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1.  Chemical-genetic profile analysis in yeast suggests that a previously uncharacterized open reading frame, YBR261C, affects protein synthesis 
BMC Genomics  2008;9:583.
Background
Functional genomics has received considerable attention in the post-genomic era, as it aims to identify function(s) for different genes. One way to study gene function is to investigate the alterations in the responses of deletion mutants to different stimuli. Here we investigate the genetic profile of yeast non-essential gene deletion array (yGDA, ~4700 strains) for increased sensitivity to paromomycin, which targets the process of protein synthesis.
Results
As expected, our analysis indicated that the majority of deletion strains (134) with increased sensitivity to paromomycin, are involved in protein biosynthesis. The remaining strains can be divided into smaller functional categories: metabolism (45), cellular component biogenesis and organization (28), DNA maintenance (21), transport (20), others (38) and unknown (39). These may represent minor cellular target sites (side-effects) for paromomycin. They may also represent novel links to protein synthesis. One of these strains carries a deletion for a previously uncharacterized ORF, YBR261C, that we term TAE1 for Translation Associated Element 1. Our focused follow-up experiments indicated that deletion of TAE1 alters the ribosomal profile of the mutant cells. Also, gene deletion strain for TAE1 has defects in both translation efficiency and fidelity. Miniaturized synthetic genetic array analysis further indicates that TAE1 genetically interacts with 16 ribosomal protein genes. Phenotypic suppression analysis using TAE1 overexpression also links TAE1 to protein synthesis.
Conclusion
We show that a previously uncharacterized ORF, YBR261C, affects the process of protein synthesis and reaffirm that large-scale genetic profile analysis can be a useful tool to study novel gene function(s).
doi:10.1186/1471-2164-9-583
PMCID: PMC2613417  PMID: 19055778
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.
doi:10.1038/msb.2011.31
PMCID: PMC3159983  PMID: 21694716
antifungal; combination; pathogen; resistance; synergism
3.  Inference of Protein Complex Activities from Chemical-Genetic Profile and Its Applications: Predicting Drug-Target Pathways 
PLoS Computational Biology  2008;4(8):e1000162.
The chemical-genetic profile can be defined as quantitative values of deletion strains' growth defects under exposure to chemicals. In yeast, the compendium of chemical-genetic profiles of genomewide deletion strains under many different chemicals has been used for identifying direct target proteins and a common mode-of-action of those chemicals. In the previous study, valuable biological information such as protein–protein and genetic interactions has not been fully utilized. In our study, we integrated this compendium and biological interactions into the comprehensive collection of ∼490 protein complexes of yeast for model-based prediction of a drug's target proteins and similar drugs. We assumed that those protein complexes (PCs) were functional units for yeast cell growth and regarded them as hidden factors and developed the PC-based Bayesian factor model that relates the chemical-genetic profile at the level of organism phenotypes to the hidden activities of PCs at the molecular level. The inferred PC activities provided the predictive power of a common mode-of-action of drugs as well as grouping of PCs with similar functions. In addition, our PC-based model allowed us to develop a new effective method to predict a drug's target pathway, by which we were able to highlight the target-protein, TOR1, of rapamycin. Our study is the first approach to model phenotypes of systematic deletion strains in terms of protein complexes. We believe that our PC-based approach can provide an appropriate framework for combining and modeling several types of chemical-genetic profiles including interspecies. Such efforts will contribute to predicting more precisely relevant pathways including target proteins that interact directly with bioactive compounds.
Author Summary
Finding the specific targets of chemicals and deciphering how drugs work in our body is important for the effective development of new drugs. Growth profiles of yeast genomewide deletion strains under many different chemicals have been used for identifying target proteins and a common mode-of-action of drugs. In this study, we integrated those growth profiles with biological information such as protein–protein interactions and genetic interactions to develop a new method to infer the mode-of-action of drugs. We assume that the protein complexes (PCs) are functional units for cell growth regulation, analogous to the transcriptional factors (TFs) for gene regulation. We also assume that the relative cell growth of a specific deletion mutant in the presence of a specific drug is determined by the interactions between the PCs and the deleted gene of the mutant. We then developed a computational model with which we were able to infer the hidden activities of PCs on the cell growth and showed that yeast growth phenotypes could be effectively modeled by PCs in a biologically meaningful way by demonstrating that the inferred activities of PCs contributed to predicting groups of similar drugs as well as proteins and pathways targeted by drugs.
doi:10.1371/journal.pcbi.1000162
PMCID: PMC2515108  PMID: 18769708
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.
doi:10.1038/msb.2010.107
PMCID: PMC3018166  PMID: 21179023
chemogenomics; evolution; modularity
5.  Genetic interactions reveal the evolutionary trajectories of duplicate genes 
Duplicate genes show significantly fewer interactions than singleton genes, and functionally similar duplicates can exhibit dissimilar profiles because common interactions are ‘hidden' due to buffering.Genetic interaction profiles provide insights into evolutionary mechanisms of duplicate retention by distinguishing duplicates under dosage selection from those retained because of some divergence in function.The genetic interactions of duplicate genes evolve in an extremely asymmetric way and the directionality of this asymmetry correlates well with other evolutionary properties of duplicate genes.Genetic interaction profiles can be used to elucidate the divergent function of specific duplicate pairs.
Gene duplication and divergence serves as a primary source for new genes and new functions, and as such has broad implications on the evolutionary process. Duplicate genes within S. cerevisiae have been shown to retain a high degree of similarity with regard to many of their functional properties (Papp et al, 2004; Guan et al, 2007; Wapinski et al, 2007; Musso et al, 2008), and perturbation of duplicate genes has been shown to result in smaller fitness defects than singleton genes (Gu et al, 2003; DeLuna et al, 2008; Dean et al, 2008; Musso et al, 2008). Individual genetic interactions between pairs of genes and profiles of such interactions across the entire genome provide a new context in which to examine the properties of duplicate compensation.
In this study we use the most recent and comprehensive set of genetic interactions in yeast produced to date (Costanzo et al, 2010) to address questions of duplicate retention and redundancy. We show that the ability for duplicate genes to buffer the deletion of a partner has three main consequences. First it agrees with previous work demonstrating that a high proportion of duplicate pairs are synthetic lethal, a classic indication of the ability to buffer one another functionally (DeLuna et al, 2008; Dean et al, 2008; Musso et al, 2008). Second, it reduces the number of genetic interactions observed between duplicate genes and the rest of the genome by masking interactions relating to common function from experimental detection. Third, this buffering of common interactions serves to reduce profile similarity in spite of common function (Figure 1). The compensatory ability of functionally similar duplicates buffers genetic interactions related to their common function (reducing the number of genetic interactions overall), while allowing the measurement of interactions related to any divergent function. Thus, even functionally similar duplicates may have dissimilar genetic interaction profiles. As previously surmised (Ihmels et al, 2007), duplicate genes under selection for dosage amplification have differing profile characteristics. We show that dosage-mediated duplicates have much higher genetic interaction profile similarity than do other duplicate pairs. Furthermore, we show in a comparison with local neighbors on a protein–protein interaction (PPI) network, that although dosage-mediated duplicates more often have higher similarity to each other than they do to their neighbors, the reverse is true for duplicates in general. That is, slightly divergent duplicate genes more often exhibit a higher similarity with a common neighbor on the PPI network than they do with each other, and that observation is consistent with the idea that common interactions are buffered while interactions corresponding to divergent functions are observed.
We then asked whether duplicates' genetic interactions that are not buffered appear in a symmetric or an asymmetric fashion. Previous work has established asymmetric patterns with regard to PPI degree (Wagner, 2002; He and Zhang, 2005), sequence divergence (Conant and Wagner, 2003; Zhang et al, 2003; Kellis et al, 2004; Scannell and Wolfe, 2008) and expression patterns (Gu et al, 2002b; Tirosh and Barkai, 2007). Although genetic interactions are further removed from mechanism than protein–protein interactions, for example, they do offer a more direct measurement of functional consequence and, thus, may give a better indication of the functional differences between a duplicate pair. We found that duplicates exhibit a strikingly asymmetric pattern of genetic interactions, with the ratio of interactions between sisters commonly exceeding 7:1 (Figure 4A). The observations differ significantly from random simulations in which genetic interactions were redistributed between sisters with equal probability (Figure 4A). Moreover, the directionality of this interaction asymmetry agrees with other physiological properties of duplicate pairs. For example, the sister with more genetic interactions also tends to have more protein–protein interactions and also tends to evolve at a slower rate (Figure 4B).
Genetic interaction degree and profiles can be used to understand the functional divergence of particular duplicates pairs. As a case example, we consider the whole-genome-duplication pair CIK1–VIK1. Each of these genes encode proteins that form distinct heterodimeric complexes with the microtubule motor protein Kar3 (Manning et al, 1999). Although each of these proteins depend on a direct physical interaction with Kar3, Cik1 has a much higher profile similarity to Kar3 than does Vik1 (r=0.5 and r=0.3, respectively). Consistent with its higher similarity, Δcik1 and Δkar3 exhibit several similar phenotypes, including abnormally short spindles, chromosome loss and delayed cell cycle progression (Page et al, 1994; Manning et al, 1999). In contrast, a Δvik1 mutant strain exhibits no overt phenotype (Manning et al, 1999).
The characterization of functional redundancy and divergence between duplicate genes is an important step in understanding the evolution of genetic systems. Large-scale genetic network analysis in Saccharomyces cerevisiae provides a powerful perspective for addressing these questions through quantitative measurements of genetic interactions between pairs of duplicated genes, and more generally, through the study of genome-wide genetic interaction profiles associated with duplicated genes. We show that duplicate genes exhibit fewer genetic interactions than other genes because they tend to buffer one another functionally, whereas observed interactions are non-overlapping and reflect their divergent roles. We also show that duplicate gene pairs are highly imbalanced in their number of genetic interactions with other genes, a pattern that appears to result from asymmetric evolution, such that one duplicate evolves or degrades faster than the other and often becomes functionally or conditionally specialized. The differences in genetic interactions are predictive of differences in several other evolutionary and physiological properties of duplicate pairs.
doi:10.1038/msb.2010.82
PMCID: PMC3010121  PMID: 21081923
duplicate genes; functional divergence; genetic interactions; paralogs; Saccharomyces cerevisiae
6.  Genome-Wide Fitness Test and Mechanism-of-Action Studies of Inhibitory Compounds in Candida albicans 
PLoS Pathogens  2007;3(6):e92.
Candida albicans is a prevalent fungal pathogen amongst the immunocompromised population, causing both superficial and life-threatening infections. Since C. albicans is diploid, classical transmission genetics can not be performed to study specific aspects of its biology and pathogenesis. Here, we exploit the diploid status of C. albicans by constructing a library of 2,868 heterozygous deletion mutants and screening this collection using 35 known or novel compounds to survey chemically induced haploinsufficiency in the pathogen. In this reverse genetic assay termed the fitness test, genes related to the mechanism of action of the probe compounds are clearly identified, supporting their functional roles and genetic interactions. In this report, chemical–genetic relationships are provided for multiple FDA-approved antifungal drugs (fluconazole, voriconazole, caspofungin, 5-fluorocytosine, and amphotericin B) as well as additional compounds targeting ergosterol, fatty acid and sphingolipid biosynthesis, microtubules, actin, secretion, rRNA processing, translation, glycosylation, and protein folding mechanisms. We also demonstrate how chemically induced haploinsufficiency profiles can be used to identify the mechanism of action of novel antifungal agents, thereby illustrating the potential utility of this approach to antifungal drug discovery.
Author Summary
Candida albicans is the principal human fungal pathogen responsible for life-threatening fungal infections. Despite an urgent need for more efficacious antifungal agents, the pace of discovery has waned using the traditional approaches. In part, this reflects the longstanding limitation of performing mechanism-of-action–based screening directly in those key fungal pathogens for which new antifungal agents are sought. Here we describe an alternative approach, first developed in Saccharomyces cerevisiae and termed the fitness test, to survey approximately 45% of the C. albicans genome for the molecular targets of growth inhibitory compounds. We demonstrate that mechanistically characterized compounds can be used as chemical probes to assist gene function annotations in C. albicans. Similarly, fitness tests performed using newly discovered compounds provide powerful insights into their mechanism of action and therapeutic potential as antifungal agents. Extending this screening paradigm to C. albicans facilitates a pathogen-focused approach to antifungal drug discovery.
doi:10.1371/journal.ppat.0030092
PMCID: PMC1904411  PMID: 17604452
7.  Automated identification of pathways from quantitative genetic interaction data 
We present a novel Bayesian learning method that reconstructs large detailed gene networks from quantitative genetic interaction (GI) data.The method uses global reasoning to handle missing and ambiguous measurements, and provide confidence estimates for each prediction.Applied to a recent data set over genes relevant to protein folding, the learned networks reflect known biological pathways, including details such as pathway ordering and directionality of relationships.The reconstructed networks also suggest novel relationships, including the placement of SGT2 in the tail-anchored biogenesis pathway, a finding that we experimentally validated.
Recent developments have enabled large-scale quantitative measurement of genetic interactions (GIs) that report on the extent to which the activity of one gene is dependent on a second. It has long been recognized (Avery and Wasserman, 1992; Hartman et al, 2001; Segre et al, 2004; Tong et al, 2004; Drees et al, 2005; Schuldiner et al, 2005; St Onge et al, 2007; Costanzo et al, 2010) that functional dependencies revealed by GI data can provide rich information regarding underlying biological pathways. Further, the precise phenotypic measurements provided by quantitative GI data can provide evidence for even more detailed aspects of pathway structure, such as differentiating between full and partial dependence between two genes (Drees et al, 2005; Schuldiner et al, 2005; St Onge et al, 2007; Jonikas et al, 2009) (Figure 1A). As GI data sets become available for a range of quantitative phenotypes and organisms, such patterns will allow researchers to elucidate pathways important to a diverse set of biological processes.
We present a new method that exploits the high-quality, quantitative nature of recent GI assays to automatically reconstruct detailed multi-gene pathway structures, including the organization of a large set of genes into coherent pathways, the connectivity and ordering within each pathway, and the directionality of each relationship. We introduce activity pathway networks (APNs), which represent functional dependencies among a set of genes in the form of a network. We present an automatic method to efficiently reconstruct APNs over large sets of genes based on quantitative GI measurements. This method handles uncertainty in the data arising from noise, missing measurements, and data points with ambiguous interpretations, by performing global reasoning that combines evidence from multiple data points. In addition, because some structure choices remain uncertain even when jointly considering all measurements, our method maintains multiple likely networks, and allows computation of confidence estimates over each structure choice.
We applied our APN reconstruction method to the recent high-quality GI data set of Jonikas et al (2009), which examined the functional interaction between genes that contribute to protein folding in the ER. Specifically, Jonikas et al used the cell's endogenous sensor (the unfolded protein response), to first identify several hundred yeast genes with functions in endoplasmic reticulum folding and then systematically characterized their functional interdependencies by measuring unfolded protein response levels in double mutants. Our analysis produced an ensemble of 500 likelihood-weighted APNs over 178 genes (Figure 2).
We performed an aggregate evaluation of our results by comparing to known biological relationships between gene pairs, including participation in pathways according to the Kyoto Encyclopedia of Genes and Genomes (KEGG), correlation of chemical genomic profiles in a recent high-throughput assay (Hillenmeyer et al, 2008) and similarity of Gene Ontology (GO) annotations. In each evaluation performed, our reconstructed APNs were significantly more consistent with the known relationships than either the raw GI values or the Pearson correlation between profiles of GI values.
Importantly, our approach provides not only an improved means for defining pairs or groups of related genes, but also enables the identification of detailed multi-gene network structures. In many cases, our method successfully reconstructed known cellular pathways, including the ER-associated degradation (ERAD) pathway, and the biosynthesis of N-linked glycans, ranking them among the highest confidence structures. In-depth examination of the learned network structures indicates agreement with many known details of these pathways. In addition, quantitative analysis indicates that our learned APNs are indicative of ordering within KEGG-annotated biological pathways.
Our results also suggest several novel relationships, including placement of uncharacterized genes into pathways, and novel relationships between characterized genes. These include the dependence of the J domain chaperone JEM1 on the PDI homolog MPD1, dependence of the Ubiquitin-recycling enzyme DOA4 on N-linked glycosylation, and the dependence of the E3 Ubiquitin ligase DOA10 on the signal peptidase complex subunit SPC2. Our APNs also place the poorly characterized TPR-containing protein SGT2 upstream of the tail-anchored protein biogenesis machinery components GET3, GET4, and MDY2 (also known as GET5), suggesting that SGT2 has a function in the insertion of tail-anchored proteins into membranes. Consistent with this prediction, our experimental analysis shows that sgt2Δ cells show a defect in localization of the tail-anchored protein GFP-Sed5 from punctuate Golgi structures to a more diffuse pattern, as seen in other genes involved in this pathway.
Our results show that multi-gene, detailed pathway networks can be reconstructed from quantitative GI data, providing a concrete computational manifestation to intuitions that have traditionally accompanied the manual interpretation of such data. Ongoing technological developments in both genetics and imaging are enabling the measurement of GI data at a genome-wide scale, using high-accuracy quantitative phenotypes that relate to a range of particular biological functions. Methods based on RNAi will soon allow collection of similar data for human cell lines and other mammalian systems (Moffat et al, 2006). Thus, computational methods for analyzing GI data could have an important function in mapping pathways involved in complex biological systems including human cells.
High-throughput quantitative genetic interaction (GI) measurements provide detailed information regarding the structure of the underlying biological pathways by reporting on functional dependencies between genes. However, the analytical tools for fully exploiting such information lag behind the ability to collect these data. We present a novel Bayesian learning method that uses quantitative phenotypes of double knockout organisms to automatically reconstruct detailed pathway structures. We applied our method to a recent data set that measures GIs for endoplasmic reticulum (ER) genes, using the unfolded protein response as a quantitative phenotype. The results provided reconstructions of known functional pathways including N-linked glycosylation and ER-associated protein degradation. It also contained novel relationships, such as the placement of SGT2 in the tail-anchored biogenesis pathway, a finding that we experimentally validated. Our approach should be readily applicable to the next generation of quantitative GI data sets, as assays become available for additional phenotypes and eventually higher-level organisms.
doi:10.1038/msb.2010.27
PMCID: PMC2913392  PMID: 20531408
computational biology; genetic interaction; pathway reconstruction; probabilistic methods
8.  Identification of Protein N-Terminal Methyltransferases in Yeast and Humans† 
Biochemistry  2010;49(25):5225-5235.
Protein modification by methylation is important in cellular function. We show here that the Saccharomyces cerevisiae YBR261C/TAE1 gene encodes an N-terminal protein methyltransferase catalyzing the modification of two ribosomal protein substrates, Rpl12ab and Rps25a/Rps25b. The YBR261C/Tae1 protein is conserved across eukaryotes; all of these proteins share sequence similarity with known seven beta strand class I methyltransferases. Wild type yeast cytosol and mouse heart cytosol catalyze the methylation of a synthetic peptide (PPKQQLSKY) that contains the first eight amino acids of the processed N-terminus of Rps25a/Rps25b. However, no methylation of this peptide is seen in yeast cytosol from a ΔYBR261C/tae1 deletion strain. Yeast YBR261C/TAE1 and the human ortholog METTL11A genes were expressed as fusion proteins in Escherichia coli and were shown to be capable of stoichiometrically dimethylating the N-terminus of the synthetic peptide. Furthermore, the YBR261C/Tae1 and METTL11A recombinant proteins methylate variants of the synthetic peptide containing N-terminal alanine and serine residues. However, methyltransferase activity is largely abolished when the proline residue in position 2 or the lysine residue in position 3 is substituted. Thus, the methyltransferases described here specifically recognize the N-terminal X-Pro-Lys sequence motif and we suggest designating the yeast enzyme Ntm1 and the human enzyme NTMT1. These enzymes may account for nearly all previously described eukaryotic protein N-terminal methylation reactions. A number of other yeast and humans proteins also share the recognition motif and may be similarly modified. We conclude that protein X-Pro-Lys N-terminal methylation reactions catalyzed by the enzymes described here may be widespread in nature.
doi:10.1021/bi100428x
PMCID: PMC2890028  PMID: 20481588
9.  Identification of Human Intracellular Targets of the Medicinal Herb St. John’s Wort by Chemical-Genetic Profiling in Yeast 
St. John’s Wort (SJW; Hypericum perforatum L.) is commonly known for its antidepressant properties and was traditionally used to promote wound healing, but its molecular mechanism of action is not known. Here, chemical-genetic profiling in yeast was used to predict the human intracellular targets of an aqueous extract of SJW. SJW source material was authenticated by TLC, digital microscopy, and HPLC, and further characterized by colorimetric methods for antioxidant activity, protein content, and total soluble phenolic content. SJW extract contained 1.76µg/mL hyperforin, 10.14µg/mL hypericin, and 46.05µg/mL pseudohypericin. The effect of SJW extract on ~5900 barcoded heterozygous diploid deletion strains of Saccharomyces cerevisiae was investigated using high-density oligonucleotide microarrays. Seventy-eight (78) yeast genes were identified as sensitive to SJW and were primarily associated with vesicle-mediated transport and signal transduction pathways. Potential human intracellular targets were identified using sequence-based comparisons and included proteins associated with neurological disease and angiogenesis-related pathways. Selected human targets were confirmed by cell-based immunocytochemical assays. The comprehensive and systematic nature of chemical-genetic profiling in yeast makes this technique attractive for elucidating the potential molecular mechanisms of action of botanical medicines and other bioactive dietary plants.
doi:10.1021/jf801593a
PMCID: PMC2645918  PMID: 18975972
St. John’s Wort; Hypericum perforatum; botanical; dietary supplement; yeast; microarray; wound healing; depression; alternative medicine
10.  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.
doi:10.1038/msb4100116
PMCID: PMC1828746  PMID: 17332758
chemical genetics; combinations and synergy; metabolic and regulatory networks; simulation and data analysis
11.  A Systems Biology Approach Reveals the Role of a Novel Methyltransferase in Response to Chemical Stress and Lipid Homeostasis 
PLoS Genetics  2011;7(10):e1002332.
Using small molecule probes to understand gene function is an attractive approach that allows functional characterization of genes that are dispensable in standard laboratory conditions and provides insight into the mode of action of these compounds. Using chemogenomic assays we previously identified yeast Crg1, an uncharacterized SAM-dependent methyltransferase, as a novel interactor of the protein phosphatase inhibitor cantharidin. In this study we used a combinatorial approach that exploits contemporary high-throughput techniques available in Saccharomyces cerevisiae combined with rigorous biological follow-up to characterize the interaction of Crg1 with cantharidin. Biochemical analysis of this enzyme followed by a systematic analysis of the interactome and lipidome of CRG1 mutants revealed that Crg1, a stress-responsive SAM-dependent methyltransferase, methylates cantharidin in vitro. Chemogenomic assays uncovered that lipid-related processes are essential for cantharidin resistance in cells sensitized by deletion of the CRG1 gene. Lipidome-wide analysis of mutants further showed that cantharidin induces alterations in glycerophospholipid and sphingolipid abundance in a Crg1-dependent manner. We propose that Crg1 is a small molecule methyltransferase important for maintaining lipid homeostasis in response to drug perturbation. This approach demonstrates the value of combining chemical genomics with other systems-based methods for characterizing proteins and elucidating previously unknown mechanisms of action of small molecule inhibitors.
Author Summary
Chemical genetics uses small molecules to perturb biological systems to study gene function. By analogy with genetic lesions, chemical probes act as fast-acting, reversible, and “tunable” conditional alleles. Furthermore, small molecules can target multiple protein targets and target pathways simultaneously to uncover phenotypes that may be masked by genes encoding partially redundant proteins. Finally, potent chemical probes can be useful starting points for the development of human therapeutics. Here, we used cantharidin, a natural toxin, to uncover otherwise “hidden” phenotypes for a methyltransferase that has resisted characterization. This enzyme, Crg1, has no phenotype in standard conditions but is indispensible for survival in the presence of cantharidin. Using this chemical genetic relationship, we characterized novel functions of Crg1, and by combining diverse genomic assays with small molecule perturbation we characterized the mechanism of cantharidin cytotoxicity. These observations are relevant beyond yeast Crg1 because cantharidin and its analogues have potent anticancer activity, yet its therapeutic use has been limited to topical applications because of its cytotoxicity. Considering that methyltransferases are an extremely abundant and diverse class of cellular proteins, chemical probes such as cantharidin are critical for understanding their cellular roles and defining potential points of therapeutic intervention.
doi:10.1371/journal.pgen.1002332
PMCID: PMC3197675  PMID: 22028670
12.  Epistatic relationships reveal the functional organization of yeast transcription factors 
A comprehensive quantitative genetic interaction map, or E-MAP, has provided a global view of the functional interdependencies between the components of the transcriptional apparatus in budding yeast.Transcription factors that display aggravating/negative genetic interactions regulate gene expression in an independent rather than coordinated manner.Parallel/compensating relationships between regulators often characterize transcriptional circuits.
Genetic interactions identify the functional interdependencies between genes (Guarente, 1993). They can be either positive (i.e. alleviating) or negative (i.e. aggravating) in nature corresponding to cases where the double mutant grows better or worse, respectively, then expected from growth of the corresponding single mutants (Beyer et al, 2007). Negative genetic interactions between non-essential genes often identify factors involved in parallel pathways, whereas positive ones often correspond to cases where the corresponding proteins are working in the same pathway and/or are physically associated (Beltrao et al, 2010). The epistatic miniarray profile (E-MAP) approach (Schuldiner et al, 2005), which quantitatively and comprehensively identifies both positive and negative genetic interactions on a logically selected set of genes, was used in this study in S. cerevisiae to genetically interrogate the set of 151 sequence-specific transcription factors (STFs) as well as 172 components of the general transcriptional machinery (GTFs).
We found a higher propensity of the group of STFs to strongly genetically interact with GTFs than with themselves (Figure 1A and B). However, within the set of STF–STF genetic interactions, there was a significant enrichment of negative over positive genetic interactions (Figure 1A and C), suggesting that parallel/compensating relationships, rather than linear pathways, predominate within the set of STFs. These genetic trends are in stark contrast to what was previously observed with factors involved in regulating signaling (e.g. kinases and phosphatases), which were significantly enriched in positive over negative genetic interactions (Fiedler et al, 2009).
In addition to providing an overview of the global relationships among TFs, the fine structure of the E-MAP can be used to address the nature of the regulatory architecture controlling individual genes. A variety of regulatory patterns have been described that serve the differing functional requirements of various biological processes (Istrail and Davidson, 2005). Our E-MAP identified several examples of the regulatory relationships between transcription factors, including (1) one TF acting as a repressor of another TF (e.g. Gal80 acting as a repressor of Gal4, the activator of the GAL genes); (2) two TFs acting redundantly to regulate a set of genes (e.g. Gln3 and Gat1, two GATA family activators involved in regulating nitrogen catabolite repression (NCR)); and (3) two TFs regulating genes in a coordinated manner (e.g. Hac1 working with the HDAC complex Rpd3C(L) to regulate expression of early meiotic genes).
Given the complex structures of promoters (Zhu and Zhang, 1999; Chin et al, 2005) and the possible types of regulatory logic (Buchler et al, 2003), we wanted to identify the types of logic that are used in nature. We explored this by combining our genetic interaction data with the information about the network connections between STFs and their targets. By initially focusing on pairs of STFs that share a set of targets defined by the genome-wide binding studies (Harbison et al, 2004; MacIsaac et al, 2006), a total of 110 STF gene pairs were identified that have statistically significant target overlap with a P-value <0.005, whereas 49 pairs have significant overlap at a more stringent cutoff (P<10−7). Several examples were examined in more detail by quantitative growth assay in liquid culture and gene expression profiling of the TF-deletion mutants. In each case, the growth rate of one of the single-deletion mutants is significantly reduced (i.e. ‘the major regulator'), whereas the growth rate of the other single-deletion mutant is similar to that of the wild type (i.e. ‘the minor regulator'). In the absence of the major regulator, the deletion of the minor regulator leads to a more severe growth defect, resulting in a negative genetic interaction (Figure 5A). We examined the response of common target genes of two pairs of TFs (Swi4-Skn7 and Gcr2-Tye7) and found an enrichment of common target genes displaying ‘OR' but not ‘AND' behavior, in the simplified language of Boolean logic. Further examination of the targets revealed that many of them are induced/repressed more by the double deletion than each of the single deletions (Figure 5D). Collectively, these results suggest that frequently TF pairs with negative interactions regulate the transcription of their common target genes in a redundant manner.
The regulation of gene expression is, in large part, mediated by interplay between the general transcription factors (GTFs) that function to bring about the expression of many genes and site-specific DNA-binding transcription factors (STFs). Here, quantitative genetic profiling using the epistatic miniarray profile (E-MAP) approach allowed us to measure 48 391 pairwise genetic interactions, both negative (aggravating) and positive (alleviating), between and among genes encoding STFs and GTFs in Saccharomyces cerevisiae. This allowed us to both reconstruct regulatory models for specific subsets of transcription factors and identify global epistatic patterns. Overall, there was a much stronger preference for negative relative to positive genetic interactions among STFs than there was among GTFs. Negative genetic interactions, which often identify factors working in non-essential, redundant pathways, were also enriched for pairs of STFs that co-regulate similar sets of genes. Microarray analysis demonstrated that pairs of STFs that display negative genetic interactions regulate gene expression in an independent rather than coordinated manner. Collectively, these data suggest that parallel/compensating relationships between regulators, rather than linear pathways, often characterize transcriptional circuits.
doi:10.1038/msb.2010.77
PMCID: PMC2990640  PMID: 20959818
genetic interaction; regulatory network; transcription factor; transcription regulation
13.  Regulatory and metabolic rewiring during laboratory evolution of ethanol tolerance in E. coli 
We have designed an experimental/computational framework for studying complex phenotypes in bacteria.Our framework relies on whole-genome fitness profiling coupled with a module-level analysis to discover pathways that directly affect fitness.As a proof-of-principle, we studied ethanol tolerance in Escherichia coli and we identified key pathways that contribute to this phenotype.We then validated our findings through genetic manipulations, gene-expression profiling, metabolite-level measurements, and stable-isotope labeling.
Elucidating the genetic basis of complex phenotypes remains a fundamental challenge in biology. We have developed a systematic framework for comprehensive genetic analysis of microbial phenotypes. Our approach combines the power of fitness profiling (Girgis et al, 2007; Amini et al, 2009) with the sensitivity of module-level analysis (Goodarzi et al, 2009a) to identify key genetic modules that directly affect a phenotype under study. We applied our technology to ethanol tolerance, a complex phenotype with broad industrial relevance. Ethanol affects a variety of cellular components and pathways, including but not limited to membrane integrity (Dombek and Ingram, 1984), enzyme activities (Millar et al, 1982), and proton flux (D'Amore et al, 1990). Given the diversity of targets, the emergence of ethanol tolerance requires modifications to multiple pathway (D'Amore and Stewart, 1987).
To reveal the genetic basis of ethanol tolerance in Escherichia coli, we used two high-coverage mutant libraries (a transposon library and an overexpression library) to assess the fitness consequences of single-locus perturbations. Each cell in our transposon library contains a random transposon insertion in its genome (Girgis et al, 2007); whereas the cells in the overexpression library carry 1–3 kb genomic fragments cloned into a cloning vector (Amini et al, 2009). We grew these libraries under mild (4% v/v) and harsh (5.5% v/v) ethanol concentrations. On growth, the abundance of each transposon insertion or overexpression mutant changes as a function of its fitness, a process that can be monitored through parallel genetic footprinting and microarray hybridization (Figure 1A). This results in a global fitness profile, where the contribution of each genetic locus to ethanol tolerance can be quantified in parallel. However, in the context of ethanol tolerance and other complex phenotypes, single-locus perturbations typically result in modest changes in fitness. Although these small differences can be amplified through multiple rounds of selection, the number of generations is limited as spontaneous beneficial mutations emerge in the population and cause strong biases in the resulting fitness profiles. To boost our analytical power without introducing these biases in the data, we used a module-level computational method to discover the pathways and components that are strongly associated with the data as opposed to focusing on the genes individually (Goodarzi et al, 2009a). Genes function in the context of pathways and modules and module-level analyses increase statistical power through combining information from multiple genes functioning as part of a given pathway (Subramanian et al, 2005).
The module-level analysis of the fitness scores from both libraries revealed a diverse set of pathways that have a direct function in ethanol tolerance. Some of these pathways, including heat-shock stress response and osmoregulation, are known modifiers of ethanol tolerance; whereas others such as acid-stress response and fimbrial structures are novel pathways. Among our findings was the important function of three regulatory proteins: FNR, ArcA, and CafA. Knocking out FNR/ArcA that upregulates aerobic respiration proteins and TCA cycle components results in a marked increase in ethanol tolerance. Similarly, knocking out CafA, a post-transcriptional regulator of alcohol dehydrogenase, is beneficial for tolerance. Given these observations, we hypothesized that selection for ethanol tolerance can result in higher ethanol degradation.
As a large fraction of discovered pathways belonged to central metabolism, we used metabolomics to evaluate our findings. To directly assess the metabolic consequences of adaptation to ethanol, we evolved ethanol-tolerant strains in minimal media plus glucose for ∼30 and 160 generations. We then compared the steady-state level of metabolites in these strains to that of the wild type (Figure 1B). In agreement with our fitness profiling results, we observed a significant increase in TCA cycle metabolites in one of our ethanol-tolerant strains. Higher concentrations of TCA cycle components along with a high free coenzyme A (CoA) to acetyl-coenzyme A (acetyl-CoA) ratio hinted at the capacity of this strain to metabolize ethanol. To test this hypothesis, we performed stable-isotope labeling on our ethanol-tolerant strain versus wild type. After growth on labeled ethanol, we measured the fraction of metabolites that were labeled at each timepoint (Figure 1B). Our results confirmed that the ethanol-tolerant strain has the capacity to consume ethanol through its conversion into acetyl-CoA and further assimilation in the TCA cycle.
By using a variety of systems-level approaches, we have been able to genetically dissect ethanol tolerance in E. coli. We have shown that fitness profiling, in combination with module-level analysis tools, can serve as a powerful approach for revealing the genetic basis of complex phenotypes. The fact that laboratory evolution ended up using the very modules that we discovered, highlights the biological and adaptive relevance of the proposed framework.
Understanding the genetic basis of adaptation is a central problem in biology. However, revealing the underlying molecular mechanisms has been challenging as changes in fitness may result from perturbations to many pathways, any of which may contribute relatively little. We have developed a combined experimental/computational framework to address this problem and used it to understand the genetic basis of ethanol tolerance in Escherichia coli. We used fitness profiling to measure the consequences of single-locus perturbations in the context of ethanol exposure. A module-level computational analysis was then used to reveal the organization of the contributing loci into cellular processes and regulatory pathways (e.g. osmoregulation and cell-wall biogenesis) whose modifications significantly affect ethanol tolerance. Strikingly, we discovered that a dominant component of adaptation involves metabolic rewiring that boosts intracellular ethanol degradation and assimilation. Through phenotypic and metabolomic analysis of laboratory-evolved ethanol-tolerant strains, we investigated naturally accessible pathways of ethanol tolerance. Remarkably, these laboratory-evolved strains, by and large, follow the same adaptive paths as inferred from our coarse-grained search of the fitness landscape.
doi:10.1038/msb.2010.33
PMCID: PMC2913397  PMID: 20531407
adaptation; ethanol tolerance; evolution; fitness profiling
14.  Identification of Two Legionella pneumophila Effectors that Manipulate Host Phospholipids Biosynthesis 
PLoS Pathogens  2012;8(11):e1002988.
The intracellular pathogen Legionella pneumophila translocates a large number of effector proteins into host cells via the Icm/Dot type-IVB secretion system. Some of these effectors were shown to cause lethal effect on yeast growth. Here we characterized one such effector (LecE) and identified yeast suppressors that reduced its lethal effect. The LecE lethal effect was found to be suppressed by the over expression of the yeast protein Dgk1 a diacylglycerol (DAG) kinase enzyme and by a deletion of the gene encoding for Pah1 a phosphatidic acid (PA) phosphatase that counteracts the activity of Dgk1. Genetic analysis using yeast deletion mutants, strains expressing relevant yeast genes and point mutations constructed in the Dgk1 and Pah1 conserved domains indicated that LecE functions similarly to the Nem1-Spo7 phosphatase complex that activates Pah1 in yeast. In addition, by using relevant yeast genetic backgrounds we examined several L. pneumophila effectors expected to be involved in phospholipids biosynthesis and identified an effector (LpdA) that contains a phospholipase-D (PLD) domain which caused lethal effect only in a dgk1 deletion mutant of yeast. Additionally, LpdA was found to enhance the lethal effect of LecE in yeast cells, a phenomenon which was found to be dependent on its PLD activity. Furthermore, to determine whether LecE and LpdA affect the levels or distribution of DAG and PA in-vivo in mammalian cells, we utilized fluorescent DAG and PA biosensors and validated the notion that LecE and LpdA affect the in-vivo levels and distribution of DAG and PA, respectively. Finally, we examined the intracellular localization of both LecE and LpdA in human macrophages during L. pneumophila infection and found that both effectors are localized to the bacterial phagosome. Our results suggest that L. pneumophila utilize at least two effectors to manipulate important steps in phospholipids biosynthesis.
Author Summary
Legionella pneumophila is an intracellular pathogen that causes a severe pneumonia known as Legionnaires' disease. Following infection, the bacteria use a Type-IVB secretion system to translocate multiple effector proteins into macrophages and generate the Legionella-containing vacuole (LCV). The formation of the LCV involves the recruitment of specific bacterial effectors and host cell factors to the LCV as well as changes in its lipids composition. By screening L. pneumophila effectors for yeast growth inhibition, we have identified an effector, named LecE, that strongly inhibits yeast growth. By using yeast genetic tools, we found that LecE activates the yeast lipin homolog – Pah1, an enzyme that catalyzes the conversion of diacylglycerol to phosphatidic acid, these two molecules function as bioactive lipid signaling molecules in eukaryotic cells. In addition, by using yeast deletion mutants in genes relevant to lipids biosynthesis, we have identified another effector, named LpdA, which function as a phospholipase-D enzyme. Both effectors were found to be localized to the LCV during infection. Our results reveal a possible mechanism by which an intravacuolar pathogen might change the lipid composition of the vacuole in which it resides, a process that might lead to the recruitment of specific bacterial and host cell factors to the vacoule.
doi:10.1371/journal.ppat.1002988
PMCID: PMC3486869  PMID: 23133385
15.  Transcriptional Regulation of Chemical Diversity in Aspergillus fumigatus by LaeA 
PLoS Pathogens  2007;3(4):e50.
Secondary metabolites, including toxins and melanins, have been implicated as virulence attributes in invasive aspergillosis. Although not definitively proved, this supposition is supported by the decreased virulence of an Aspergillus fumigatus strain, ΔlaeA, that is crippled in the production of numerous secondary metabolites. However, loss of a single LaeA-regulated toxin, gliotoxin, did not recapitulate the hypovirulent ΔlaeA pathotype, thus implicating other toxins whose production is governed by LaeA. Toward this end, a whole-genome comparison of the transcriptional profile of wild-type, ΔlaeA, and complemented control strains showed that genes in 13 of 22 secondary metabolite gene clusters, including several A. fumigatus–specific mycotoxin clusters, were expressed at significantly lower levels in the ΔlaeA mutant. LaeA influences the expression of at least 9.5% of the genome (943 of 9,626 genes in A. fumigatus) but positively controls expression of 20% to 40% of major classes of secondary metabolite biosynthesis genes such as nonribosomal peptide synthetases (NRPSs), polyketide synthases, and P450 monooxygenases. Tight regulation of NRPS-encoding genes was highlighted by quantitative real-time reverse-transcription PCR analysis. In addition, expression of a putative siderophore biosynthesis NRPS (NRPS2/sidE) was greatly reduced in the ΔlaeA mutant in comparison to controls under inducing iron-deficient conditions. Comparative genomic analysis showed that A. fumigatus secondary metabolite gene clusters constitute evolutionarily diverse regions that may be important for niche adaptation and virulence attributes. Our findings suggest that LaeA is a novel target for comprehensive modification of chemical diversity and pathogenicity.
Author Summary
Patients with suppressed immune systems due to cancer treatments, HIV/AIDS, or organ transplantation are at high risk of infection from microbes. Some of the most deadly infections for such patients arise from a fungal pathogen, Aspergillus fumigatus. This species, like several of its close relatives, can produce an array of small chemical compounds that influences both the infection process and its environmental niche outside of the host. The genes dedicated to production of each compound are clustered adjacent to each other in the genome. One protein named LaeA is a master regulator of such clustered small molecule genes, and removal of the gene encoding LaeA cripples the organism's ability to infect. We conducted a genome-wide microarray experiment to identify small molecule gene clusters controlled by the presence of LaeA in A. fumigatus. In doing so, we identified actively expressed gene clusters critical for small molecule production and potentially involved in disease progression. These results also provide insight into evolutionary events shaping the organism's collection of chemical compounds.
doi:10.1371/journal.ppat.0030050
PMCID: PMC1851976  PMID: 17432932
16.  The Antifungal Eugenol Perturbs Dual Aromatic and Branched-Chain Amino Acid Permeases in the Cytoplasmic Membrane of Yeast 
PLoS ONE  2013;8(10):e76028.
Eugenol is an aromatic component of clove oil that has therapeutic potential as an antifungal drug, although its mode of action and precise cellular target(s) remain ambiguous. To address this knowledge gap, a chemical-genetic profile analysis of eugenol was done using ∼4700 haploid Saccharomyces cerevisiae gene deletion mutants to reveal 21 deletion mutants with the greatest degree of susceptibility. Cellular roles of deleted genes in the most susceptible mutants indicate that the main targets for eugenol include pathways involved in biosynthesis and transport of aromatic and branched-chain amino acids. Follow-up analyses showed inhibitory effects of eugenol on amino acid permeases in the yeast cytoplasmic membrane. Furthermore, phenotypic suppression analysis revealed that eugenol interferes with two permeases, Tat1p and Gap1p, which are both involved in dual transport of aromatic and branched-chain amino acids through the yeast cytoplasmic membrane. Perturbation of cytoplasmic permeases represents a novel antifungal target and may explain previous observations that exposure to eugenol results in leakage of cell contents. Eugenol exposure may also contribute to amino acid starvation and thus holds promise as an anticancer therapeutic drug. Finally, this study provides further evidence of the usefulness of the yeast Gene Deletion Array approach in uncovering the mode of action of natural health products.
doi:10.1371/journal.pone.0076028
PMCID: PMC3799837  PMID: 24204588
17.  Chemical combinations elucidate pathway interactions and regulation relevant to Hepatitis C replication 
SREBP-2, oxidosqualene cyclase (OSC) or lanosterol demethylase were identified as novel sterol pathway-associated targets that, when probed with chemical agents, can inhibit hepatitis C virus (HCV) replication.Using a combination chemical genetics approach, combinations of chemicals targeting sterol pathway enzymes downstream of and including OSC or protein geranylgeranyl transferase I (PGGT) produce robust and selective synergistic inhibition of HCV replication. Inhibition of enzymes upstream of OSC elicit proviral responses that are dominant to the effects of inhibiting all downstream targets.Inhibition of the sterol pathway without inhibition of regulatory feedback mechanisms ultimately results in an increase in HCV replication because of a compensatory upregulation of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) expression. Increases in HMGCR expression without inhibition of HMGCR enzymatic activity ultimately stimulate HCV replication through increasing the cellular pool of geranylgeranyl pyrophosphate (GGPP).Chemical inhibitors that ultimately prevent SREBP-2 activation, inhibit PGGT or encourage the production of polar sterols have great potential as HCV therapeutics if associated toxicities can be reduced.
Chemical inhibition of enzymes in either the cholesterol or the fatty acid biosynthetic pathways has been shown to impact viral replication, both positively and negatively (Su et al, 2002; Ye et al, 2003; Kapadia and Chisari, 2005; Sagan et al, 2006; Amemiya et al, 2008). FBL2 has been identified as a 50 kDa geranylgeranylated host protein that is necessary for localization of the hepatitis C virus (HCV) replication complex to the membranous web through its close association with the HCV protein NS5A and is critical for HCV replication (Wang et al, 2005). Inhibition of the protein geranylgeranyl transferase I (PGGT), an enzyme that transfers geranylgeranyl pyrophosphate (GGPP) to cellular proteins such as FBL2 for the purpose of membrane anchoring, negatively impacts HCV replication (Ye et al, 2003). Conversely, chemical agents that increase intracellular GGPP concentrations promote viral replication (Kapadia and Chisari, 2005). Statin compounds that inhibit 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), the rate-limiting enzyme in the sterol pathway (Goldstein and Brown, 1990), have been suggested to inhibit HCV replication through ultimately reducing the cellular pool of GGPP (Ye et al, 2003; Kapadia and Chisari, 2005; Ikeda et al, 2006). However, inhibition of the sterol pathway with statin drugs has not yielded consistent results in patients. The use of statins for the treatment of HCV is likely to be complicated by the reported compensatory increase in HMGCR expression in vitro and in vivo (Stone et al, 1989; Cohen et al, 1993) in response to treatment. Enzymes in the sterol pathway are regulated on a transcriptional level by sterol regulatory element-binding proteins (SREBPs), specifically SREBP-2 (Hua et al, 1993; Brown and Goldstein, 1997). When cholesterol stores in cells are depleted, SREBP-2 activates transcription of genes in the sterol pathway such as HMGCR, HMG-CoA synthase, farnesyl pyrophosphate (FPP) synthase, squalene synthase (SQLS) and the LDL receptor (Smith et al, 1988, 1990; Sakai et al, 1996; Brown and Goldstein, 1999; Horton et al, 2002). The requirement of additional downstream sterol pathway metabolites for HCV replication has not been completely elucidated.
To further understand the impact of the sterol pathway and its regulation on HCV replication, we conducted a high-throughput combination chemical genetic screen using 16 chemical probes that are known to modulate the activity of target enzymes relating to the sterol biosynthesis pathway (Figure 1). Using this approach, we identified several novel antiviral targets including SREBP-2 as well as targets downstream of HMGCR in the sterol pathway such as oxidosqualene cyclase (OSC) and lanosterol demethylase. Many of our chemical probes, specifically SR-12813, farnesol and squalestatin, strongly promoted replicon replication. The actions of both farnesol and squalestatin ultimately result in an increase in the cellular pool of GGPP, which is known to increase HCV replication (Ye et al, 2003; Kapadia and Chisari, 2005; Wang et al, 2005).
Chemical combinations targeting enzymes upstream of squalene epoxidase (SQLE) at the top of the sterol pathway (Figure 4A) elicited Bateson-type epistatic responses (Boone et al, 2007), where the upstream agent's response predominates over the effects of inhibiting all downstream targets. This was especially notable for combinations including simvastatin and either U18666A or squalestatin, and for squalestatin in combination with Ro48-8071. Treatment with squalestatin prevents the SQLS substrate, farnesyl pyrophosphate (FPP) from being further metabolized by the sterol pathway. As FPP concentrations increase, the metabolite can be shunted away from the sterol pathway toward farnesylation and GGPP synthetic pathways, resulting in an increase in host protein geranylgeranylation, including FBL2, and consequently replicon replication. This increase in replicon replication explains the source of the observed epistasis over Ro48-8071 treatment.
Combinations between probes targeting enzymes downstream of and including OSC produced robust synergies with each other or with a PGGT inhibitor. Figure 4B highlights examples of antiviral synergy resulting from treatment of cells with an OSC inhibitor in combination with an inhibitor of either an enzyme upstream or downstream of OSC. A combination of terconazole and U18666A is synergistic without similar combination effects in the host proliferation screen. Likewise, clomiphene was also synergistic when added to replicon cells in combination with U18666A. One of the greatest synergies observed downstream in the sterol pathway is a combination of amorolfine and AY 9944, suggesting that there is value in developing combinations of drugs that target enzymes in the sterol pathway, which are downstream of HMGCR.
Interactions with the protein prenylation pathway also showed strong mechanistic patterns (Figure 4C). GGTI-286 is a peptidomimetic compound resembling the CAAX domain of a protein to be geranylgeranylated and is a competitive inhibitor of protein geranylgeranylation. Simvastatin impedes the antiviral effect of GGTI-286 at low concentrations but that antagonism is balanced by comparable synergy at higher concentrations. At the low simvastatin concentrations, a compensatory increase in HMGCR expression leads to increased cellular levels of GGPP, which are likely to result in an increase in PGGT enzymatic turnover and decreased GGTI-286 efficacy. The antiviral synergy observed at the higher inhibitor concentrations is likely nonspecific as synergy was also observed in a host viability assay. Further downstream, however, a competitive interaction was observed between GGTI-286 and squalestatin, where the opposing effect of one compound obscures the other compound's effect. This competitive relationship between GGTI and SQLE explains the epistatic response observed between those two agents. For inhibitors of targets downstream of OSC, such as amorolfine, there are strong antiviral synergies with GGTI-286. Notably, combinations with OSC inhibitors and GGTI-286 were selective, in that comparable synergy was not found in a host viability assay. This selectivity suggests that jointly targeting OSC and PGGT is a promising avenue for future HCV therapy development.
This study provides a comprehensive and unique perspective into the impact of sterol pathway regulation on HCV replication and provides compelling insight into the use of chemical combinations to maximize antiviral effects while minimizing proviral consequences. Our results suggest that HCV therapeutics developed against sterol pathway targets must consider the impact on underlying sterol pathway regulation. We found combinations of inhibitors of the lower part of the sterol pathway that are effective and synergistic with each other when tested in combination. Furthermore, the combination effects observed with simvastatin suggest that, though statins inhibit HMGCR activity, the resulting regulatory consequences of such inhibition ultimately lead to undesirable epistatic effects. Inhibitors that prevent SREBP-2 activation, inhibit PGGT or encourage the production of polar sterols have great potential as HCV therapeutics if associated toxicities can be reduced.
The search for effective Hepatitis C antiviral therapies has recently focused on host sterol metabolism and protein prenylation pathways that indirectly affect viral replication. However, inhibition of the sterol pathway with statin drugs has not yielded consistent results in patients. Here, we present a combination chemical genetic study to explore how the sterol and protein prenylation pathways work together to affect hepatitis C viral replication in a replicon assay. In addition to finding novel targets affecting viral replication, our data suggest that the viral replication is strongly affected by sterol pathway regulation. There is a marked transition from antagonistic to synergistic antiviral effects as the combination targets shift downstream along the sterol pathway. We also show how pathway regulation frustrates potential hepatitis C therapies based on the sterol pathway, and reveal novel synergies that selectively inhibit hepatitis C replication over host toxicity. In particular, combinations targeting the downstream sterol pathway enzymes produced robust and selective synergistic inhibition of hepatitis C replication. Our findings show how combination chemical genetics can reveal critical pathway connections relevant to viral replication, and can identify potential treatments with an increased therapeutic window.
doi:10.1038/msb.2010.32
PMCID: PMC2913396  PMID: 20531405
chemical genetics; combinations and synergy; hepatitis C; replicon; sterol biosynthesis
18.  SCS3 and YFT2 Link Transcription of Phospholipid Biosynthetic Genes to ER Stress and the UPR 
PLoS Genetics  2012;8(8):e1002890.
The ability to store nutrients in lipid droplets (LDs) is an ancient function that provides the primary source of metabolic energy during periods of nutrient insufficiency and between meals. The Fat storage-Inducing Transmembrane (FIT) proteins are conserved ER–resident proteins that facilitate fat storage by partitioning energy-rich triglycerides into LDs. FIT2, the ancient ortholog of the FIT gene family first identified in mammals has two homologs in Saccharomyces cerevisiae (SCS3 and YFT2) and other fungi of the Saccharomycotina lineage. Despite the coevolution of these genes for more than 170 million years and their divergence from higher eukaryotes, SCS3, YFT2, and the human FIT2 gene retain some common functions: expression of the yeast genes in a human embryonic kidney cell line promotes LD formation, and expression of human FIT2 in yeast rescues the inositol auxotrophy and chemical and genetic phenotypes of strains lacking SCS3. To better understand the function of SCS3 and YFT2, we investigated the chemical sensitivities of strains deleted for either or both genes and identified synthetic genetic interactions against the viable yeast gene-deletion collection. We show that SCS3 and YFT2 have shared and unique functions that connect major biosynthetic processes critical for cell growth. These include lipid metabolism, vesicular trafficking, transcription of phospholipid biosynthetic genes, and protein synthesis. The genetic data indicate that optimal strain fitness requires a balance between phospholipid synthesis and protein synthesis and that deletion of SCS3 and YFT2 impacts a regulatory mechanism that coordinates these processes. Part of this mechanism involves a role for SCS3 in communicating changes in the ER (e.g. due to low inositol) to Opi1-regulated transcription of phospholipid biosynthetic genes. We conclude that SCS3 and YFT2 are required for normal ER membrane biosynthesis in response to perturbations in lipid metabolism and ER stress.
Author Summary
The ability to form lipid droplets is a conserved property of eukaryotic cells that allows the storage of excess metabolic energy in a form that can be readily accessed. In adipose tissue, the storage of excess calories in lipid droplets normally protects other tissues from lipotoxicity and insulin resistance, but this protection is lost with chronic over-nutrition. The FAT storage-inducing transmembrane (FIT) proteins were recently identified as a conserved family of proteins that reside in the lipid bilayer of the endoplasmic reticulum and are implicated in lipid droplet formation. In this work we show that specific functions of the FIT proteins are conserved between yeast and humans and that SCS3 and YFT2, the yeast homologs of mammalian FIT2, are part of a large genetic interaction network connecting lipid metabolism, vesicle trafficking, transcription, and protein synthesis. From these interactions we determined that yeast strains lacking SCS3 and YFT2 are defective in their response to chronic ER stress and cannot induce the unfolded protein response pathway or transcription of phospholipid biosynthetic genes in low inositol. Our findings suggest that the mammalian FIT genes may play an important role in ER stress pathways, which are linked to obesity and type 2 diabetes.
doi:10.1371/journal.pgen.1002890
PMCID: PMC3426550  PMID: 22927826
19.  Detection of compound mode of action by computational integration of whole-genome measurements and genetic perturbations 
BMC Bioinformatics  2006;7:51.
Background
A key problem of drug development is to decide which compounds to evaluate further in expensive clinical trials (Phase I- III). This decision is primarily based on the primary targets and mechanisms of action of the chemical compounds under consideration. Whole-genome expression measurements have shown to be useful for this process but current approaches suffer from requiring either a large number of mutant experiments or a detailed understanding of the regulatory networks.
Results
We have designed an algorithm, CutTree that when applied to whole-genome expression datasets identifies the primary affected genes (PAGs) of a chemical compound by separating them from downstream, indirectly affected genes. Unlike previous methods requiring whole-genome deletion libraries or a complete map of gene network architecture, CutTree identifies PAGs from a limited set of experimental perturbations without requiring any prior information about the underlying pathways. The principle for CutTree is to iteratively filter out PAGs from other recurrently active genes (RAGs) that are not PAGs. The in silico validation predicted that CutTree should be able to identify 3–4 out of 5 known PAGs (~70%). In accordance, when we applied CutTree to whole-genome expression profiles from 17 genetic perturbations in the presence of galactose in Yeast, CutTree identified four out of five known primary galactose targets (80%). Using an exhaustive search strategy to detect these PAGs would not have been feasible (>1012 combinations).
Conclusion
In combination with genetic perturbation techniques like short interfering RNA (siRNA) followed by whole-genome expression measurements, CutTree sets the stage for compound target identification in less well-characterized but more disease-relevant mammalian cell systems.
doi:10.1186/1471-2105-7-51
PMCID: PMC1403807  PMID: 16451737
20.  Structure and Biosynthesis of Heat-Stable Antifungal Factor (HSAF), a Broad-Spectrum Antimycotic with a Novel Mode of Action▿  
A screen for antifungal compounds from Lysobacter enzymogenes strain C3, a bacterial biological control agent of fungal diseases, has previously led to the isolation of heat-stable antifungal factor (HSAF). HSAF exhibits inhibitory activities against a wide range of fungal species and shows a novel mode of antifungal action by disrupting the biosynthesis of a distinct group of sphingolipids. We have now determined the chemical structure of HSAF, which is identical to that of dihydromaltophilin, an antifungal metabolite with a unique macrocyclic lactam system containing a tetramic acid moiety and a 5,5,6-tricyclic skeleton. We have also identified the genetic locus responsible for the biosynthesis of HSAF in strain C3. DNA sequencing of this locus revealed genes for a hybrid polyketide synthase-nonribosomal peptide synthetase (PKS-NRPS), a sterol desaturase, a ferredoxin reductase, and an arginase. The disruption of the PKS-NRPS gene generated C3 mutants that lost the ability to produce HSAF and to inhibit fungal growth, demonstrating a hybrid PKS-NRPS that catalyzed the biosynthesis of the unique macrolactam system that is found in many biologically active natural products isolated from marine organisms. In addition, we have generated mutants with disrupted sterol desaturase, ferredoxin reductase, and arginase and examined the metabolites produced in these mutants. The work represents the first study of the genetic basis for the biosynthesis of the tetramic acid-containing macrolactams. The elucidation of the chemical structure of HSAF and the identification of the genetic locus for its biosynthesis establish the foundation for future exploitation of this group of compounds as new fungicides or antifungal drugs.
doi:10.1128/AAC.00931-06
PMCID: PMC1797680  PMID: 17074795
21.  Robustness and evolvability in natural chemical resistance: identification of novel systems properties, biochemical mechanisms and regulatory interactions 
Molecular bioSystems  2010;6(8):1475-1491.
A vast amount of data on the natural resistance of Saccharomyces cerevisiae to a diverse array of chemicals has been generated over the past decade (chemical genetics). We endeavored to use this data to better characterize the “systems” level properties of this phenomenon. By collating data from over 30 different genome-scale studies on growth of gene deletion mutants in presence of diverse chemicals, we assembled the largest currently available gene-chemical network. We also derived a second gene-gene network that links genes with significantly overlapping chemical-genetic profiles. We analyzed properties of these networks and investigated their significance by overlaying various sources of information, such as presence of TATA boxes in their promoters (which typically correlate with transcriptional noise), association with TFIID or SAGA, and propensity to function as phenotypic capacitors. We further combined these networks with ubiquitin and protein kinase-substrate networks to understand chemical tolerance in the context of major post-translational regulatory processes. Hubs in the gene-chemical network (multidrug resistance genes) are notably enriched for phenotypic capacitors (buffers against phenotypic variation), suggesting the generality of these players in buffering mechanistically unrelated deleterious forces impinging on the cell. More strikingly, analysis of the gene-gene network derived from the gene-chemical network uncovered another set of genes that appear to function in providing chemical tolerance in a cooperative manner. These appear to be enriched in lineage-specific and rapidly diverging members that also show a corresponding tendency for SAGA-dependent regulation, evolutionary divergence and noisy expression patterns. This set represents a previously underappreciated component of the chemical response that enables cells to explore alternative survival strategies. Thus, systems robustness and evolvability are simultaneously active as general forces in tolerating environmental variation. We also recover the actual genes involved in the above-discussed network properties and predict the biochemistry of their products. Certain key components of the ubiquitin system (e.g. Rcy1, Wss1 and Ubp16), peroxisome recycling (e.g. Irs4) and phosphorylation cascades (e.g. NPR1, MCK1 and HOG) are major participants and regulators of chemical resistance. We also show that a major subnetwork boosting mitochondrial protein synthesis is important for exploration of alternative survival strategies under chemical stress. Further, we find evidence that cellular exploration of survival strategies under chemical stress and secondary metabolism draw from a common pool of biochemical players (e.g. acetyltransferases and a novel NTN hydrolase).
doi:10.1039/c002567b
PMCID: PMC3236069  PMID: 20517567
22.  Cell Cycle-Independent Phospho-Regulation of Fkh2 during Hyphal Growth Regulates Candida albicans Pathogenesis 
PLoS Pathogens  2015;11(1):e1004630.
The opportunistic human fungal pathogen, Candida albicans, undergoes morphological and transcriptional adaptation in the switch from commensalism to pathogenicity. Although previous gene-knockout studies have identified many factors involved in this transformation, it remains unclear how these factors are regulated to coordinate the switch. Investigating morphogenetic control by post-translational phosphorylation has generated important regulatory insights into this process, especially focusing on coordinated control by the cyclin-dependent kinase Cdc28. Here we have identified the Fkh2 transcription factor as a regulatory target of both Cdc28 and the cell wall biosynthesis kinase Cbk1, in a role distinct from its conserved function in cell cycle progression. In stationary phase yeast cells 2D gel electrophoresis shows that there is a diverse pool of Fkh2 phospho-isoforms. For a short window on hyphal induction, far before START in the cell cycle, the phosphorylation profile is transformed before reverting to the yeast profile. This transformation does not occur when stationary phase cells are reinoculated into fresh medium supporting yeast growth. Mass spectrometry and mutational analyses identified residues phosphorylated by Cdc28 and Cbk1. Substitution of these residues with non-phosphorylatable alanine altered the yeast phosphorylation profile and abrogated the characteristic transformation to the hyphal profile. Transcript profiling of the phosphorylation site mutant revealed that the hyphal phosphorylation profile is required for the expression of genes involved in pathogenesis, host interaction and biofilm formation. We confirmed that these changes in gene expression resulted in corresponding defects in pathogenic processes. Furthermore, we identified that Fkh2 interacts with the chromatin modifier Pob3 in a phosphorylation-dependent manner, thereby providing a possible mechanism by which the phosphorylation of Fkh2 regulates its specificity. Thus, we have discovered a novel cell cycle-independent phospho-regulatory event that subverts a key component of the cell cycle machinery to a role in the switch from commensalism to pathogenicity.
Author Summary
The fungus Candida albicans is a commensal in the human microbiota, responsible for superficial infections such as oral and vaginal thrush. However, it can become highly virulent, causing life-threatening systemic candidemia in severely immunocompromised patients, including those taking immunosuppressive drugs for transplantation, sufferers of AIDS and neutropenia, and individuals undergoing chemotherapy or at extremes of age. With a rapidly increasing ageing population worldwide, C. albicans and other fungal pathogens will become more prevalent, demanding a greater understanding of their pathogenesis for the development of effective therapeutics. Fungal pathogenicity requires a coordinated change in the pattern of gene expression orchestrated by a set of transcription factors. Here we have discovered that a transcription factor, Fkh2, is modified by phosphorylation under the control of the kinases Cdc28 and Cbk1 in response to conditions that activate virulence factor expression. Fkh2 is involved in a wide variety of cellular processes including cell proliferation, but this phosphorylation endows it with a specialized function in promoting the expression of genes required for tissue invasion, biofilm formation, and pathogenesis in the host. This study highlights the role of protein phosphorylation in regulating pathogenesis and furthers our understanding of the pathogenic switch in this important opportunistic fungal pathogen.
doi:10.1371/journal.ppat.1004630
PMCID: PMC4305328  PMID: 25617770
23.  Oncogenic K-Ras decouples glucose and glutamine metabolism to support cancer cell growth 
A systems approach using 13C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD), and transcriptional profiling was applied to investigate the role of oncogenic K-Ras in metabolic transformation.K-Ras transformed cells exhibit an increased glycolytic rate and lower flux through the oxidative tricarboxylic acid (TCA) cycle.K-Ras transformed cells show a relative increase in glutamine anaplerosis and reductive TCA metabolism.Transcriptional changes driven by oncogenic K-Ras suggest control nodes associated with the metabolic reprogramming of cancer cells.
The ras and myc oncogenes drive pleiotropic changes in cell signaling, nutrient uptake, and intracellular metabolism (Chiaradonna et al, 2006b; Yuneva et al, 2007; Wise et al, 2008; Vander Heiden et al, 2009). Mutated ras proteins, identified in 25% of human cancers (Bos, 1989; Downward, 2003), correlate with an increased rate of glucose consumption, lactate accumulation, altered expression of mitochondrial genes, increased ROS production, and reduced mitochondrial activity (Bos, 1989; Downward, 2003; Vizan et al, 2005; Chiaradonna et al, 2006a; Yun et al, 2009; Baracca et al, 2010; Weinberg et al, 2010). Furthermore, K-Ras transformed cancer cells are dependent upon glucose and glutamine availability, since their withdrawal induces apoptosis and cell-cycle arrest, respectively (Ramanathan et al, 2005; Telang et al, 2006; Yun et al, 2009). However, the precise metabolic effects downstream of oncogenic Ras signaling as well as the mechanisms by which intracellular glucose and glutamine metabolism change have not been completely elucidated.
In this report, we have investigated the reprogramming of central carbon metabolism in cancer cells and its regulation by the K-ras oncogene, applying a systems level approach using 13C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD), and transcriptional profiling. These data reveal a coordinated decoupling of glycolysis and the tricarboxylic acid (TCA) cycle. K-Ras transformed mouse and human cells exhibited a high glucose to lactate flux and relatively lower oxidative metabolism of pyruvate. Such changes were supported by increased expression of glycolytic genes as well as several pyruvate dehydrogenase kinases. In contrast to glucose, the contribution of glutamine carbon to TCA cycle intermediates through both oxidative and reductive metabolism was significantly increased upon K-Ras transformation. Despite this increase in glutamine anaplerosis, oxidative TCA flux was significantly decreased. Additionally, we observed elevated levels of glutamine-derived nitrogen in various biosynthetic metabolites in transformed cells, including amino acids, 5-oxoproline, and the nucleobase adenine. Consistent with these changes, we detected increased transcription of genes associated with glutamine metabolism and nucleotide biosynthesis in cells expressing oncogenic K-Ras.
Taken together, these findings indicate an important role of oncogenic K-Ras in cancer cell metabolism. The observed decoupling of glucose and glutamine metabolism enables the efficient utilization of both carbon and nitrogen from glutamine for biosynthetic processes. In accord with these alterations, oncogenic K-Ras induces gene expression changes that may drive this metabolic reprogramming. Finally, these results may enable the identification of metabolic and transcriptional targets throughout the network and allow more effective cancer therapies.
Oncogenes such as K-ras mediate cellular and metabolic transformation during tumorigenesis. To analyze K-Ras-dependent metabolic alterations, we employed 13C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD) of 15N-labeled glutamine, and transcriptomic profiling in mouse fibroblast and human carcinoma cell lines. Stable isotope-labeled glucose and glutamine tracers and computational determination of intracellular fluxes indicated that cells expressing oncogenic K-Ras exhibited enhanced glycolytic activity, decreased oxidative flux through the tricarboxylic acid (TCA) cycle, and increased utilization of glutamine for anabolic synthesis. Surprisingly, a non-canonical labeling of TCA cycle-associated metabolites was detected in both transformed cell lines. Transcriptional profiling detected elevated expression of several genes associated with glycolysis, glutamine metabolism, and nucleotide biosynthesis upon transformation with oncogenic K-Ras. Chemical perturbation of enzymes along these pathways further supports the decoupling of glycolysis and TCA metabolism, with glutamine supplying increased carbon to drive the TCA cycle. These results provide evidence for a role of oncogenic K-Ras in the metabolic reprogramming of cancer cells.
doi:10.1038/msb.2011.56
PMCID: PMC3202795  PMID: 21847114
cancer; metabolic flux analysis; metabolism; Ras; transcriptional analysis
24.  Chemical–Genetic Profiling of Imidazo[1,2-a]pyridines and -Pyrimidines Reveals Target Pathways Conserved between Yeast and Human Cells 
PLoS Genetics  2008;4(11):e1000284.
Small molecules have been shown to be potent and selective probes to understand cell physiology. Here, we show that imidazo[1,2-a]pyridines and imidazo[1,2-a]pyrimidines compose a class of compounds that target essential, conserved cellular processes. Using validated chemogenomic assays in Saccharomyces cerevisiae, we discovered that two closely related compounds, an imidazo[1,2-a]pyridine and -pyrimidine that differ by a single atom, have distinctly different mechanisms of action in vivo. 2-phenyl-3-nitroso-imidazo[1,2-a]pyridine was toxic to yeast strains with defects in electron transport and mitochondrial functions and caused mitochondrial fragmentation, suggesting that compound 13 acts by disrupting mitochondria. By contrast, 2-phenyl-3-nitroso-imidazo[1,2-a]pyrimidine acted as a DNA poison, causing damage to the nuclear DNA and inducing mutagenesis. We compared compound 15 to known chemotherapeutics and found resistance required intact DNA repair pathways. Thus, subtle changes in the structure of imidazo-pyridines and -pyrimidines dramatically alter both the intracellular targeting of these compounds and their effects in vivo. Of particular interest, these different modes of action were evident in experiments on human cells, suggesting that chemical–genetic profiles obtained in yeast are recapitulated in cultured cells, indicating that our observations in yeast can: (1) be leveraged to determine mechanism of action in mammalian cells and (2) suggest novel structure–activity relationships.
Author Summary
We have shown that chemical–genetic screening allows structure–activity studies of chemical compounds at a very high resolution. In analyzing the effects of closely related imidazo-pyridine and -pyrimidine compounds, we found two compounds that likely act as oxidizing agents, yet target different organelles. The imidazo-pyridine affected mitochondrial functions whereas the imidazo-pyrimidine caused nuclear DNA damage. Remarkably, the only difference between these two compounds is the presence of a nitrogen atom at position 8. Thus, in addition to demonstrating the potential for high resolution in chemical–genetic studies, our work suggests that subtle changes in compound chemistry can be exploited to target different intracellular compartments with very different biological effects. Finally, we show that chemical–genetic profiling in yeast can be used to infer mode of action in mammalian cells. The specificity of compound 15 in eliciting a nuclear DNA damage response in evolutionarily diverse eukaryotes suggests that it will be of great utility in studying the cellular response to nuclear oxidative damage.
doi:10.1371/journal.pgen.1000284
PMCID: PMC2583946  PMID: 19043571
25.  Combining metabolomics and transcriptomics to characterize tanshinone biosynthesis in Salvia miltiorrhiza 
BMC Genomics  2014;15:73.
Background
Plant natural products have been co-opted for millennia by humans for various uses such as flavor, fragrances, and medicines. These compounds often are only produced in relatively low amounts and are difficult to chemically synthesize, limiting access. While elucidation of the underlying biosynthetic processes might help alleviate these issues (e.g., via metabolic engineering), investigation of this is hindered by the low levels of relevant gene expression and expansion of the corresponding enzymatic gene families. However, the often-inducible nature of such metabolic processes enables selection of those genes whose expression pattern indicates a role in production of the targeted natural product.
Results
Here, we combine metabolomics and transcriptomics to investigate the inducible biosynthesis of the bioactive diterpenoid tanshinones from the Chinese medicinal herb, Salvia miltiorrhiza (Danshen). Untargeted metabolomics investigation of elicited hairy root cultures indicated that tanshinone production was a dominant component of the metabolic response, increasing at later time points. A transcriptomic approach was applied to not only define a comprehensive transcriptome (comprised of 20,972 non-redundant genes), but also its response to induction, revealing 6,358 genes that exhibited differential expression, with significant enrichment for up-regulation of genes involved in stress, stimulus and immune response processes. Consistent with our metabolomics analysis, there appears to be a slower but more sustained increased in transcript levels of known genes from diterpenoid and, more specifically, tanshinone biosynthesis. Among the co-regulated genes were 70 transcription factors and 8 cytochromes P450, providing targets for future investigation.
Conclusions
Our results indicate a biphasic response of Danshen terpenoid metabolism to elicitation, with early induction of sesqui- and tri- terpenoid biosynthesis, followed by later and more sustained production of the diterpenoid tanshinones. Our data provides a firm foundation for further elucidation of tanshinone and other inducible natural product metabolism in Danshen.
doi:10.1186/1471-2164-15-73
PMCID: PMC3913955  PMID: 24467826

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