Detecting differential growth of microbial populations with Gaussian process regression

Identification and characterization of a skin microbiome on Caenorhabditis elegans suggests environmental microbes confer cuticle protection

In the wild, C. elegans are emersed in environments teeming with a veritable menagerie of microorganisms. The C. elegans cuticular surface serves as a barrier and first point of contact with their microbial environments. In this study, we identify microbes from C. elegans natural habitats that associate with its cuticle, constituting a simple "skin microbiome." We rear our animals on a modified CeMbio, mCeMbio, a consortium of ecologically relevant microbes. We first combine standard microbiological methods with an adapted micro skin-swabbing tool to describe the skin-resident bacteria on the C. elegans surface. Furthermore, we conduct 16S rRNA gene sequencing studies to identify relative shifts in the proportion of mCeMbio bacteria upon surface-sterilization, implying distinct skin- and gut-microbiomes. We find that some strains of bacteria, including Enterobacter sp. JUb101, are primarily found on the nematode skin, while others like Stenotrophomonas indicatrix JUb19 and Ochrobactrum vermis MYb71 are predominantly found in the animal's gut. Finally, we show that this skin microbiome promotes host cuticle integrity in harsh environments. Together, we identify a skin microbiome for the well-studied nematode model and propose its value in conferring host fitness advantages in naturalized contexts.

IMPORTANCE

The genetic model organism C. elegans has recently emerged as a tool for understanding host-microbiome interactions. Nearly all of these studies either focus on pathogenic or gut-resident microbes. Little is known about the existence of native, nonpathogenic skin microbes or their function. We demonstrate that members of a modified C. elegans model microbiome, mCeMbio, can adhere to the animal's cuticle and confer protection from noxious environments. We combine a novel micro-swab tool, the first 16S microbial sequencing data from relatively unperturbed C. elegans, and physiological assays to demonstrate microbially mediated protection of the skin. This work serves as a foundation to explore wild C. elegans skin microbiomes and use C. elegans as a model for skin research.

gcplyr: an R package for microbial growth curve data analysis

BACKGROUND

Characterization of microbial growth is of both fundamental and applied interest. Modern platforms can automate collection of high-throughput microbial growth curves, necessitating the development of computational tools to handle and analyze these data to produce insights.

RESULTS

To address this need, here I present a newly-developed R package: gcplyr. gcplyr can flexibly import growth curve data in common tabular formats, and reshapes it under a tidy framework that is flexible and extendable, enabling users to design custom analyses or plot data with popular visualization packages. gcplyr can also incorporate metadata and generate or import experimental designs to merge with data. Finally, gcplyr carries out model-free (non-parametric) analyses. These analyses do not require mathematical assumptions about microbial growth dynamics, and gcplyr is able to extract a broad range of important traits, including growth rate, doubling time, lag time, maximum density and carrying capacity, diauxie, area under the curve, extinction time, and more.

CONCLUSIONS

gcplyr makes scripted analyses of growth curve data in R straightforward, streamlines common data wrangling and analysis steps, and easily integrates with common visualization and statistical analyses.

Reverse engineering morphogenesis through Bayesian optimization of physics-based models

Morphogenetic programs coordinate cell signaling and mechanical interactions to shape organs. In systems and synthetic biology, a key challenge is determining optimal cellular interactions for predicting organ shape, size, and function. Physics-based models defining the subcellular force distribution facilitate this, but it is challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the experimentally observed organ shapes. This integrative framework employs Gaussian Process Regression, a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that maintain the final organ shape. We calibrated and tested the method on Drosophila wing imaginal discs to study mechanisms that regulate epithelial processes ranging from development to cancer. The parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with imaging data of wing discs perturbed with collagenase. The computational pipeline identifies distinct parameter sets mimicking wild-type shapes. It enables a global sensitivity analysis to support the regulation of actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with experimental imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This workflow is extensible toward reverse-engineering morphogenesis across organ systems and for real-time control of complex multicellular systems.

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.

Heterotrophic Selenium Incorporation into Chlorella vulgaris K-01: Selenium Tolerance, Assimilation, and Removal through Microalgal Cells

Chlorella has been applied in the production of selenium (Se) enriched organic biomass. However, limited information exists regarding heterotrophic selenium tolerance and its incorporation into Chlorella. This study aimed to investigate the potential of using Chlorella vulgaris K-01 for selenium biotransformation. To assess the dose-response effect of Se stress on the strain, time-series growth curves were recorded, growth productivity parameters were calculated, and Gaussian process (GP) regression analysis was performed. The strain's carbon and energy metabolism were evaluated by measuring residual glucose in the medium. Characterization of different forms of intracellular Se and residual Se in the medium was conducted using inductively coupled plasma-mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectrometer (ICP-OES). The EC50 value for the strain in response to Se stress was 38.08 mg/L. The maximum biomass productivity was 0.26 g/L/d. GP regression analysis revealed that low-level Se treatment could increase the biomass accumulation and the carrying capacity of Chlorella vulgaris K-01 in a heterotrophic culture. The maximum organic Se in biomass was 154.00 μg/g DW. These findings lay the groundwork for understanding heterotrophic microalgal production of Se-containing nutraceuticals, offering valuable insights into Se tolerance, growth dynamics, and metabolic responses in Chlorella vulgaris K-01.

Application of Machine Learning Algorithms to Describe the Characteristics of Dairy Sheep Lactation Curves

In recent years, machine learning (ML) algorithms have emerged as powerful tools for predicting and modeling complex data. Therefore, the aim of this study was to evaluate the prediction ability of different ML algorithms and a traditional empirical model to estimate the parameters of lactation curves. A total of 1186 monthly records from 156 sheep lactations were used. The model development process involved training and testing models using ML algorithms. In addition to these algorithms, lactation curves were also fitted using the Wood model. The goodness of fit was assessed using correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and relative root mean square error (RRSE). SMOreg was the algorithm with the best estimates of the characteristics of the sheep lactation curve, with higher values of r compared to the Wood model (0.96 vs. 0.68) for the total milk yield. The results of the current study showed that ML algorithms are able to adequately predict the characteristics of the lactation curve, using a relatively small number of input data. Some ML algorithms provide an interpretable architecture, which is useful for decision-making at the farm level to maximize the use of available information.

Reverse engineering morphogenesis through Bayesian optimization of physics-based models

Morphogenetic programs direct the cell signaling and nonlinear mechanical interactions between multiple cell types and tissue layers to define organ shape and size. A key challenge for systems and synthetic biology is determining optimal combinations of intra- and inter-cellular interactions to predict an organ's shape, size, and function. Physics-based mechanistic models that define the subcellular force distribution facilitate this, but it is extremely challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the desired organ shapes observed within the experimental imaging data. This integrative framework employs Gaussian Process Regression (GPR), a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that generate and maintain the final organ shape. We calibrated and tested the method on cross-sections of Drosophila wing imaginal discs, a highly informative model organ system, to study mechanisms that regulate epithelial processes that range from development to cancer. As a specific test case, the parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with time series imaging data of wing discs perturbed with collagenase. Unexpectedly, the framework also identifies multiple distinct parameter sets that generate shapes similar to wild-type organ shapes. This platform enables an efficient, global sensitivity analysis to support the necessity of both actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with fixed tissue imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This framework is extensible toward reverse-engineering the morphogenesis of any organ system and can be utilized in real-time control of complex multicellular systems.

Machine learning-assisted discovery of growth decision elements by relating bacterial population dynamics to environmental diversity

Microorganisms growing in their habitat constitute a complex system. How the individual constituents of the environment contribute to microbial growth remains largely unknown. The present study focused on the contribution of environmental constituents to population dynamics via a high-throughput assay and data-driven analysis of a wild-type Escherichia coli strain. A large dataset constituting a total of 12,828 bacterial growth curves with 966 medium combinations, which were composed of 44 pure chemical compounds, was acquired. Machine learning analysis of the big data relating the growth parameters to the medium combinations revealed that the decision-making components for bacterial growth were distinct among various growth phases, e.g., glucose, sulfate, and serine for maximum growth, growth rate, and growth delay, respectively. Further analyses and simulations indicated that branched-chain amino acids functioned as global coordinators for population dynamics, as well as a survival strategy of risk diversification to prevent the bacterial population from undergoing extinction.

ScanGrow: Deep Learning-Based Live Tracking of Bacterial Growth in Broth

Monitoring the growth of bacterial cultures is one of the most common techniques in microbiology. This is usually achieved by using expensive and bulky spectrophotometric plate readers which periodically measure the optical density of bacterial cultures during the incubation period. In this study, we present a completely novel way of obtaining bacterial growth curves based on the classification of scanned images of cultures rather than using spectrophotometric measurements. We trained a deep learning model with images of bacterial broths contained in microplates, and we integrated it into a custom-made software application that triggers a flatbed scanner to timely capture images, automatically processes the images, and represents all growth curves. The developed tool, ScanGrow, is presented as a low-cost and high-throughput alternative to plate readers, and it only requires a computer connected to a flatbed scanner and equipped with our open-source ScanGrow application. In addition, this application also assists in the pre-processing of data to create and evaluate new models, having the potential to facilitate many routine microbiological techniques.

Formulation of Chemically Defined Media and Growth Evaluation of Ligilactobacillus salivarius ZJ614 and Limosilactobacillus reuteri ZJ625

Lactic acid bacteria are increasingly becoming important dietary supplements due to their health benefits when consumed in adequate quantity. The increasing attention on these important microbes has necessitated an in-depth understanding of their physiological processes, such as nutritional requirements and growth patterns, to better harness their probiotic potentials. This study was carried out to determine the nutritional requirements for the growth of L. salivarius ZJ614 and L. reuteri ZJ625 from a chemically defined medium and evaluate growth kinetics by fitting different sigmoidal growth models. The complete CDM contains 49 nutritional ingredients such as glucose, Tween 80^®, mineral salts, buffers, amino acids, vitamins, and nucleotides at defined concentrations. In addition, the minimal nutritional requirements of the isolates were determined in a series of single-omission experiments (SOEs) to compose the MDM. Growth curve data were generated by culturing in an automated 96-well micro-plate reader at 37°C for 36 h, and photometric readings (optical density: OD₆₀₀) were taken. The data were summarized in tables and charts using Microsoft Excel, while growth evaluation was carried out using open-source software (Curveball) on Python. The results revealed that omission of the amino acids, vitamins, and nucleotides groups resulted in 2.0, 20.17, and 60.24% (for L. salivarius ZJ614) and 0.95, 42.7, and 70.5% (for L. reuteri ZJ625) relative growths, respectively. Elimination of the individual CDM components also indicates varying levels of growth by the strains. The growth curve data revealed LogisticLag2 and Baranyi–Roberts models as the best fits for L. reuteri ZJ625 and L. salivarius ZJ614, respectively. All the strains showed appreciable growth on the CDM and MDM as observed in de Man–Rogosa–Sharpe (MRS) broth. We also described the growth kinetics of L. reuteri ZJ625 and L. salivarius ZJ614 in the CDM, and the best models revealed the estimated growth parameters.

Systems biology approach to functionally assess the Clostridioides difficile pangenome reveals genetic diversity with discriminatory power

SignificanceClostridioides difficile infections are the most common source of hospital-acquired infections and are responsible for an extensive burden on the health care system. Strains of the C. difficile species comprise diverse lineages and demonstrate genome variability, with advantageous trait acquisition driving the emergence of endemic lineages. Here, we present a systems biology analysis of C. difficile that evaluates strain-specific genotypes and phenotypes to investigate the overall diversity of the species. We develop a strain typing method based on similarity of accessory genomes to identify and contextualize genetic loci capable of discriminating between strain groups.

The Interactions among Isolates of Lactiplantibacillus plantarum and Dairy Yeast Contaminants: Towards Biocontrol Applications

Yeast diversity in the cheese manufacturing process and in the cheeses themselves includes indispensable species for the production of specific cheeses and undesired species that cause cheese defects and spoilage. The control of yeast contaminants is problematic due to limitations in sanitation methods and chemicals used in the food industry. The utilisation of lactic acid bacteria and their antifungal products is intensively studied. Lactiplantibacillus plantarum is one of the most frequently studied species producing a wide spectrum of bioactive by-products. In the present study, twenty strains of L. plantarum from four sources were tested against 25 species of yeast isolated from cheeses, brines, and dairy environments. The functional traits of L. plantarum strains, such as the presence of class 2a bacteriocin and chitinase genes and in vitro production of organic acids, were evaluated. The extracellular production of bioactive peptides and proteins was tested using proteomic methods. Antifungal activity against yeast was screened using in vitro tests. Testing of antifungal activity on artificial media and reconstituted milk showed significant variability within the strains of L. plantarum and its group of origin. Strains from sourdoughs (CCDM 3018, K19-3) and raw cheese (L12, L24, L32) strongly inhibited the highest number of yeast strains on medium with reconstituted milk. These strains showed a consistent spectrum of genes belonging to class 2a bacteriocins, the gene of chitinase and its extracellular product 9 LACO Chitin-binding protein. Strain CCDM 3018 with the spectrum of class 2a bacteriocin gene, chitinase and significant production of lactic acid in all media performed significant antifungal effects in artificial and reconstituted milk-based media.

Is skipping the definition of primary and secondary models possible? Prediction of Escherichia coli O157 growth by machine learning

To predict bacterial population behavior in food, statistical models with specific function form have been applied in the field of predictive microbiology. Modelers need to consider the linear or non-linear relationship between the response and explanatory variables in the statistical modeling approach. In the present study, we focused on machine learning methods to skip definition of primary and secondary structure model. Support vector regression, extremely randomized trees regression, and Gaussian process regression were used to predict population growth of Escherichia coli O157 at 15 and 25 °C without defining the primary and secondary models. Furthermore, the support vector regression model was applied to predict small population of bacteria cells with probability theory. The model performance of the machine learning models were nearly equal to that of the current statistical models. Machine learning models have a potential for predicting bacterial population behavior.

The pitfalls of using Gaussian Process Regression for normative modeling

Normative modeling, a group of methods used to quantify an individual's deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity. Gaussian Processes Regression includes an estimate of variable uncertainty across the input domain, which at face value makes it an attractive method to normalize the cohort heterogeneity where the deviation between predicted value and true observation is divided by the derived uncertainty directly from Gaussian Processes Regression. However, we show that the uncertainty directly from Gaussian Processes Regression is irrelevant to the cohort heterogeneity in general.

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.

Integrated knowledge mining, genome-scale modeling, and machine learning for predicting Yarrowia lipolytica bioproduction

Predicting bioproduction titers from microbial hosts has been challenging due to complex interactions between microbial regulatory networks, stress responses, and suboptimal cultivation conditions. This study integrated knowledge mining, feature extraction, genome-scale modeling (GSM), and machine learning (ML) to develop a model for predicting Yarrowia lipolytica chemical titers (i.e., organic acids, terpenoids, etc.). First, Y. lipolytica production data, including cultivation conditions, genetic engineering strategies, and product information, was manually collected from literature (~100 papers) and stored as either numerical (e.g., substrate concentrations) or categorical (e.g., bioreactor modes) variables. For each case recorded, central pathway fluxes were estimated using GSMs and flux balance analysis (FBA) to provide metabolic features. Second, a ML ensemble learner was trained to predict strain production titers. Accurate predictions on the test data were obtained for instances with production titers >1 g/L (R² = 0.87). However, the model had reduced predictability for low performance strains (0.01-1 g/L, R² = 0.29) potentially due to biosynthesis bottlenecks not captured in the features. Feature ranking indicated that the FBA fluxes, the number of enzyme steps, the substrate inputs, and thermodynamic barriers (i.e., Gibbs free energy of reaction) were the most influential factors. Third, the model was evaluated on other oleaginous yeasts and indicated there were conserved features for some hosts that can be potentially exploited by transfer learning. The platform was also designed to assist computational strain design tools (such as OptKnock) to screen genetic targets for improved microbial production in light of experimental conditions.

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.

Quantifying relative virulence: when μmax fails and AUC alone just is not enough

A challenge in virology is quantifying relative virulence (VR) between two (or more) viruses that exhibit different replication dynamics in a given susceptible host. Host growth curve analysis is often used to mathematically characterize virus-host interactions and to quantify the magnitude of detriment to host due to viral infection. Quantifying VR using canonical parameters, like maximum specific growth rate (μmax), can fail to provide reliable information regarding virulence. Although area-under-the-curve (AUC) calculations are more robust, they are sensitive to limit selection. Using empirical data from Sulfolobus Spindle-shaped Virus (SSV) infections, we introduce a novel, simple metric that has proven to be more robust than existing methods for assessing VR. This metric (ISC) accurately aligns biological phenomena with quantified metrics to determine VR. It also addresses a gap in virology by permitting comparisons between different non-lytic virus infections or non-lytic versus lytic virus infections on a given host in single-virus/single-host infections.

Pleiotropy and epistasis within and between signaling pathways defines the genetic architecture of fungal virulence

Cryptococcal disease is estimated to affect nearly a quarter of a million people annually. Environmental isolates of Cryptococcus deneoformans, which make up 15 to 30% of clinical infections in temperate climates such as Europe, vary in their pathogenicity, ranging from benign to hyper-virulent. Key traits that contribute to virulence, such as the production of the pigment melanin, an extracellular polysaccharide capsule, and the ability to grow at human body temperature have been identified, yet little is known about the genetic basis of variation in such traits. Here we investigate the genetic basis of melanization, capsule size, thermal tolerance, oxidative stress resistance, and antifungal drug sensitivity using quantitative trait locus (QTL) mapping in progeny derived from a cross between two divergent C. deneoformans strains. Using a "function-valued" QTL analysis framework that exploits both time-series information and growth differences across multiple environments, we identified QTL for each of these virulence traits and drug susceptibility. For three QTL we identified the underlying genes and nucleotide differences that govern variation in virulence traits. One of these genes, RIC8, which encodes a regulator of cAMP-PKA signaling, contributes to variation in four virulence traits: melanization, capsule size, thermal tolerance, and resistance to oxidative stress. Two major effect QTL for amphotericin B resistance map to the genes SSK1 and SSK2, which encode key components of the HOG pathway, a fungal-specific signal transduction network that orchestrates cellular responses to osmotic and other stresses. We also discovered complex epistatic interactions within and between genes in the HOG and cAMP-PKA pathways that regulate antifungal drug resistance and resistance to oxidative stress. Our findings advance the understanding of virulence traits among diverse lineages of Cryptococcus, and highlight the role of genetic variation in key stress-responsive signaling pathways as a major contributor to phenotypic variation.

Comparison of Machine Learning Algorithms for Structure State Prediction in Operational Load Monitoring

The aim of operational load monitoring is to make predictions about the remaining usability time of structures, which is extremely useful in aerospace industry where in-service life of aircraft structural components can be maximized, taking into account safety. In order to make such predictions, strain sensors are mounted to the structure, from which data are acquired during operational time. This allows to determine how many load cycles has the structure withstood so far. Continuous monitoring of the strain distribution of the whole structure can be complicated due to vicissitude nature of the loads. Sensors should be mounted in places where stress and strain accumulations occur, and due to experiencing variable loads, the number of required sensors may be high. In this work, different machine learning and artificial intelligence algorithms are implemented to predict the current safety factor of the structure in its most stressed point, based on relatively low number of strain measurements. Adaptive neuro-fuzzy inference systems (ANFIS), support-vector machines (SVM) and Gaussian processes for machine learning (GPML) are trained with simulation data, and their effectiveness is measured using data obtained from experiments. The proposed methods are compared to the earlier work where artificial neural networks (ANN) were proven to be efficiently used for reduction of the number of sensors in operational load monitoring processes. A numerical comparison of accuracy and computational time (taking into account possible real-time applications) between all considered methods is provided.

Systems biology analysis of the Clostridioides difficile core-genome contextualizes microenvironmental evolutionary pressures leading to genotypic and phenotypic divergence

Hospital acquired Clostridioides (Clostridium) difficile infection is exacerbated by the continued evolution of C. difficile strains, a phenomenon studied by multiple laboratories using stock cultures specific to each laboratory. Intralaboratory evolution of strains contributes to interlaboratory variation in experimental results adding to the challenges of scientific rigor and reproducibility. To explore how microevolution of C. difficile within laboratories influences the metabolic capacity of an organism, three different laboratory stock isolates of the C. difficile 630 reference strain were whole-genome sequenced and profiled in over 180 nutrient environments using phenotypic microarrays. The results identified differences in growth dynamics for 32 carbon sources including trehalose, fructose, and mannose. An updated genome-scale model for C. difficile 630 was constructed and used to contextualize the 28 unique mutations observed between the stock cultures. The integration of phenotypic screens with model predictions identified pathways enabling catabolism of ethanolamine, salicin, arbutin, and N-acetyl-galactosamine that differentiated individual C. difficile 630 laboratory isolates. The reconstruction was used as a framework to analyze the core-genome of 415 publicly available C. difficile genomes and identify areas of metabolism prone to evolution within the species. Genes encoding enzymes and transporters involved in starch metabolism and iron acquisition were more variable while C. difficile distinct metabolic functions like Stickland fermentation were more consistent. A substitution in the trehalose PTS system was identified with potential implications in strain virulence. Thus, pairing genome-scale models with large-scale physiological and genomic data enables a mechanistic framework for studying the evolution of pathogens within microenvironments and will lead to predictive modeling to combat pathogen emergence.

Environmental and Physiological Factors Affecting High-Throughput Measurements of Bacterial Growth

Bacterial growth under nutrient-rich and starvation conditions is intrinsically tied to the environmental history and physiological state of the population. While high-throughput technologies have enabled rapid analyses of mutant libraries, technical and biological challenges complicate data collection and interpretation. Here, we present a framework for the execution and analysis of growth measurements with improved accuracy over that of standard approaches. Using this framework, we demonstrate key biological insights that emerge from consideration of culturing conditions and history. We determined that quantification of the background absorbance in each well of a multiwell plate is critical for accurate measurements of maximal growth rate. Using mathematical modeling, we demonstrated that maximal growth rate is dependent on initial cell density, which distorts comparisons across strains with variable lag properties. We established a multiple-passage protocol that alleviates the substantial effects of glycerol on growth in carbon-poor media, and we tracked growth rate-mediated fitness increases observed during a long-term evolution of Escherichia coli in low glucose concentrations. Finally, we showed that growth of Bacillus subtilis in the presence of glycerol induces a long lag in the next passage due to inhibition of a large fraction of the population. Transposon mutagenesis linked this phenotype to the incorporation of glycerol into lipoteichoic acids, revealing a new role for these envelope components in resuming growth after starvation. Together, our investigations underscore the complex physiology of bacteria during bulk passaging and the importance of robust strategies to understand and quantify growth.IMPORTANCE How starved bacteria adapt and multiply under replete nutrient conditions is intimately linked to their history of previous growth, their physiological state, and the surrounding environment. While automated equipment has enabled high-throughput growth measurements, data interpretation and knowledge gaps regarding the determinants of growth kinetics complicate comparisons between strains. Here, we present a framework for growth measurements that improves accuracy and attenuates the effects of growth history. We determined that background absorbance quantification and multiple passaging cycles allow for accurate growth rate measurements even in carbon-poor media, which we used to reveal growth-rate increases during long-term laboratory evolution of Escherichia coli Using mathematical modeling, we showed that maximum growth rate depends on initial cell density. Finally, we demonstrated that growth of Bacillus subtilis with glycerol inhibits the future growth of most of the population, due to lipoteichoic acid synthesis. These studies highlight the challenges of accurate quantification of bacterial growth behaviors.

A Bayesian non-parametric mixed-effects model of microbial growth curves

Substantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

A Gaussian process regression model to predict energy contents of corn for poultry

The present study proposes a Gaussian process regression (GPR) approach to develop a model to predict true metabolizable energy corrected for nitrogen (TMEn) content of corn samples (as model output) for poultry given levels of feed chemical compositions of crude protein, ether extract, crude fiber, and ash (as model inputs). A 30 corn samples obtained from 5 origins [Brazil (n = 9), China (n = 5), Iran (n = 7), and Ukraine (n = 9)] were assayed to determine chemical composition and TMEn content using chemical analyses and bioassay technique. In addition to GPR model, data were also analyzed by multiple linear regression (MLR) model. Results revealed that corn samples of different origins differ in their gross energy and chemical composition of crude protein, crude fiber, and ash, but no differences were observed for their ether extract and TMEn contents. Based on model evaluation criteria of R² and root mean square error (RMSE), the GPR model showed satisfactory performance (R² = 0.92 and RMSE = 33.68 kcal/kg DM) in predicting TMEn and produced relatively better prediction values than those produce by MLR (R² = 0.23 and RMSE = 104.85 kcal/kg DM). The GPR model may be capable of improving our aptitude and capacity to precisely predict energy contents of feed ingredients to formulate optimal diets for poultry.

The Ribbon-Helix-Helix Domain Protein CdrS Regulates the Tubulin Homolog ftsZ2 To Control Cell Division in Archaea

Precise control of the cell cycle is central to the physiology of all cells. In prior work we demonstrated that archaeal cells maintain a constant size; however, the regulatory mechanisms underlying the cell cycle remain unexplored in this domain of life. Here, we use genetics, functional genomics, and quantitative imaging to identify and characterize the novel CdrSL gene regulatory network in a model species of archaea. We demonstrate the central role of these ribbon-helix-helix family transcription factors in the regulation of cell division through specific transcriptional control of the gene encoding FtsZ2, a putative tubulin homolog. Using time-lapse fluorescence microscopy in live cells cultivated in microfluidics devices, we further demonstrate that FtsZ2 is required for cell division but not elongation. The cdrS-ftsZ2 locus is highly conserved throughout the archaeal domain, and the central function of CdrS in regulating cell division is conserved across hypersaline adapted archaea. We propose that the CdrSL-FtsZ2 transcriptional network coordinates cell division timing with cell growth in archaea.IMPORTANCE Healthy cell growth and division are critical for individual organism survival and species long-term viability. However, it remains unknown how cells of the domain Archaea maintain a healthy cell cycle. Understanding the archaeal cell cycle is of paramount evolutionary importance given that an archaeal cell was the host of the endosymbiotic event that gave rise to eukaryotes. Here, we identify and characterize novel molecular players needed for regulating cell division in archaea. These molecules dictate the timing of cell septation but are dispensable for growth between divisions. Timing is accomplished through transcriptional control of the cell division ring. Our results shed light on mechanisms underlying the archaeal cell cycle, which has thus far remained elusive.

The Klebsiella pneumoniae citrate synthase gene, gltA, influences site specific fitness during infection

Klebsiella pneumoniae (Kp), one of the most common causes of healthcare-associated infections, increases patient morbidity, mortality, and hospitalization costs. Kp must acquire nutrients from the host for successful infection; however, the host is able to prevent bacterial nutrient acquisition through multiple systems. This includes the innate immune protein lipocalin 2 (Lcn2), which prevents Kp iron acquisition. To identify novel Lcn2-dependent Kp factors that mediate evasion of nutritional immunity during lung infection, we undertook an InSeq study using a pool of >20,000 transposon mutants administered to Lcn2^+/+ and Lcn2^-/- mice. Comparing transposon mutant frequencies between mouse genotypes, we identified the Kp citrate synthase, GltA, as potentially interacting with Lcn2, and this novel finding was independently validated. Interestingly, in vitro studies suggest that this interaction is not direct. Given that GltA is involved in oxidative metabolism, we screened the ability of this mutant to use a variety of carbon and nitrogen sources. The results indicated that the gltA mutant has a distinct amino acid auxotrophy rendering it reliant upon glutamate family amino acids for growth. Deletion of Lcn2 from the host leads to increased amino acid levels in bronchioloalveolar lavage fluid, corresponding to increased fitness of the gltA mutant in vivo and ex vivo. Accordingly, addition of glutamate family amino acids to Lcn2^+/+ bronchioloalveolar lavage fluid rescued growth of the gltA mutant. Using a variety of mouse models of infection, we show that GltA is an organ-specific fitness factor required for complete fitness in the spleen, liver, and gut, but dispensable in the bloodstream. Similar to bronchioloalveolar lavage fluid, addition of glutamate family amino acids to Lcn2^+/+ organ lysates was sufficient to rescue the loss of gltA. Together, this study describes a critical role for GltA in Kp infection and provides unique insight into how metabolic flexibility impacts bacterial fitness during infection.

The bacteria Klebsiella pneumoniae (Kp) is an important cause of infection in healthcare settings. These infections can be difficult to treat, as they frequently occur in chronically ill patients and the bacteria have the ability to acquire multiple antibiotic resistance markers. Kp is a common colonizer of the intestinal tract in hospitalized patients, and can progress to infections of the bloodstream, respiratory, and urinary tract. However, the bacterial factors that allow Kp to replicate in these different body sites are unclear. In this study, we found that the Kp citrate synthase, GltA, enables bacterial replication in the lung and intestine by enhancing the ability of Kp to use diverse nutrients in a mechanism known as metabolic flexibility. Kp lacking GltA require specific amino acids that are abundant in blood, but not other body sites. The work in this study provides novel insight into why Kp is a successful hospital pathogen that can colonize and infect multiple body sites.

A unified-models analysis of the development of sexual size dimorphism in Damaraland mole-rats, Fukomys damarensis

Individual variation in growth rates often generates variation in fitness. However, the ability to draw meaningful inferences from growth data depends on the use of growth models that allow for direct comparisons of growth between the sexes, between populations, and between species. Unlike traditional sigmoid functions, a recently parameterized family of unified growth models provides a reliable basis for comparisons since each parameter affects a single curve characteristic and parameters are directly comparable across the unified family. Here, we use the unified-models approach to examine the development of sexual size dimorphism in Damaraland mole-rats (Fukomys damarensis), where breeding males are larger than breeding females. Using skeletal measurements, we show here that the larger size of male Damaraland mole-rats arises from an increased growth rate across the entire period of development, rather than through sex differences in the duration or timing of growth. Male-biased skeletal size dimorphism is not unusual among rodents, and our measures of sex differences in size in captive mole-rats are close to sexual size differences in the wild, where size dimorphism = 1.04 (male:female). We hope our study will encourage the wide use of unified growth models by mammalogists.

Predicting the decision making chemicals used for bacterial growth

Predicting the contribution of media components to bacterial growth was first initiated by introducing machine learning to high-throughput growth assays. A total of 1336 temporal growth records corresponding to 225 different media, which were composed of 13 chemical components, were generated. The growth rate and saturated density of each growth curve were automatically calculated with the newly developed data processing program. To identify the decision making factors related to growth among the 13 chemicals, big datasets linking the growth parameters to the chemical combinations were subjected to decision tree learning. The results showed that the only carbon source, glucose, determined bacterial growth, but it was not the first priority. Instead, the top decision making chemicals in relation to the growth rate and saturated density were ammonium and ferric ions, respectively. Three chemical components (NH₄⁺, Mg²⁺ and glucose) commonly appeared in the decision trees of the growth rate and saturated density, but they exhibited different mechanisms. The concentration ranges for fast growth and high density were overlapped for glucose but distinguished for NH₄⁺ and Mg²⁺. The results suggested that these chemicals were crucial in determining the growth speed and growth maximum in either a universal use or a trade-off manner. This differentiation might reflect the diversity in the resource allocation mechanisms for growth priority depending on the environmental restrictions. This study provides a representative example for clarifying the contribution of the environment to population dynamics through an innovative viewpoint of employing modern data science within traditional microbiology to obtain novel findings.

Synergistic Impacts of Organic Acids and pH on Growth of Pseudomonas aeruginosa: A Comparison of Parametric and Bayesian Non-parametric Methods to Model Growth

Different weak organic acids have significant potential as topical treatments for wounds infected by opportunistic pathogens that are recalcitrant to standard treatments. These acids have long been used as bacteriostatic compounds in the food industry, and in some cases are already being used in the clinic. The effects of different organic acids vary with pH, concentration, and the specific organic acid used, but no studies to date on any opportunistic pathogens have examined the detailed interactions between these key variables in a controlled and systematic way. We have therefore comprehensively evaluated the effects of several different weak organic acids on growth of the opportunistic pathogen Pseudomonas aeruginosa. We used a semi-automated plate reader to generate growth profiles for two different strains (model laboratory strain PAO1 and clinical isolate PA1054 from a hospital burns unit) in a range of organic acids at different concentrations and pH, with a high level of replication for a total of 162,960 data points. We then compared two different modeling approaches for the interpretation of this time-resolved dataset: parametric logistic regression (with or without a component to include lag phase) vs. non-parametric Gaussian process (GP) regression. Because GP makes no prior assumptions about the nature of the growth, this method proved to be superior in cases where growth did not follow a standard sigmoid functional form, as is common when bacteria grow under stress. Acetic, propionic and butyric acids were all more detrimental to growth than the other acids tested, and although PA1054 grew better than PAO1 under non-stress conditions, this difference largely disappeared as the levels of stress increased. As expected from knowledge of how organic acids behave, their effect was significantly enhanced in combination with low pH, with this interaction being greatest in the case of propionic acid. Our approach lends itself to the characterization of combinatorial interactions between stressors, especially in cases where their impacts on growth render logistic growth models unsuitable.

Advances in online methods for monitoring microbial growth

Understanding the characteristics of microbial growth is of great significance to many fields including in scientific research, the food industry, health care, and agriculture. Many methods have been established to characterize the process of microbial growth. Online and automated methods, in which sample transfer is avoided, are popular because they can facilitate the development of simple, safe, and effective growth monitoring. This review focuses on advances in online monitoring methods over the last decade (2008-2018). We specifically focus on optic- and electrochemistry-based techniques, either through contact measurements or contactless measurement. Strengths and weaknesses of each set of methods are described and we also speculate on forthcoming trends in the field.

A decay effect of the growth rate associated with genome reduction in Escherichia coli

Background

Bacterial growth is an important topic in microbiology and of crucial importance to better understand living cells. Bacterial growth dynamics are quantitatively examined using various methods to determine the physical, chemical or biological features of growing populations. Due to methodological differences, the exponential growth rate, which is a parameter that is representative of growth dynamics, should be differentiated. Ignoring such differentiation in the growth analysis might overlook somehow slight but significant changes in cellular features of the growing population. Both experimental and theoretical investigations are required to address these issues.

Results

This study experimentally verified the differentiation in growth rates attributed to different methodologies, and demonstrated that the most popular method, optical turbidity, led to the determination of a lower growth rate in comparison to the methods based on colony formation and cellular adenosine triphosphate, due to a decay effect of reading OD₆₀₀ during a population increase. Accordingly, the logistic model, which is commonly applied to the high-throughput growth data reading the OD₆₀₀, was revised by introducing a new parameter: the decay rate, to compensate for the lowered estimation in growth rates. An improved goodness of fit in comparison to the original model was acquired due to this revision. Applying the modified logistic model to hundreds of growth data acquired from an assortment of Escherichia coli strains carrying the reduced genomes led to an intriguing finding of a correlation between the decay rate and the genome size. The decay effect seemed to be partially attributed to the decrease in cell size accompanied by a population increase and was medium dependent.

Conclusions

The present study provides not only an improved theoretical tool for the high-throughput studies on bacterial growth dynamics linking with optical turbidity to biological meaning, but also a novel insight of the genome reduction correlated decay effect, which potentially reflects the changing cellular features during population increase. It is valuable for understanding the genome evolution and the fitness increase in microbial life.

Electronic supplementary material

The online version of this article (10.1186/s12866-018-1242-4) contains supplementary material, which is available to authorized users.

Microbial communities as dynamical systems

Nowadays, microbial communities are frequently monitored over long periods of time and the interactions between their members are explored in vitro. This development has opened the way to apply mathematical models to characterize community structure and dynamics, to predict responses to perturbations and to explore general dynamical properties such as stability, alternative stable states and periodicity. Here, we highlight the role of dynamical systems theory in the exploration of microbial communities, with a special emphasis on the generalized Lotka-Volterra (gLV) equations. In particular, we discuss applications, assumptions and limitations of the gLV model, mention modifications to address these limitations and review stochastic extensions. The development of dynamical models, together with the generation of time series data, can improve the design and control of microbial communities.

Transcriptional Regulation in Archaea: From Individual Genes to Global Regulatory Networks

Archaea are major contributors to biogeochemical cycles, possess unique metabolic capabilities, and resist extreme stress. To regulate the expression of genes encoding these unique programs, archaeal cells use gene regulatory networks (GRNs) composed of transcription factor proteins and their target genes. Recent developments in genetics, genomics, and computational methods used with archaeal model organisms have enabled the mapping and prediction of global GRN structures. Experimental tests of these predictions have revealed the dynamical function of GRNs in response to environmental variation. Here, we review recent progress made in this area, from investigating the mechanisms of transcriptional regulation of individual genes to small-scale subnetworks and genome-wide global networks. At each level, archaeal GRNs consist of a hybrid of bacterial, eukaryotic, and uniquely archaeal mechanisms. We discuss this theme from the perspective of the role of individual transcription factors in genome-wide regulation, how these proteins interact to compile GRN topological structures, and how these topologies lead to emergent, high-level GRN functions. We conclude by discussing how systems biology approaches are a fruitful avenue for addressing remaining challenges, such as discovering gene function and the evolution of GRNs.

A transcription network of interlocking positive feedback loops maintains intracellular iron balance in archaea

Iron is required for key metabolic processes but is toxic in excess. This circumstance forces organisms across the tree of life to tightly regulate iron homeostasis. In hypersaline lakes dominated by archaeal species, iron levels are extremely low and subject to environmental change; however, mechanisms regulating iron homeostasis in archaea remain unclear. In previous work, we demonstrated that two transcription factors (TFs), Idr1 and Idr2, collaboratively regulate aspects of iron homeostasis in the model species Halobacterium salinarum. Here we show that Idr1 and Idr2 are part of an extended regulatory network of four TFs of the bacterial DtxR family that maintains intracellular iron balance. We demonstrate that each TF directly regulates at least one of the other DtxR TFs at the level of transcription. Dynamical modeling revealed interlocking positive feedback loop architecture, which exhibits bistable or oscillatory network dynamics depending on iron availability. TF knockout mutant phenotypes are consistent with model predictions. Together, our results support that this network regulates iron homeostasis despite variation in extracellular iron levels, consistent with dynamical properties of interlocking feedback architecture in eukaryotes. These results suggest that archaea use bacterial-type TFs in a eukaryotic regulatory network topology to adapt to harsh environments.

Systematic Discovery of Archaeal Transcription Factor Functions in Regulatory Networks through Quantitative Phenotyping Analysis

To ensure survival in the face of stress, microorganisms employ inducible damage repair pathways regulated by extensive and complex gene networks. Many archaea, microorganisms of the third domain of life, persist under extremes of temperature, salinity, and pH and under other conditions. In order to understand the cause-effect relationships between the dynamic function of the stress network and ultimate physiological consequences, this study characterized the physiological role of nearly one-third of all regulatory proteins known as transcription factors (TFs) in an archaeal organism. Using a unique quantitative phenotyping approach, we discovered functions for many novel TFs and revealed important secondary functions for known TFs. Surprisingly, many TFs are required for resisting multiple stressors, suggesting cross-regulation of stress responses. Through extensive validation experiments, we map the physiological roles of these novel TFs in stress response back to their position in the regulatory network wiring. This study advances understanding of the mechanisms underlying how microorganisms resist extreme stress. Given the generality of the methods employed, we expect that this study will enable future studies on how regulatory networks adjust cellular physiology in a diversity of organisms.

Gene regulatory networks (GRNs) are critical for dynamic transcriptional responses to environmental stress. However, the mechanisms by which GRN regulation adjusts physiology to enable stress survival remain unclear. Here we investigate the functions of transcription factors (TFs) within the global GRN of the stress-tolerant archaeal microorganism Halobacterium salinarum. We measured growth phenotypes of a panel of TF deletion mutants in high temporal resolution under heat shock, oxidative stress, and low-salinity conditions. To quantitate the noncanonical functional forms of the growth trajectories observed for these mutants, we developed a novel modeling framework based on Gaussian process regression and functional analysis of variance (FANOVA). We employ unique statistical tests to determine the significance of differential growth relative to the growth of the control strain. This analysis recapitulated known TF functions, revealed novel functions, and identified surprising secondary functions for characterized TFs. Strikingly, we observed that the majority of the TFs studied were required for growth under multiple stress conditions, pinpointing regulatory connections between the conditions tested. Correlations between quantitative phenotype trajectories of mutants are predictive of TF-TF connections within the GRN. These phenotypes are strongly concordant with predictions from statistical GRN models inferred from gene expression data alone. With genome-wide and targeted data sets, we provide detailed functional validation of novel TFs required for extreme oxidative stress and heat shock survival. Together, results presented in this study suggest that many TFs function under multiple conditions, thereby revealing high interconnectivity within the GRN and identifying the specific TFs required for communication between networks responding to disparate stressors.

IMPORTANCE To ensure survival in the face of stress, microorganisms employ inducible damage repair pathways regulated by extensive and complex gene networks. Many archaea, microorganisms of the third domain of life, persist under extremes of temperature, salinity, and pH and under other conditions. In order to understand the cause-effect relationships between the dynamic function of the stress network and ultimate physiological consequences, this study characterized the physiological role of nearly one-third of all regulatory proteins known as transcription factors (TFs) in an archaeal organism. Using a unique quantitative phenotyping approach, we discovered functions for many novel TFs and revealed important secondary functions for known TFs. Surprisingly, many TFs are required for resisting multiple stressors, suggesting cross-regulation of stress responses. Through extensive validation experiments, we map the physiological roles of these novel TFs in stress response back to their position in the regulatory network wiring. This study advances understanding of the mechanisms underlying how microorganisms resist extreme stress. Given the generality of the methods employed, we expect that this study will enable future studies on how regulatory networks adjust cellular physiology in a diversity of organisms.