Christin Koch

Electroactive mixed culture derived biofilms in microbial bioelectrochemical systems: the role of pH on biofilm formation, performance and composition.

The pH-value played a crucial role for the development and current production of anodic microbial electroactive biofilms. It was demonstrated that only a narrow pH-window, ranging from pH 6 to 9, was suitable for growth and operation of biofilms derived from pH-neutral wastewater. Any stronger deviation from pH neutral conditions led to a substantial decrease in the biofilm performance. Thus, average current densities of 151, 821 and 730 μA cm(-2) were measured for anode biofilms grown and operated at pH 6, 7 and 9 respectively. The microbial diversity of the anode chamber community during the biofilm selection process was studied using the low cost method flow-cytometry. Thereby, it was demonstrated that the pH value as well as the microbial inocula had an impact on the resulting anode community structure. As shown by cyclic voltammetry the electron transfer thermodynamics of the biofilms was strongly depending on the solutions pH-value.

Cytometric fingerprinting for analyzing microbial intracommunity structure variation and identifying subcommunity function

Functions of complex natural microbial communities are realized by single cells that contribute differently to the overall performance of a community. Usually, molecular and, more recently, deep-sequencing techniques are used for detailed but resource-consuming phylogenetic or functional analyses of microbial communities. Here we present a method for analyzing dynamic community structures that rapidly detects functional (rather than phylogenetic) coherent subcommunities by monitoring changes in cell-specific and abiotic microenvironmental parameters. The protocol involves the use of flow cytometry to analyze elastic light scattering and fluorescent cell labeling, with subsequent determination of cell gate abundance and finally the creation of a cytometric community fingerprint. Abiotic parameter analysis data are correlated with the dynamic cytometric fingerprint to obtain a time-bound functional heat map. The map facilitates the identification of activity hot spots in communities, which can be further resolved by subsequent cell sorting of key subcommunities and concurrent phylogenetic analysis (terminal restriction fragment length polymorphism, tRFLP). The cytometric fingerprint information is based on gate template settings and the functional heat maps are created using an R script. Cytometric fingerprinting and evaluation can be accomplished in 1 d, and additional subcommunity composition information can be obtained in a further 6 d.

Revealing the electrochemically driven selection in natural community derived microbial biofilms using flow-cytometry

In this communication we demonstrate that electrochemically active microbial biofilms and their enrichment at the anode of a microbial bioelectrochemical system (BES) can be quantitatively and easily characterized by flow-cytometry. This analysis revealed that the anodic biofilm of a BES, formed from a highly diverse microbial community and fed with single substrate artificial wastewater, was dominated by only one phylotype.

Correlation of community dynamics and process parameters as a tool for the prediction of the stability of wastewater treatment.

Wastewater treatment often suffers from instabilities and the failure of specific functions such as biological phosphorus removal by polyphosphate accumulating organisms. Since most of the microorganisms involved in water clarification are unknown it is challenging to operate the process accounting for the permanent varying abiotic parameters and the complex composition and unrevealed metabolic capacity of a wastewater microbial community. Fulfilling the demands for water quality irrespective of substrate inflow conditions may emit severe problems if the limited management resources of municipal wastewater treatment plants are regarded. We used flow cytometric analyses of cellular DNA and polyphosphate to create patterns mirroring dynamics in community structure. These patterns were resolved in up to 15 subclusters, the presence and abundances of which correlated with abiotic data. The study used biostatistics to determine the kind and strength of the correlation. Samples investigated were obtained from a primary clarifier and two activated sludge basins. The stability of microbial community structure was found to be high in the basins and low in the primary clarifier. Despite major abiotic changes certain subcommunities were dominantly present (up to 80% stability), whereas others emerged only sporadically (down to 3% stability, both according to equivalence testing). Additionally, subcommunities of diagnostic value were detected showing positive correlation with substrate influxes. For instance blackwater (r(s) = 0.5) and brewery inflow (both r(s) = 0.6) were mirrored by increases in cell abundances in subclusters 1 and 6 as well as 4 and 8, respectively. Phosphate accumulation was obviously positively correlated with nitrate (r(s) = 0.4) and the presence of denitrifying organisms (Rhodacyclaceae). Various other correlations between community structure and abiotic parameters were apparent. The bacterial composition of certain subcommunities was determined by cell sorting and phylogenetic tools like T-RFLP. In essence, we developed a monitoring tool which is quick, cheap and causal in its interpretation. It will make laborious PCR based technique less obligatory as it allows reliable process monitoring and control in wastewater treatment plants.

CHIC—an automated approach for the detection of dynamic variations in complex microbial communities

Altering environmental conditions change structures of microbial communities. These effects have an impact on the single‐cell level and can be sensitively detected using community flow cytometry. However, although highly accurate, microbial monitoring campaigns are still rarely performed applying this technique. One reason is the limited access to pattern analysis approaches for the evaluation of microbial cytometric data. In this article, a new analyzing tool, Cytometric Histogram Image Comparison (CHIC), is presented, which realizes trend interpretation of variations in microbial community structures (i) without any previous definition of gates, by working (ii) person independent, and (iii) with low computational demand. Various factors influencing a sensitive determination of changes in community structures were tested. The sensitivity of this technique was found to discriminate down to 0.5% internal variation. The final protocol was exemplarily applied to a complex microbial community dataset, and correlations to experimental variation were successfully shown.

Cytometric fingerprints: evaluation of new tools for analyzing microbial community dynamics

Optical characteristics of individual bacterial cells of natural communities can be measured with flow cytometry (FCM) in high throughput. The resulting data are visualized in cytometric histograms. These histograms represent individual cytometric fingerprints of microbial communities, e.g., at certain time points or microenvironmental conditions. Up to now four tools for analyzing the variation in these cytometric fingerprints are available but have not yet been systematically compared regarding application: Dalmatian Plot, Cytometric Histogram Image Comparison (CHIC), Cytometric Barcoding (CyBar), and FlowFP. In this article these tools were evaluated concerning (i) the required experience of the operator in handling cytometric data sets, (ii) the detection level of changes, (iii) time demand for analysis, and (iv) software requirements. As an illustrative example, FCM was used to characterize the microbial community structure of electroactive microbial biofilms. Their cytometric fingerprints were determined, analyzed with all four tools, and correlated to experimental and functional parameters. The source of inoculum (four different types of wastewater samples) showed the strongest influence on the microbial community structure and biofilm performance while the choice of substrate (acetate or lactate) had no significant effect in the present study. All four evaluation tools were found suitable to monitor structural changes of natural microbial communities. The Dalmatian Plot was shown to be most sensitive to operator impact but nevertheless provided an overview on community shifts. CHIC, CyBar, and FlowFP showed less operator dependence and gave highly resolved information on community structure variation on different detection levels. In conclusion, experimental and productivity parameters correlated with the biofilm structures and practical process integration details were available from cytometric fingerprint analysis.

Monitoring functions in managed microbial systems by cytometric bar coding

Cytometric monitoring of microbial community dynamics can be used to estimate stability of technical microbial processes like biogas production by analysis of segregated cell abundance changes. In this study, structure variations of a biogas community were cytometrically recorded over 9 months and found to be of diagnostic value for process details. The reactor regime was intentionally disturbed with regard to substrate overload or H(2)S accumulation. A single-cell based approach called cytometric bar coding (CyBar) for fast identification of reactive subcommunities was used. Functionality of specific subcommunities was uncovered by processing CyBar data with abiotic reactor parameters using Spearmans correlation coefficient. Twenty subcommunities showed a discrete and divergent behavior. For example, a 4-fold substrate overload increased the cell number of two acidogenic index subcommunities to 176 and 193% within three days. Supplementary analyses were done using DNA fingerprinting, cloning, and sequencing. Bioreactor perturbations were shown to create cell abundance changes in subcommunities rather than variations in their phylogenetic composition. The used workflow and macros are ready-to-use tools and allow on-site monitoring and interpretation of variation in microbial community functions within a few hours.

Coupling electric energy and biogas production in anaerobic digesters – impacts on the microbiome

The combination of anaerobic digestion (AD) and microbial electrochemical technologies provides the opportunity to efficiently produce methane and electrical energy from complex biomass. Enhanced methane production and system stability have been reported but the causes (electrolysis or microbial–electrochemical interactions) are less understood. Using the model substrate corn silage it is demonstrated that, for conditions allowing microbiome growth and adaptation, the methane yield of combined reactors remains constant (216 (±29) mL godm−1) while a second product, electrons (q = 14.4 (±0.8) kC, jmax = 1.34 mA cm−2 geometric current density), is also generated. The combined strategy allowed up to a 27% increase in total yield while the reactor community and its dynamics over time were not affected. A typical AD composition of Firmicutes, Bacteroidetes, Proteobacteria, and Synergistetes (bacteria) as well as Methanosarcina, Methanoculleus and Methanobacterium (archaea) was found in the bulk liquid. Specific enrichments of Geobacter (anode) and Methanobacterium (cathode) were of functional relevance.

Dynamics in the microbial cytome-single cell analytics in natural systems.

Natural microbial systems are highly dynamic due to the short generation times of the comprised organisms and their rapid and distinct reactions to changing environments. Microbial flow cytometry approaches are capable techniques for following such community dynamics in a fast and inexpensive way. Newly developed bioinformatics tools not only enable quantification of single cell dynamics, they also make nearly on-line evaluation of community attributes possible, enable interpretation of community trends, and reveal possible constraints that influence community structure and function. Microbial flow cytometry is poised to make the microbial cytome accessible for ambitious ecosystem studies. Functions of cells within the cytome can be determined either by cell sorting in combination with other omics-approaches of choice or by simple correlation analyses.

Monitoring and engineering reactor microbiomes of denitrifying bioelectrochemical systems

Denitrifying bioelectrochemical systems (d-BES) are a promising technology for nitrate removal from wastewaters. Microbial community monitoring is required to pave the way to application. In this study, for the first time flow cytometry combined with molecular biology techniques is exploited to monitor and determine the structure–function relationship of the microbiome of a denitrifying biocathode. Stable cathode performance at poised potential (−0.32 V vs. Ag/AgCl) was monitored, and different stress-tests were applied (reactor leakage, nitrate concentration, buffer capacity). Stress-tests shifted the reactor microbiome and performance. The monitoring campaign covered a wide range of nitrate consumption rates (from 15 to 157 mg N LNCC−1 d−1), current densities (from 0 to 25 mA LNCC−1) and denitrification intermediates (nitrite and nitrous oxide consumption rates varied from 0 to 56 mg N LNCC−1 d−1). The reactor microbiome (composed of 21 subcommunities) was characterized and its structure–function relationship was revealed. A key role for Thiobacillus sp. in the bioelectrochemical reduction of nitrate was suggested, while a wider number of subcommunities were involved in NO2− and N2O reduction. It was demonstrated that different bacteria catalyze each denitrification step in a biocathode. This study contributed significantly to understanding denitrifying biocathodes, paving the way for their knowledge-driven engineering.