SummaryTechnical developments in molecular biology have found extensive applications in the field of microbial ecology. Among these techniques, fingerprinting methods such as denaturing gel electrophoresis (DGE, including the three options: DGGE, TGGE and TTGE) has been applied to environmental samples over this last decade. Microbial ecologists took advantage of this technique, originally developed for the detection of single mutations, for the analysis of whole bacterial communities. However, until recently, the results of these high quality fingerprinting patterns were restricted to a visual interpretation, neglecting the analytical potential of the method in terms of statistical significance and ecological interpretation. A brief recall is presented here about the principles and limitations of DGE fingerprinting analysis, with an emphasis on the need of standardization of the whole analytical process. The main content focuses on statistical strategies for analysing the gel patterns, from single band examination to the analysis of whole fingerprinting profiles. Applying statistical method make the DGE fingerprinting technique a promising tool. Numerous samples can be analysed simultaneously, permitting the monitoring of microbial communities or simply bacterial groups for which occurrence and relative frequency are affected by any environmental parameter. As previously applied in the fields of plant and animal ecology, the use of statistics provides a significant advantage for the nonambiguous interpretation of the spatial and temporal functioning of microbial communities.

Quantifying diversity is of central importance for the study of structure, function and evolution of microbial communities. The estimation of microbial diversity has received renewed attention with the advent of large-scale metagenomic studies. Here, we consider what the diversity observed in a sample tells us about the diversity of the community being sampled. First, we argue that one cannot reliably estimate the absolute and relative number of microbial species present in a community without making unsupported assumptions about species abundance distributions. The reason for this is that sample data do not contain information about the number of rare species in the tail of species abundance distributions. We illustrate the difficulty in comparing species richness estimates by applying Chao's estimator of species richness to a set of in silico communities: they are ranked incorrectly in the presence of large numbers of rare species. Next, we extend our analysis to a general family of diversity metrics ('Hill diversities'), and construct lower and upper estimates of diversity values consistent with the sample data. The theory generalizes Chao's estimator, which we retrieve as the lower estimate of species richness. We show that Shannon and Simpson diversity can be robustly estimated for the in silico communities. We analyze nine metagenomic data sets from a wide range of environments, and show that our findings are relevant for empirically-sampled communities. Hence, we recommend the use of Shannon and Simpson diversity rather than species richness in efforts to quantify and compare microbial diversity.

Microorganisms often respond to their environment by growing as densely packed communities in biofilms, flocs or granules. One major advantage of life in these aggregates is the retention of its community in an ecosystem despite flowing water. We describe here a novel type of granule dominated by filamentous and motile cyanobacteria of the order Oscillatoriales. These bacteria form a mat-like photoactive outer layer around an otherwise unconsolidated core. The spatial organization of the phototrophic layer resembles microbial mats growing on sediments but is spherical. We describe the production of these oxygenic photogranules under static batch conditions, as well as in turbulently mixed bioreactors. Photogranulation defies typically postulated requirements for granulation in biotechnology, i.e., the need for hydrodynamic shear and selective washout. Photogranulation as described here is a robust phenomenon with respect to inoculum characteristics and environmental parameters like carbon sources. A bioprocess using oxygenic photogranules is an attractive candidate for energy-positive wastewater treatment as it biologically couples CO2 and O2 fluxes. As a result, the external supply of oxygen may become obsolete and otherwise released CO2 is fixed by photosynthesis for the production of an organic-rich biofeedstock as a renewable energy source.

This study presents the oxygenic photogranule (OPG) process, a light-driven process for wastewater treatment, developed based on photogranulation of filamentous cyanobacteria, nonphototrophic bacteria, and microalgae. Unlike other biogranular processes requiring airlift or upflow-based mixing, the OPG process was operated in stirred-tank reactors without aeration. Reactors were seeded with hydrostatically grown photogranules and operated in a sequencing-batch mode for five months to treat wastewater. The new reactor biomass propagated with progression of photogranulation under periodic light/dark cycles. Due to effective biomass separation from water, the system was operated with short settling time (10 min) with effective decoupling of hydraulic and solids retention times (0.75 d vs 21-42 d). During quasi-steady state, the diameter of the OPGs ranged between 0.1 and 4.5 mm. The reactors produced effluents with average total chemical oxygen demand less than 30 mg/L. Nitrogen removal (28-71%) was achieved by bioassimilation and nitrification/denitrification pathways. Oxygen needed for the oxidation of organic matter and nitrification was produced by OPGs at a rate of 12.6 ± 2.4 mg O/g biomass-h. The OPG system presents a new biogranule process, which can potentially use simple mixing and natural light to treat wastewater.