Colonyzer: automated quantification of micro-organism growth characteristics on solid agar

Long-term stability of acquired drug resistance and resistance associated mutations in the fungal pathogen Nakaseomyces glabratus (Candida glabrata)

The limited number of available antifungal drugs and the increasing number of fungal isolates that show drug or multidrug resistance pose a serious medical threat. Several yeast pathogens, such as Nakaseomyces glabratus (Candida glabrata), show a remarkable ability to develop drug resistance during treatment through the acquisition of genetic mutations. However, how stable this resistance and the underlying mutations are in non-selective conditions remains poorly characterized. The stability of acquired drug resistance has fundamental implications for our understanding of the appearance and spread of drug-resistant outbreaks and for defining efficient strategies to combat them. Here, we used an in vitro evolution approach to assess the stability under optimal growth conditions of resistance phenotypes and resistance-associated mutations that were previously acquired under exposure to antifungals. Our results reveal a remarkable stability of the resistant phenotype and the underlying mutations in a significant number of evolved populations, which conserved their phenotype for at least two months in the absence of drug-selective pressure. We observed a higher stability of anidulafungin resistance over fluconazole resistance, and of resistance-conferring point mutations as compared with aneuploidies. In addition, we detected accumulation of novel mutations in previously altered resistance-associated genes in non-selective conditions, which suggest a possible compensatory role. We conclude that acquired resistance, particularly to anidulafungin, is a long-lasting phenotype, which has important implications for the persistence and propagation of drug-resistant clinical outbreaks.

Quantifying massively parallel microbial growth with spatially mediated interactions

Quantitative understanding of microbial growth is an essential prerequisite for successful control of pathogens as well as various biotechnology applications. Even though the growth of cell populations has been extensively studied, microbial growth remains poorly characterised at the spatial level. Indeed, even isogenic populations growing at different locations on solid growth medium typically show significant location-dependent variability in growth. Here we show that this variability can be attributed to the initial physiological states of the populations, the interplay between populations interacting with their local environment and the diffusion of nutrients and energy sources coupling the environments. We further show how the causes of this variability change throughout the growth of a population. We use a dual approach, first applying machine learning regression models to discover that location dominates growth variability at specific times, and, in parallel, developing explicit population growth models to describe this spatial effect. In particular, treating nutrient and energy source concentration as a latent variable allows us to develop a mechanistic resource consumer model that captures growth variability across the shared environment. As a consequence, we are able to determine intrinsic growth parameters for each local population, removing confounders common to location-dependent variability in growth. Importantly, our explicit low-parametric model for the environment paves the way for massively parallel experimentation with configurable spatial niches for testing specific eco-evolutionary hypotheses.

Equations describing semi-confluent cell growth (I) Analytical approximations

A set of differential equations with analytical solutions are presented that can quantitatively account for variable degrees of contact inhibition on cell growth in two- and three-dimensional cultures. The developed equations can be used for comparative purposes when assessing contribution of higher-order effects, such as culture geometry and nutrient depletion, on mean cell growth rate. These equations also offer experimentalists the opportunity to characterize cell culture experiments using a single reductive parameter.

A Matlab-based application for quantification of yeast cell growth on solid media

Quantitative assessment of growth and survival is a suitable technique in studying biochemical, genetic and physiological processes in the cells. The budding yeast Saccharomyces cerevisiae is one of the most widely used eukaryotic model organisms for studying cellular mechanisms and processes in evolutionarily distant species, including humans. Yeast growth can be evaluated on both liquid and solid media by measuring cell suspension turbidity and colony forming units, respectively. Several software tools utilizing different parameters have been proposed to quantify yeast growth on solid media. Here, we developed a Matlab-based application which provides a rapid and robust quantitative yeast growth analysis from spot plating assay. Spot plating assay is a typical procedure to evaluate yeast growth in low-throughput laboratory settings, including growth on different nutrient sources or treatment with specific stressors. The app has a one-step installation process, a self-explanatory interface and shorter analysis steps compared with previous established methods, providing a useful tool for both expert and non-expert yeast researchers.

Building a eukaryotic chromosome arm by de novo design and synthesis

The genome of an organism is inherited from its ancestor and continues to evolve over time, however, the extent to which the current version could be altered remains unknown. To probe the genome plasticity of Saccharomyces cerevisiae, here we replace the native left arm of chromosome XII (chrXIIL) with a linear artificial chromosome harboring small sets of reconstructed genes. We find that as few as 12 genes are sufficient for cell viability, whereas 25 genes are required to recover the partial fitness defects observed in the 12-gene strain. Next, we demonstrate that these genes can be reconstructed individually using synthetic regulatory sequences and recoded open-reading frames with a "one-amino-acid-one-codon" strategy to remain functional. Finally, a synthetic neochromsome with the reconstructed genes is assembled which could substitute chrXIIL for viability. Together, our work not only highlights the high plasticity of yeast genome, but also illustrates the possibility of making functional eukaryotic chromosomes from entirely artificial sequences.

An experimental setup and segmentation method for CFU counting on agar plate for the assessment of drinking water

Quantification of bacterial colonies on an agar plate is a daily routine for a microbiologist to determine the number of viable microorganisms in the sample. In general, microbiologists perform a visual assessment of bacterial colonies which is time-consuming (takes 2 min per plate), tedious, and subjective. Some automatic counting algorithms are developed that save labour and time, but their results are affected by the non-illumination on an agar plate. To improve this, the present manuscript aims to develop an inexpensive and efficient device to acquire S.aureus images via an automatic counting method using image processing techniques under real laboratory conditions. The proposed method (P_ColonyCount) includes the region of interest extraction and color space transformation followed by filtering, thresholding, morphological operation, distance transform, and watershed technique for the quantification of isolated and overlapping colonies. The present work also shows a comparative study on grayscale, K, and green channels by applying different filter and thresholding techniques on 42 images. The results of all channels were compared with the score provided by the expert (manual count). Out of all the proposed method (P_ColonyCount), the K channel gives the best outcome in comparison with the other two channels (grayscale and green) in terms of precision, recall, and F-measure which are 0.99, 0.99, and 0.99 (2 h), 0.98, 0.99, and 0.98 (4 h), and 0.98, 0.98, 0.98 (6 h) respectively. The execution time of the manual and the proposed method (P_ColonyCount) for 42 images ranges from 19 to 113 s and 15 to 31 s respectively. Apart from this, a user-friendly graphical user interface is also developed for the convenient enumeration of colonies without any expert knowledge/training. The developed imaging device will be useful for researchers and teaching lab settings.

Origin of fungal hybrids with pathogenic potential from warm seawater environments

Hybridisation is a common event in yeasts often leading to genomic variability and adaptation. The yeast Candida orthopsilosis is a human-associated opportunistic pathogen belonging to the Candida parapsilosis species complex. Most C. orthopsilosis clinical isolates are hybrids resulting from at least four independent crosses between two parental lineages, of which only one has been identified. The rare presence or total absence of parentals amongst clinical isolates is hypothesised to be a consequence of a reduced pathogenicity with respect to their hybrids. Here, we sequence and analyse the genomes of environmental C. orthopsilosis strains isolated from warm marine ecosystems. We find that a majority of environmental isolates are hybrids, phylogenetically closely related to hybrid clinical isolates. Furthermore, we identify the missing parental lineage, thus providing a more complete overview of the genomic evolution of this species. Additionally, we discover phenotypic differences between the two parental lineages, as well as between parents and hybrids, under conditions relevant for pathogenesis. Our results suggest a marine origin of C. orthopsilosis hybrids, with intrinsic pathogenic potential, and pave the way to identify pre-existing environmental adaptations that rendered hybrids more prone than parental lineages to colonise and infect the mammalian host.

A Normalization Protocol Reduces Edge Effect in High-Throughput Analyses of Hydroxyurea Hypersensitivity in Fission Yeast

Edge effect denotes better growth of microbial organisms situated at the edge of the solid agar media. Although the precise reason underlying edge effect is unresolved, it is generally attributed to greater nutrient availability with less competing neighbors at the edge. Nonetheless, edge effect constitutes an unavoidable confounding factor that results in misinterpretation of cell fitness, especially in high-throughput screening experiments widely employed for genome-wide investigation using microbial gene knockout or mutant libraries. Here, we visualize edge effect in high-throughput high-density pinning arrays and report a normalization approach based on colony growth rate to quantify drug (hydroxyurea)-hypersensitivity in fission yeast strains. This normalization procedure improved the accuracy of fitness measurement by compensating cell growth rate discrepancy at different locations on the plate and reducing false-positive and -negative frequencies. Our work thus provides a simple and coding-free solution for a struggling problem in robotics-based high-throughput screening experiments.

Automation of yeast spot assays using an affordable liquid handling robot

The spot assay of the budding yeast Saccharomyces cerevisiae is an experimental method that is used to evaluate the effect of genotypes, medium conditions, and environmental stresses on cell growth and survival. Automation of the spot assay experiments from preparing a dilution series to spotting to observing spots continuously has been implemented based on large laboratory automation devices and robots, especially for high-throughput functional screening assays. However, there has yet to be an affordable solution for the automated spot assays suited to researchers in average laboratories and with high customizability for end-users. To make reproducible spot assay experiments widely available, we have automated the plate-based yeast spot assay of budding yeast using Opentrons OT-2 (OT-2), an affordable liquid-handling robot, and a flatbed scanner. We prepared a 3D-printed mount for the Petri dish to allow for precise placement of the Petri dish inside the OT-2. To account for the uneven height of the agar plates, which were made by human hands, we devised a method to adjust the z-position of the pipette tips based on the weight of each agar plate. During the incubation of the agar plates, a flatbed scanner was used to automatically take images of the agar plates over time, allowing researchers to quantify and compare the cell density within the spots at optimal time points a posteriori. Furthermore, the accuracy of the newly developed automated spot assay was verified by performing spot assays with human experimenters and the OT-2 and quantifying the yeast-grown area of the spots. This study will contribute to the introduction of automated spot assays and the automated acquisition of growth processes in conventional laboratories that are not adapted for high-throughput laboratory automation.

Tetraploidy accelerates adaptation under drug selection in a fungal pathogen

Baseline ploidy significantly impacts evolutionary trajectories and, specifically, tetraploidy is associated with higher rates of adaptation relative to haploidy and diploidy. While the majority of experimental evolution studies investigating ploidy use the budding yeast Saccharomyces cerivisiae, the fungal pathogen Candida albicans is a powerful system to investigate ploidy dynamics, particularly in the context of acquiring antifungal drug resistance. C. albicans laboratory and clinical strains are predominantly diploid, but have been isolated as haploid and polyploid. Here, we evolved diploid and tetraploid C. albicans for ~60 days in the antifungal drug caspofungin. Tetraploid-evolved lines adapted faster than diploid-evolved lines and reached higher levels of caspofungin resistance. While diploid-evolved lines generally maintained their initial genome size, tetraploid-evolved lines rapidly underwent genome-size reductions and did so prior to caspofungin adaptation. While clinical resistance was largely due to mutations in FKS1, these mutations were caused by substitutions in diploid, and indels in tetraploid isolates. Furthermore, fitness costs in the absence of drug selection were significantly less in tetraploid-evolved lines compared to the diploid-evolved lines. Taken together, this work supports a model of adaptation in which the tetraploid state is transient but its ability to rapidly transition ploidy states improves adaptive outcomes and may drive drug resistance in fungal pathogens.

Using colony size to measure fitness in Saccharomyces cerevisiae

Competitive fitness assays in liquid culture have been a mainstay for characterizing experimental evolution of microbial populations. Growth of microbial strains has also been extensively characterized by colony size and could serve as a useful alternative if translated to per generation measurements of relative fitness. To examine fitness based on colony size, we established a relationship between cell number and colony size for strains of Saccharomyces cerevisiae robotically pinned onto solid agar plates in a high-density format. This was used to measure growth rates and estimate relative fitness differences between evolved strains and their ancestors. After controlling for edge effects through both normalization and agar-trimming, we found that colony size is a sensitive measure of fitness, capable of detecting 1% differences. While fitnesses determined from liquid and solid mediums were not equivalent, our results demonstrate that colony size provides a sensitive means of measuring fitness that is particularly well suited to measurements across many environments.

High-Throughput, High-Precision Colony Phenotyping with Pyphe

Colony fitness screens are powerful approaches for functional genomics and genetics. This protocol describes experimental and computational procedures for assaying the fitness of thousands of microbial strains in numerous conditions in parallel. Data analysis is based on pyphe, an all-in-one bioinformatics toolbox for scanning, image analysis, data normalization, and interpretation. We describe a standard protocol where endpoint colony areas are used as fitness proxy and two variations on this, one using colony growth curves and one using colony viability staining with phloxine B. Different strategies for experimental design, normalization and quality control are discussed. Using these approaches, it is possible to collect hundreds of thousands of data points, with low technical noise levels around 5%, in an experiment typically lasting 2 weeks or less.

Narrow mutational signatures drive acquisition of multidrug resistance in the fungal pathogen Candida glabrata

Fungal infections are a growing medical concern, in part due to increased resistance to one or multiple antifungal drugs. However, the evolutionary processes underpinning the acquisition of antifungal drug resistance are poorly understood. Here, we used experimental microevolution to study the adaptation of the yeast pathogen Candida glabrata to fluconazole and anidulafungin, two widely used antifungal drugs with different modes of action. Our results show widespread ability of rapid adaptation to one or both drugs. Resistance, including multidrug resistance, is often acquired at moderate fitness costs and mediated by mutations in a limited set of genes that are recurrently and specifically mutated in strains adapted to each of the drugs. Importantly, we uncover a dual role of ERG3 mutations in resistance to anidulafungin and cross-resistance to fluconazole in a subset of anidulafungin-adapted strains. Our results shed light on the mutational paths leading to resistance and cross-resistance to antifungal drugs.

LI Detector: a framework for sensitive colony-based screens regardless of the distribution of fitness effects

Microbial growth characteristics have long been used to investigate fundamental questions of biology. Colony-based high-throughput screens enable parallel fitness estimation of thousands of individual strains using colony growth as a proxy for fitness. However, fitness estimation is complicated by spatial biases affecting colony growth, including uneven nutrient distribution, agar surface irregularities, and batch effects. Analytical methods that have been developed to correct for these spatial biases rely on the following assumptions: (1) that fitness effects are normally distributed, and (2) that most genetic perturbations lead to minor changes in fitness. Although reasonable for many applications, these assumptions are not always warranted and can limit the ability to detect small fitness effects. Beneficial fitness effects, in particular, are notoriously difficult to detect under these assumptions. Here, we developed the linear interpolation-based detector (LI Detector) framework to enable sensitive colony-based screening without making prior assumptions about the underlying distribution of fitness effects. The LI Detector uses a grid of reference colonies to assign a relative fitness value to every colony on the plate. We show that the LI Detector is effective in correcting for spatial biases and equally sensitive toward increase and decrease in fitness. LI Detector offers a tunable system that allows the user to identify small fitness effects with unprecedented sensitivity and specificity. LI Detector can be utilized to develop and refine gene-gene and gene-environment interaction networks of colony-forming organisms, including yeast, by increasing the range of fitness effects that can be reliably detected.

Quantifying Variation in Bacterial Reproductive Fitness: a High-Throughput Method

Reproductive fitness of bacteria is a major factor in the evolution and persistence of antimicrobial resistance and may play an important role as a pathogen factor in severe infections. With a computational approach to quantify fitness in bacteria growing competitively on agar plates, our high-throughput method has been designed to obtain additional phenotypic data for antimicrobial resistance analysis at a low cost.

To evaluate changes in reproductive fitness of bacteria, e.g., after acquisition of antimicrobial resistance, a low-cost high-throughput method to analyze bacterial growth on agar is desirable for broad usability. In our bacterial quantitative fitness analysis (BaQFA), arrayed cultures are spotted on agar and photographed sequentially while growing. These time-lapse images are analyzed using a purpose-built open-source software to derive normalized image intensity (NI) values for each culture spot. Subsequently, a Gompertz growth model is fitted to NI values, and fitness is calculated from model parameters. To represent a range of clinically important pathogenic bacteria, we used different strains of Enterococcus faecium, Escherichia coli, and Staphylococcus aureus, with and without antimicrobial resistance. Relative competitive fitness (RCF) was defined as the mean fitness ratio of two strains growing competitively on one plate.BaQFA permitted the accurate construction of growth curves from bacteria grown on semisolid agar plates and fitting of Gompertz models. Normalized image intensity values showed a strong association with the total CFU/ml count per spotted culture (P < 0.001) for all strains of the three species. BaQFA showed relevant reproductive fitness differences between individual strains, suggesting substantially higher fitness of methicillin-resistant S. aureus JE2 than Cowan (RCF, 1.58; P < 0.001). Similarly, the vancomycin-resistant E. faecium ST172b showed higher competitive fitness than susceptible E. faecium ST172 (RCF, 1.59; P < 0.001). Our BaQFA method allows detection of fitness differences between bacterial strains and may help to estimate epidemiological antimicrobial persistence or contribute to the prediction of clinical outcomes in severe infections.

IMPORTANCE Reproductive fitness of bacteria is a major factor in the evolution and persistence of antimicrobial resistance and may play an important role in severe infections. With a computational approach to quantify fitness in bacteria growing competitively on agar plates, our high-throughput method has been designed to obtain additional phenotypic data for antimicrobial resistance analysis at a low cost. Furthermore, our bacterial quantitative fitness analysis (BaQFA) enables the investigation of a link between bacterial fitness and clinical outcomes in severe invasive bacterial infections. This may allow future use of our method for patient management and risk stratification of clinical outcomes. Our proposed method uses open-source software and a hardware setup that can utilize consumer electronics. This will enable a wider community of researchers, including those from low-resource countries, where the burden of antimicrobial resistance is highest, to obtain valuable information about emerging bacterial strains.

Pyphe, a python toolbox for assessing microbial growth and cell viability in high-throughput colony screens

Microbial fitness screens are a key technique in functional genomics. We present an all-in-one solution, pyphe, for automating and improving data analysis pipelines associated with large-scale fitness screens, including image acquisition and quantification, data normalisation, and statistical analysis. Pyphe is versatile and processes fitness data from colony sizes, viability scores from phloxine B staining or colony growth curves, all obtained with inexpensive transilluminating flatbed scanners. We apply pyphe to show that the fitness information contained in late endpoint measurements of colony sizes is similar to maximum growth slopes from time series. We phenotype gene-deletion strains of fission yeast in 59,350 individual fitness assays in 70 conditions, revealing that colony size and viability provide complementary, independent information. Viability scores obtained from quantifying the redness of phloxine-stained colonies accurately reflect the fraction of live cells within colonies. Pyphe is user-friendly, open-source and fully documented, illustrated by applications to diverse fitness analysis scenarios.

Activation of Meiotic Genes Mediates Ploidy Reduction during Cryptococcal Infection

Cryptococcus neoformans is a global human fungal pathogen that causes fatal meningoencephalitis in mostly immunocompromised individuals. During pulmonary infection, cryptococcal cells form large polyploid cells that exhibit increased resistance to host immune attack and are proposed to contribute to the latency of cryptococcal infection. These polyploid titan cells can generate haploid and aneuploid progeny that may result in systemic infection. What triggers cryptococcal polyploidization and how ploidy reduction is achieved remain open questions. Here, we discovered that Cryptococcus cells polyploidize in response to genotoxic stresses that cause DNA double-strand breaks. Intriguingly, meiosis-specific genes are activated in C. neoformans and contribute to ploidy reduction, both in vitro and during infection in mice. Cryptococcal cells that activated their meiotic genes in mice were resistant to specific genotoxic stress compared to sister cells recovered from the same host tissue but without activation of meiotic genes. Our findings support the idea that meiotic genes, in addition to their conventional roles in classic sexual reproduction, contribute to adaptation of eukaryotic cells that undergo dramatic genome changes in response to genotoxic stress. The discovery has additional implications for evolution of sexual reproduction and the paradox of the presence of meiotic machinery in asexual species. Finally, our findings in this eukaryotic microbe mirror the revolutionary discoveries of the polyploidization and meiosis-like ploidy reduction process in cancer cells, suggesting that the reversible ploidy change itself could provide a general mechanism for rejuvenation to promote individual survival in response to stress.

Automated counting of bacterial colonies on agar plates based on images captured at near-infrared light

Counting colonies is usually used in microbiological analysis to assess if samples meet microbiological criteria. Although manual counting remains gold standard, the process is subjective, tedious, and time-consuming. Some developed automatic counting methods could save labors and time, but their results are easily affected by uneven illumination and reflection of visible light. To offer a method which counts colonies automatically and is robust to light, we constructed a convenient and cost-effective system to obtain images of colonies at near-infrared light, and proposed an automatic method to detect and count colonies by processing images. The colonies cultured by using raw cows' milk were used as identification objects. The developed system mainly consisted of a visible/near-infrared camera and a circular near-infrared illuminator. The automatic method proposed to count colonies includes four steps, i.e., eliminating noises outside agar plate, removing plate rim and wall, identifying and separating clustered or overlapped colonies, and counting colonies by using connected region labelling, distance transform, and watershed algorithms, etc. A user-friendly graphic user interface was also developed for the proposed method. The relative error and counting time of the automatic counting method were compared with those of manual counting. The results showed that the relative error of the automatic counting method was -7.4%~ + 8.3%, with average relative error of 0.2%, and the time used for counting colonies on each agar plate was 11-21 s, which was 15-75% of the time used in manual counting, depending on the numbers of colonies on agar plates. The proposed system and automatic counting method demonstrate promising performance in terms of precision, and they are robust and efficient in terms of labor- and time-savings.

In vitro antimicrobial susceptibility testing methods: agar dilution to 3D tissue-engineered models

In the field of orthopaedic surgery, bacterial invasion of implants and the resulting periprosthetic infections are a common and unresolved problem. Antimicrobial susceptibility testing methods help to define the optimal treatment and identify antimicrobial resistance. This review discusses proven gold-standard techniques and recently developed models for antimicrobial susceptibility testing, while also providing a future outlook. Conventional, gold-standard methods, such as broth microdilution, are still widely applied in clinical settings. Although recently developed methods based on microfluidics and microdroplets have shown advantages over conventional methods in terms of testing speed, safety and the potential to provide a deeper insight into resistance mechanisms, extensive validation is required to translate this research to clinical practice. Recent optical and mechanical methods are complex and expensive and, therefore, not immediately clinically applicable. Novel osteoblast infection and tissue models best resemble infections in vivo. However, the integration of biomaterials into these models remains challenging and they require a long tissue culture, making their rapid clinical implementation unlikely. A method applicable for both clinical and research environments is difficult to realise. With a continuous increase in antimicrobial resistance, there is an urgent need for methods that analyse recurrent infections to identify the optimal treatment approaches. Graphical abstract Timeline of published and partly applied antimicrobial susceptibility testing methods, listed according to their underlying mechanism, complexity and application in research or clinics.

High-Throughput Quantitative Genetic Interaction Mapping in the Fission Yeast Schizosaccharomyces pombe

Epistasis mapping, in which the phenotype that emerges from combining pairs of mutations is measured quantitatively, is a powerful tool for unbiased study of gene function. When performed at a large scale, this approach has been used to assign function to previously uncharacterized genes, define functional modules and pathways, and study their cross talk. These experiments rely heavily on methods for rapid sampling of binary combinations of mutant alleles by systematic generation of a series of double mutants. Epistasis mapping technologies now exist in various model systems. Here we provide an overview of different epistasis mapping technologies, including the pombe epistasis mapper (PEM) system designed for the collection of quantitative genetic interaction data in fission yeast Schizosaccharomyces pombe Comprising a series of high-throughput selection steps for generation and characterization of double mutants, the PEM system has provided insight into a wide range of biological processes as well as facilitated evolutionary analysis of genetic interactomes across different species.

PHENOS: a high-throughput and flexible tool for microorganism growth phenotyping on solid media

Background

Microbial arrays, with a large number of different strains on a single plate printed with robotic precision, underpin an increasing number of genetic and genomic approaches. These include Synthetic Genetic Array analysis, high-throughput Quantitative Trait Loci (QTL) analysis and 2-hybrid techniques. Measuring the growth of individual colonies within these arrays is an essential part of many of these techniques but is useful for any work with arrays. Measurement is typically done using intermittent imagery fed into complex image analysis software, which is not especially accurate and is challenging to use effectively. We have developed a simple and fast alternative technique that uses a pinning robot and a commonplace microplate reader to continuously measure the thickness of colonies growing on solid agar, complemented by a technique for normalizing the amount of cells initially printed to each spot of the array in the first place. We have developed software to automate the process of combining multiple sets of readings, subtracting agar absorbance, and visualizing colony thickness changes in a number of informative ways.

Results

The “PHENOS” pipeline (PHENotyping On Solid media), optimized for Saccharomyces yeasts, produces highly reproducible growth curves and is particularly sensitive to low-level growth. We have empirically determined a formula to estimate colony cell count from an absorbance measurement, and shown this to be comparable with estimates from measurements in liquid. We have also validated the technique by reproducing the results of an earlier QTL study done with conventional liquid phenotyping, and found PHENOS to be considerably more sensitive.

Conclusions

“PHENOS” is a cost effective and reliable high-throughput technique for quantifying growth of yeast arrays, and is likely to be equally very useful for a range of other types of microbial arrays. A detailed guide to the pipeline and software is provided with the installation files at https://github.com/gact/phenos.

Electronic supplementary material

The online version of this article (10.1186/s12866-017-1143-y) contains supplementary material, which is available to authorized users.

Genome-Wide Quantitative Fitness Analysis (QFA) of Yeast Cultures

We provide a detailed protocol for robot-assisted, genome-wide measurement of fitness in the model yeast Saccharomyces cerevisiae using Quantitative Fitness Analysis (QFA). We first describe how we construct thousands of double or triple mutant yeast strains in parallel using Synthetic Genetic Array (SGA) procedures. Strains are inoculated onto solid agar surfaces by liquid spotting followed by repeated photography of agar plates. Growth curves are constructed and the fitness of each strain is estimated. Robot-assisted QFA, can be used to identify genetic interactions and chemical sensitivity/resistance in genome-wide experiments, but QFA can also be used in smaller scale, manual workflows.

A tool named Iris for versatile high-throughput phenotyping in microorganisms

Advances in our ability to systematically introduce and track controlled genetic variance in microbes have fueled high-throughput reverse genetics approaches in the past decade. When coupled to quantitative readouts, such approaches are extremely powerful at elucidating gene function and providing insights into the underlying pathways and the overall cellular network organization. Yet, until now all efforts for quantifying microbial macroscopic phenotypes have been restricted to monitoring growth in a small number of model microbes. We developed an image analysis software named Iris, which allows for systematic exploration of a number of orthogonal-to-growth processes, including biofilm formation, colony morphogenesis, envelope biogenesis, sporulation and reporter activity. In addition, Iris provides more sensitive growth measurements than current available software, and is compatible with a variety of different microbes, as well as with endpoint or kinetic data. We used Iris to reanalyze existing chemical genomics data in Escherichia coli and to perform proof-of-principle screens on colony biofilm formation and morphogenesis of different bacterial species and the pathogenic fungus, Candida albicans. Thereby we recapitulated existing knowledge but also identified a plethora of additional genes and pathways involved in both processes.

One-Cell Doubling Evaluation by Living Arrays of Yeast, ODELAY!

Cell growth is a complex phenotype widely used in systems biology to gauge the impact of genetic and environmental perturbations. Due to the magnitude of genome-wide studies, resolution is often sacrificed in favor of throughput, creating a demand for scalable, time-resolved, quantitative methods of growth assessment. We present ODELAY (One-cell Doubling Evaluation by Living Arrays of Yeast), an automated and scalable growth analysis platform. High measurement density and single-cell resolution provide a powerful tool for large-scale multiparameter growth analysis based on the modeling of microcolony expansion on solid media. Pioneered in yeast but applicable to other colony forming organisms, ODELAY extracts the three key growth parameters (lag time, doubling time, and carrying capacity) that define microcolony expansion from single cells, simultaneously permitting the assessment of population heterogeneity. The utility of ODELAY is illustrated using yeast mutants, revealing a spectrum of phenotypes arising from single and combinatorial growth parameter perturbations.

Genome-wide identification of genes involved in growth and fermentation activity at low temperature in Saccharomyces cerevisiae

Fermentation at low temperatures is one of the most popular current winemaking practices because of its reported positive impact on the aromatic profile of wines. However, low temperature is an additional hurdle to develop Saccharomyces cerevisiae wine yeasts, which are already stressed by high osmotic pressure, low pH and poor availability of nitrogen sources in grape must. Understanding the mechanisms of adaptation of S. cerevisiae to fermentation at low temperature would help to design strategies for process management, and to select and improve wine yeast strains specifically adapted to this winemaking practice. The problem has been addressed by several approaches in recent years, including transcriptomic and other high-throughput strategies. In this work we used a genome-wide screening of S. cerevisiae diploid mutant strain collections to identify genes that potentially contribute to adaptation to low temperature fermentation conditions. Candidate genes, impaired for growth at low temperatures (12°C and 18°C), but not at a permissive temperature (28°C), were deleted in an industrial homozygous genetic background, wine yeast strain FX10, in both heterozygosis and homozygosis. Some candidate genes were required for growth at low temperatures only in the laboratory yeast genetic background, but not in FX10 (namely the genes involved in aromatic amino acid biosynthesis). Other genes related to ribosome biosynthesis (SNU66 and PAP2) were required for low-temperature fermentation of synthetic must (SM) in the industrial genetic background. This result coincides with our previous findings about translation efficiency with the fitness of different wine yeast strains at low temperature.

Spotsizer: High-throughput quantitative analysis of microbial growth

Microbial colony growth can serve as a useful readout in assays for studying complex genetic interactions or the effects of chemical compounds. Although computational tools for acquiring quantitative measurements of microbial colonies have been developed, their utility can be compromised by inflexible input image requirements, non-trivial installation procedures, or complicated operation. Here, we present the Spotsizer software tool for automated colony size measurements in images of robotically arrayed microbial colonies. Spotsizer features a convenient graphical user interface (GUI), has both single-image and batch-processing capabilities, and works with multiple input image formats and different colony grid types. We demonstrate how Spotsizer can be used for high-throughput quantitative analysis of fission yeast growth. The user-friendly Spotsizer tool provides rapid, accurate, and robust quantitative analyses of microbial growth in a high-throughput format. Spotsizer is freely available at https://data.csiro.au/dap/landingpage?pid=csiro:15330 under a proprietary CSIRO license.

Scan-o-matic: High-Resolution Microbial Phenomics at a Massive Scale

The capacity to map traits over large cohorts of individuals—phenomics—lags far behind the explosive development in genomics. For microbes, the estimation of growth is the key phenotype because of its link to fitness. We introduce an automated microbial phenomics framework that delivers accurate, precise, and highly resolved growth phenotypes at an unprecedented scale. Advancements were achieved through the introduction of transmissive scanning hardware and software technology, frequent acquisition of exact colony population size measurements, extraction of population growth rates from growth curves, and removal of spatial bias by reference-surface normalization. Our prototype arrangement automatically records and analyzes close to 100,000 growth curves in parallel. We demonstrate the power of the approach by extending and nuancing the known salt-defense biology in baker’s yeast. The introduced framework represents a major advance in microbial phenomics by providing high-quality data for extensive cohorts of individuals and generating well-populated and standardized phenomics databases

Pho4 mediates phosphate acquisition in Candida albicans and is vital for stress resistance and metal homeostasis

This study provides the first evidence that the phosphate-responsive transcription factor Pho4 is vital for survival of Candida albicans to diverse and physiologically relevant stresses. Pho4 is important for C. albicans pathogenesis, and thus these findings illustrate how metabolic adaptation promotes C. albicans survival in the host.

During interactions with its mammalian host, the pathogenic yeast Candida albicans is exposed to a range of stresses such as superoxide radicals and cationic fluxes. Unexpectedly, a nonbiased screen of transcription factor deletion mutants revealed that the phosphate-responsive transcription factor Pho4 is vital for the resistance of C. albicans to these diverse stresses. RNA-Seq analysis indicated that Pho4 does not induce stress-protective genes directly. Instead, we show that loss of Pho4 affects metal cation toxicity, accumulation, and bioavailability. We demonstrate that pho4Δ cells are sensitive to metal and nonmetal cations and that Pho4-mediated polyphosphate synthesis mediates manganese resistance. Significantly, we show that Pho4 is important for mediating copper bioavailability to support the activity of the copper/zinc superoxide dismutase Sod1 and that loss of Sod1 activity contributes to the superoxide sensitivity of pho4Δ cells. Consistent with the key role of fungal stress responses in countering host phagocytic defenses, we also report that C. albicans pho4Δ cells are acutely sensitive to macrophage-mediated killing and display attenuated virulence in animal infection models. The novel connections between phosphate metabolism, metal homeostasis, and superoxide stress resistance presented in this study highlight the importance of metabolic adaptation in promoting C. albicans survival in the host.

PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics

BACKGROUND

Phenomics is a field in functional genomics that records variation in organismal phenotypes in the genetic, epigenetic or environmental context at a massive scale. For microbes, the key phenotype is the growth in population size because it contains information that is directly linked to fitness. Due to technical innovations and extensive automation our capacity to record complex and dynamic microbial growth data is rapidly outpacing our capacity to dissect and visualize this data and extract the fitness components it contains, hampering progress in all fields of microbiology.

RESULTS

To automate visualization, analysis and exploration of complex and highly resolved microbial growth data as well as standardized extraction of the fitness components it contains, we developed the software PRECOG (PREsentation and Characterization Of Growth-data). PRECOG allows the user to quality control, interact with and evaluate microbial growth data with ease, speed and accuracy, also in cases of non-standard growth dynamics. Quality indices filter high- from low-quality growth experiments, reducing false positives. The pre-processing filters in PRECOG are computationally inexpensive and yet functionally comparable to more complex neural network procedures. We provide examples where data calibration, project design and feature extraction methodologies have a clear impact on the estimated growth traits, emphasising the need for proper standardization in data analysis.

CONCLUSIONS

PRECOG is a tool that streamlines growth data pre-processing, phenotypic trait extraction, visualization, distribution and the creation of vast and informative phenomics databases.

Synthetic Genetic Arrays: Automation of Yeast Genetics

Genome-sequencing efforts have led to great strides in the annotation of protein-coding genes and other genomic elements. The current challenge is to understand the functional role of each gene and how genes work together to modulate cellular processes. Genetic interactions define phenotypic relationships between genes and reveal the functional organization of a cell. Synthetic genetic array (SGA) methodology automates yeast genetics and enables large-scale and systematic mapping of genetic interaction networks in the budding yeast,Saccharomyces cerevisiae SGA facilitates construction of an output array of double mutants from an input array of single mutants through a series of replica pinning steps. Subsequent analysis of genetic interactions from SGA-derived mutants relies on accurate quantification of colony size, which serves as a proxy for fitness. Since its development, SGA has given rise to a variety of other experimental approaches for functional profiling of the yeast genome and has been applied in a multitude of other contexts, such as genome-wide screens for synthetic dosage lethality and integration with high-content screening for systematic assessment of morphology defects. SGA-like strategies can also be implemented similarly in a number of other cell types and organisms, includingSchizosaccharomyces pombe,Escherichia coli, Caenorhabditis elegans, and human cancer cell lines. The genetic networks emerging from these studies not only generate functional wiring diagrams but may also play a key role in our understanding of the complex relationship between genotype and phenotype.

Automated inference procedure for the determination of cell growth parameters

The growth rate and carrying capacity of a cell population are key to the characterization of the population's viability and to the quantification of its responses to perturbations such as drug treatments. Accurate estimation of these parameters necessitates careful analysis. Here, we present a rigorous mathematical approach for the robust analysis of cell count data, in which all the experimental stages of the cell counting process are investigated in detail with the machinery of Bayesian probability theory. We advance a flexible theoretical framework that permits accurate estimates of the growth parameters of cell populations and of the logical correlations between them. Moreover, our approach naturally produces an objective metric of avoidable experimental error, which may be tracked over time in a laboratory to detect instrumentation failures or lapses in protocol. We apply our method to the analysis of cell count data in the context of a logistic growth model by means of a user-friendly computer program that automates this analysis, and present some samples of its output. Finally, we note that a traditional least squares fit can provide misleading estimates of parameter values, because it ignores available information with regard to the way in which the data have actually been collected.

Cell-based screens and phenomics with fission yeast

Next-generation sequencing approaches have considerably advanced our understanding of genome function and regulation. However, the knowledge of gene function and complex cellular processes remains a challenge and bottleneck in biological research. Phenomics is a rapidly emerging area, which seeks to rigorously characterize all phenotypes associated with genes or gene variants. Such high-throughput phenotyping under different conditions can be a potent approach toward gene function. The fission yeast Schizosaccharomyces pombe (S. pombe) is a proven eukaryotic model organism that is increasingly used for genomewide screens and phenomic assays. In this review, we highlight current large-scale, cell-based approaches used with S. pombe, including computational colony-growth measurements, genetic interaction screens, parallel profiling using barcodes, microscopy-based cell profiling, metabolomic methods and transposon mutagenesis. These diverse methods are starting to offer rich insights into the relationship between genotypes and phenotypes.

Bayesian hierarchical modelling for inferring genetic interactions in yeast

Quantitative fitness analysis (QFA) is a high throughput experimental and computational methodology for measuring the growth of microbial populations. QFA screens can be used to compare the health of cell populations with and without a mutation in a query gene to infer genetic interaction strengths genomewide, examining thousands of separate genotypes. We introduce Bayesian hierarchical models of population growth rates and genetic interactions that better reflect QFA experimental design than current approaches. Our new approach models population dynamics and genetic interaction simultaneously, thereby avoiding passing information between models via a univariate fitness summary. Matching experimental structure more closely, Bayesian hierarchical approaches use data more efficiently and find new evidence for genes which interact with yeast telomeres within a published data set.

Human Milk Oligosaccharides Inhibit Candida albicans Invasion of Human Premature Intestinal Epithelial Cells

BACKGROUND

Human milk oligosaccharides (HMOs) are a highly abundant, diverse group of unique glycans that are postulated to promote the development of a protective bacterial microbiota in the intestine and prevent adhesive and invasive interactions of pathogenic bacteria with mucosal epithelia. Candida albicans, a prevalent fungal colonizer of the neonatal gut, causes the majority of fungal disease in premature infants and is highly associated with life-threatening intestinal disorders.

OBJECTIVE

The objective of the current study was to test the hypothesis that HMOs protect human premature intestinal epithelial cells (pIECs) from invasion by C. albicans.

METHODS

To study fungal invasion, a quantitative immunocytochemical assay was used to distinguish invading from noninvading C. albicans cells in the presence and absence of HMOs. To understand how HMOs affect C. albicans invasion of pIECs, the expression of C. albicans virulence traits that are important for invasiveness (hyphal morphogenesis and ability to associate with host cells) were quantified.

RESULTS

Treatment with HMOs reduced invasion of pIECs by C. albicans in a dose-dependent manner by 14-67%, with a physiologic concentration (15mg/mL) of HMOs causing a 52% reduction in invasion (P < 0.05). The decreased invasive ability of C. albicans was associated with hyphal lengths that were ∼30% shorter (P < 0.05), likely because of a delay in the induction of hyphal morphogenesis after inoculation of yeast onto pIECs, which correlated with a 23% reduction in the combined expression level of hyphal-specific genes (P < 0.05). In addition, HMOs caused a 40% decrease in the number of C. albicans cells able to associate with pIECs at the time of hyphal induction (P < 0.05).

CONCLUSIONS

These results, obtained with the use of a primary pIEC model, indicate that HMOs reduce virulence characteristics of C. albicans and suggest a role for HMOs in protecting the premature infant intestine from invasion and damage by C. albicans hyphae.

Genetic Networks Required to Coordinate Chromosome Replication by DNA Polymerases α, δ, and ε in Saccharomyces cerevisiae

Three major DNA polymerases replicate the linear eukaryotic chromosomes. DNA polymerase α-primase (Pol α) and DNA polymerase δ (Pol δ) replicate the lagging-strand and Pol α and DNA polymerase ε (Pol ε) the leading-strand. To identify factors affecting coordination of DNA replication, we have performed genome-wide quantitative fitness analyses of budding yeast cells containing defective polymerases. We combined temperature-sensitive mutations affecting the three replicative polymerases, Pol α, Pol δ, and Pol ε with genome-wide collections of null and reduced function mutations. We identify large numbers of genetic interactions that inform about the roles that specific genes play to help Pol α, Pol δ, and Pol ε function. Surprisingly, the overlap between the genetic networks affecting the three DNA polymerases does not represent the majority of the genetic interactions identified. Instead our data support a model for division of labor between the different DNA polymerases during DNA replication. For example, our genetic interaction data are consistent with biochemical data showing that Pol ε is more important to the Pre-Loading complex than either Pol α or Pol δ. We also observed distinct patterns of genetic interactions between leading- and lagging-strand DNA polymerases, with particular genes being important for coupling proliferating cell nuclear antigen loading/unloading (Ctf18, Elg1) with nucleosome assembly (chromatin assembly factor 1, histone regulatory HIR complex). Overall our data reveal specialized genetic networks that affect different aspects of leading- and lagging-strand DNA replication. To help others to engage with these data we have generated two novel, interactive visualization tools, DIXY and Profilyzer.

Quantitative Fitness Analysis Identifies exo1∆ and Other Suppressors or Enhancers of Telomere Defects in Schizosaccharomyces pombe

Synthetic genetic array (SGA) has been successfully used to identify genetic interactions in S. cerevisiae and S. pombe. In S. pombe, SGA methods use either cycloheximide (C) or heat shock (HS) to select double mutants before measuring colony size as a surrogate for fitness. Quantitative Fitness Analysis (QFA) is a different method for determining fitness of microbial strains. In QFA, liquid cultures are spotted onto solid agar and growth curves determined for each spot by photography and model fitting. Here, we compared the two S. pombe SGA methods and found that the HS method was more reproducible for us. We also developed a QFA procedure for S. pombe. We used QFA to identify genetic interactions affecting two temperature sensitive, telomere associated query mutations (taz1Δ and pot1-1). We identify exo1∆ and other gene deletions as suppressors or enhancers of S. pombe telomere defects. Our study identifies known and novel gene deletions affecting the fitness of strains with telomere defects. The interactions we identify may be relevant in human cells.

Phenomic assessment of genetic buffering by kinetic analysis of cell arrays

Quantitative high-throughput cell array phenotyping (Q-HTCP) is applied to the genomic collection of yeast gene deletion mutants for systematic, comprehensive assessment of the contribution of genes and gene combinations to any phenotype of interest (phenomic analysis). Interacting gene networks influence every phenotype. Genetic buffering refers to how gene interaction networks stabilize or destabilize a phenotype. Like genomics, phenomics varies in its resolution with there being a trade-off allocating a greater number of measurements per sample to enhance quantification of the phenotype vs. increasing the number of different samples by obtaining fewer measurements per sample. The Q-HTCP protocol we describe assesses 50,000-70,000 cultures per experiment by obtaining kinetic growth curves from time series imaging of agar cell arrays. This approach was developed for the yeast gene deletion strains, but it could be applied as well to other microbial mutant arrays grown on solid agar media. The methods we describe are for creation and maintenance of frozen stocks, liquid source array preparation, agar destination plate printing, image scanning, image analysis, curve fitting, and evaluation of gene interaction.

Genetic Interaction Scoring Procedure for Bacterial Species

A genetic interaction occurs when the phenotype of an organism carrying two mutant genes differs from what should have been observed given their independent influence. Such unexpected outcome indicates a mechanistic connection between the perturbed genes, providing a key source of functional information about the cell. Large-scale screening for genetic interactions involves measuring phenotypes of single and double mutants, which for microorganisms is usually done by automated analysis of images of ordered colonies. Obtaining accurate colony sizes, and using them to identify genetic interactions from such screens remains a challenging and time-consuming task. Here, we outline steps to compute genetic interaction scores in E. coli by measuring colony sizes from plate images, performing normalisation, and quantifying the strength of the effect.

Colony-live--a high-throughput method for measuring microbial colony growth kinetics--reveals diverse growth effects of gene knockouts in Escherichia coli

Background

Precise quantitative growth measurements and detection of small growth changes in high-throughput manner is essential for fundamental studies of bacterial cell. However, an inherent tradeoff for measurement quality in high-throughput methods sacrifices some measurement quality. A key challenge has been how to enhance measurement quality without sacrificing throughput.

Results

We developed a new high-throughput measurement system, termed Colony-live. Here we show that Colony-live provides accurate measurement of three growth values (lag time of growth (LTG), maximum growth rate (MGR), and saturation point growth (SPG)) by visualizing colony growth over time. By using a new normalization method for colony growth, Colony-live gives more precise and accurate growth values than the conventional method. We demonstrated the utility of Colony-live by measuring growth values for the entire Keio collection of Escherichia coli single-gene knockout mutants. By using Colony-live, we were able to identify subtle growth defects of single-gene knockout mutants that were undetectable by the conventional method quantified by fixed time-point camera imaging. Further, Colony-live can reveal genes that influence the length of the lag-phase and the saturation point of growth.

Conclusions

Measurement quality is critical to achieving the resolution required to identify unique phenotypes among a diverse range of phenotypes. Sharing high-quality genome-wide datasets should benefit many researchers who are interested in specific gene functions or the architecture of cellular systems. Our Colony-live system provides a new powerful tool to accelerate accumulation of knowledge of microbial growth phenotypes.

Fast Bayesian parameter estimation for stochastic logistic growth models

The transition density of a stochastic, logistic population growth model with multiplicative intrinsic noise is analytically intractable. Inferring model parameter values by fitting such stochastic differential equation (SDE) models to data therefore requires relatively slow numerical simulation. Where such simulation is prohibitively slow, an alternative is to use model approximations which do have an analytically tractable transition density, enabling fast inference. We introduce two such approximations, with either multiplicative or additive intrinsic noise, each derived from the linear noise approximation (LNA) of a logistic growth SDE. After Bayesian inference we find that our fast LNA models, using Kalman filter recursion for computation of marginal likelihoods, give similar posterior distributions to slow, arbitrarily exact models. We also demonstrate that simulations from our LNA models better describe the characteristics of the stochastic logistic growth models than a related approach. Finally, we demonstrate that our LNA model with additive intrinsic noise and measurement error best describes an example set of longitudinal observations of microbial population size taken from a typical, genome-wide screening experiment.

gitter: a robust and accurate method for quantification of colony sizes from plate images

Colony-based screens that quantify the fitness of clonal populations on solid agar plates are perhaps the most important source of genome-scale functional information in microorganisms. The images of ordered arrays of mutants produced by such experiments can be difficult to process because of laboratory-specific plate features, morphed colonies, plate edges, noise, and other artifacts. Most of the tools developed to address this problem are optimized to handle a single setup and do not work out of the box in other settings. We present gitter, an image analysis tool for robust and accurate processing of images from colony-based screens. gitter works by first finding the grid of colonies from a preprocessed image and then locating the bounds of each colony separately. We show that gitter produces comparable colony sizes to other tools in simple cases but outperforms them by being able to handle a wider variety of screens and more accurately quantify colony sizes from difficult images. gitter is freely available as an R package from http://cran.r-project.org/web/packages/gitter under the LGPL. Tutorials and demos can be found at http://omarwagih.github.io/gitter.

Heterosis is prevalent among domesticated but not wild strains of Saccharomyces cerevisiae

Crosses between inbred but unrelated individuals often result in an increased fitness of the progeny. This phenomenon is known as heterosis and has been reported for wild and domesticated populations of plants and animals. Analysis of heterosis is often hindered by the fact that the genetic relatedness between analyzed organisms is only approximately known. We studied a collection of Saccharomyces cerevisiae isolates from wild and human-created habitats whose genomes were sequenced and thus their relatedness was fully known. We reasoned that if these strains accumulated different deleterious mutations at an approximately constant rate, then heterosis should be most visible in F1 heterozygotes from the least related parents. We found that heterosis was substantial and positively correlated with sequence divergence, but only in domesticated strains. More than 80% of the heterozygous hybrids were more fit than expected from the mean of their homozygous parents, and approximately three-quarters of those exceeded even the fittest parent. Our results support the notion that domestication brings about relaxation of selection and accumulation of deleterious mutations. However, other factors may have contributed as well. In particular, the observed build-up of genetic load might be facilitated by a decrease, and not increase, in the rate of inbreeding.

Selective cultivation and rapid detection of Staphylococcus aureus by computer vision

In this paper, we developed a selective growth medium and a more rapid detection method based on computer vision for selective isolation and identification of Staphylococcus aureus from foods. The selective medium consisted of tryptic soy broth basal medium, 3 inhibitors (NaCl, K2 TeO3 , and phenethyl alcohol), and 2 accelerators (sodium pyruvate and glycine). After 4 h of selective cultivation, bacterial detection was accomplished using computer vision. The total analysis time was 5 h. Compared to the Baird-Parker plate count method, which requires 4 to 5 d, this new detection method offers great time savings. Moreover, our novel method had a correlation coefficient of greater than 0.998 when compared with the Baird-Parker plate count method. The detection range for S. aureus was 10 to 10(7) CFU/mL.

PRACTICAL APPLICATION

Our new, rapid detection method for microorganisms in foods has great potential for routine food safety control and microbiological detection applications.

Pentose phosphate pathway function affects tolerance to the G-quadruplex binder TMPyP4

G-quadruplexes form in guanine-rich regions of DNA and the presence of these structures at telomeres prevents the activity of telomerase in vitro. Ligands such as the cationic porphyrin TMPyP4 stabilise G-quadruplexes and are therefore under investigation for their potential use as anti-cancer drugs. In order to investigate the mechanism of action of TMPyP4 in vivo, we carried out a genome-wide screen in the budding yeast Saccharomyces cerevisiae. We found that deletion of key pentose phosphate pathway (PPP) genes increased the sensitivity of yeast to the presence of TMPyP4. The PPP plays an important role in the oxidative stress response and sensitivity to TMPyP4 also increased when genes involved in the oxidative stress response, CCS1 and YAP1, were deleted. For comparison we also report genome wide-screens using hydrogen peroxide, which causes oxidative stress, RHPS4, another G-quadruplex binder and hydroxyurea, an S phase poison. We found that a number of TMPyP4-sensitive strains are also sensitive to hydrogen peroxide in a genome-wide screen. Overall our results suggest that treatment with TMPyP4 results in light-dependent oxidative stress response in budding yeast, and that this, rather than G-quadruplex binding, is the major route to cytotoxicity. Our results have implications for the usefulness and mechanism of action of TMPyP4.

SGAtools: one-stop analysis and visualization of array-based genetic interaction screens

Screening genome-wide sets of mutants for fitness defects provides a simple but powerful approach for exploring gene function, mapping genetic networks and probing mechanisms of drug action. For yeast and other microorganisms with global mutant collections, genetic or chemical-genetic interactions can be effectively quantified by growing an ordered array of strains on agar plates as individual colonies, and then scoring the colony size changes in response to a genetic or environmental perturbation. To do so, requires efficient tools for the extraction and analysis of quantitative data. Here, we describe SGAtools (http://sgatools.ccbr.utoronto.ca), a web-based analysis system for designer genetic screens. SGAtools outlines a series of guided steps that allow the user to quantify colony sizes from images of agar plates, correct for systematic biases in the observations and calculate a fitness score relative to a control experiment. The data can also be visualized online to explore the colony sizes on individual plates, view the distribution of resulting scores, highlight genes with the strongest signal and perform Gene Ontology enrichment analysis.

The tRNA thiolation pathway modulates the intracellular redox state in Escherichia coli

We have performed a screening of hydroxyurea (HU)-sensitive mutants using a single-gene-deletion mutant collection in Escherichia coli K-12. HU inhibits ribonucleotide reductase (RNR), an enzyme that catalyzes the formation of deoxyribonucleotides. Unexpectedly, seven of the mutants lacked genes that are required for the incorporation of sulfur into a specific tRNA modification base, 5-methylaminomethyl-2-thiouridine (mnm(5)s(2)U), via persulfide relay. We found that the expression of RNR in the mutants was reduced to about one-third both in the absence and presence of HU, while sufficient deoxynucleoside triphosphate (dNTP) was maintained in the mutants in the absence of HU but a shortage occurred in the presence of HU. Trans-supply of an RNR R2 subunit rescued the HU sensitivity of these mutants. The mutants showed high intracellular ATP/ADP ratios, and overexpression of Hda, which catalyzes the conversion of DnaA-ATP to DnaA-ADP, rescued the HU sensitivity of the mutants, suggesting that DnaA-ATP represses RNR expression. The high intracellular ATP/ADP ratios were due to high respiration activity in the mutants. Our data suggested that intracellular redox was inclined toward the reduced state in these mutants, which may explain a change in RNR activity by reduction of the catalytically formed disulfide bond and high respiration activity by the NADH reducing potential. The relation between persulfide relay and intracellular redox is discussed.

Genome-wide screening with hydroxyurea reveals a link between nonessential ribosomal proteins and reactive oxygen species production

We performed a screening of hydroxyurea (HU)-sensitive mutants using a single-gene-deletion mutant collection in Escherichia coli. HU inhibits ribonucleotide reductase (RNR), which leads to arrest of the replication fork. Surprisingly, the wild-type was less resistant to HU than the average for the Keio Collection. Respiration-defective mutants were significantly more resistant to HU, suggesting that the generation of reactive oxygen species (ROS) contributes to cell death. High-throughput screening revealed that 15 mutants were completely sensitive on plates containing 7.5 mM HU. Unexpectedly, translation-related mutants based on COG categorization were the most enriched, and three of them were deletion mutants of nonessential ribosomal proteins (L1, L32, and L36). We found that, in these mutants, an increased membrane stress response was provoked, resulting in increased ROS generation. The addition of OH radical scavenger thiourea rescued the HU sensitivity of these mutants, suggesting that ROS generation is the direct cause of cell death. Conversely, both the deletion of rpsF and the deletion of rimK, which encode S6 and S6 modification enzymes, respectively, showed an HU-resistant phenotype. These mutants increased the copy number of the p15A-based plasmid and exhibited reduced basal levels of SOS response. The data suggest that nonessential proteins indirectly affect the DNA-damaging process.

A quantitative fitness analysis workflow

Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel^1,2,3,4. QFA can be applied to focused observations of single cultures but is most useful for genome-wide genetic interaction or drug screens investigating up to thousands of independent cultures. The central experimental method is the inoculation of independent, dilute liquid microbial cultures onto solid agar plates which are incubated and regularly photographed. Photographs from each time-point are analyzed, producing quantitative cell density estimates, which are used to construct growth curves, allowing quantitative fitness measures to be derived. Culture fitnesses can be compared to quantify and rank genetic interaction strengths or drug sensitivities. The effect on culture fitness of any treatments added into substrate agar (e.g. small molecules, antibiotics or nutrients) or applied to plates externally (e.g. UV irradiation, temperature) can be quantified by QFA.

The QFA workflow produces growth rate estimates analogous to those obtained by spectrophotometric measurement of parallel liquid cultures in 96-well or 200-well plate readers. Importantly, QFA has significantly higher throughput compared with such methods. QFA cultures grow on a solid agar surface and are therefore well aerated during growth without the need for stirring or shaking.

QFA throughput is not as high as that of some Synthetic Genetic Array (SGA) screening methods^5,6. However, since QFA cultures are heavily diluted before being inoculated onto agar, QFA can capture more complete growth curves, including exponential and saturation phases³. For example, growth curve observations allow culture doubling times to be estimated directly with high precision, as discussed previously¹.

Here we present a specific QFA protocol applied to thousands of S. cerevisiae cultures which are automatically handled by robots during inoculation, incubation and imaging. Any of these automated steps can be replaced by an equivalent, manual procedure, with an associated reduction in throughput, and we also present a lower throughput manual protocol. The same QFA software tools can be applied to images captured in either workflow.

We have extensive experience applying QFA to cultures of the budding yeast S. cerevisiae but we expect that QFA will prove equally useful for examining cultures of the fission yeast S. pombe and bacterial cultures.