Wilfred Otten

The role of the natural environment in the emergence of antibiotic resistance in Gram-negative bacteria

During the past 10 years, multidrug-resistant Gram-negative Enterobacteriaceae have become a substantial challenge to infection control. It has been suggested by clinicians that the effectiveness of antibiotics is in such rapid decline that, depending on the pathogen concerned, their future utility can be measured in decades or even years. Unless the rise in antibiotic resistance can be reversed, we can expect to see a substantial rise in incurable infection and fatality in both developed and developing regions. Antibiotic resistance develops through complex interactions, with resistance arising by de-novo mutation under clinical antibiotic selection or frequently by acquisition of mobile genes that have evolved over time in bacteria in the environment. The reservoir of resistance genes in the environment is due to a mix of naturally occurring resistance and those present in animal and human waste and the selective effects of pollutants, which can co-select for mobile genetic elements carrying multiple resistant genes. Less attention has been given to how anthropogenic activity might be causing evolution of antibiotic resistance in the environment. Although the economics of the pharmaceutical industry continue to restrict investment in novel biomedical responses, action must be taken to avoid the conjunction of factors that promote evolution and spread of antibiotic resistance.

Microbial Distribution in Soils: Physics and Scaling

In a handful of fertile soil there are billions of microorganisms and yet, even with a conservative estimate, the surface area covered by these organisms is considerably less than 1%. What does this tell us about the function of the physical structure in which soil organisms reside and function, collecting, and separating micropopulations from each other and from resources? It would seem that most of the soil is akin to desert regions with little life been supported on its terrains, yet with vast communities of individuals, from an amazing array of species, supported in small-scale habitats, connected or disconnected by saturated or unsaturated pore space over relatively short time-scales. The biodiversity of these communities remains impressive yet overall functionally illusive, bar some considerations of inbuilt redundancy. What is far more impressive is the range of habitats on offer to populations with short-term evolutionary time frames. The availability of spatially and temporally diverse habitats probably gives rise to the biodiversity that we seeAbstract In a handful of fertile soil there are billions of microorganisms and yet, even with a conservative estimate, the surface area covered by these organisms is considerably less than 1%. What does this tell us about the function of the physical structure in which soil organisms reside and function, collecting, and separating micropopulations from each other and from resources? It would seem that most of the soil is akin to desert regions with little life been supported on its terrains, yet with vast communities of individuals, from an amazing array of species, supported in small-scale habitats, connected or disconnected by saturated or unsaturated pore space over relatively short time-scales. The biodiversity of these communities remains impressive yet overall functionally illusive, bar some considerations of inbuilt redundancy. What is far more impressive is the range of habitats on offer to populations with short-term evolutionary time frames. The availability of spatially and temporally diverse habitats probably gives rise to the biodiversity that we see in soil. It is not too far fetched to state that the majority of habitats on Earth (and indeed extraterrestrial) are revealed in that handful of soil. The key question is what is the functional consequence of such habitat heterogeneity? To answer this it is clear that we need to bring together a new discipline that combines the biology and physics of the soil ecosystem. This biophysical approach, combined, where required, with important mineral-microbe knowledge is needed to help us understand the mechanisms by which soils remain productive, and to identify the tipping-points at which there may be no return to sustainability. This review aims to highlight the importance of addressing the soil ecosystem as a dynamic heterogeneous system focusing on microbiota–habitat interactions.

Chapter 4 Microbial Distribution in Soils: Physics and Scaling

In a handful of fertile soil there are billions of microorganisms and yet, even with a conservative estimate, the surface area covered by these organisms is considerably less than 1%. What does this tell us about the function of the physical structure in which soil organisms reside and function, collecting, and separating micropopulations from each other and from resources? It would seem that most of the soil is akin to desert regions with little life been supported on its terrains, yet with vast communities of individuals, from an amazing array of species, supported in small-scale habitats, connected or disconnected by saturated or unsaturated pore space over relatively short time-scales. The biodiversity of these communities remains impressive yet overall functionally illusive, bar some considerations of inbuilt redundancy. What is far more impressive is the range of habitats on offer to populations with short-term evolutionary time frames. The availability of spatially and temporally diverse habitats probably gives rise to the biodiversity that we seeAbstract In a handful of fertile soil there are billions of microorganisms and yet, even with a conservative estimate, the surface area covered by these organisms is considerably less than 1%. What does this tell us about the function of the physical structure in which soil organisms reside and function, collecting, and separating micropopulations from each other and from resources? It would seem that most of the soil is akin to desert regions with little life been supported on its terrains, yet with vast communities of individuals, from an amazing array of species, supported in small-scale habitats, connected or disconnected by saturated or unsaturated pore space over relatively short time-scales. The biodiversity of these communities remains impressive yet overall functionally illusive, bar some considerations of inbuilt redundancy. What is far more impressive is the range of habitats on offer to populations with short-term evolutionary time frames. The availability of spatially and temporally diverse habitats probably gives rise to the biodiversity that we see in soil. It is not too far fetched to state that the majority of habitats on Earth (and indeed extraterrestrial) are revealed in that handful of soil. The key question is what is the functional consequence of such habitat heterogeneity? To answer this it is clear that we need to bring together a new discipline that combines the biology and physics of the soil ecosystem. This biophysical approach, combined, where required, with important mineral-microbe knowledge is needed to help us understand the mechanisms by which soils remain productive, and to identify the tipping-points at which there may be no return to sustainability. This review aims to highlight the importance of addressing the soil ecosystem as a dynamic heterogeneous system focusing on microbiota–habitat interactions.

Transparent Soil for Imaging the Rhizosphere

Understanding of soil processes is essential for addressing the global issues of food security, disease transmission and climate change. However, techniques for observing soil biology are lacking. We present a heterogeneous, porous, transparent substrate for in situ 3D imaging of living plants and root-associated microorganisms using particles of the transparent polymer, Nafion, and a solution with matching optical properties. Minerals and fluorescent dyes were adsorbed onto the Nafion particles for nutrient supply and imaging of pore size and geometry. Plant growth in transparent soil was similar to that in soil. We imaged colonization of lettuce roots by the human bacterial pathogen Escherichia coli O157:H7 showing micro-colony development. Micro-colonies may contribute to bacterial survival in soil. Transparent soil has applications in root biology, crop genetics and soil microbiology.

Effect of bulk density on the spatial organisation of the fungus Rhizoctonia solani in soil

Abstract The mycelial growth form of eucarpic fungi allows for a highly effective spatial exploration of the soil habitat. However, understanding mycelial spread through soil has been limited by difficulties of observation and quantification of fungi as they spread through this matrix. We report on a study on the effects of soil structure by altering the soil bulk density, on the spatial exploration of soil by the fungus Rhizoctonia solani using a soil thin-sectioning technique. First we quantified fungal densities in microscopic images (0.44 mm(2)). At this scale, hyphae were either absent, or present as minor fragments, typically occupying less than 1% surface area of the thin section. From contiguous microscopic images we then produced large-scale (6.21 cm(2)) spatial distribution maps of fungal hyphae. These maps were superimposed onto soil structural maps, which quantify the degree of porosity in each microscopic image. Alterations in soil structure by changing the bulk density are shown to affect the distribution of the fungus within the soil. The volume of soil explored by the fungus increased with increasing bulk density. This was associated with a shift from a few large pore spaces to more evenly distributed small-scale pores. Fungal hyphae were present in all porosity classes within each bulk density, including areas that contain less than 5% visible pore space. However, fungal hyphae were more often found in areas with a higher porosity, in particular at low soil bulk densities. The results show that soil structure is a major component in the spatial exploration of soil by fungi.

Continuity of air-filled pores and invasion thresholds for a soil-borne fungal plant pathogen, Rhizoctonia solani

Abstract Fundamental knowledge of the way fungi explore the pore volume within a soil is crucial if we are to understand how soil physical conditions affect population dynamics and invasion of many important fungal parasites and saprophytes within soil. In this study, spread of the fungus Rhizoctonia solani Kuhn (AG4), an economically important soil-borne plant pathogen and saprophyte, was quantified in relation to tortuosity and continuity of the air-filled pore space. Samples containing fine or coarse sand were equilibrated at matric potentials ranging from −1 to −7 kPa and inoculated with R. solani . We quantified the colony extent and biomass of R. solani spreading from a localized source of inoculum using microscopy and a monoclonal antibody-based immunosorbent assay. Air permeability rapidly increased when the air-filled pore space became continuous below a threshold matric potential of −2.0 kPa for the fine sand and −1.0 kPa for the coarse sand. Fungal spread was limited at high matric potentials with a colony extent of 6.6 and 4.6 mm for the fine and coarse sand, respectively, but increased to 32.6 and 28.5 mm, with a sharp transition below a threshold matric potential of −2.2 and −1.3 kPa. The average colony biomass dropped markedly at the same threshold matric potential. We conclude that the ability of the fungus to invade soil depends on the connectivity and tortuosity of the air-filled pore volume. At near-saturated conditions, the spread was spatially constrained to a relatively small volume, forming small dense colonies. In a well connected air-filled pore volume, larger colonies with low biomass density were formed. The broader implications for invasion of soil by a fungus and for transmission of fungal diseases are discussed.

Estimation of multiple transmission rates for epidemics in heterogeneous populations

One of the principal challenges in epidemiological modeling is to parameterize models with realistic estimates for transmission rates in order to analyze strategies for control and to predict disease outcomes. Using a combination of replicated experiments, Bayesian statistical inference, and stochastic modeling, we introduce and illustrate a strategy to estimate transmission parameters for the spread of infection through a two-phase mosaic, comprising favorable and unfavorable hosts. We focus on epidemics with local dispersal and formulate a spatially explicit, stochastic set of transition probabilities using a percolation paradigm for a susceptible–infected (S–I) epidemiological model. The S–I percolation model is further generalized to allow for multiple sources of infection including external inoculum and host-to-host infection. We fit the model using Bayesian inference and Markov chain Monte Carlo simulation to successive snapshots of damping-off disease spreading through replicated plant populations that differ in relative proportions of favorable and unfavorable hosts and with time-varying rates of transmission. Epidemiologically plausible parametric forms for these transmission rates are compared by using the deviance information criterion. Our results show that there are four transmission rates for a two-phase system, corresponding to each combination of infected donor and susceptible recipient. Knowing the number and magnitudes of the transmission rates allows the dominant pathways for transmission in a heterogeneous population to be identified. Finally, we show how failure to allow for multiple transmission rates can overestimate or underestimate the rate of spread of epidemics in heterogeneous environments, which could lead to marked failure or inefficiency of control strategies.

Challenges and opportunities for quantifying roots and rhizosphere interactions through imaging and image analysis

The morphology of roots and root systems influences the efficiency by which plants acquire nutrients and water, anchor themselves and provide stability to the surrounding soil. Plant genotype and the biotic and abiotic environment significantly influence root morphology, growth and ultimately crop yield. The challenge for researchers interested in phenotyping root systems is, therefore, not just to measure roots and link their phenotype to the plant genotype, but also to understand how the growth of roots is influenced by their environment. This review discusses progress in quantifying root system parameters (e.g. in terms of size, shape and dynamics) using imaging and image analysis technologies and also discusses their potential for providing a better understanding of root:soil interactions. Significant progress has been made in image acquisition techniques, however trade-offs exist between sample throughput, sample size, image resolution and information gained. All of these factors impact on downstream image analysis processes. While there have been significant advances in computation power, limitations still exist in statistical processes involved in image analysis. Utilizing and combining different imaging systems, integrating measurements and image analysis where possible, and amalgamating data will allow researchers to gain a better understanding of root:soil interactions.

Adaptive-window indicator kriging: A thresholding method for computed tomography images of porous media

Image segmentation is a crucial step in understanding the structure of porous materials, subsequent analyses being profoundly dependent upon segmentation accuracy. Computed tomography images of naturally occurring heterogeneous materials such as soils are particularly challenging to segment reliably, due to the prevalence of partial volume effects, noise, and other artefacts induced during the image acquisition process. As a result, boundaries between classes of objects, typically pore versus solid in the case of soil, are difficult to identify. Indicator kriging can address these problems in a robust fashion but the computational cost can be very great under some circumstances; this is particularly true under conditions that occur regularly in images of soil. The kriging window size parameter is decisive in obtaining a good quality result at reasonable cost, but is difficult to estimate for an image exhibiting significant heterogeneity. This work demonstrates that, allowing the kriging window size to adapt locally throughout the image provides a very efficient solution. Moreover, it is shown that this can be achieved using a conceptually simple mechanism that involves negligible extra processing cost. The adaptive-window indicator kriging method described in this study achieves easily an order of magnitude improvement in computational performance over a fixed size window implementation without sacrificing quality. In addition, it is shown that, by improving the locality of estimation, the new method is robust when applied to soil images.

Bayesian estimation for percolation models of disease spread in plant populations

Statistical methods are formulated for fitting and testing percolation-based, spatio-temporal models that are generally applicable to biological or physical processes that evolve in spatially distributed populations. The approach is developed and illustrated in the context of the spread of Rhizoctonia solani, a fungal pathogen, in radish but is readily generalized to other scenarios. The particular model considered represents processes of primary and secondary infection between nearest-neighbour hosts in a lattice, and time-varying susceptibility of the hosts. Bayesian methods for fitting the model to observations of disease spread through space and time in replicate populations are developed. These use Markov chain Monte Carlo methods to overcome the problems associated with partial observation of the process. We also consider how model testing can be achieved by embedding classical methods within the Bayesian analysis. In particular we show how a residual process, with known sampling distribution, can be defined. Model fit is then examined by generating samples from the posterior distribution of the residual process, to which a classical test for consistency with the known distribution is applied, enabling the posterior distribution of the P-value of the test used to be estimated. For the Rhizoctonia-radish system the methods confirm the findings of earlier non-spatial analyses regarding the dynamics of disease transmission and yield new evidence of environmental heterogeneity in the replicate experiments.