PMAnalyzer: a new web interface for bacterial growth curve analysis

Inferring fungal growth rates from optical density data

Quantifying fungal growth underpins our ability to effectively treat severe fungal infections. Current methods quantify fungal growth rates from time-course morphology-specific data, such as hyphal length data. However, automated large-scale collection of such data lies beyond the scope of most clinical microbiology laboratories. In this paper, we propose a mathematical model of fungal growth to estimate morphology-specific growth rates from easy-to-collect, but indirect, optical density (OD600) data of Aspergillus fumigatus growth (filamentous fungus). Our method accounts for OD600 being an indirect measure by explicitly including the relationship between the indirect OD600 measurements and the calibrating true fungal growth in the model. Therefore, the method does not require de novo generation of calibration data. Our model outperformed reference models at fitting to and predicting OD600 growth curves and overcame observed discrepancies between morphology-specific rates inferred from OD600 versus directly measured data in reference models that did not include calibration.

Dashing Growth Curves: a web application for rapid and interactive analysis of microbial growth curves

BACKGROUND

Recording and analyzing microbial growth is a routine task in the life sciences. Microplate readers that record dozens to hundreds of growth curves simultaneously are increasingly used for this task raising the demand for their rapid and reliable analysis.

RESULTS

Here, we present Dashing Growth Curves, an interactive web application ( http://dashing-growth-curves.ethz.ch/ ) that enables researchers to quickly visualize and analyze growth curves without the requirement for coding knowledge and independent of operating system. Growth curves can be fitted with parametric and non-parametric models or manually. The application extracts maximum growth rates as well as other features such as lag time, length of exponential growth phase and maximum population size among others. Furthermore, Dashing Growth Curves automatically groups replicate samples and generates downloadable summary plots for of all growth parameters.

CONCLUSIONS

Dashing Growth Curves is an open-source web application that reduces the time required to analyze microbial growth curves from hours to minutes.

BactEXTRACT: an R Shiny app to quickly extract, plot and analyse bacterial growth and gene expression data

To streamline the analysis and visualization of bacterial growth and gene expression data obtained by microtitre plate readers, we developed BactEXTRACT, an intuitive, easy-to-use R Shiny application. BactEXTRACT simplifies the transition from raw optical density, fluorescence and luminescence measurements to publication-ready plots. This package offers a user-friendly interface that reduces the complexity involved in growth curve and gene expression analysis and is generally applicable. BactEXTRACT is available at https://veeninglab.com/bactextract.

AMiGA: Software for Automated Analysis of Microbial Growth Assays

The analysis of microbial growth is one of the central methods in the field of microbiology. Microbial growth dynamics can be characterized by meaningful parameters, including carrying capacity, exponential growth rate, and growth lag. However, microbial assays with clinical isolates, fastidious organisms, or microbes under stress often produce atypical growth shapes that do not follow the classical microbial growth pattern. Here, we introduce the analysis of microbial growth assays (AMiGA) software, which streamlines the analysis of growth curves without any assumptions about their shapes. AMiGA can pool replicates of growth curves and infer summary statistics for biologically meaningful growth parameters. In addition, AMiGA can quantify death phases and characterize diauxic shifts. It can also statistically test for differential growth under distinct experimental conditions. Altogether, AMiGA streamlines the organization, analysis, and visualization of microbial growth assays. IMPORTANCE Our current understanding of microbial physiology relies on the simple method of measuring microbial populations' sizes over time and under different conditions. Many advances have increased the throughput of those assays and enabled the study of nonlab-adapted microbes under diverse conditions that widely affect their growth dynamics. Our software provides an all-in-one tool for estimating the growth parameters of microbial cultures and testing for differential growth in a high-throughput and user-friendly fashion without any underlying assumptions about how microbes respond to their growth conditions.

Plant Polysaccharides Modulate Biofilm Formation and Insecticidal Activities of Bacillus thuringiensis Strains

After the biological pesticide Bacillus thuringiensis (Bt) is applied to the field, it has to remain on the surface of plants to have the insecticidal activities against insect pests. Bt can form biofilms on the surface of vegetable leaves, which were rich in polysaccharides. However, the relationship between polysaccharides of the leaves and the biofilm formation as well as the insecticidal activities of Bt is still unknown. Herein, this study focused on the effects of plant polysaccharides pectin and xylan on biofilm formation and the insecticidal activities of Bt strains. By adding pectin, there were 88 Bt strains with strong biofilm formation, 69 strains with weak biofilm formation, and 13 strains without biofilm formation. When xylan was added, 13 Bt strains formed strong biofilms, 98 strains formed weak biofilms, and 59 strains did not form biofilms. This indicated that two plant polysaccharides, especially pectin, modulate the biofilm formation of Bt strains. The ability of pectin to induce biofilm formation was not related to Bt serotypes. Pectin promoted the biofilms formed by Bt cells in the logarithmic growth phase and lysis phase at the air–liquid interface, while it inhibited the biofilms formed by Bt cells in the sporangial phase at the air–liquid interface. The dosage of pectin was positively correlated with the yield of biofilms formed by Bt cells in the logarithmic growth phase or lysis phase at the solid–liquid interfaces. Pectin did not change the free-living growth and the cell motility of Bt strains. Pectin can improve the biocontrol activities of the spore–insecticidal crystal protein mixture of Bt and BtK commercial insecticides, as well as the biofilms formed by the logarithmic growth phase or lysis phase of Bt cells. Our findings confirmed that plant polysaccharides modulate biofilm formation and insecticidal activities of Bt strains and built a foundation for the construction of biofilm-type Bt biopesticides.

sparse-growth-curve: a Computational Pipeline for Parsing Cellular Growth Curves with Low Temporal Resolution

Here, we introduce a Python-based repository, sparse-growth-curve, a software package designed for parsing cellular growth curves with low temporal resolution. The repository uses cell density and time data as the input, automatically separates different growth phases, calculates the exponential growth rates, and produces multiple graphs to aid in interpretation.

Dietary prophage inducers and antimicrobials: toward landscaping the human gut microbiome

The approximately 10¹¹ viruses and microbial cells per gram of fecal matter (dry weight) in the large intestine are important to human health. The responses of three common gut bacteria species, and one opportunistic pathogen, to 117 commonly consumed foods, chemical additives, and plant extracts were tested. Many compounds, including Stevia rebaudiana and bee propolis extracts, exhibited species-specific growth inhibition by prophage induction. Overall, these results show that various foods may change the abundances of gut bacteria by modulating temperate phage and suggests a novel path for landscaping the human gut microbiome.

Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics

Genomics-based metabolic models of microorganisms currently have no easy way of corroborating predicted biomass with the actual metabolites being produced. This study uses untargeted mass spectrometry-based metabolomics data to generate a list of accurate metabolite masses produced from the human commensal bacteria Citrobacter sedlakii grown in the presence of a simple glucose carbon source. A genomics-based flux balance metabolic model of this bacterium was previously generated using the bioinformatics tool PyFBA and phenotypic growth curve data. The high-resolution mass spectrometry data obtained through timed metabolic extractions were integrated with the predicted metabolic model through a program called MS_FBA. This program correlated untargeted metabolomics features from C. sedlakii with 218 of the 699 metabolites in the model using an exact mass match, with 51 metabolites further confirmed using predicted isotope ratios. Over 1400 metabolites were matched with additional metabolites in the ModelSEED database, indicating the need to incorporate more specific gene annotations into the predictive model through metabolomics-guided gap filling.

De novo design of symmetric ferredoxins that shuttle electrons in vivo

A symmetric origin for bacterial ferredoxins was first proposed over 50 y ago, yet, to date, no functional symmetric molecule has been constructed. It is hypothesized that extant proteins have drifted from their symmetric roots via gene duplication followed by mutations. Phylogenetic analyses of extant ferredoxins support the independent evolution of N- and C-terminal sequences, thereby allowing consensus-based design of symmetric 4Fe-4S molecules. All designs bind two [4Fe-4S] clusters and exhibit strongly reducing midpoint potentials ranging from -405 to -515 mV. One of these constructs efficiently shuttles electrons through a designed metabolic pathway in Escherichia coli These finding establish that ferredoxins consisting of a symmetric core can be used as a platform to design novel electron transfer carriers for in vivo applications. Outer-shell asymmetry increases sequence space without compromising electron transfer functionality.

Online Monitoring of Bacterial Growth with an Electrical Sensor

Herein, we developed an automatic electrical bacterial growth sensor (EBGS) based on a multichannel capacitively coupled contactless conductivity detector (C⁴D). With the use of the EBGS, up to eight culture samples of E. coli in disposable tubes were online monitored simultaneously in a noninvasive manner. Growth curves with high resolution (on the order of a time scale of seconds) were generated by plotting normalized apparent conductivity value against incubation time. The characteristic data of E. coli growth (e.g., growth rate) obtained here were more accurate than those obtained with optical density and contact conductivity methods. And the correlation coefficient of the regression line ( r) for quantitative determination of viable bacteria was 0.9977. Moreover, it also could be used for other tasks, such as the investigation of toxic/stress effects from chemicals and antimicrobial susceptibility testing. All of these performances required neither auxiliary devices nor additional chemicals and biomaterials. Taken together, this strategy has the advantages of simplicity, accuracy, reproducibility, affordability, versatility, and miniaturization, liberating the users greatly from financial and labor costs.

Growth Score: a single metric to define growth in 96-well phenotype assays

High-throughput phenotype assays are a cornerstone of systems biology as they allow direct measurements of mutations, genes, strains, or even different genera. High-throughput methods also require data analytic methods that reduce complex time-series data to a single numeric evaluation. Here, we present the Growth Score, an improvement on the previous Growth Level formula. There is strong correlation between Growth Score and Growth Level, but the new Growth Score contains only essential growth curve properties while the formula of the previous Growth Level was convoluted and not easily interpretable. Several programs can be used to estimate the parameters required to calculate the Growth Score metric, including our PMAnalyzer pipeline.