Correlating methane production to microbiota in anaerobic digesters fed synthetic wastewater.

A quantitative structure activity relationship (QSAR) between relative abundance values and digester methane production rate was developed. For this, 50 triplicate anaerobic digester sets (150 total digesters) were each seeded with different methanogenic biomass samples obtained from full-scale, engineered methanogenic systems. Although all digesters were operated identically for at least 5 solids retention times (SRTs), their quasi steady-state function varied significantly, with average daily methane production rates ranging from 0.09 ± 0.004 to 1 ± 0.05 L-CH4/LR-day (LR = Liter of reactor volume) (average ± standard deviation). Digester microbial community structure was analyzed using more than 4.1 million partial 16S rRNA gene sequences of Archaea and Bacteria. At the genus level, 1300 operational taxonomic units (OTUs) were observed across all digesters, whereas each digester contained 158 ± 27 OTUs. Digester function did not correlate with typical biomass descriptors such as volatile suspended solids (VSS) concentration, microbial richness, diversity or evenness indices. However, methane production rate did correlate notably with relative abundances of one Archaeal and nine Bacterial OTUs. These relative abundances were used as descriptors to develop a multiple linear regression (MLR) QSAR equation to predict methane production rates solely based on microbial community data. The model explained over 66% of the variance in the experimental data set based on 149 anaerobic digesters with a standard error of 0.12 L-CH4/LR-day. This study provides a framework to relate engineered process function and microbial community composition which can be further expanded to include different feed stocks and digester operating conditions in order to develop a more robust QSAR model.

Anaerobic digester bioaugmentation influences quasi steady state performance and microbial community.

Nine anaerobic digesters, each seeded with biomass from a different source, were operated identically and their quasi steady state function was compared. Subsequently, digesters were bioaugmented with a methanogenic culture previously shown to increase specific methanogenic activity. Before bioaugmentation, different seed biomass resulted in different quasi steady state function, with digesters clustering into three groups distinguished by methane (CH4) production. Digesters with similar functional performance contained similar archaeal communities based on clustering of Illumina sequence data of the V4V5 region of the 16S rRNA gene. High CH4 production correlated with neutral pH and high Methanosarcina abundance, whereas low CH4 production correlated to low pH as well as high Methanobacterium and DHVEG 6 family abundance. After bioaugmentation, CH4 production from the high CH4 producing digesters transiently increased by 11 ± 3% relative to non-bioaugmented controls (p < 0.05, n = 3), whereas no functional changes were observed for medium and low CH4 producing digesters that all had pH higher than 6.7. The CH4 production increase after bioaugmentation was correlated to increased relative abundance of Methanosaeta and Methaospirillum originating from the bioaugment culture. In conclusion, different anaerobic digester seed biomass can result in different quasi steady state CH4 production, SCOD removal, pH and effluent VFA concentration in the timeframe studied. The bioaugmentation employed can result in a period of increased methane production. Future research should address extending the period of increased CH4 production by employing pH and VFA control concomitant with bioaugmentation, developing improved bioaugments, or employing a membrane bioreactor to retain the bioaugment.

Conservation of acquired morphology and community structure in aged biofilms after facing environmental stress.

The influence of growth history on biofilm morphology and microbial community structure is poorly studied despite its important role for biofilm development. Here, biofilms were exposed to a change in hydrodynamic conditions at different growth stages and we observed how biofilm age affected the change in morphology and bacterial community structure. Biofilms were developed in two bubble column reactors, one operated under constant shear stress and one under variable shear stress. Biofilms were transferred from one reactor to the other at different stages in their development by withdrawing and inserting the support medium from one reactor to the other. The developments of morphology and microbial community structure were followed by image analysis and molecular tools. When transferred early in biofilm development, biofilms adapted to the new hydrodynamic conditions and adopted features of the biofilm already developed in the receiving reactor. Biofilms transferred at a late state of biofilm development continued their initial trajectories of morphology and community development even in a new environment. These biofilms did not immediately adapt to their new environment and kept features acquired during their early growth phase, a property we called memory effect.

An automated method for the quantification of moving predators such as rotifers in biofilms by image analysis.

In natural environments as well as in industrial processes, microorganisms form biofilms. Eukaryotic microorganisms, like metazoans and protozoans, can shape the microbial communities because of their grazing activity. However, their influence on biofilm structure is often neglected because of the lack of appropriate methods to quantify their presence. In the present work, a method has been developed to quantify moving population of rotifers within a biofilm. We developed an automated approach to characterize the rotifer population density. Two time lapse images are recorded per biofilm location at an interval of 1s. By subtracting the two images from each other, rotifer displacements that occurred between the two images acquisition can be quantified. A comparison of the image analysis approach with manually counted rotifers showed a correlation of R(2)=0.90, validating the automated method. We verified our method with two biofilms of different superficial and community structures and measured rotifer densities of up to 1700 per cm(2). The method can be adapted for other types of moving organisms in biofilms like nematodes and ciliates.

Heterogeneity and spatial distribution of bacterial background contamination in pulp and process water of a paper mill.

Identifying the source and the distribution of bacterial contaminant communities in water circuits of industrial applications is critical even when the process may not show signs of acute biofouling. The endemic contamination of facilities can cause adverse effects on process runability but may be masked by the observed daily variability. The distribution of background communities of bacterial contaminants may therefore be critical in the development of new site-specific antifouling strategies. In a paper mill as one example for a full-scale production process, bacterial contaminants in process water and pulp suspensions were mapped using molecular fingerprints at representative locations throughout the plant. These ecological data were analyzed in the process-engineering context of pulp and water flow in the facilities. Dispersal limits within the plant environment led to the presence of distinct groups of contaminant communities in the primary units of the plant, despite high flows of water and paper pulp between units. In the paper machine circuit, community profiles were more homogeneous than in the other primary units. The variability between sampled communities in each primary unit was used to identify a possible point source of microbial contamination, in this case a storage silo for reused pulp. Part of the contamination problem in the paper mill is likely related to indirect effects of microbial activity under the local conditions in the silo rather than to the direct presence of accumulated microbial biomass.

Analyzing characteristic length scales in biofilm structures.

The quantification of biofilm structure based on image analysis requires a statistical measure like representative elemental areas (REA) to determine the necessary size of biofilm area to be imaged. In this study, REAs for biofilm structure were calculated for the descriptors Gray level and Correlation (COR) derived from a spatial gray level dependence matrix analysis (SGLDM). An important difference between these two descriptors is their response to structural features at different spatial scales. Gray level is a scale-independent descriptor, whereas COR is scale-dependent. For scale-independent descriptors, the size of the individual images is not relevant when determining REAs. This is in contrast to scale-dependent descriptors for which REAs can only be determined when the area of each image covers the range of structural variability of the biofilm. We used COR to analyze scale dependence of structural heterogeneity at different length scales. A characteristic length of 400 microm in biofilm images provides structural information relevant for mass transport phenomena in biofilms. Overall REAs for gray level and COR were on average 3.4 mm(2). The scale-dependent descriptor COR could not in all cases accurately be determined from combining individual image analysis results--even when the combined area resulted in the REA. Microscope and camera specifications define the upper and lower limit of detectable characteristic length that can be extracted from images and should therefore be considered in the experimental design when choosing an imaging technique.

Textural fingerprints: a comprehensive descriptor for biofilm structure development.

Automated tools to determine biofilm structure are necessary to interpret large time series of biofilm images. Image analysis based on the evaluation of Spatial Gray Level Dependence Matrices (SGLDM) enabled us to monitor biofilm structure development in response to external disturbances (i.e., periodic increases of wall shear stress) at a large scale (i.e., >1 mm). We applied our method to an experiment conducted in an annular reactor over a 10-week period. Six states of biofilm development were differentiated by their unique structure. Previous exposure to rapidly increased shear influenced the resulting biofilm structure after additional shear increases. In addition, on the scale of the biofilm images, the biofilm structure after a shear increase was spatially heterogeneous and resulted in spatially differentiated regrowth after detachment at different locations in the biofilm. SGLDM was developed further as an alternative to approaches based on image binarization as binarization leads to information loss for low-magnification and low-resolution images. During post-processing of image data, structural states of biofilm development were identified by K-means clustering and data display in Principal Component plots. Quantitatively selected representative images were used to illustrate the meaning of the clusters. Post-treatment of image data was essential for managing several thousands of raw biofilm images and therefore improved the usefulness of the image analysis.

Texture analysis of spatial biofilm development.

The quantitative evaluation of images taken during biofilm experiments is an important step in determining the relation between biofilm performance and biofilm architecture. Whereas areal descriptors are used by some researchers, descriptors of biofilm texture have received limited attention. In our research, the texture of images documenting long-term biofilm experiments was evaluated using a spatial grey level dependence matrices (SGLDM) approach. By calculating SGLDM for a wide range of position operators (angle-distance combinations), the discriminatory power of this approach was extended. For some descriptors, surface plots allowed the direct spatial interpretation of texture. Using principal component analysis (PCA) a subset of independent textural descriptors was identified. It is suggested to determine textural fingerprints of stages during biofilm development by making use of PCA and biplots.

Optical method for long-term and large-scale monitoring of spatial biofilm development.

A method was developed that allows biofilm monitoring on the square centimeter scale over extended periods of time. The method is based on image acquisition using a desktop scanner and subsequent image analysis. It was shown that results from grey level analysis are highly correlated with physical properties of the biofilm like average biomass and biofilm thickness. The scanner method was applied to monitor overall biofilm growth, detachment, and surface roughness during two 3 and 4 week long experiments. Two significantly different growth dynamics during the biofilm development could be identified, depending on the biofilm history. Surface roughness on transects in flow direction was always higher than on transects perpendicular to the flow, reflecting the anisotropic characteristics of biofilms growing in a flow field.