Lactose drives Enterococcus expansion to promote graft-versus-host disease.

Disruption of intestinal microbial communities appears to underlie many human illnesses, but the mechanisms that promote this dysbiosis and its adverse consequences are poorly understood. In patients who received allogeneic hematopoietic cell transplantation (allo-HCT), we describe a high incidence of enterococcal expansion, which was associated with graft-versus-host disease (GVHD) and mortality. We found that Enterococcus also expands in the mouse gastrointestinal tract after allo-HCT and exacerbates disease severity in gnotobiotic models. Enterococcus growth is dependent on the disaccharide lactose, and dietary lactose depletion attenuates Enterococcus outgrowth and reduces the severity of GVHD in mice. Allo-HCT patients carrying lactose-nonabsorber genotypes showed compromised clearance of postantibiotic Enterococcus domination. We report lactose as a common nutrient that drives expansion of a commensal bacterium that exacerbates an intestinal and systemic inflammatory disease.

Stochastic modeling for characterisation of biofilm development with discrete detachment events (sloughing).

The monitoring of biofilm development at a small-scale is often observed to be a stochastic process. This raises important issues concerning the reproducibility of biofilm growth monitoring experiments. By realising that there are limits to the latter, a model of biofilm accumulation curves that takes into account the dynamics of seemingly random fluctuations resulting from sloughing events is proposed. The model is derived from a stochastic differential equation (SDE) based on the logistic equation, adding a stochastic term for the sloughing events and measurement noise. Experimental light absorbance data that correlate with biofilm biomass obtained from the development of phototrophic biofilms are analysed to illustrate the use of SDE modeling.

Kinetic model of a granular sludge SBR: influences on nutrient removal.

A mathematical model was developed that can be used to describe an aerobic granular sludge reactor, fed with a defined influent, capable of simultaneously removing COD, nitrogen and phosphate in one sequencing batch reactor (SBR). The model described the experimental data from this complex system sufficiently. The effect of process parameters on the nutrient removal rates could therefore be reliably evaluated. The influence of oxygen concentration, temperature, granule diameter, sludge loading rate, and cycle configuration were analyzed. Oxygen penetration depth in combination with the position of the autotrophic biomass played a crucial role in the conversion rates of the different components and thus on overall nutrient removal efficiencies. The ratio between aerobic and anoxic volume in the granule strongly determines the N-removal efficiency as it was shown by model simulations with varying oxygen concentration, temperature, and granule size. The optimum granule diameter for maximum N- and P-removal in the standard case operating conditions (DO 2 mg L(-1), 20 degrees C) was found between 1.2 and 1.4 mm and the optimum COD loading rate was 1.9 kg COD m(-3) day(-1). When all ammonia is oxidized, oxygen diffuses to the core of the granule inhibiting the denitrification process. In order to optimize the process, anoxic phases can be implemented in the SBR-cycle configuration, leading to a more efficient overall N-removal. Phosphate removal efficiency mainly depends on the sludge age; if the SRT exceeds 30 days not enough biomass is removed from the system to keep effluent phosphate concentrations low.

Assessment of three-dimensional biofilm models through direct comparison with confocal microscopy imaging.

The mathematical modeling of spatial biofilm formation that provides the capability to predict biofilm structure from first principles has been in development for the past six years. However, a direct and quantitative link between model predictions and the experimentally observed structure formation still remains to be established. This work assesses the capability of a state-of-the-art technique for three-dimensional (3D) modeling of biofilm structure, individual based modeling (IbM), to quantitatively describe the early development of a multispecies denitrifying biofilm. Model evaluation was carried out by comparison of predicted structure with that observed from two experimental datasets using confocal laser scanning microscopy (CLSM) monitoring of biofilm development in laboratory flowcells. Experimental conditions provided biofilm growth without substrate limitation, which was confirmed from substrate profiles computed by the model. 3D structures were compared quantitatively using a set of morphological parameters including the biovolume, filled-space profiles, substratum coverage, average thickness and normalized roughness. In spite of the different morphologies detectable in the two independent short-term experiments analyzed here, the model was capable of accurate fitting data from both experiments. Prediction of structure formation was precise, as expressed by the set of morphology parameters used.

Automated biofilm morphology quantification from confocal laser scanning microscopy imaging.

In spite of the immediate visual appeal of confocal laser scanning microscopy images, the extraction of accurate reconstitutions of biofilm morphology requires a lengthy and computational intensive succession of processing steps. However, once performed, it provides ample reward by enabling the quantitative study of biofilm structure. A software suite of image processing tools for full automation of biofilm morphology quantification was developed by integrating preprocessing, segmentation and morphology quantification operations. This software toolbox was implemented in a web server and a user friendly interface was developed to facilitate image submission, storage and sharing, its access being unrestricted for scientific applications. The image bioinformatics tool which results from the integration of the processing operations can be accessed at http://www.itqb.unl.pt:111/clsmip/. Its use is described in this paper and is illustrated with an example of processing of experimental data describing the growth of a mixed species dentrifying biofilm.

Objective threshold selection procedure (OTS) for segmentation of scanning laser confocal microscope images.

The determination of volumes and interface areas from confocal laser scanning microscopy (CLSM) images requires the identification of component objects by segmentation. An automated method for the determination of segmentation thresholds for CLSM imaging of biofilms was developed. The procedure, named objective threshold selection (OTS), is a three-dimensional development of the approach introduced by the popular robust automatic threshold selection (RATS) method. OTS is based on the statistical properties of local gray-values and gradients in the image. By characterizing the dependence between a volumetric feature and the intensity threshold used for image segmentation, the former can be determined with an arbitrary confidence level, with no need for user intervention. The identification of an objective segmentation procedure renders the possibility for the full automation of volume and interfacial area measurement. Images from two distinct biofilm systems, acquired using different experimental techniques and instrumental setups were segmented by OTS to determine biofilm volume and interfacial area. The reliability of measurements for each case was analyzed to identify optimal procedure for image acquisition. The automated OTS method was shown to reproduce values obtained manually by an experienced operator.