Distinct growth strategies of soil bacteria as revealed by large-scale colony tracking

Dynamics of bacterial growth, and life-history tradeoffs, explain differences in soil carbon cycling due to land-use

Soil contains a considerable fraction of Earth's organic carbon. Bacterial growth and mortality drive the microbial carbon pump, influencing carbon use efficiency and necromass production, key determinants for organic carbon persistence in soils. However, bacterial growth dynamics in soil are poorly characterized. We used an internal standard approach to normalize 16S ribosomal RNA gene sequencing data allowing us to quantify growth dynamics for 30 days following plant litter input to soil. We show that clustering taxa into three groups optimized variation of bacterial growth parameters in situ. These three clusters differed significantly with respect to their lag time, growth rate, growth duration, and change in abundance due to growth (ΔNg) and mortality (ΔNd), matching predictions of Grime's CSR life-history framework. In addition, we show a striking relationship between ΔNg and ΔNd, which reveals that growth in soil is tightly coupled to death. This result suggests a fitness paradox whereby some bacteria can optimize fitness in soil by minimizing mortality rather than maximizing growth. We hypothesized that land-use constrains microbial growth dynamics by favoring different life-history strategies and that these constraints control carbon mineralization. We show that life-history groups vary in prevalence with respect to land-use, and that bacterial growth dynamics correlated with carbon mineralization rate and net growth efficiency. Meadow soil supported more bacterial growth, greater mortality, and higher growth efficiency than agricultural soils, pointing toward more efficient conversion of plant litter into microbial necromass, which should promote long-term C stabilization.

Construction and characterisation of a structured, tuneable, and transparent 3D culture platform for soil bacteria

We have developed a tuneable workflow for the study of soil microbes in an imitative 3D soil environment that is compatible with routine and advanced optical imaging, is chemically customisable, and is reliably refractive index matched based on the carbon catabolism of the study organism. We demonstrate our transparent soil pipeline with two representative soil organisms, Bacillus subtilis and Streptomyces coelicolor, and visualise their colonisation behaviours using fluorescence microscopy and mesoscopy. This spatially structured, 3D approach to microbial culture has the potential to further study the behaviour of bacteria in conditions matching their native environment and could be expanded to study microbial interactions, such as competition and warfare.

Land use alters bacterial growth dynamics in soil

Microbial growth and mortality are major determinants of soil carbon cycling. We measured in situ growth dynamics of individual bacterial taxa in cropped and successional soils in response to a resource pulse. We hypothesized that land use imposes selection pressures on growth characteristics. We estimated growth and death for 453 and 73 taxa, respectively. The average generation time was 5.04 ± 6.28 (SD; range 0.7-63.5) days. Lag times were shorter in cultivated than successional soils and resource amendment decreased lag times. Taxa exhibiting the greatest growth response also exhibited the greatest mortality, indicative of boom-and-bust dynamics. We observed a bimodal growth rate distribution, representing fast- and slow-growing clusters. Both clusters grew more rapidly in successional soils, which had more organic matter, than cultivated soils. Resource amendment increased the growth rate of the slower growing but not the faster-growing cluster via a mixture of increased growth rates and species turnover, indicating that competitive dynamics constrain growth rates in situ. These two clusters show that copiotrophic bacteria in soils may be subdivided into different life history groups and that these subgroups respond independently to land use and resource availability.

Survival of Salmonella spp. under varying temperature and soil conditions

Soils can serve as suitable reservoirs for or barriers against microbial contamination of water resources and plant produce. The magnitude of water or food contamination risks through soil depends on several factors, including the survival potential of microorganisms in the soil. This study assessed and compared the survival/persistence of 14 Salmonella spp. strains in loam and sandy soils at 5, 10, 20, 25, 30, 35, 37 °C and under uncontrolled ambient temperature conditions in Campinas Sao Paulo. The ambient temperature ranged from 6 °C (minimum) to 36 °C (maximum). Bacterial population densities were determined by the conventional culture method (plate counts) and monitored for 216 days. Statistical differences among the test parameters were determined by Analysis of Variance, while relationships between temperature and soil type were evaluated using Pearson correlation analysis. Similarly, relationships between time and temperature for survival of the various strains were evaluated using Pearson correlation analysis. Results obtained indicate that temperature and soil type influence the survival of Salmonella spp. in soils. All 14 strains survived for up to 216 days in the organic-rich loam soil under at least three of the temperature conditions evaluated. However, comparatively lower survival rates were recorded in sandy soil, especially at lower temperature. The optimum temperature for survival varied among the strains, where some survived best at 5 °C and others between 30 and 37 °C. Under uncontrolled temperature conditions, the Salmonella strains survived better in loam than in sandy soils. Bacterial growth over post inoculation storage period was overall more impressive in loam soil. In general, the results indicate that temperature and soil type can interact to influence the survival of Salmonella spp. strains in soil. For the survival of some strains, there were significant correlations between soil type and temperature, while for some others, no significant relationship between soil and temperature was determined. A similar trend was observed for the correlation between time and temperature.

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.

Influence of planting yellowhorn (Xanthoceras sorbifolium Bunge) on the bacterial and fungal diversity of fly ash

Phytoremediation is a low-cost solution to fly ash pollution and the rhizosphere interactions between plant roots and the fly ash microbiome were important for the phytoremediation. To analyze the dynamic changes of the rhizosphere microbiome during yellowhorn cultivation in fly ash, the bacterial 16S rRNA gene V3-V4 region and the fungal ITS region of the rhizosphere microbiome were sequenced using Illumina MiSeq technology. The changes in fly ash physicochemical properties and the heavy metal content of different yellowhorn tissues were also analyzed. The results showed that both the bacterial and fungal communities were noticeably different after yellowhorn cultivation compared with the control sample. Proteobacteria and Acidobacteria levels increased (p < 0.05) and Firmicutes and Actinobacteria decreased (p < 0.05) in the bacterial community after yellowhorn cultivation. In the fungal community, Ascomycota and Mortierellomycota decreased (p < 0.05), while Chytridiomycota increased (p < 0.05). The levels of four heavy metals (Cr, Cd, Hg, Pb and As) decreased in the fly ash after yellowhorn cultivation. These metals were absorbed by the yellowhorn plants and accumulated in the fibrous root, taproot, stem and leaf tissues of these plants. Accordingly, the abundance of bacteria that could solubilize heavy metals increased (p < 0.05). In summary, the cultivation of yellowhorn affected the composition of the rhizosphere microbial communities in fly ash, which is of great significance for the biological remediation of fly ash.

Contribution of the genomic and nutritional differentiation to the spatial distribution of bacterial colonies

Colony growth is a common phenomenon of structured populations dispersed in nature; nevertheless, studies on the spatial distribution of colonies are largely insufficient. Here, we performed a systematic survey to address the questions of whether and how the spatial distribution of colonies was influenced by the genome and environment. Six Escherichia coli strains carrying either the wild-type or reduced genomes and eight media of varied nutritional richness were used to evaluate the genomic and environmental impacts, respectively. The genome size and nutritional variation contributed to the mean size and total area but not the variation and shape of size distribution of the colonies formed within the identical space and of equivalent spatial density. The spatial analysis by means of the Voronoi diagram found that the Voronoi correlation remained nearly constant in common, in comparison to the Voronoi response decreasing in correlation to genome reduction and nutritional enrichment. Growth analysis at the single colony level revealed positive correlations of the relative growth rate to both the maximal colony size and the Voronoi area, regardless of the genomic and nutritional variety. This result indicated fast growth for the large space assigned and supported homeostasis in the Voronoi correlation. Taken together, the spatial distribution of colonies might benefit efficient clonal growth. Although the mechanisms remain unclear, the findings provide quantitative insights into the genomic and environmental contributions to the growth and distribution of spatially or geographically isolated populations.

Metabolic Phenotyping of Marine Heterotrophs on Refactored Media Reveals Diverse Metabolic Adaptations and Lifestyle Strategies

Microbial communities, through their metabolism, drive carbon cycling in marine environments. These complex communities are composed of many different microorganisms including heterotrophic bacteria, each with its own nutritional needs and metabolic capabilities. Yet, models of ecosystem processes typically treat heterotrophic bacteria as a “black box,” which does not resolve metabolic heterogeneity nor address ecologically important processes such as the successive modification of different types of organic matter. Here we directly address the heterogeneity of metabolism by characterizing the carbon source utilization preferences of 63 heterotrophic bacteria representative of several major marine clades. By systematically growing these bacteria on 10 media containing specific subsets of carbon sources found in marine biomass, we obtained a phenotypic fingerprint that we used to explore the relationship between metabolic preferences and phylogenetic or genomic features. At the class level, these bacteria display broadly conserved patterns of preference for different carbon sources. Despite these broad taxonomic trends, growth profiles correlate poorly with phylogenetic distance or genome-wide gene content. However, metabolic preferences are strongly predicted by a handful of key enzymes that preferentially belong to a few enriched metabolic pathways, such as those involved in glyoxylate metabolism and biofilm formation. We find that enriched pathways point to enzymes directly involved in the metabolism of the corresponding carbon source and suggest potential associations between metabolic preferences and other ecologically relevant traits. The availability of systematic phenotypes across multiple synthetic media constitutes a valuable resource for future quantitative modeling efforts and systematic studies of interspecies interactions.

IMPORTANCE Half of the Earth’s annual primary production is carried out by phytoplankton in the surface ocean. However, this metabolic activity is heavily impacted by heterotrophic bacteria, which dominate the transformation of organic matter released from phytoplankton. Here, we characterize the diversity of metabolic preferences across many representative heterotrophs by systematically growing them on different fractions of dissolved organic carbon. Our analysis suggests that different clades of bacteria have substantially distinct preferences for specific carbon sources, in a way that cannot be simply mapped onto phylogeny. These preferences are associated with the presence of specific genes and pathways, reflecting an association between metabolic capabilities and ecological lifestyles. In addition to helping understand the importance of heterotrophs under different conditions, the phenotypic fingerprint we obtained can help build higher resolution quantitative models of global microbial activity and biogeochemical cycles in the oceans.

Microbiome composition of disturbed soils from sandy-gravel mining complexes with different reclamation approaches

Activities connected to mineral mining disrupt the soil layer and bring parent rock material to the surface. It leads to altering the environmental conditions and leaves behind vast areas of disturbed lands. Returning these lands to natural ecosystems is an important contemporary challenge, which can be acquired by reclamation practices. Soil microbiome composition reflects changes happening to disturbed lands; thus, its analysis is a powerful tool for evaluating the disturbance degree and estimating the effect of the implementation of reclamation techniques. Additionally, factors connected to the characteristics of a particular geographical region have a certain impact on the microbiome and should be taken into account. Thereby, studies of soil microbiomes of disturbed soils of different origins are essential in understanding the dynamics of soil restoration. Here, we focus on soil microbiomes from two sandy-gravel mining complexes in mountainous areas with a moderate continental climate of the Central Caucasus. These quarries share the same parent rock material, but differ in benchmark soil type and reclamation approach - one was left for passive recovery and the other was technically reclaimed with overburden material. Comparative analysis of microbiome composition, based on sequencing of 16S rRNA gene libraries, showed that region and disturbance are the key factors explaining microbiome variation, which surpass the influence of local factors. However, the application of reclamation techniques greatly reduces the dissimilarity of soil microbiomes caused by disturbance. Linking of soil chemical parameters to microbiome composition showed that the disturbance factor correlates with a lack of organic carbon. Other chemical parameters, like pH, ammonium, nitrates and total carbon explain microbiome variability on a smaller scale between sampling sites. Thus, while regional and disturbance factors reflected differentiation of soil microbiomes, soil chemical parameters explained local variation of certain groups of microorganisms.

Pareto optimality between growth-rate and lag-time couples metabolic noise to phenotypic heterogeneity in Escherichia coli

Despite mounting evidence that in clonal bacterial populations, phenotypic variability originates from stochasticity in gene expression, little is known about noise-shaping evolutionary forces and how expression noise translates to phenotypic differences. Here we developed a high-throughput assay that uses a redox-sensitive dye to couple growth of thousands of bacterial colonies to their respiratory activity and show that in Escherichia coli, noisy regulation of lower glycolysis and citric acid cycle is responsible for large variations in respiratory metabolism. We found that these variations are Pareto optimal to maximization of growth rate and minimization of lag time, two objectives competing between fermentative and respiratory metabolism. Metabolome-based analysis revealed the role of respiratory metabolism in preventing the accumulation of toxic intermediates of branched chain amino acid biosynthesis, thereby supporting early onset of cell growth after carbon starvation. We propose that optimal metabolic tradeoffs play a key role in shaping and preserving phenotypic heterogeneity and adaptation to fluctuating environments.

Correlation between the spatial distribution and colony size was common for monogenetic bacteria in laboratory conditions

Background

Geographically separated population growth of microbes is a common phenomenon in microbial ecology. Colonies are representative of the morphological characteristics of this structured population growth. Pattern formation by single colonies has been intensively studied, whereas the spatial distribution of colonies is poorly investigated.

Results

The present study describes a first trial to address the questions of whether and how the spatial distribution of colonies determines the final colony size using the model microorganism Escherichia coli, colonies of which can be grown under well-controlled laboratory conditions. A computational tool for image processing was developed to evaluate colony density, colony size and size variation, and the Voronoi diagram was applied for spatial analysis of colonies with identical space resources. A positive correlation between the final colony size and the Voronoi area was commonly identified, independent of genomic and nutritional differences, which disturbed the colony size and size variation.

Conclusions

This novel finding of a universal correlation between the spatial distribution and colony size not only indicated the fair distribution of spatial resources for monogenetic colonies growing with identical space resources but also indicated that the initial localization of the microbial colonies decided by chance determined the fate of the subsequent population growth. This study provides a valuable example for quantitative analysis of the complex microbial ecosystems by means of experimental ecology.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02180-8.

Definition of Core Bacterial Taxa in Different Root Compartments of Dactylis glomerata, Grown in Soil under Different Levels of Land Use Intensity

Plant-associated bacterial assemblages are critical for plant fitness. Thus, identifying a consistent plant-associated core microbiome is important for predicting community responses to environmental changes. Our target was to identify the core bacterial microbiome of orchard grass Dactylis glomerata L. and to assess the part that is most sensitive to land management. Dactylis glomerata L. samples were collected from grassland sites with contrasting land use intensities but comparable soil properties at three different timepoints. To assess the plant-associated bacterial community structure in the compartments rhizosphere, bulk soil and endosphere, a molecular barcoding approach based on high throughput 16S rRNA amplicon sequencing was used. A distinct composition of plant-associated core bacterial communities independent of land use intensity was identified. Pseudomonas, Rhizobium and Bradyrhizobium were ubiquitously found in the root bacterial core microbiome. In the rhizosphere, the majority of assigned genera were Rhodoplanes, Methylibium, Kaistobacter and Bradyrhizobium. Due to the frequent occurrence of plant-promoting abilities in the genera found in the plant-associated core bacterial communities, our study helps to identify “healthy” plant-associated bacterial core communities. The variable part of the plant-associated microbiome, represented by the fluctuation of taxa at the different sampling timepoints, was increased under low land use intensity. This higher compositional variation in samples from plots with low land use intensity indicates a more selective recruitment of bacteria with traits required at different timepoints of plant development compared to samples from plots with high land use intensity.

Efficient microbial colony growth dynamics quantification with ColTapp, an automated image analysis application

Populations of genetically identical bacteria are phenotypically heterogeneous, giving rise to population functionalities that would not be possible in homogeneous populations. For instance, a proportion of non-dividing bacteria could persist through antibiotic challenges and secure population survival. This heterogeneity can be studied in complex environmental or clinical samples by spreading the bacteria on agar plates and monitoring time to growth resumption in order to infer their metabolic state distribution. We present ColTapp, the Colony Time-lapse application for bacterial colony growth quantification. Its intuitive graphical user interface allows users to analyze time-lapse images of agar plates to monitor size, color and morphology of colonies. Additionally, images at isolated timepoints can be used to estimate lag time. Using ColTapp, we analyze a dataset of Staphylococcus aureus time-lapse images including populations with heterogeneous lag time. Colonies on dense plates reach saturation early, leading to overestimation of lag time from isolated images. We show that this bias can be corrected by taking into account the area available to each colony on the plate. We envision that in clinical settings, improved analysis of colony growth dynamics may help treatment decisions oriented towards personalized antibiotic therapies.

A Multiplex Fluidic Chip for Rapid Phenotypic Antibiotic Susceptibility Testing

Prompt and effective antimicrobial therapy is crucial for the management of patients with severe bacterial infections but is becoming increasingly difficult to provide due to emerging antibiotic resistance. The traditional methods for antibiotic susceptibility testing (AST) used in most clinical laboratories are reliable but slow with turnaround times of 2 to 3 days, which necessitates the use of empirical therapy with broad-spectrum antibiotics. There is a great need for fast and reliable AST methods that enable starting targeted treatment within a few hours to improve patient outcome and reduce the overuse of broad-spectrum antibiotics. The multiplex fluidic chip for phenotypic AST described in the present study may enable data on antimicrobial resistance within 2 to 4 h, allowing for an early initiation of appropriate antibiotic therapy.

Many patients with severe infections receive inappropriate empirical treatment, and rapid detection of bacterial antibiotic susceptibility can improve clinical outcome and reduce mortality. To this end, we have developed a multiplex fluidic chip for rapid phenotypic antibiotic susceptibility testing of bacteria. A total of 21 clinical isolates of Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus were acquired from the EUCAST Development Laboratory and tested against amikacin, ceftazidime, and meropenem (Gram-negative bacteria) or gentamicin, ofloxacin, and tetracycline (Gram-positive bacteria). The bacterial samples were mixed with agarose and loaded in an array of growth chambers in the chip where bacterial microcolony growth was monitored over time using automated image analysis. MIC values were automatically obtained by tracking the growth rates of individual microcolonies in different regions of antibiotic gradients. Stable MIC values were obtained within 2 to 4 h, and the results showed categorical agreement with reference MIC values as determined by broth microdilution in 86% of the cases.

Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations

While microbiologists often make the simplifying assumption that genotype determines phenotype in a given environment, it is becoming increasingly apparent that phenotypic heterogeneity (in which one genotype generates multiple phenotypes simultaneously even in a uniform environment) is common in many microbial populations. The importance of phenotypic heterogeneity has been demonstrated in a number of model systems involving binary phenotypic states (e.g., growth/non-growth); however, less is known about systems involving phenotype distributions that are continuous across an environmental gradient, and how those distributions change when the environment changes. Here, we describe a novel instance of phenotypic diversity in tolerance to a metabolic toxin within wild-type populations of Methylobacterium extorquens, a ubiquitous phyllosphere methylotroph capable of growing on the methanol periodically released from plant leaves. The first intermediate in methanol metabolism is formaldehyde, a potent cellular toxin that is lethal in high concentrations. We have found that at moderate concentrations, formaldehyde tolerance in M. extorquens is heterogeneous, with a cell's minimum tolerance level ranging between 0 mM and 8 mM. Tolerant cells have a distinct gene expression profile from non-tolerant cells. This form of heterogeneity is continuous in terms of threshold (the formaldehyde concentration where growth ceases), yet binary in outcome (at a given formaldehyde concentration, cells either grow normally or die, with no intermediate phenotype), and it is not associated with any detectable genetic mutations. Moreover, tolerance distributions within the population are dynamic, changing over time in response to growth conditions. We characterized this phenomenon using bulk liquid culture experiments, colony growth tracking, flow cytometry, single-cell time-lapse microscopy, transcriptomics, and genome resequencing. Finally, we used mathematical modeling to better understand the processes by which cells change phenotype, and found evidence for both stochastic, bidirectional phenotypic diversification and responsive, directed phenotypic shifts, depending on the growth substrate and the presence of toxin.

Scientists tend to appreciate microbes for their simplicity and predictability: a population of genetically identical cells inhabiting a uniform environment is expected to behave in a uniform way. However, counter-examples to this assumption are frequently being discovered, forcing a re-examination of the relationship between genotype and phenotype. In most such examples, bacterial cells are found to split into two discrete populations, for instance growing and non-growing. Here, we report the discovery of a novel example of microbial phenotypic heterogeneity in which cells are distributed along a gradient of phenotypes, ranging from low to high tolerance of a toxic chemical. Furthermore, we demonstrate that the distribution of phenotypes changes in different growth conditions, and we use mathematical modeling to show that cells may change their phenotype either randomly or in a particular direction in response to the environment. Our work expands our understanding of how a bacterial cell's genome, family history, and environment all contribute to its behavior, with implications for the diverse situations in which we care to understand the growth of any single-celled populations.

Resistance of aerobic microorganisms and soil enzyme response to soil contamination with Ekodiesel Ultra fuel

This study determined the susceptibility of cultured soil microorganisms to the effects of Ekodiesel Ultra fuel (DO), to the enzymatic activity of soil and to soil contamination with PAHs. Studies into the effects of any type of oil products on reactions taking place in soil are necessary as particular fuels not only differ in the chemical composition of oil products but also in the composition of various fuel improvers and antimicrobial fuel additives. The subjects of the study included loamy sand and sandy loam which, in their natural state, have been classified into the soil subtype 3.1.1 Endocalcaric Cambisols. The soil was contaminated with the DO in amounts of 0, 5 and 10 cm³ kg^-1. Differences were noted in the resistance of particular groups or genera of microorganisms to DO contamination in loamy sand (LS) and sandy loam (SL). In loamy sand and sandy loam, the most resistant microorganisms were oligotrophic spore-forming bacteria. The resistance of microorganisms to DO contamination was greater in LS than in SL. It decreased with the duration of exposure of microorganisms to the effects of DO. The factor of impact (IF_DO) on the activity of particular enzymes varied. For dehydrogenases, urease, arylsulphatase and β-glucosidase, it had negative values, while for catalase, it had positive values and was close to 0 for acid phosphatase and alkaline phosphatase. However, in both soils, the noted index of biochemical activity of soil (BA) decreased with the increase in DO contamination. In addition, a positive correlation occurred between the degree of soil contamination and its PAH content.

Soil organic carbon distribution in Mediterranean areas under a climate change scenario via multiple linear regression analysis

Over time, the interest on soil studies has increased due to its role in carbon sequestration in terrestrial ecosystems, which could contribute to decreasing atmospheric CO₂ rates. In many studies, independent variables were related to soil organic carbon (SOC) alone, however, the contribution degree of each variable with the experimentally determined SOC content were not considered. In this study, samples from 612 soil profiles were obtained in a natural protected (Red Natura 2000) of Sierra Morena (Mediterranean area, South Spain), considering only the topsoil 0-25cm, for better comparison between results. 24 independent variables were used to define it relationship with SOC content. Subsequently, using a multiple linear regression analysis, the effects of these variables on the SOC correlation was considered. Finally, the best parameters determined with the regression analysis were used in a climatic change scenario. The model indicated that SOC in a future scenario of climate change depends on average temperature of coldest quarter (41.9%), average temperature of warmest quarter (34.5%), annual precipitation (22.2%) and annual average temperature (1.3%). When the current and future situations were compared, the SOC content in the study area was reduced a 35.4%, and a trend towards migration to higher latitude and altitude was observed.

Community-level physiological profiling analyses show potential to identify the copiotrophic bacteria present in soil environments

Community-level physiological profiling (CLPP) analyses from very diverse environments are frequently used with the aim of characterizing the metabolic versatility of whole environmental bacterial communities. While the limitations of the methodology for the characterization of whole communities are well known, we propose that CLPP combined with high-throughput sequencing and qPCR can be utilized to identify the copiotrophic, fast-growing fraction of the bacterial community of soil environments, where oligotrophic taxa are usually dominant. In the present work we have used this approach to analyze samples of litter and soil from a coniferous forest in the Czech Republic using BIOLOG GN2 plates. Monosaccharides and amino acids were utilized significantly faster than other C substrates, such as organic acids, in both litter and soil samples. Bacterial biodiversity in CLPP wells was significantly lower than in the original community, independently of the carbon source. Bacterial communities became highly enriched in taxa that typically showed low abundance in the original soil, belonging mostly to the Gammaproteobacteria and the genus Pseudomonas, indicating that the copiotrophic strains, favoured by the high nutrient content, are rare in forest litter and soil. In contrast, taxa abundant in the original samples were rarely found to grow at sufficient rates under the CLPP conditions. Our results show that CLPP is useful to detect copiotrophic bacteria from the soil environments and that bacterial growth is substrate specific.

The Influence of Land Use Intensity on the Plant-Associated Microbiome of Dactylis glomerata L

In this study, we investigated the impact of different land use intensities (LUI) on the root-associated microbiome of Dactylis glomerata (orchardgrass). For this purpose, eight sampling sites with different land use intensity levels but comparable soil properties were selected in the southwest of Germany. Experimental plots covered land use levels from natural grassland up to intensively managed meadows. We used 16S rRNA gene based barcoding to assess the plant-associated community structure in the endosphere, rhizosphere and bulk soil of D. glomerata. Samples were taken at the reproductive stage of the plant in early summer. Our data indicated that roots harbor a distinct bacterial community, which clearly differed from the microbiome of the rhizosphere and bulk soil. Our results revealed Pseudomonadaceae, Enterobacteriaceae and Comamonadaceae as the most abundant endophytes independently of land use intensity. Rhizosphere and bulk soil were dominated also by Proteobacteria, but the most abundant families differed from those obtained from root samples. In the soil, the effect of land use intensity was more pronounced compared to root endophytes leading to a clearly distinct pattern of bacterial communities under different LUI from rhizosphere and bulk soil vs. endophytes. Overall, a change of community structure on the plant-soil interface was observed, as the number of shared OTUs between all three compartments investigated increased with decreasing land use intensity. Thus, our findings suggest a stronger interaction of the plant with its surrounding soil under low land use intensity. Furthermore, the amount and quality of available nitrogen was identified as a major driver for shifts in the microbiome structure in all compartments.

Competition for space during bacterial colonization of a surface

Competition for space is ubiquitous in the ecology of both microorganisms and macro-organisms. We introduce a bacterial model system in which the factors influencing competition for space during colonization of an initially empty habitat can be tracked directly. Using fluorescence microscopy, we follow the fate of individual Escherichia coli bacterial cell lineages as they undergo expansion competition (the race to be the first to colonize a previously empty territory), and as they later compete at boundaries between clonal territories. Our experiments are complemented by computer simulations of a lattice-based model. We find that both expansion competition, manifested as differences in individual cell lag times, and boundary competition, manifested as effects of neighbour cell geometry, can play a role in colonization success, particularly when lineages expand exponentially. This work provides a baseline for investigating how ecological interactions affect colonization of space by bacterial populations, and highlights the potential of bacterial model systems for the testing and development of ecological theory.

Serial image analysis of Mycobacterium tuberculosis colony growth reveals a persistent subpopulation in sputum during treatment of pulmonary TB

Faster elimination of drug tolerant ‘persister’ bacteria may shorten treatment of tuberculosis (TB) but no method exists to quantify persisters in clinical samples. We used automated image analysis to assess whether studying growth characteristics of individual Mycobacterium tuberculosis colonies from sputum on solid media during early TB treatment facilitates ‘persister’ phenotyping. As Time to Detection (TTD) in liquid culture inversely correlates with total bacterial load we also evaluated the relationship between individual colony growth parameters and TTD. Sputum from TB patients in Malawi was prepared for solid and liquid culture after 0, 2 and 4 weeks of treatment. Serial photography of agar plates was used to measure time to appearance (lag time) and radial growth rate for each colony. Mixed-effects modelling was used to analyse changing growth characteristics from serial samples. 20 patients had colony measurements recorded at ≥1 time-point. Overall lag time increased by 6.5 days between baseline and two weeks (p = 0.0001). Total colony count/ml showed typical biphasic elimination, but long lag time colonies (>20days) had slower, monophasic decline. TTD was associated with minimum lag time (time to appearance of first colony1). Slower elimination of long lag time colonies suggests that these may represent a persister subpopulation of bacilli.

Molecular survey of bacterial communities associated with bacterial chondronecrosis with osteomyelitis (BCO) in broilers

Bacterial chondronecrosis with osteomyelitis (BCO) is recognized as an important cause of lameness in commercial broiler chickens (meat-type chickens). Relatively little is known about the microbial communities associated with BCO. This study was conducted to increase our understanding of the microbial factors associated with BCO using a culture-independent approach. Using Illumina sequencing of the hyper-variable region V6 in the 16S rRNA gene, we characterized the bacterial communities in 97 femoral or tibial heads from normal and lame broilers carefully selected to represent diverse variations in age, line, lesion type, floor type, clinical status and bone type. Our in-depth survey based on 14 million assembled sequence reads revealed that complex bacterial communities exist in all samples, including macroscopically normal bones from clinically healthy birds. Overall, Proteobacteria (mean 90.9%) comprised the most common phylum, followed by Firmicutes (6.1%) and Actinobacteria (2.6%), accounting for more than 99% of all reads. Statistical analyses demonstrated that there are differences in bacterial communities in different types of bones (femur vs. tibia), lesion types (macroscopically normal femora or tibiae vs. those with pathognomonic BCO lesions), and among individual birds. This analysis also showed that BCO samples overrepresented genera Staphylococcus, whose species have been frequently isolated in BCO samples in previous studies. Rarefaction analysis demonstrated the general tendency that increased severities of BCO lesions were associated with reduced species diversity in both femoral and tibial samples when compared to macroscopically normal samples. These observations suggest that certain bacterial subgroups are preferentially selected in association with the development of BCO lesions. Understanding the microbial species associated with BCO will identify opportunities for understanding and modulating the pathogenesis of this form of lameness in broilers.

Evolutionary limits to cooperation in microbial communities

Microbes produce many compounds that are costly to a focal cell but promote the survival and reproduction of neighboring cells. This observation has led to the suggestion that microbial strains and species will commonly cooperate by exchanging compounds. Here, we examine this idea with an ecoevolutionary model where microbes make multiple secretions, which can be exchanged among genotypes. We show that cooperation between genotypes only evolves under specific demographic regimes characterized by intermediate genetic mixing. The key constraint on cooperative exchanges is a loss of autonomy: strains become reliant on complementary genotypes that may not be reliably encountered. Moreover, the form of cooperation that we observe arises through mutual exploitation that is related to cheating and "Black Queen" evolution for a single secretion. A major corollary is that the evolution of cooperative exchanges reduces community productivity relative to an autonomous strain that makes everything it needs. This prediction finds support in recent work from synthetic communities. Overall, our work suggests that natural selection will often limit cooperative exchanges in microbial communities and that, when exchanges do occur, they can be an inefficient solution to group living.

ScanLag: high-throughput quantification of colony growth and lag time

Growth dynamics are fundamental characteristics of microorganisms. Quantifying growth precisely is an important goal in microbiology. Growth dynamics are affected both by the doubling time of the microorganism and by any delay in growth upon transfer from one condition to another, the lag. The ScanLag method enables the characterization of these two independent properties at the level of colonies originating each from a single cell, generating a two-dimensional distribution of the lag time and of the growth time. In ScanLag, measurement of the time it takes for colonies on conventional nutrient agar plates to be detected is automated on an array of commercial scanners controlled by an in house application. Petri dishes are placed on the scanners, and the application acquires images periodically. Automated analysis of colony growth is then done by an application that returns the appearance time and growth rate of each colony. Other parameters, such as the shape, texture and color of the colony, can be extracted for multidimensional mapping of sub-populations of cells. Finally, the method enables the retrieval of rare variants with specific growth phenotypes for further characterization. The technique could be applied in bacteriology for the identification of long lag that can cause persistence to antibiotics, as well as a general low cost technique for phenotypic screens.

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.

Using surface plasmon resonance imaging to study bacterial biofilms

This paper describes the use of Surface Plasmon Resonance imaging (SPRi) as an emerging technique to study bacterial physiology in real-time without labels. The overwhelming majority of bacteria on earth exist in large multicellular communities known as biofilms. Biofilms are especially problematic because they facilitate the survival of pathogens, leading to chronic and recurring infections as well as costly industrial complications. Monitoring biofilm accumulation and removal is therefore critical in these and other applications. SPRi uniquely provides label-free, high-resolution images of biomass coverage on large channel surfaces up to 1 cm(2) in real time, which allow quantitative assessment of biofilm dynamics. The rapid imaging capabilities of this technique are particularly relevant for multicellular bacterial studies, as these cells can swim several body lengths per second and divide multiple times per hour. We present here the first application of SPRi to image Escherichia coli and Pseudomonas aeruginosa cells moving, attaching, and forming biofilms across a large surface. This is also the first time that biofilm removal has been visualized with SPRi, which has important implications for monitoring the biofouling and regeneration of fluidic systems. Initial images of the removal process show that the biofilm releases from the surface as a wave along the direction of the fluid flow.

The genotypic view of social interactions in microbial communities

Dense and diverse microbial communities are found in many environments. Disentangling the social interactions between strains and species is central to understanding microbes and how they respond to perturbations. However, the study of social evolution in microbes tends to focus on single species. Here, we broaden this perspective and review evolutionary and ecological theory relevant to microbial interactions across all phylogenetic scales. Despite increased complexity, we reduce the theory to a simple null model that we call the genotypic view. This states that cooperation will occur when cells are surrounded by identical genotypes at the loci that drive interactions, with genetic identity coming from recent clonal growth or horizontal gene transfer (HGT). In contrast, because cooperation is only expected to evolve between different genotypes under restrictive ecological conditions, different genotypes will typically compete. Competition between two genotypes includes mutual harm but, importantly, also many interactions that are beneficial to one of the two genotypes, such as predation. The literature offers support for the genotypic view with relatively few examples of cooperation between genotypes. However, the study of microbial interactions is still at an early stage. We outline the logic and methods that help to better evaluate our perspective and move us toward rationally engineering microbial communities to our own advantage.

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.

Toward the development of microbial indicators for wetland assessment

Wetland assessment tools are being developed and employed in wetland monitoring and conservation based on physical, chemical and biological characterization. In wetland biological assessment, various ecological functions have been described by biological traits of an entire species pool that adapts to different types of wetland environments. Since microorganisms play a key role in wetland biogeochemical processes and respond quickly to environmental disturbances, this review paper describes the different macro indicators used in wetland biological monitoring and expands the potential use of microbial indicators in wetland assessment and management. Application of molecular microbial technologies paves the path to an integrated measure of wetland health conditions. For example, the ratio of ammonia-oxidizing archaeal and bacterial populations has been proposed to serve as a microbial indicator of wetland nutrient conditions. The microbial indicators coupled with physical, chemical and other biological parameters are vital to the development of multi-metric index for measuring wetland health conditions. Inclusion of microbial indicators will lead to a more comprehensive wetland assessment for wetland restoration and management practices.