and decay in yeast.
The first step in the expression of the genome is the synthesis of messenger-RNA (mRNA). In all cells, the regulation of mRNA levels in response to changing environmental conditions is a fundamental process. Classical methods to study such changes in mRNA levels, however, fail to unravel whether such changes are due to changes in mRNA synthesis (transcription) or changes in mRNA decay, which both contribute to setting mRNA levels. Therefore, the regulation of mRNA stability and turnover is poorly understood, and new methods for a quantitative analysis of mRNA synthesis and decay are urgenlty sought.
In this study, we describe a novel method termed dynamic transcriptome analysis (DTA), which can be used to determine synthesis and decay rates of mRNAs on a genome-wide level in yeast and other eukaryotic cells. We applied DTA to the model organism Saccharomyces cerevisiae and analyzed the dynamics of the transcriptome under standard growth conditions as well as under osmotic stress conditions. DTA relies on a combination of biochemistry, high-throughput data acquisition, and computational biology. It uses metabolic labeling of newly synthesised RNA with the nucleoside analogon 4-thiouridine (4sU), purification of labeled, newly synthesized RNA, and subsequent microarray hybridization. An improved mathematical model enables synthesis and decay rates of esentially all mRNAs in the cell to be determined with accuracy.
In this study, we found that under normal growth conditions the synthesis rates for most mRNAs are low and that the decay rates are not correlated with synthesis. Addition of salt to the culture, however, induced three phases of changes in mRNA synthesis and decay. During the initial shock phase, there is a global repression of synthesis and a reduction of decay of most mRNAs. The subsequent induction phase involves strongly increased synthesis of stress mRNAs, which are also destabilized. Finally, the recovery phase restores decay rates, but leaves synthesis rates altered, apparently to allow for cellular growth under the new conditions.
DTA shows a higher sensitivity and better temporal resolution than classical methods such as transcriptomics. Also, DTA is non-perturbing and allows for an unbiased monitoring of genomic regulatory systems in living cells. Previously used methods are invasive and likely alter cellular physiology and thereby mRNA dynamics. DTA has a high potential to become a standard technique in molecular biology that may replace standard transcriptomics to study gene regulatory systems. In the future, DTA may be used to study dynamic changes in cellular mRNA metabolism induced by chemical inhibitors or defined mutations or changes in the environment.
To obtain rates of mRNA synthesis and decay in yeast, we established dynamic transcriptome analysis (DTA). DTA combines non-perturbing metabolic RNA labeling with dynamic kinetic modeling. DTA reveals that most mRNA synthesis rates are around several transcripts per cell and cell cycle, and most mRNA half-lives range around a median of 11 min. DTA can monitor the cellular response to osmotic stress with higher sensitivity and temporal resolution than standard transcriptomics. In contrast to monotonically increasing total mRNA levels, DTA reveals three phases of the stress response. During the initial shock phase, mRNA synthesis and decay rates decrease globally, resulting in mRNA storage. During the subsequent induction phase, both rates increase for a subset of genes, resulting in production and rapid removal of stress-responsive mRNAs. During the recovery phase, decay rates are largely restored, whereas synthesis rates remain altered, apparently enabling growth at high salt concentration. Stress-induced changes in mRNA synthesis rates are predicted from gene occupancy with RNA polymerase II. DTA-derived mRNA synthesis rates identified 16 stress-specific pairs/triples of cooperative transcription factors, of which seven were known. Thus, DTA realistically monitors the dynamics in mRNA metabolism that underlie gene regulatory systems.
PMCID: PMC3049410  PMID: 21206491
gene expression; gene regulation; mRNA transcription; mRNA turnover; salt stress response
20.  Control of ATP homeostasis during the respiro-fermentative transition in yeast 
Respiring Saccharomyces cerevisiae cells respond to a sudden increase in glucose concentration by a pronounced drop of their adenine nucleotide content. Transient accumulation of the purine salvage pathway intermediate inosine accounts for the apparent loss of adenine nucleotides.Inosine formation in response to perturbations of cellular energy balance depends on the presence of a fermentable carbon source. Under respiratory conditions, AMP accumulates instead and no inosine is formed.Conversion of AXPs into inosine is facilitated by AMP deaminase, Amd1, and IMP-specific 5'-nucleotidase, Isn1. Inosine recycling into the AXP pool is facilitated by the purine nucleoside phosphorylase, Pnp1, and joint action of the phosphoribosyltransferases, Hpt1 and Xpt1.Impaired inosine formation results in altered metabolite pool dynamics in response to glucose addition, but does not change glycolytic flux. However, mutants blocked in inosine formation exhibit delayed growth acceleration after glucose addition.
Yeast cells are exposed to strongly fluctuating nutrient concentrations in their natural environment, which requires rapid and efficient adaptation through rearrangements on all levels of their metabolism. The quantitative understanding of these adaptation processes represents the basis for a directed optimization of the microorganism to suit the needs of biochemical production processes that often impose non-uniform or harsh cultivation conditions (Lara et al, 2006), or require a redirection of metabolic fluxes to improve productivity (Bailey, 1991). In this context, controlled perturbation experiments represent a valuable tool, as they allow studying the transition from one defined physiological state to another under well-characterized conditions. Measurements of metabolite pool dynamics and enzymatic activities in response to different perturbations enable the quantitative mathematical analysis of glycolytic dynamics, which is ultimately meant to discriminate the impact of allosteric regulation and changes in the enzymatic make-up of the cell on the overall metabolic response (Rizzi et al, 1997; Teusink et al, 2000; Daran-Lapujade et al, 2007; van den Brink et al, 2008).
A long-standing problem in the context of these studies was the apparent loss of adenine nucleotides, which immediately follows the relief from glucose limitation (Theobald et al, 1997; Kresnowati et al, 2006). In this study, we showed that the transient accumulation of the purine salvage pathway (PSP) metabolites, IMP and inosine, account for the loss of AXP nucleotides. The pathway for inosine formation and recycling was identified, and the interplay between different pathways during the respiro-fermentative transition is schematically summarized in Figure 10. The presence of a quickly metabolizable sugar causes accumulation of the phosphorylated metabolites F1,6P, T6P and G3P, which results in a transient imbalance of ATP-consuming and ATP-regenerating reactions, and provokes a drop in both ATP and intracellular phosphate (Pi) concentrations. The joint action of fast ATP consumption and the Adk1 reaction results in the net-production of AMP. The accumulation of AMP, however, is prevented by Amd1 and Isn1, which readily convert AMP via IMP into inosine. Recycling of inosine into IMP is facilitated by Pnp1 and the concomitant action of Hpt1 and Xpt1, with Hpt1 having the predominant role. We suggest that inosine formation serves to prevent unscheduled AMP accumulation, and to store AXP nucleotides in a metabolically ‘neutral' form until re-equilibration of glycolysis eventually allows recovery of ATP levels. Recycling of inosine via IMP represents an energy-saving way to replenish the AXP pool, as de novo synthesis of IMP starting from PRPP requires 4 ATP molecules, whereas IMP production from inosine and PRPP is an ATP-neutral process (Figure 10).
In contrast to the behavior observed in the presence of fermentable carbon sources, neither IMP nor inosine were formed in response to perturbations of the cellular energy balance under respiratory conditions. Instead, the drop in ATP level caused a concomitant increase in AMP concentration. Therefore, we conclude that the conversion of AXP nucleotides into inosine represents a specific short-term metabolic response to the perturbation of cellular energy homeostasis, which is controlled by the presence of a fermentable carbon source (or a sugar derivative that can undergo rapid phosphorylation). The discrimination between AMP accumulation and AMP-to-inosine conversion seems to be controlled by Amd1 activity. At the present stage of investigation, we cannot conclude whether regulation of Amd1 is brought about by allosteric control or by posttranslational modification. However, given that inorganic phosphate is a potent inhibitor of Amd1 (Merkler et al, 1989) and intracellular phosphate concentration transiently drops when cells become exposed to fermentable carbon sources (our data, and e.g. Hohmann et al, 1996), phosphate availability is likely to have a pivotal role in the regulation of AXP pool size and inosine formation.
The impact of defective AXP cycling on the global metabolic response to glucose addition was tested under conditions in which respiration was inhibited by antimycin A. The amd1 mutant, in which adenine nucleotide cycling was completely blocked, showed the strongest deviations from the wild-type behavior. In this mutant, adenylic energy charge exhibited a strong drop after glucose addition, and recovered much slower than in wild-type cells. Furthermore, deletion of AMD1 resulted in strong accumulation of AMP and pronounced changes in the dynamics of trehalose-6-phosphate, glycerol-3-phosphate, and PRPP after glucose addition (Figure 6).
Despite pronounced changes in metabolite pool dynamics caused by the deletion of AMD1, no alterations in the production of the fermentative end products, ethanol and glycerol, nor in the consumption of glucose were observed (Figure 9B). However, the amd1 and isn1 mutant strains showed delayed growth acceleration after glucose addition (Figure 9C). The observation of unaltered fermentation capacity but concomitant delay in growth acceleration in the amd and isn1 mutants points to the possibility that regulation of glycolysis is not the major target of AMP. Indeed, our study revealed a delayed increase of PRPP concentration in the amd1 strain after glucose addition. The PRPP protein has a central role as a precursor for purine nucleotide de novo synthesis and the synthesis of amino acids (Vavassori et al, 2005). Hence, limitation of this important precursor may negatively affect growth. In addition, AMP accumulation may have an important signaling function acting, for example, through the cAMP/PKA pathway (Thevelein et al, 2005; Zaman et al, 2009) and/or via Snf1 (Celenza and Carlson, 1984). However, experimental evidence to support this potential link is missing. Thus, the assumption of a signaling role of AMP remains speculative, although intriguing, when asking for the actual function of AXP-to-inosine conversion during the respiro-fermentative transition.
On the basis of this study, we put forward the implication of adenine nucleotide cycling through the PSP in energy homeostasis in yeast. Answering the question whether this pathway is also active in humans could potentially contribute to a better understanding of metabolic processes that control the metabolic transition from respiratory to respiro-fermentative energy supply in muscle upon heavy exercise. In particular, it may help to understand conflicting results on the impaired physical performance of individuals carrying an AMP deaminase dysfunction under these conditions (Tarnopolsky et al, 2001; Fischer et al, 2007).
Respiring Saccharomyces cerevisiae cells respond to a sudden increase in glucose concentration by a pronounced drop of their adenine nucleotide content ([ATP]+[ADP]+[AMP]=[AXP]). The unknown fate of ‘lost' AXP nucleotides represented a long-standing problem for the understanding of the yeast's physiological response to changing growth conditions. Transient accumulation of the purine salvage pathway intermediate, inosine, accounted for the apparent loss of adenine nucleotides. Conversion of AXPs into inosine was facilitated by AMP deaminase, Amd1, and IMP-specific 5′-nucleotidase, Isn1. Inosine recycling into the AXP pool was facilitated by purine nucleoside phosphorylase, Pnp1, and joint action of the phosphoribosyltransferases, Hpt1 and Xpt1. Analysis of changes in 24 intracellular metabolite pools during the respiro-fermentative growth transition in wild-type, amd1, isn1, and pnp1 strains revealed that only the amd1 mutant exhibited significant deviations from the wild-type behavior. Moreover, mutants that were blocked in inosine production exhibited delayed growth acceleration after glucose addition. It is proposed that interconversion of adenine nucleotides and inosine facilitates rapid and energy-cost efficient adaptation of the AXP pool size to changing environmental conditions.
PMCID: PMC2824524  PMID: 20087341
ATP homeostasis; metabolic regulation; purine nucleotide metabolism; respiro-fermentative transition; yeast
21.  Tissue dynamics spectroscopy for phenotypic profiling of drug effects in three-dimensional culture 
Biomedical Optics Express  2012;3(11):2825-2841.
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.
PMCID: PMC3493238  PMID: 23162721
(090.1995) Digital holography; (170.0170) Medical optics and biotechnology; (170.3880) Medical and biological imaging; (110.1650) Coherence imaging
22.  Mapping the Hsp90 Genetic Interaction Network in Candida albicans Reveals Environmental Contingency and Rewired Circuitry 
PLoS Genetics  2012;8(3):e1002562.
The molecular chaperone Hsp90 regulates the folding of diverse signal transducers in all eukaryotes, profoundly affecting cellular circuitry. In fungi, Hsp90 influences development, drug resistance, and evolution. Hsp90 interacts with ∼10% of the proteome in the model yeast Saccharomyces cerevisiae, while only two interactions have been identified in Candida albicans, the leading fungal pathogen of humans. Utilizing a chemical genomic approach, we mapped the C. albicans Hsp90 interaction network under diverse stress conditions. The chaperone network is environmentally contingent, and most of the 226 genetic interactors are important for growth only under specific conditions, suggesting that they operate downstream of Hsp90, as with the MAPK Hog1. Few interactors are important for growth in many environments, and these are poised to operate upstream of Hsp90, as with the protein kinase CK2 and the transcription factor Ahr1. We establish environmental contingency in the first chaperone network of a fungal pathogen, novel effectors upstream and downstream of Hsp90, and network rewiring over evolutionary time.
Author Summary
Hsp90 is an essential and conserved molecular chaperone in eukaryotes that assists with folding diverse proteins, especially regulators of cellular signaling. By activating signaling in response to environmental cues, Hsp90 has a profound impact on myriad aspects of biology. In fungi, Hsp90 influences development, drug resistance, and evolution. In the model yeast Saccharomyces cerevisiae, Hsp90 interacts with ∼10% of proteins. In the leading human fungal pathogen, Candida albicans, only two interactions have been identified. We conducted a chemical genetic screen to elucidate the C. albicans Hsp90 interaction network under diverse stress conditions. The majority of the 226 genetic interactors are important for growth under specific conditions, suggesting that they act downstream of Hsp90 and that the network is environmentally contingent. For example, the kinase Hog1 depends upon Hsp90 for activation. Only a few interactors are important for growth in many conditions, suggesting that they act upstream of Hsp90. For example, the protein kinase CK2 regulates function of the Hsp90 chaperone machine and the transcription factor Ahr1 governs HSP90 expression. Thus, we identify novel effectors upstream and downstream of Hsp90, and establish the first chaperone network of a fungal pathogen, with evidence for environmental contingency and network rewiring over evolutionary time.
PMCID: PMC3305360  PMID: 22438817
23.  Identification of Host-Dependent Survival Factors for Intracellular Mycobacterium tuberculosis through an siRNA Screen 
PLoS Pathogens  2010;6(4):e1000839.
The stable infection of host macrophages by Mycobacterium tuberculosis (Mtb) involves, and depends on, the attenuation of the diverse microbicidal responses mounted by the host cell. This is primarily achieved through targeted perturbations of the host cellular signaling machinery. Therefore, in view of the dependency of the pathogen on host molecules for its intracellular survival, we wanted to test whether targeting such factors could provide an alternate route for the therapeutic management of tuberculosis. To first identify components of the host signaling machinery that regulate intracellular survival of Mtb, we performed an siRNA screen against all known kinases and phosphatases in murine macrophages infected with the virulent strain, H37Rv. Several validated targets could be identified by this method where silencing led either to a significant decrease, or enhancement in the intracellular mycobacterial load. To further resolve the functional relevance of these targets, we also screened against these identified targets in cells infected with different strains of multiple drug-resistant mycobacteria which differed in terms of their intracellular growth properties. The results obtained subsequently allowed us to filter the core set of host regulatory molecules that functioned independently of the phenotypic variations exhibited by the pathogen. Then, using a combination of both in vitro and in vivo experimentation, we could demonstrate that at least some of these host factors provide attractive targets for anti-TB drug development. These results provide a “proof-of-concept” demonstration that targeting host factors subverted by intracellular Mtb provides an attractive and feasible strategy for the development of anti-tuberculosis drugs. Importantly, our findings also emphasize the advantage of such an approach by establishing its equal applicability to infections with Mtb strains exhibiting a range of phenotypic diversifications, including multiple drug-resistance. Thus the host factors identified here may potentially be exploited for the development of anti-tuberculosis drugs.
Author Summary
The adaptation of Mycobacterium tuberculosis (Mtb) involves dynamic interactions with the molecular components of the host cellular machinery. Therefore, targeting relevant host factors may provide an alternate approach for the chemotherapy of tuberculosis (TB). To test this, we first performed an siRNA screen targeting all known kinases and phosphatases in murine macrophages infected with a virulent strain of Mtb. A subsequent validation of this screen then identified several host molecules whose depletion severely affected the intracellular survival of mycobacteria. We also then screened against the identified host targets in cells infected with independent isolates of MDR-Mtb. This exercise identified those host molecules that were indispensable for supporting infection, independent of the phenotypic variations exhibited by the pathogen. Then, by using a pharmacological inhibitor that simultaneously targeted two of these molecules, we were able to demonstrate clearance of both drug-sensitive and drug-resistant strains of Mtb from infected cells. Importantly, this inhibitor was also effective in mice infected with the virulent strain of Mtb. Thus, in addition to demonstrating the feasibility of targeting host molecules involved in supporting intracellular persistence of pathogen for TB therapy, our studies also identify several such molecules that may be exploited for the purposes of drug development.
PMCID: PMC2855445  PMID: 20419122
24.  Flow-Based Cytometric Analysis of Cell Cycle via Simulated Cell Populations 
PLoS Computational Biology  2010;6(4):e1000741.
We present a new approach to the handling and interrogating of large flow cytometry data where cell status and function can be described, at the population level, by global descriptors such as distribution mean or co-efficient of variation experimental data. Here we link the “real” data to initialise a computer simulation of the cell cycle that mimics the evolution of individual cells within a larger population and simulates the associated changes in fluorescence intensity of functional reporters. The model is based on stochastic formulations of cell cycle progression and cell division and uses evolutionary algorithms, allied to further experimental data sets, to optimise the system variables. At the population level, the in-silico cells provide the same statistical distributions of fluorescence as their real counterparts; in addition the model maintains information at the single cell level. The cell model is demonstrated in the analysis of cell cycle perturbation in human osteosarcoma tumour cells, using the topoisomerase II inhibitor, ICRF-193. The simulation gives a continuous temporal description of the pharmacodynamics between discrete experimental analysis points with a 24 hour interval; providing quantitative assessment of inter-mitotic time variation, drug interaction time constants and sub-population fractions within normal and polyploid cell cycles. Repeated simulations indicate a model accuracy of ±5%. The development of a simulated cell model, initialized and calibrated by reference to experimental data, provides an analysis tool in which biological knowledge can be obtained directly via interrogation of the in-silico cell population. It is envisaged that this approach to the study of cell biology by simulating a virtual cell population pertinent to the data available can be applied to “generic” cell-based outputs including experimental data from imaging platforms.
Author Summary
One of the key challenges facing cell biologists today is understanding the influence of molecular controls in shaping and controlling cell growth and proliferation. There is growing recognition that abnormal progression through the cell cycle and the associated effects on the growth of cell populations has a major impact on a wide range of biological and clinical problems, including: tumour growth, developmental control, origins of chromosomal instability and drug resistance. Multiparameter flow cytometry is frequently used to assess proliferation dynamics of cellular populations using fluorescent reporters generating large data sets that can inform simulation models. We have developed stochastic computing approaches allied to evolutionary algorithms to produce simulated cell populations—providing a new approach to the analysis of real multi-variate data sets obtained by flow cytometry. The methodology delivers new insight on biological processes in delivering a continuous simulation of the dynamic evolution of a cellular system between fixed sampling points, hence, converting static data into dynamic data revealing the effective traverse of the cell cycle, restriction points and commitment gateways. The approach also permits the visualisation of the variation between individual cells reflecting biological heterogeneity and potentially Darwinian fitness, given that the simulation delivers a report on population dynamics in which each and every cell can be tracked.
PMCID: PMC2855319  PMID: 20419143
25.  Screening and Rapid Molecular Diagnosis of Tuberculosis in Prisons in Russia and Eastern Europe: A Cost-Effectiveness Analysis 
PLoS Medicine  2012;9(11):e1001348.
Daniel Winetsky and colleagues investigate eight strategies for screening and diagnosis of tuberculosis within prisons of the former Soviet Union.
Prisons of the former Soviet Union (FSU) have high rates of multidrug-resistant tuberculosis (MDR-TB) and are thought to drive general population tuberculosis (TB) epidemics. Effective prison case detection, though employing more expensive technologies, may reduce long-term treatment costs and slow MDR-TB transmission.
Methods and Findings
We developed a dynamic transmission model of TB and drug resistance matched to the epidemiology and costs in FSU prisons. We evaluated eight strategies for TB screening and diagnosis involving, alone or in combination, self-referral, symptom screening, mass miniature radiography (MMR), and sputum PCR with probes for rifampin resistance (Xpert MTB/RIF). Over a 10-y horizon, we projected costs, quality-adjusted life years (QALYs), and TB and MDR-TB prevalence. Using sputum PCR as an annual primary screening tool among the general prison population most effectively reduced overall TB prevalence (from 2.78% to 2.31%) and MDR-TB prevalence (from 0.74% to 0.63%), and cost US$543/QALY for additional QALYs gained compared to MMR screening with sputum PCR reserved for rapid detection of MDR-TB. Adding sputum PCR to the currently used strategy of annual MMR screening was cost-saving over 10 y compared to MMR screening alone, but produced only a modest reduction in MDR-TB prevalence (from 0.74% to 0.69%) and had minimal effect on overall TB prevalence (from 2.78% to 2.74%). Strategies based on symptom screening alone were less effective and more expensive than MMR-based strategies. Study limitations included scarce primary TB time-series data in FSU prisons and uncertainties regarding screening test characteristics.
In prisons of the FSU, annual screening of the general inmate population with sputum PCR most effectively reduces TB and MDR-TB prevalence, doing so cost-effectively. If this approach is not feasible, the current strategy of annual MMR is both more effective and less expensive than strategies using self-referral or symptom screening alone, and the addition of sputum PCR for rapid MDR-TB detection may be cost-saving over time.
Please see later in the article for the Editors' Summary
Editors' Summary
Tuberculosis (TB)—a contagious bacterial disease—is a major public health problem, particularly in low- and middle-income countries. In 2010, about nine million people developed TB, and about 1.5 million people died from the disease. Mycobacterium tuberculosis, the bacterium that causes TB, is spread in airborne droplets when people with active disease cough or sneeze. The characteristic symptoms of TB include fever, a persistent cough, and night sweats. Diagnostic tests include sputum smear microscopy (examination of mucus from the lungs for M. tuberculosis bacilli), mycobacterial culture (growth of M. tuberculosis from sputum), and chest X-rays. TB can also be diagnosed by looking for fragments of the M. tuberculosis genetic blueprint in sputum samples (sputum PCR). Importantly, sputum PCR can detect the genetic changes that make M. tuberculosis resistant to rifampicin, a constituent of the cocktail of antibiotics that is used to cure TB. Rifampicin resistance is an indicator of multidrug-resistant TB (MDR-TB), the emergence of which is thwarting ongoing global efforts to control TB.
Why Was This Study Done?
Prisons present unique challenges for TB control. Overcrowding, poor ventilation, and inadequate medical care increase the spread of TB among prisoners, who often come from disadvantaged populations where the prevalence of TB (the proportion of the population with TB) is already high. Prisons also act as reservoirs for TB, recycling the disease back into the civilian population. The prisons of the former Soviet Union, for example, which have extremely high rates of MDR-TB, are thought to drive TB epidemics in the general population. Because effective identification of active TB among prison inmates has the potential to improve TB control outside prisons, the World Health Organization recommends active TB case finding among prisoners using self-referral, screening with symptom questionnaires, or screening with chest X-rays or mass miniature radiography (MMR). But which of these strategies will reduce the prevalence of TB in prisons most effectively, and which is most cost-effective? Here, the researchers evaluate the relative effectiveness and cost-effectiveness of alternative strategies for screening and diagnosis of TB in prisons by modeling TB and MDR-TB epidemics in prisons of the former Soviet Union.
What Did the Researchers Do and Find?
The researchers used a dynamic transmission model of TB that simulates the movement of individuals in prisons in the former Soviet Union through different stages of TB infection to estimate the costs, quality-adjusted life years (QALYs; a measure of disease burden that includes both the quantity and quality of life) saved, and TB and MDR-TB prevalence for eight TB screening/diagnostic strategies over a ten-year period. Compared to annual MMR alone (the current strategy), annual screening with sputum PCR produced the greatest reduction in the prevalence of TB and of MDR-TB among the prison population. Adding sputum PCR for detection of MDR-TB to annual MMR screening did not affect the overall TB prevalence but slightly reduced the MDR-TB prevalence and saved nearly US$2,000 over ten years per model prison of 1,000 inmates, compared to MMR screening alone. Annual sputum PCR was the most cost-effective strategy, costing US$543/QALY for additional QALYs gained compared to MMR screening plus sputum PCR for MDR-TB detection. Other strategies tested, including symptom screening alone or combined with sputum PCR, were either more expensive and less effective or less cost-effective than these two options.
What Do These Findings Mean?
These findings suggest that, in prisons in the former Soviet Union, annual screening with sputum PCR will most effectively reduce TB and MDR-TB prevalence and will be cost-effective. That is, the cost per QALY saved of this strategy is less than the per-capita gross domestic product of any of the former Soviet Union countries. The paucity of primary data on some facets of TB epidemiology in prisons in the former Soviet Union and the assumptions built into the mathematical model limit the accuracy of these findings. Moreover, because most of the benefits of sputum PCR screening come from treating the MDR-TB cases that are detected using this screening approach, these findings cannot be generalized to prison settings without a functioning MDR-TB treatment program or with a very low MDR-TB prevalence. Despite these and other limitations, these findings provide valuable information about the screening strategies that are most likely to interrupt the TB cycle in prisons, thereby saving resources and averting preventable deaths both inside and outside prisons.
Additional Information
Please access these websites via the online version of this summary at
The World Health Organization provides information (in several languages) on all aspects of tuberculosis, including general information on tuberculosis diagnostics and on tuberculosis in prisons; a report published in the Bulletin of the World Health Organization in 2006 describes tough measures taken in Russian prisons to slow the spread of TB
The Stop TB Partnership is working towards tuberculosis elimination; patient stories about tuberculosis are available (in English and Spanish)
The US Centers for Disease Control and Prevention has information about tuberculosis, about its diagnosis, and about tuberculosis in prisons (some information in English and Spanish)
A PLOS Medicine Research Article by Iacapo Baussano et al. describes a systematic review of tuberculosis incidence in prisons; a linked editorial entitled The Health Crisis of Tuberculosis in Prisons Extends beyond the Prison Walls is also available
The Tuberculosis Survival Project, which aims to raise awareness of tuberculosis and provide support for people with tuberculosis, provides personal stories about treatment for tuberculosis; the Tuberculosis Vaccine Initiative also provides personal stories about dealing with tuberculosis
MedlinePlus has links to further information about tuberculosis (in English and Spanish)
PMCID: PMC3507963  PMID: 23209384

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