Chemical-Chemical Combinations Map Uncharted Interactions in Escherichia coli under Nutrient Stress.
ABSTRACT: Of the ?4,400 genes that constitute Escherichia coli's genome, ?300 genes are indispensable for its growth in nutrient-rich conditions. These encode housekeeping functions, including cell wall, DNA, RNA, and protein syntheses. Under conditions in which nutrients are limited to a carbon source, nitrogen source, essential phosphates, and salts, more than 100 additional genes become essential. These largely code for the synthesis of amino acids, vitamins, and nucleobases. Although much is known about this collection of ?400 genes, their interactions under nutrient stress are uncharted. Using a chemical biology approach, we focused on 45 chemical probes targeting encoded proteins in this collection and mapped their interactions under nutrient-limited conditions. Encompassing 990 unique pairwise chemical combinations, we revealed a highly connected network of 186 interactions, of which 81 were synergistic and 105 were antagonistic. The network revealed signature interactions for each probe and highlighted new connectivity between housekeeping functions and those essential in nutrient stress.
Project description:Small molecule screening libraries cover only a small fraction of the astronomical number of possible drug-like compounds, limiting the success of ligand discovery efforts. Computational screening of virtual libraries representing unexplored chemical space could potentially bridge this gap. Drug development for adenosine receptors (ARs) as targets for inflammation and cardiovascular diseases has been hampered by the paucity of agonist scaffolds. To identify novel AR agonists, a virtual library of synthetically tractable nucleosides with alternative bases was generated and structure-based virtual screening guided selection of compounds for synthesis. Pharmacological assays were carried out at three AR subtypes for 13 ribosides. Nine compounds displayed significant activity at the ARs, and several of these represented atypical agonist scaffolds. The discovered ligands also provided insights into receptor activation and revealed unknown interactions of endogenous and clinical compounds with the ARs. The results demonstrate that virtual compound databases provide access to bioactive matter from regions of chemical space that are sparsely populated in commercial libraries, an approach transferrable to numerous drug targets.
Project description:Sulfur mustard (SM) was used as a chemical weapon in Iraq-Iran war. Exposed people have major complications in important organs such as pulmonary system. Some studies have shown that SM could affect the expression of endogenous genes and non-housekeeping genes, time dependently. To understand the accurate molecular mechanism of the delayed effect of SM, the identification of the gene expression pattern in these patients is essential. Hence, we have evaluated mRNA expression of four common housekeeping genes (ACTIN, PGK1, ?2m, GAPDH) in SM-exposed and non-exposed (control) formalin-fixed, paraffin-embedded (FFPE) human lung tissues.Paraffin block of lung biopsy of SM-exposed people (11 cases) and people without exposure to SM as control group (9 cases) have been selected. The mRNA expression of four endogenous control genes has been evaluated by qRT-PCR. The stability value of each gene was calculated by different methods.It was found that ACTIN mRNA has the highest expression (30.26±2.87) and PGK1 has the lowest standard deviation (SD) (30.885±2.215) between pooled groups. The best correlation was between ACTIN and PGK1 expressions. The M value has shown that ACTIN and then PGK1 are the most stable housekeeping genes among. The results obtained from the GeNorm and NormFinder have indicated that the pair ACTIN- PGK1 is the most suitable choice for endogenous control genes.ACTIN and PGK1 genes are stable in studied lung tissues and are the better than two other housekeeping genes. In addition, mustard gas does not affect their expression in long term.
Project description:Hypopigmentation is a feature of copper deficiency in humans, as caused by mutation of the copper (Cu(2+)) transporter ATP7A in Menkes disease, or an inability to absorb copper after gastric surgery. However, many causes of copper deficiency are unknown, and genetic polymorphisms might underlie sensitivity to suboptimal environmental copper conditions. Here, we combined phenotypic screens in zebrafish for compounds that affect copper metabolism with yeast chemical-genetic profiles to identify pathways that are sensitive to copper depletion. Yeast chemical-genetic interactions revealed that defects in intracellular trafficking pathways cause sensitivity to low-copper conditions; partial knockdown of the analogous Ap3s1 and Ap1s1 trafficking components in zebrafish sensitized developing melanocytes to hypopigmentation in low-copper environmental conditions. Because trafficking pathways are essential for copper loading into cuproproteins, our results suggest that hypomorphic alleles of trafficking components might underlie sensitivity to reduced-copper nutrient conditions. In addition, we used zebrafish-yeast screening to identify a novel target pathway in copper metabolism for the small-molecule MEK kinase inhibitor U0126. The zebrafish-yeast screening method combines the power of zebrafish as a disease model with facile genome-scale identification of chemical-genetic interactions in yeast to enable the discovery and dissection of complex multigenic interactions in disease-gene networks.
Project description:Research using the zebrafish model has experienced a rapid growth in recent years. Although real-time reverse transcription PCR (QPCR), normalized to an internal reference ("housekeeping") gene, is a frequently used method for quantifying gene expression changes in zebrafish, many commonly used housekeeping genes are known to vary with experimental conditions. To identify housekeeping genes that are stably expressed under different experimental conditions, and thus suitable as normalizers for QPCR in zebrafish, the present study evaluated the expression of eight commonly used housekeeping genes as a function of stage and hormone/toxicant exposure during development, and by tissue type and sex in adult fish.QPCR analysis was used to quantify mRNA levels of bactin1, tubulin alpha 1(tuba1), glyceraldehyde-3-phosphate dehydrogenase (gapdh), glucose-6-phosphate dehydrogenase (g6pd), TATA-box binding protein (tbp), beta-2-microglobulin (b2m), elongation factor 1 alpha (elfa), and 18s ribosomal RNA (18s) during development (2 - 120 hr postfertilization, hpf); in different tissue types (brain, eye, liver, heart, muscle, gonads) of adult males and females; and after treatment of embryos/larvae (24 - 96 hpf) with commonly used vehicles for administration and agents that represent known environmental endocrine disruptors. All genes were found to have some degree of variability under the conditions tested here. Rank ordering of expression stability using geNorm analysis identified 18s, b2m, and elfa as most stable during development and across tissue types, while gapdh, tuba1, and tpb were the most variable. Following chemical treatment, tuba1, bactin1, and elfa were the most stably expressed whereas tbp, 18s, and b2m were the least stable. Data also revealed sex differences that are gene- and tissue-specific, and treatment effects that are gene-, vehicle- and ligand-specific. When the accuracy of QPCR analysis was tested using different reference genes to measure suppression of cyp19a1b by an estrogen receptor antagonist and induction of cyp1a by an arylhydrocarbon receptor agonist, the direction and magnitude of effects with stable and unstable genes differed.This study provides data that can be expected to aid zebrafish researchers in their initial choice of housekeeping genes for future studies, but underlines the importance of further validating housekeeping genes for each new experimental paradigm and fish species.
Project description:The "small molecule universe" (SMU), the set of all synthetically feasible organic molecules of 500 Da molecular weight or less, is estimated to contain over 10(60) structures, making exhaustive searches for structures of interest impractical. Here, we describe the construction of a "representative universal library" spanning the SMU that samples the full extent of feasible small molecule chemistries. This library was generated using the newly developed Algorithm for Chemical Space Exploration with Stochastic Search (ACSESS). ACSESS makes two important contributions to chemical space exploration: it allows the systematic search of the unexplored regions of the small molecule universe, and it facilitates the mining of chemical libraries that do not yet exist, providing a near-infinite source of diverse novel compounds.
Project description:Determining the mode of action of bioactive chemicals is of interest to a broad range of academic, pharmaceutical, and industrial scientists. Saccharomyces cerevisiae, or budding yeast, is a model eukaryote for which a complete collection of ~6,000 gene deletion mutants and hypomorphic essential gene mutants are commercially available. These collections of mutants can be used to systematically detect chemical-gene interactions, i.e. genes necessary to tolerate a chemical. This information, in turn, reports on the likely mode of action of the compound. Here we describe a protocol for the rapid identification of chemical-genetic interactions in budding yeast. We demonstrate the method using the chemotherapeutic agent 5-fluorouracil (5-FU), which has a well-defined mechanism of action. Our results show that the nuclear TRAMP RNA exosome and DNA repair enzymes are needed for proliferation in the presence of 5-FU, which is consistent with previous microarray based bar-coding chemical genetic approaches and the knowledge that 5-FU adversely affects both RNA and DNA metabolism. The required validation protocols of these high-throughput screens are also described.
Project description:A dataset of chemical-gene interactions was created by extracting data from the Comparative Toxicogenomics Database (CTD) with the following filtering criteria: data was extracted only from experiments that used human, rat, or mouse cells/tissues and used high-throughput approaches for gene expression analysis. Genes not present in genomes of all three species were filtered out. The resulting dataset included 591,084 chemical-gene interaction. All chemical compounds in the database were annotated for their major uses. For every gene in the database number of chemical-gene interactions was calculated and used as a metric of gene sensitivity to a variety of chemical exposures. The lists of genes with corresponding numbers of chemical-gene interactions were used in gene-set enrichment analysis (GSEA) to identify potential sensitivity to chemical exposures of molecular pathways in Hallmark, KEGG and Reactome collections. Thus, data presented here represent unbiased and searchable datasets of sensitivity of genes and molecular pathways to a broad range of chemical exposures. As such the data can be used for a diverse range of toxicological and regulatory applications. Approach for the identification of molecular mechanisms sensitive to chemical exposures may inform regulatory toxicology about best toxicity testing strategies. Analysis of sensitivity of genes and molecular pathways to chemical exposures based on these datasets was published in Chemosphere (Suvorov et al., 2021) .
Project description:Excessive pollen harvesting by bees can compromise the reproductive success of plants. Plants have therefore evolved different morphological structures and floral cues to narrow the spectrum of pollen feeding visitors. Among "filtering" mechanisms, the chemical and mechanical protection of pollen might shape bee-flower interactions and restrict pollen exploitation to a specific suite of visitors such as observed in Asteraceae. Asteraceae pollen is indeed only occasionally exploited by generalist bee species but plentifully foraged by specialist ones (i.e., Asteraceae paradox). During our bioassays, we observed that micro-colonies of generalist bumblebee (Bombus terrestris L.) feeding on Taraxacum pollen (Asteraceae) reduced their pollen collection and offspring production. Bees also experienced physiological effects of possible defenses in the form of digestive damage. Overall, our results suggest the existence of an effective chemical defense in Asteraceae pollen, while the hypothesis of a mechanical defense appeared more unlikely. Pre- and post-ingestive effects of such chemical defenses (i.e., nutrient deficit or presence of toxic compounds), as well as their role in the shaping of bee-flower interactions, are discussed. Our results strongly suggest that pollen chemical traits may act as drivers of plant selection by bees and partly explain why Asteraceae pollen is rare in generalist bee diets.
Project description:The structure of genetic interaction networks predicts that, analogous to synthetic lethal interactions between non-essential genes, combinations of compounds with latent activities may exhibit potent synergism. To test this hypothesis, we generated a chemical-genetic matrix of 195 diverse yeast deletion strains treated with 4,915 compounds. This approach uncovered 1,221 genotype-specific inhibitors, which we termed cryptagens. Synergism between 8,128 structurally disparate cryptagen pairs was assessed experimentally and used to benchmark predictive algorithms. A model based on the chemical-genetic matrix and the genetic interaction network failed to accurately predict synergism. However, a combined random forest and Naive Bayesian learner that associated chemical structural features with genotype-specific growth inhibition had strong predictive power. This approach identified previously unknown compound combinations that exhibited species-selective toxicity toward human fungal pathogens. This work demonstrates that machine learning methods trained on unbiased chemical-genetic interaction data may be widely applicable for the discovery of synergistic combinations in different species.
Project description:Conventional efforts to describe essential genes in bacteria have typically emphasized nutrient-rich growth conditions. Of note, however, are the set of genes that become essential when bacteria are grown under nutrient stress. For example, more than 100 genes become indispensable when the model bacterium Escherichia coli is grown on nutrient-limited media, and many of these nutrient stress genes have also been shown to be important for the growth of various bacterial pathogens in vivo To better understand the genetic network that underpins nutrient stress in E. coli, we performed a genome-scale cross of strains harboring deletions in some 82 nutrient stress genes with the entire E. coli gene deletion collection (Keio) to create 315,400 double deletion mutants. An analysis of the growth of the resulting strains on rich microbiological media revealed an average of 23 synthetic sick or lethal genetic interactions for each nutrient stress gene, suggesting that the network defining nutrient stress is surprisingly complex. A vast majority of these interactions involved genes of unknown function or genes of unrelated pathways. The most profound synthetic lethal interactions were between nutrient acquisition and biosynthesis. Further, the interaction map reveals remarkable metabolic robustness in E. coli through pathway redundancies. In all, the genetic interaction network provides a powerful tool to mine and identify missing links in nutrient synthesis and to further characterize genes of unknown function in E. coli Moreover, understanding of bacterial growth under nutrient stress could aid in the development of novel antibiotic discovery platforms.<h4>Importance</h4>With the rise of antibiotic drug resistance, there is an urgent need for new antibacterial drugs. Here, we studied a group of genes that are essential for the growth of Escherichia coli under nutrient limitation, culture conditions that arguably better represent nutrient availability during an infection than rich microbiological media. Indeed, many such nutrient stress genes are essential for infection in a variety of pathogens. Thus, the respective proteins represent a pool of potential new targets for antibacterial drugs that have been largely unexplored. We have created all possible double deletion mutants through a genetic cross of nutrient stress genes and the E. coli deletion collection. An analysis of the growth of the resulting clones on rich media revealed a robust, dense, and complex network for nutrient acquisition and biosynthesis. Importantly, our data reveal new genetic connections to guide innovative approaches for the development of new antibacterial compounds targeting bacteria under nutrient stress.