Jeff S. Piotrowski

Combining functional genomics and chemical biology to identify targets of bioactive compounds

Genome sequencing projects have revealed thousands of suspected genes, challenging researchers to develop efficient large-scale functional analysis methodologies. Determining the function of a gene product generally requires a means to alter its function. Genetically tractable model organisms have been widely exploited for the isolation and characterization of activating and inactivating mutations in genes encoding proteins of interest. Chemical genetics represents a complementary approach involving the use of small molecules capable of either inactivating or activating their targets. Saccharomyces cerevisiae has been an important test bed for the development and application of chemical genomic assays aimed at identifying targets and modes of action of known and uncharacterized compounds. Here we review yeast chemical genomic assays strategies for drug target identification.

Death by a thousand cuts: the challenges and diverse landscape of lignocellulosic hydrolysate inhibitors

Lignocellulosic hydrolysate (LCH) inhibitors are a large class of bioactive molecules that arise from pretreatment, hydrolysis, and fermentation of plant biomass. These diverse compounds reduce lignocellulosic biofuel yields by inhibiting cellular processes and diverting energy into cellular responses. LCH inhibitors present one of the most significant challenges to efficient biofuel production by microbes. Development of new strains that lessen the effects of LCH inhibitors is an economically favorable strategy relative to expensive detoxification methods that also can reduce sugar content in deconstructed biomass. Systems biology analyses and metabolic modeling combined with directed evolution and synthetic biology are successful strategies for biocatalyst development, and methods that leverage state-of-the-art tools are needed to overcome inhibitors more completely. This perspective considers the energetic costs of LCH inhibitors and technologies that can be used to overcome their drain on conversion efficiency. We suggest academic and commercial research groups could benefit by sharing data on LCH inhibitors and implementing “translational biofuel research.”

Forazoline A: Marine-Derived Polyketide with Antifungal In Vivo Efficacy†

Forazoline A, a novel antifungal polyketide with in vivo efficacy against Candida albicans, was discovered using LCMS-based metabolomics to investigate marine-invertebrate-associated bacteria. Forazoline A had a highly unusual and unprecedented skeleton. Acquisition of (13)C-(13)C gCOSY and (13)C-(15)N HMQC NMR data provided the direct carbon-carbon and carbon-nitrogen connectivity, respectively. This approach represents the first example of determining direct (13)C-(15)N connectivity for a natural product. Using yeast chemical genomics, we propose that forazoline A operated through a new mechanism of action with a phenotypic outcome of disrupting membrane integrity.

Chemical-genomic profiling: systematic analysis of the cellular targets of bioactive molecules.

Chemical-genomic (CG) profiling of bioactive compounds is a powerful approach for drug target identification and mode of action studies. Within the last decade, research focused largely on the development and application of CG approaches in the model yeast Saccharomyces cerevisiae. The success of these methods has sparked interest in transitioning CG profiling to other biological systems to extend clinical and evolutionary relevance. Additionally, CG profiling has proven to enhance drug-synergy screens for developing combinatorial therapies. Herein, we briefly review CG profiling, focusing on emerging cross-species technologies and novel drug-synergy applications, as well as outlining needs within the field.

Padanamides A and B, Highly Modified Linear Tetrapeptides Produced in Culture by a Streptomyces sp. Isolated from a Marine Sediment

Two highly modified linear tetrapeptides, padanamides A (1) and B (2), are produced by laboratory cultures of a Streptomyces sp. obtained from a marine sediment. Padanamide B is cytotoxic to Jurkat cells, and a chemical genomics analysis using Saccharomyces cerevisiae deletion mutants suggested that padanamide A inhibits cysteine and methionine biosynthesis or that these amino acids are involved in the yeasts response to the peptide.

Ecological and genetic barriers differentiate natural populations of Saccharomyces cerevisiae

How populations that inhabit the same geographical area become genetically differentiated is not clear. To investigate this, we characterized phenotypic and genetic differences between two populations of Saccharomyces cerevisiae that in some cases inhabit the same environment but show relatively little gene flow. We profiled stress sensitivity in a group of vineyard isolates and a group of oak-soil strains and found several niche-related phenotypes that distinguish the populations. We performed bulk-segregant mapping on two of the distinguishing traits: The vineyard-specific ability to grow in grape juice and oak-specific tolerance to the cell wall damaging drug Congo red. To implicate causal genes, we also performed a chemical genomic screen in the lab-strain deletion collection and identified many important genes that fell under quantitative trait loci peaks. One gene important for growth in grape juice and identified by both the mapping and the screen was SSU1, a sulfite-nitrite pump implicated in wine fermentations. The beneficial allele is generated by a known translocation that we reasoned may also serve as a genetic barrier. We found that the translocation is prevalent in vineyard strains, but absent in oak strains, and presents a postzygotic barrier to spore viability. Furthermore, the translocation was associated with a fitness cost to the rapid growth rate seen in oak-soil strains. Our results reveal the translocation as a dual-function locus that enforces ecological differentiation while producing a genetic barrier to gene flow in these sympatric populations.

Functional annotation of chemical libraries across diverse biological processes.

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells to a compound, revealing chemical-genetic interactions that can elucidate a compound’s mode of action. We developed a highly parallel and unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized, diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode sequencing protocol, enabling assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened 7 different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

Quantifying pretreatment degradation compounds in solution and accumulated by cells during solids and yeast recycling in the Rapid Bioconversion with Integrated recycling Technology process using AFEX™ corn stover.

Effects of degradation products (low molecular weight compounds produced during pretreatment) on the microbes used in the RaBIT (Rapid Bioconversion with Integrated recycling Technology) process that reduces enzyme usage up to 40% by efficient enzyme recycling were studied. Chemical genomic profiling was performed, showing no yeast response differences in hydrolysates produced during RaBIT enzymatic hydrolysis. Concentrations of degradation products in solution were quantified after different enzymatic hydrolysis cycles and fermentation cycles. Intracellular degradation product concentrations were also measured following fermentation. Degradation product concentrations in hydrolysate did not change between RaBIT enzymatic hydrolysis cycles; the cell population retained its ability to oxidize/reduce (detoxify) aldehydes over five RaBIT fermentation cycles; and degradation products accumulated within or on the cells as RaBIT fermentation cycles increased. Synthetic hydrolysate was used to confirm that pretreatment degradation products are the sole cause of decreased xylose consumption during RaBIT fermentations.

Accumulation of heme biosynthetic intermediates contributes to the antibacterial action of the metalloid tellurite.

The metalloid tellurite is highly toxic to microorganisms. Several mechanisms of action have been proposed, including thiol depletion and generation of hydrogen peroxide and superoxide, but none of them can fully explain its toxicity. Here we use a combination of directed evolution and chemical and biochemical approaches to demonstrate that tellurite inhibits heme biosynthesis, leading to the accumulation of intermediates of this pathway and hydroxyl radical. Unexpectedly, the development of tellurite resistance is accompanied by increased susceptibility to hydrogen peroxide. Furthermore, we show that the heme precursor 5-aminolevulinic acid, which is used as an antimicrobial agent in photodynamic therapy, potentiates tellurite toxicity. Our results define a mechanism of tellurite toxicity and warrant further research on the potential use of the combination of tellurite and 5-aminolevulinic acid in antimicrobial therapy.

Large-scale interpretation of chemical-genetic interaction profiles using a genetic interaction network

Chemical-genetic interactions – observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes – contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. CG-TARGET compared favorably to a baseline enrichment approach across a variety of benchmarks, achieving similar accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. We applied CG-TARGET to a recent screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. Upon investigation of the compatibility of chemical-genetic and genetic interaction profiles, we observed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We present here a detailed characterization of the CG-TARGET method along with experimental validation of predicted biological process targets, focusing on inhibitors of tubulin polymerization and cell cycle progression. Our approach successfully demonstrates the use of genetic interaction networks in the functional annotation of compounds to biological processes.Genetic interactions provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. However, they have remained underutilized in this capacity across recent chemical-genetic interaction screening efforts and their ability to interpret chemical-genetic interaction profiles on a large scale has not been tested. We developed a method, which we refer to as CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates the data from large-scale chemical-genetic interaction screens with genetic interaction data to predict the biological processes perturbed by compounds. CG-TARGET compared favorably to a standard enrichment approach across a variety of benchmarks, achieving similar performance on measures of accuracy and substantial improvement in the ability to control the false discovery rate of its predictions. We found that one-third to one-half of gene mutants in the data contribute to the highest-confidence biological process predictions and that these contributions overwhelmingly come from negative chemical-genetic interactions. This method was used to prioritize over 1500 out of over 13,000 compounds for further study in a recently-completed chemical-genetic interaction screen in Saccharomyces cerevisiae, enabling the rapid functional annotation of unknown compounds to biological processes through targeted biological validations. We present here a detailed characterization of the method and further biological validations to demonstrate the utility of genetic interactions in the interpretation of chemical-genetic interaction profiles and the effectiveness of our implementation of this concept.