Final Report Summary - MAPPING THE CELL (Mapping the Cell)
A growing number of academic labs are pursuing model organism screens to identify chemical probes for use as powerful molecular tools to probe biological function and map the cell. Chemical probes complement standard genetic approaches to elucidate gene function while offering distinct advantages. For example, when applied to a cell or whole organism, the effects induced by chemical probes are often rapid, reversible and tuneable. Moreover, chemical probes can often be transferred across model organisms, regardless of their genetic tractability. During this project, we developed computational and experimental tools to improve the efficiency with which chemical probes are identified from model organism screening programs. In addition, we also identified and validated a number of chemical probes that can be used to study wide range biological processes in a number of model organisms. This work will directly benefit the recently announced call by the Innovative Medicines Initiative for the 'Joint European Compound Library and Screening Centre'.
Firstly, we developed a novel method for improving the efficiency of screening compounds in the tiny nematode, C. elegans. Because of the worms rapid life cycle, small size and hermaphroditism, libraries of small molecules can be screened for bioactivity in the context of the whole animal and over its entire life cycle in a high-throughput fashion. Furthermore, the power of C. elegans genetic analysis has repeatedly uncovered the mechanism of action of both small-molecule tools and novel anthelmintics. The worm also shares extensive genetic conservation with more complex animals, and the growing list of human disease models in the worm further distinguish C. elegans as a unique tool for the discovery of novel therapeutics. Unfortunately, C. elegans is relatively resistant to perturbation by pharmacologically active molecules, only 2% of pharmacologically active compounds can induce a robust phenotype in the worm. By analyzing the structures of compounds that accumulated in the animal versus those that dont, we were able to build a probabilistic structural model that could be used to prioritize compounds for screening in C. elegans. This computational model is able to dramatically reduce the cost and time required for a screening program in C. elegans.
We expanded on this work to reduce the costs of identifying chemical probes by overcoming a major bottleneck in the model organism screening paradigm. A key challenge of chemical and chemical-genetic screens is that the percentage of compounds that results in a desired phenotype is often small; for example, E. coli strain, only 3.5% of compounds resulted in growth inhibition. Using the physicochemical properties of compounds that induce a phenotype in yeast, we designed a two-property compound filter and built a Naïve Bayes model to prioritize compounds that are most likely to induce a phenotype of interest. The application of these approaches to compound library design before compound purchase resulted in an enrichment for phenotype-inducing compounds in diverse model organisms, potentially reducing the cost of such a screen by 90%.
In the course of the project, we identified and validated a number of such tool compounds. One such compound is Erg7.253 which we demonstrated inhibits lanosterol synthase in a number of organisms including yeast, worm and humans. In yeast, lanosterol synthase is a promising antifungal target based on the success of antifungal agents that target other steps of ergosterol biosynthesis pathway, in which lanosterol performs an essential function. In addition, this target has potential therapeutic relevance in humans as a cholesterol-lowering agent. We discovered this compound through the using our well-validated genome-wide HaploInsufficiency Profiling (HIP) assay. The HIP assays allows an unbiased, in vivo quantitative measure of the relative drug sensitivity of all ~1100 essential yeast proteins in a single assay. We validated the target of this compound by analyzing the lipid metabolites from cells (yeast and mammalian) grown in the presence of this inhibitor. As predicted for a bona fide inhibitor, the substrate of lanosterol, oxidosqualene, showed significant accumulation in the presence of inhibitor compared to vehicle alone.
Finally, we also identified a novel compound, erodoxin, that we demonstrated inhibited the yeast gene Ero1. We were involved in the project which constructed a genome-scale genetic interaction map by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for ~75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. Specifically, we compared compound chemical genetic profiles with genetic interaction profiles. Compound profiles similar to the profile of a query gene knock out double-mutant screen likely indicate the compound targets the query gene. We showed that compounds often clustered to dense regions of the genetic network indicative of specific bioprocesses. For example, hydroxyurea, a compound that inhibits ribonucleotide reductase and blocks DNA synthesis, clusters with the gene cohort annotated with roles in DNA replication and repair. These results demonstrate that clustering of chemical-genetic and genetic interaction profiles complements haploinsufficiency profiling, which has the potential to identify drug targets directly. We used this network approach to examine the previously uncharacterised compound, CB 0428-0027, which we have subsequently named erodoxin. Erodoxin clustered with genes associated with protein folding, glycosylation, and cell wall biosynthesis functions because the erodoxin chemical-genetic profile most closely resembled the genetic interaction profile of ERO1. Two additional lines of evidence suggested that Ero1 is the target of erodoxin. First, ero1?/+ and fad1?/+ heterozygotes were the most hypersensitive mutants identified in the HIP profile. Second, we found that erodoxin leads to inhibition of Trx1 oxidation and delayed carboxy peptidase Y (CPY) processing, which suggests that it inhibits Ero1 activity both in vitro and in vivo.
In summary, during this project, we have made substantial contributions to the field of chemical biology by both reducing the cost of identifying chemical probes in model organisms as well as identifying novel chemical tools for the study of diverse biological processes.
Firstly, we developed a novel method for improving the efficiency of screening compounds in the tiny nematode, C. elegans. Because of the worms rapid life cycle, small size and hermaphroditism, libraries of small molecules can be screened for bioactivity in the context of the whole animal and over its entire life cycle in a high-throughput fashion. Furthermore, the power of C. elegans genetic analysis has repeatedly uncovered the mechanism of action of both small-molecule tools and novel anthelmintics. The worm also shares extensive genetic conservation with more complex animals, and the growing list of human disease models in the worm further distinguish C. elegans as a unique tool for the discovery of novel therapeutics. Unfortunately, C. elegans is relatively resistant to perturbation by pharmacologically active molecules, only 2% of pharmacologically active compounds can induce a robust phenotype in the worm. By analyzing the structures of compounds that accumulated in the animal versus those that dont, we were able to build a probabilistic structural model that could be used to prioritize compounds for screening in C. elegans. This computational model is able to dramatically reduce the cost and time required for a screening program in C. elegans.
We expanded on this work to reduce the costs of identifying chemical probes by overcoming a major bottleneck in the model organism screening paradigm. A key challenge of chemical and chemical-genetic screens is that the percentage of compounds that results in a desired phenotype is often small; for example, E. coli strain, only 3.5% of compounds resulted in growth inhibition. Using the physicochemical properties of compounds that induce a phenotype in yeast, we designed a two-property compound filter and built a Naïve Bayes model to prioritize compounds that are most likely to induce a phenotype of interest. The application of these approaches to compound library design before compound purchase resulted in an enrichment for phenotype-inducing compounds in diverse model organisms, potentially reducing the cost of such a screen by 90%.
In the course of the project, we identified and validated a number of such tool compounds. One such compound is Erg7.253 which we demonstrated inhibits lanosterol synthase in a number of organisms including yeast, worm and humans. In yeast, lanosterol synthase is a promising antifungal target based on the success of antifungal agents that target other steps of ergosterol biosynthesis pathway, in which lanosterol performs an essential function. In addition, this target has potential therapeutic relevance in humans as a cholesterol-lowering agent. We discovered this compound through the using our well-validated genome-wide HaploInsufficiency Profiling (HIP) assay. The HIP assays allows an unbiased, in vivo quantitative measure of the relative drug sensitivity of all ~1100 essential yeast proteins in a single assay. We validated the target of this compound by analyzing the lipid metabolites from cells (yeast and mammalian) grown in the presence of this inhibitor. As predicted for a bona fide inhibitor, the substrate of lanosterol, oxidosqualene, showed significant accumulation in the presence of inhibitor compared to vehicle alone.
Finally, we also identified a novel compound, erodoxin, that we demonstrated inhibited the yeast gene Ero1. We were involved in the project which constructed a genome-scale genetic interaction map by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for ~75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. Specifically, we compared compound chemical genetic profiles with genetic interaction profiles. Compound profiles similar to the profile of a query gene knock out double-mutant screen likely indicate the compound targets the query gene. We showed that compounds often clustered to dense regions of the genetic network indicative of specific bioprocesses. For example, hydroxyurea, a compound that inhibits ribonucleotide reductase and blocks DNA synthesis, clusters with the gene cohort annotated with roles in DNA replication and repair. These results demonstrate that clustering of chemical-genetic and genetic interaction profiles complements haploinsufficiency profiling, which has the potential to identify drug targets directly. We used this network approach to examine the previously uncharacterised compound, CB 0428-0027, which we have subsequently named erodoxin. Erodoxin clustered with genes associated with protein folding, glycosylation, and cell wall biosynthesis functions because the erodoxin chemical-genetic profile most closely resembled the genetic interaction profile of ERO1. Two additional lines of evidence suggested that Ero1 is the target of erodoxin. First, ero1?/+ and fad1?/+ heterozygotes were the most hypersensitive mutants identified in the HIP profile. Second, we found that erodoxin leads to inhibition of Trx1 oxidation and delayed carboxy peptidase Y (CPY) processing, which suggests that it inhibits Ero1 activity both in vitro and in vivo.
In summary, during this project, we have made substantial contributions to the field of chemical biology by both reducing the cost of identifying chemical probes in model organisms as well as identifying novel chemical tools for the study of diverse biological processes.