Michaela Spitzer

BoxPlotR: a web tool for generation of box plots

To the Editor In biomedical research, it is often necessary to compare multiple data sets with different distributions. The bar plot, or histogram, is typically used to compare data sets on the basis of simple statistical measures, usually the mean with s.d. or s.e.m. However, summary statistics alone may fail to convey underlying differences in the structure of the primary data (Fig. 1a), which may in turn lead to erroneous conclusions. The box plot, also known as the box-and-whisker plot, represents both the summary statistics and the distribution of the primary data. The box plot thus enables visualization of the minimum, lower quartile, median, upper quartile and maximum of any data set (Fig. 1b). The first documented description of a box plot–like graph by Spear1 defined a range bar to show the median and interquartile range (IQR, or middle 50%) of a data set, with whiskers extended to minimum and maximum values. The most common implementation of the box plot, as defined by Tukey2, has a box that represents the IQR, with whiskers that extend 1.5 times the IQR from the box edges; it also allows for identification of outliers in the data set. Whiskers can also be defined to span the 95% central range of the data3. Other variations, including bean plots4 and violin plots, reveal additional details of the data distribution. These latter variants are less statistically informative but allow better visualization of the data distribution, such as bimodality (Fig. 1b), that may be hidden in a standard box plot. Figure 1 Data visualization with box plots Despite the obvious advantages of the box plot for simultaneous representation of data set and statistical parameters, this method is not in common use, in part because few available software tools allow the facile generation of box plots. For example, the standard spreadsheet tool Excel is unable to generate box plots. Here we describe an open-source application, called BoxPlotR, and an associated web portal that allow rapid generation of customized box plots. A user-defined data matrix is uploaded as a file or pasted directly into the application to generate a basic box plot with options for additional features. Sample size may be represented by the width of each box in proportion to the square root of the number of observations5. Whiskers may be defined according to the criteria of Spear1, Tukey2 or Altman3. The underlying data distribution may be visualized as a violin or bean plot or, alternatively, the actual data may be displayed as overlapping or nonoverlapping points. The 95% confidence interval that two medians are different may be illustrated as notches defined as ±(1.58 × IQR/√n) (ref. 5). There is also an op on to plot the sample means and their confidence intervals. More complex statistical comparisons may be required to ascertain significance according to the specific experimental design6. The output plots may be labeled; customized by color, dimensions and orientation; and exported as publication-quality .eps, .pdf or .svg files. To help ensure that generated plots are accurately described in publications, the application generates a description of the plot for incorporation into a figure legend. The interactive web application is written in R (ref. 7) with the R packages shiny, beanplot4, vioplot, beeswarm and RColorBrewer, and it is hosted on a shiny server to allow for interactive data analysis. User data are held only temporarily and discarded as soon as the session terminates. BoxPlotR is available at http://boxplot.tyerslab.com/ and may be downloaded to run locally or as a virtual machine for VMware and VirtualBox.

Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole

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.

Distinct XPPX sequence motifs induce ribosome stalling, which is rescued by the translation elongation factor EF-P

Significance During protein synthesis, ribosomes catalyze peptide-bond formation between amino acids with differing efficiency. We show that two or more consecutive prolines induce ribosome stalling, and that stalling strength is influenced by the amino acid preceding and following the prolines. In bacteria, the elongation factor EF-P efficiently rescues the ribosome stalling irrespective of the XPP or PPX motif. Ribosomes are the protein synthesizing factories of the cell, polymerizing polypeptide chains from their constituent amino acids. However, distinct combinations of amino acids, such as polyproline stretches, cannot be efficiently polymerized by ribosomes, leading to translational stalling. The stalled ribosomes are rescued by the translational elongation factor P (EF-P), which by stimulating peptide-bond formation allows translation to resume. Using metabolic stable isotope labeling and mass spectrometry, we demonstrate in vivo that EF-P is important for expression of not only polyproline-containing proteins, but also for specific subsets of proteins containing diprolyl motifs (XPP/PPX). Together with a systematic in vitro and in vivo analysis, we provide a distinct hierarchy of stalling triplets, ranging from strong stallers, such as PPP, DPP, and PPN to weak stallers, such as CPP, PPR, and PPH, all of which are substrates for EF-P. These findings provide mechanistic insight into how the characteristics of the specific amino acid substrates influence the fundamentals of peptide bond formation.

Combined zebrafish-yeast chemical-genetic screens reveal gene–copper-nutrition interactions that modulate melanocyte pigmentation

SUMMARY Hypopigmentation is a feature of copper deficiency in humans, as caused by mutation of the copper (Cu2+) 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.

An Antifungal Combination Matrix Identifies a Rich Pool of Adjuvant Molecules that Enhance Drug Activity against Diverse Fungal Pathogens

There is an urgent need to identify new treatments for fungal infections. By combining sub-lethal concentrations of the known antifungals fluconazole, caspofungin, amphotericin B, terbinafine, benomyl, and cyprodinil with ∼3,600 compounds in diverse fungal species, we generated a deep reservoir of chemical-chemical interactions termed the Antifungal Combinations Matrix (ACM). Follow-up susceptibility testing against a fluconazole-resistant isolate of C. albicans unveiled ACM combinations capable of potentiating fluconazole in this clinical strain. We used chemical genetics to elucidate the mode of action of the antimycobacterial drug clofazimine, a compound with unreported antifungal activity that synergized with several antifungals. Clofazimine induces a cell membrane stress for which the Pkc1 signaling pathway is required for tolerance. Additional tests against additional fungal pathogens, including Aspergillus fumigatus, highlighted that clofazimine exhibits efficacy as a combination agent against multiple fungi. Thus, the ACM is a rich reservoir of chemical combinations with therapeutic potential against diverse fungal pathogens.

Combinatorial strategies for combating invasive fungal infections

ABSTRACT Invasive fungal infections are an important cause of human mortality and morbidity, particularly for immunocompromised populations. However, there remains a paucity of antifungal drug treatments available to combat these fungal pathogens. Further, antifungal compounds are plagued with problems such as host toxicity, fungistatic activity, and the emergence of drug resistance in pathogen populations. A promising therapeutic strategy to increase drug effectiveness and mitigate the emergence of drug resistance is through the use of combination drug therapy. In this review we describe the current arsenal of antifungals in medicine and elaborate on the benefits of combination therapy to expand our current antifungal drug repertoire. We examine those antifungal combinations that have shown potential against fungal pathogens and discuss strategies being employed to discover novel combination therapeutics, in particular combining antifungal agents with non-antifungal bioactive compounds. The findings summarized in this review highlight the promise of combinatorial strategies in combatting invasive mycoses.

Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning

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.

ALDH2 Mediates 5-Nitrofuran Activity in Multiple Species

Summary Understanding how drugs work in vivo is critical for drug design and for maximizing the potential of currently available drugs. 5-nitrofurans are a class of prodrugs widely used to treat bacterial and trypanosome infections, but despite relative specificity, 5-nitrofurans often cause serious toxic side effects in people. Here, we use yeast and zebrafish, as well as human in vitro systems, to assess the biological activity of 5-nitrofurans, and we identify a conserved interaction between aldehyde dehydrogenase (ALDH) 2 and 5-nitrofurans across these species. In addition, we show that the activity of nifurtimox, a 5-nitrofuran anti-trypanosome prodrug, is dependent on zebrafish Aldh2 and is a substrate for human ALDH2. This study reveals a conserved and biologically relevant ALDH2-5-nitrofuran interaction that may have important implications for managing the toxicity of 5-nitrofuran treatment.

A Yeast Chemical Genetic Screen Identifies Inhibitors of Human Telomerase

Summary Telomerase comprises a reverse transcriptase and an internal RNA template that maintains telomeres in many eukaryotes, and it is a well-validated cancer target. However, there is a dearth of small molecules with efficacy against human telomerase in vivo. We developed a surrogate yeast high-throughput assay to identify human telomerase inhibitors. The reversibility of growth arrest induced by active human telomerase was assessed against a library of 678 compounds preselected for bioactivity in S. cerevisiae. Four of eight compounds identified reproducibly restored growth to strains expressing active human telomerase, and three of these four compounds also specifically inhibited purified human telomerase in vitro. These compounds represent probes for human telomerase function, and potential entry points for development of lead compounds against telomerase-positive cancers.

Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism

The network structure of biological systems suggests that effective therapeutic intervention may require combinations of agents that act synergistically. However, a dearth of systematic chemical combination datasets have limited the development of predictive algorithms for chemical synergism. Here, we report two large datasets of linked chemical-genetic and chemical-chemical interactions in the budding yeast Saccharomyces cerevisiae. We screened 5,518 unique compounds against 242 diverse yeast gene deletion strains to generate an extended chemical-genetic matrix (CGM) of 492,126 chemical-gene interaction measurements. This CGM dataset contained 1,434 genotype-specific inhibitors, termed cryptagens. We selected 128 structurally diverse cryptagens and tested all pairwise combinations to generate a benchmark dataset of 8,128 pairwise chemical-chemical interaction tests for synergy prediction, termed the cryptagen matrix (CM). An accompanying database resource called ChemGRID was developed to enable analysis, visualisation and downloads of all data. The CGM and CM datasets will facilitate the benchmarking of computational approaches for synergy prediction, as well as chemical structure-activity relationship models for anti-fungal drug discovery.