Monitoring Functions in Managed Microbial Systems by Cytometric

Article pubs.acs.org/est

Monitoring Functions in Managed Microbial Systems by Cytometric Bar Coding Christin Koch,† Ingo Fetzer,‡ Thomas Schmidt,§ Hauke Harms,‡ and Susann Müller*,‡ †

Department of Bioenergy, UFZ − Helmholtz Centre for Environmental Research, Leipzig, Germany Department of Environmental Microbiology, UFZ − Helmholtz Centre for Environmental Research, Leipzig, Germany § Department Biochemical Conversion, DBFZ − German Biomass Research Centre, Leipzig, Germany ‡

S Supporting Information *

ABSTRACT: 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 H2S 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 Spearman’s 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.

■

INTRODUCTION

between HNA and LNA already give trend information on community behavior.8 Apart from single cell based technologies researchers most often rely on PCR-based fingerprinting techniques as well as sequencing and metagenome approaches to describe natural community composition and behavior.2,9,10 The new deep sequencing approaches have the potential to resolve communities down to the targeted sequences of every cell and can also give quantitative information on sequence abundance within isolated community DNA or RNA. Nevertheless, also these powerful techniques have certain limitations like technically caused bias due to the DNA extraction and, method dependent, primer choice, and amplification efficiency.9 Classified results have been shown to be valuable in correlating phylogenetic and functional markers to abiotic influences on community performances.2,11 But dense sample coverage and on-site analysis and evaluation still remain limiting steps in natural microbial community studies. Microorganisms have generation times of minutes and can change their abundances quickly in response to varying environmental conditions.12 A quick evaluation

Understanding or even controlling the multifaceted actions of cells in managed natural communities is a key objective in microbial system ecology.1,2 The microorganisms’ performances are the result of thousands of different unknown interrelationships, metabolisms, and regulations.1−3 It is a major goal of process monitoring of microbial systems to assign performances and functions to subcommunities, populations, or even individuals. To approach this aim we used flow cytometry as a high-throughput and cultivation-independent technology. It determines cellular characteristics on both qualitative and quantitative levels. Our approach relied on chromosome number staining using the small and highly DNA specific dye 4′,6-diamidino-2-phenylindole (DAPI) and the cell size related forward scatter signal (FSC).4 DAPI labels all cells independent of species identity. It is advantageous over FISH technologies, which require specific probes and careful protocol adjustment (permeabilization, hybridization, specificity) with pure cultures.5 Their quantitative application to natural communities is, therefore, limited.5,6 As an alternative to DAPI nucleic acid stains like Syto 9 or Sybr Green can be used which create the so-called HNA and LNA subcommunities (cells with high nucleic acid or low nucleic acid contents).7 Although the resolution of the community is not as high as for the DAPI/ FSC approach used in this study, the cell abundance variations © 2012 American Chemical Society

Received: Revised: Accepted: Published: 1753

October 9, 2012 December 19, 2012 December 19, 2012 December 19, 2012 dx.doi.org/10.1021/es3041048 | Environ. Sci. Technol. 2013, 47, 1753−1760

Environmental Science & Technology

Article

Figure 1. The CyBar tool determines community variation on subcommunity level. Using the CyBar tool the abundances of cells within the subcommunities are illustrated, allowing the gate dependent variations in cell abundances to be followed in reaction to changing abiotic conditions. A 164 cytometric data set of a biogas community was analyzed with CyBar based on a template of 20 gates (G1 to G20). The color code shows the height of deviation from the average cell abundance value per gate (average shown in white (= 1), higher values in increasing red color, lower values in increasing blue color). The more intense the color is the higher is the deviation from the mean value. A long period of the same color within a gate is a marker for cell number stability for that gate. The CyBar plot represents the stable phase S1 at the top that is followed by phase 1 where FerroSorpDG was depleted. The subsequent phase 2 served as restabilization phase resulting in a stable process for six months (S2) according to abiotic parameters. SO perturbation schemes characterized phases 3 and 4. Selected corresponding abiotic variations are displayed on the right. In the lower part the box plot represents cell abundances per gate as percentage of total cell number. The low cell number gate G17 represented between 0.38 and 2.51% and the high cell number gate G20 7.72 and 23.17% of all cells.

about 1010 cells per mL may contain over 40 bacterial operational taxonomic units (OTUs)18 and about 1000 species.19 In this study, the dynamics of 20 different subcommunities were analyzed by using cytometric bar coding (Cybar) and high density sampling. In addition, we applied common correlation analysis for connecting specific functions (acidogenesis, methanogenesis) to hot spot activities of certain subcommunities. We found the method easy to handle during sampling, analysis, and evaluation and to give surprisingly causal interpretations of functional relationships between abiotic and subcommunity data.

would allow an immediate interference, support, or control of microbial community performances. In this study, we use community flow cytometry as a promising option for monitoring dynamics in microbial communities. Precise cell abundances of subcommunities (subsets of cells, more details in Supporting Information S1) can be measured. Using further developed R13 based biostatistic tools, which are also powerful for interpretation of fingerprinting or genomic data sets,14,15 index subcommunities were defined that are responsible for certain functions in the investigated ecosystem. We applied community flow cytometry to a long-term biogas cultivation experiment. The successful performance of a biogas reactor depends on the interrelationships of organisms on four trophic levels (hydrolysis, acidogenesis, acetogenesis, methanogenesis, please see ref 16 for details). Because levels of trophic interactions are carried out by different groups of organisms, a stable reactor performance can only be realized if the organisms are able to react flexibly toward perturbations.17 The complex composition of such biogas communities has been determined by techniques like T-RFLP and pyrosequencing showing that

■

EXPERIMENTAL SECTION Digester Configuration and Running Conditions. A continuous stirred tank reactor (CSTR) of 5 L working volume was agitated at 80−120 rpm and 38 °C. The reactor had a stable performance over nearly two years before starting the study for a time interval of 267 days. Dried distillers grains with solubles (DDGS, ProtiGrain, CropEnergies, Germany, diluted with water to a total solids content of 13%) were fed once a day. ProtiGrain is a protein rich feeding product, and its stable 1754

dx.doi.org/10.1021/es3041048 | Environ. Sci. Technol. 2013, 47, 1753−1760


Article

function ‘boxplot’. Data correlation was done with R stats13 and the ‘cor’ function. Ready to use macros are available.20

composition is guaranteed by quality control of the manufacturer. The reactor was operated with an organic loading rate (OLR) of 5 g L−1 day−1 of volatile solids (VS) and a hydraulic retention time (HRT) of 25 days. From day 240− 249 and day 260−262 the OLR was increased to 10 and 20 g VS L−1 day−1, respectively, and the HRT reduced to 12.5 and 6.5 days, respectively. All experimental details, abiotic parameters, and sampling days are given in Supporting Tables S1 and S2. An iron additive (FerroSorpDG, P.U.S. GmbH, Germany) was added to the substrate at a dosage of 0.049 g g−1 of VS for hydrogen sulfide removal and trace element supply. Its addition was intermitted from day 8 to 30. The community structure was analyzed weekly, daily, or hourly by flow cytometry. For sampling and fixation procedures see Supporting Experimental Procedures S1. Controls for cell preparation and cell stability after fixation are shown in Supporting Figures S1 and S2. Analytical Methods. Gas production and composition, pH, acid capacity, FOS/TAC (ratio of volatile organic acids to total inorganic carbonate in g FOS g−1 CaCO3), ammonium concentration, and volatile fatty acids (acetic acid, propionic acid, iso-butyric acid, n-butyric acid, iso-valeric acid, n-valeric acid, hexanoic acid) concentrations were measured regularly over the course of the experiment (details in Supporting Experimental Procedures S2 and Supporting Table S1). Flow Cytometry. Instrumental Settings, Staining, and Analyzing Procedure. Please see Supporting Experimental Procedures S3 for details. The Principle of CyBar. The analysis of samples and visualization of the cell distributions was performed using FlowJo (Tree Star Inc., Switzerland). All visible subcommunities were marked with gates (details see Supporting Experimental Procedures S3 and Supporting Information S1). The relevant areas of interest were separated from the instrumental noise and unstained events. All designed gates were used to create a gate template. The template was then overlaid over all measured samples. Cell number in gates were determined and normalized using the mean value per gate as 1. The variation of cell number per gate can then be visualized by heat map based visualization using the macro provided in ref 20. Molecular Analysis. DNA extraction was performed on frozen samples with two DNA extraction kits (A: NucleoSpin Tissue kit, Machery-Nagel, B: FastDNA Spin Kit for Soil, MP Biomedicals) and on sorted cells using a chelex based approach20 (Chelex Resin 10% w/v). PCR was performed with the primer set UniBac27f (FAM labeled) and Univ1492r and the primer set UniArc8f (FAM labeled) and UniArc931r to amplify 16S rDNA of both bacteria and archaea (details see Supporting Experimental Procedures S4). T-RFLP analysis was performed after digestion with the restriction endonucleases HaeIII and RsaI (New England Biolabs, Germany). For technical details see ref 21. A reactor sample taken in autumn 2010 was used to construct a 16S rDNA library consisting of 162 clones to allow for phylogenetic assignment of terminal restriction fragments (Supporting Tables S3 and S4). Additionally, sequence data of ref 18 were used for assignment of archaea. Statistical Analyses. Statistical analyses were performed with R.13 The heat map in Figure 1 was constructed using the ‘heatmap.2’ function in the package gplots.14 The box-andwhisker plots in the same figure were drawn with the generic

■

RESULTS AND DISCUSSION Community Structure Mirrored by Cell States. Different from macrobiology, where generation times are generally long, in microbiology generations comprise often only minutes, resulting in microbial systems that are normally highly dynamic and have only short reaction times toward external perturbations.4,12 Marker molecules representing proliferation activity and generation time are chromosomes. The number of chromosomes and abundance of their copies per cell reveal the upcoming and disappearance of organisms as a marker for reproductiveness.22,23 We used flow cytometry to record chromosome number using DAPI as DNA probe. Forward scattering (FSC) which is dependent on a cell’s size and morphology was used as second cell specific parameter. The cytometric analysis of cells generated clusters (Supporting Information S1) with bacteria and archaea cells contributing equally based on their single cell characteristics. As a result, the recorded cytometric patterns mirrored the complete microbial community structure.8,24 We used this structure as community fingerprint which is defined by i) number of clusters in a histogram, ii) positions of clusters in a histogram, and iii) number of cells within each cluster. Changes in the community structure can be documented by determination of cell abundances in these clusters in a bar code like manner which we call cytometric bar coding using the abbreviation ‘CyBar’. The CyBar tool gives deep and valuable insights into community variations over time by independently analyzing each subcommunity’s reaction to changing environmental conditions. Because changes in cell abundance normally mirror cell growth,22,23 the CyBar plot mirrors activity of cells in the environment revealing time points of changes and number of involved subcommunities. Detection of Community Variation with CyBar. We monitored a labscale biogas reactor over a time frame of 267 days to test the sensitivity of the CyBar tool for community structure variations. A data set of 164 measurements was generated. We divided the data set into 8 phases that were chosen according to artificially induced perturbation and recovery periods. Stable phase S1 (8 days sampling for cytometric measurement) was followed by perturbation phase 1 (22 days) where the depletion of both a hydrogen sulfide removal agent and trace element supplementation (FerroSorpDG depletion = FD) was induced resulting in a surplus of H2S and various acid species. Phase 2 (30 days) was characterized by the microbial consumption of these high acid concentrations down to concentrations known from the start of the bioreactor experiment in phase S1. This condition was maintained over the next 179 days (S2). Following, substrate overloading (SO) was provoked 2-fold (phase 3, 10 days) and 4-fold (phase 4, 3 days) after an intermittent period (S3, 10 days). The last four samples were measured after end of 4-fold SO (S4, 5 days). To create the CyBar plot, 200,000 cells of every of the 164 samples were analyzed. A so-called gate template was constructed representing 20 cell clusters, namely subcommunities, emerging from all samples, leading to evaluations of cell abundances in 3280 gates over the measured time period. As a result a bar code like map, the CyBar plot (Figure 1), accrued showing the relative variation in cell abundances per gate (x1755



Article

Figure 2. The heat map represents correlations of cytometric data with abiotic parameter variation. A correlation analysis between all measured abiotic parameters and the subcommunity abundances from the CyBar plot was performed using Spearman’s rank correlation coefficient and the data set of the 4-fold SO. The strength of correlation is defined by a color code with yellow-white indicating strong positive correlations, orange a neutral context, and intensive red a strong negative correlation. A cluster of highly positive correlated parameters was found including different VFA, AC, and FOS/TAC and several gates like G8 and G11 which were already defined as index gates for the SO. Abbreviations: aa...acetic acid, capacity...acid capacity, CH4...methane concentration, CO2...carbon dioxide concentration, FOS/TAC...ratio of volatile organic acids to total inorganic carbonate, FS...FerroSorpDG addition, gas...specific gas production, H2S...hydrogen sulfide concentration, ha...hexanoic acid, iba...iso-butyric acid, iva...iso-valeric acid, load...organic loading rate, nba...n-butyric acid, NH4−N...ammonium concentration, nva...n-valeric acid, pa...propionic acid.

abundance decreases were obvious for the gates G9, G20, and G6. The most eye catching amplification was observed for cell abundances in G17 at the beginning of FD but also other gates showed prominent cell number increases (G15 and G16). While some phylotypes can be assumed to be inhibited in their metabolism by high H2S concentrations25 and trace element deficiency26,27 others may use increasing acid concentrations for growth.28 A readdition of FerroSorpDG in phase 2 led immediately to reduced H2S and, following, VFA concentrations. We expected a recovery of the community structure in the following 179 days (phase S2) of routine cultivation. However, to our surprise, a restructuration of the community occurred although the measured abiotic parameters had the same values as determined in S1. The CyBar tool revealed that the community did not restore its original structure after the FD but transformed stepwise over time. The origin of the variation cannot have been caused by external influences during S2 because the reactor was stably maintained but might be caused by the destabilization of the community as a result of the disturbance. Such behavior toward perturbations was also described for other microbial communities.29,30 As a result, a new state that can be defined as a new functional stable state31,32 was established.

axis) as well as the time points and phase affiliations of the samples (y-axis). We used the CyBar plot for a quick overview on community structure dynamics. The variation in cell number per gate over the 267 days of the experiment was normalized in the CyBar plot to enable a visual comparison of abundance changes between all gates. A color key was created with the white color as a respective gate mean value. Blue colors represent decreases, whereas red ones represent increases in cell abundances. The total abundance of cells per every gate was shown in the box plots below the CyBar plot. There were gates with general high cell number like gates G20 and G9 (with 14.36 and 14.23% of all cells), respectively, and those that contained only a very low number like G17 (0.98%). To interpret the meaning of the community structure variation visualized by the CyBar plot the associated abiotic data have to be considered. Due to FD in phase 1 of the experiment the H2S values increased rapidly up to 5000 ppm accompanied by a slight decrease in methane production from 62.1 to 59.2% of the total biogas volume. Volatile fatty acids (VFA) accumulated (acetic acid 2.250 mg L−1, propionic acid 355 mg L−1, iso-butyric acid 80 mg L−1) indicating imbalances between the trophic groups in the biogas reactor. Cell 1756



Article

Figure 3. Composition of sorted subcommunities. T-RFLP analysis of selected gates after cell sorting revealed individual community composition in the gates. Some gates revealed a similar structure as the complete community samples while others differed strongly. Most gates remained stable community composition despite the disturbance in phase 3 and 4. Identification of T-RFs in gates with low cell abundance (e.g., G2, G10, G12) is limited as these representatives are very rare in the clone library which was established with a complete community sample.

concentration. These abiotic changes mirrored increased production of acids in the biogas system as a result of the organic substrate overload. Abiotic and biotic correlation of data revealed positive correlations between increases in the acid values and cell abundances in gates G4, G6, G8, G11, and G13-G15. Therefore, it is assumed that these cells were able to grow on the provided substrate and produced the acid metabolites. Bacteria involved in acidogenesis are described to act immediately in case of high substrate amounts by increasing both their cell abundance and metabolic activity.28,33,34 Gates G8 and G11 were the most dominant index gates under these conditions and increased cell abundance to 176% and 193% respectively. Negative correlations were found for gates G1, G3, G5, G12, G17, and G18. Cell number decreases might result from limited activity and reproductiveness as is typical for acetogens and methanogens under such conditions.33,35 Sudden acid accumulation cannot be handled by these organisms. They are described to need time to acclimatize to higher acid values.33 Some cells did not react to the bioreactor scheme (gates G2, G7, G9, G16, G19, and G20) with the latter representing the subcommunity with the highest cell abundance, therefore, probably a stabilizing factor of the community. Heterogeneous reactions of microbial subcommunities are described,17 and functional stability of a microbial system can be the result of resilience of present organisms and changes in the community structure.17,29,36 Functional dependencies were also identified for the FD data set and are shown in Supporting Figure S3. As an outcome, we developed a quick visualization tool that correlated microbial subcommunities with abiotic data sets and provided functional information on hot spot active or specific inactive members within a managed microbial system. In the biogas experiment the CyBar visualized index subcommunities that changed their cell abundances due to perturbations. The functional heat map, fed with the CyBar data set, provided interdependencies within the community substructure and affiliated the found index subcommunities to distinct process parameters. This developed workflow is useful to discover the rationale for process perturbations, thereby guaranteeing stable methane production, and avoiding process break downs. As a

In a second intervention a 2-fold (phase 3) followed by a 4fold (phase 4) SO was processed. An increase in acid capacity values of 3.09 g L−1 to 6.47 g L−1 was found. Maximum concentrations of VFA were reached (acetic acid 552 mg L−1, propionic acid 978 mg L−1) and the specific gas production decreased by 40%, finally. The SO resulted in cell abundance increases in, e.g., the gates G8 and G11 and decreases in gates G1 and G17. These findings may indicate an imbalance between substrate degrading acidogenic bacteria and further utilizing acetogenic bacteria and methanogenic archaea which can be inhibited in their metabolism by the accumulating acids.28,33 Correlation of the CyBar Tool with Cell Function. Correlation of quantitative biotic data with on-site abiotic parameters using Spearman’s rank correlation coefficient can be used to reveal functions of community members as was already shown for a wastewater community.12 Every pair of parameters was considered for possible coherency, but the systematic extraction of information, especially for huge data sets, was not possible. Therefore, the method was further developed with an R-based approach that allowed type and strength of correlations as well as classification regarding groups or clusters of equally reacting parameters to be revealed. In the following, the biotic information contained in the CyBar plot and abiotic data are processed in functional heat maps. The correlation procedure itself is performed within seconds using R based scripts,20 and data interpretation becomes easily accessible. The functional heat map approach (Figure 2) was used to interpret data from the 4-fold SO period correlating 17 cytometric measurements resulting in 340 cell abundance values with affiliated reactor parameters comprising 139 measured abiotic values. The complete data set is available in Supporting Table S5. The heat map gives information on abiotic−abiotic, abiotic−biotic, and biotic−biotic correlations. For instance, the additional substrate provided by the SO led to 5-, 100-, 40-, and 13-fold increases in acetic acid, propionic acid, iso-butyric acid, and iso-valeric acid, respectively. Strong positive correlations between these abiotic parameters can clearly be visualized in the heat map (Figure 2). Negative correlations between acid intermediates and other abiotic parameters were observed for CH4 production and NH4−N 1757



Article

Prospects for Community Monitoring in Managed Microbial Systems. Assessment of diversity is a main objective in system ecology. High diversity is generally considered to be a guarantor for an ecosystem’s stable functionality and resilience against perturbation.2,38,39 High throughput cell based analyses can be a valuable and quick alternative to sequence based technologies when microbial community dynamics need to be monitored. We showed in this study that with two-parametric cytometric measurements a good community resolution is achieved and a sensitive reaction toward environmental variations can be followed. We showed an approach where, within a few minutes, 200,000 cells per sample are measured and substructures below the classical 1%−2% threshold (e.g., G12 0.26−2.68% and G17 0.38−2.51% of total cell number) are resolved. Such data sets taken over several months were shown to be reliably processed by the developed CyBar tool. We recently also introduced an image based tool for cytometric data analysis.40 The so-called Cytometric Histogram Image Comparison (CHIC) allows a quick overview on microbial dynamics independent from personal pre-experience regarding gating and data handling. Therefore, CHIC provides basic analysis of structural community changes which can be linked to driving forces from environmental factors (unpublished data). But as the CHIC is only a very basic tool, it does not allow deeper investigation of individual cell abundance changes. It will reliably detect structural changes but cannot define if they result from an intense abundance change within one gate or from several smaller changes over several gates. For this information and, in addition, the identification of functional dependencies the CyBar tool is necessary. The methodical approach is suitable for every microbial monitoring experiment. The time necessary for sample preparation and measurement does not exceed five hours. Data collection and running the CyBar tool will take another few hours, depending on sample number. Ready to use R-based macros are available.20 Thereafter, data can be evaluated and correlated with parameters obtained from the abiotic surroundings. The procedure has a high automatization potential and could also be envisaged to be performed on site in managed microbial systems.

result, more efficient processes control strategies can be established. Subcommunity Composition Differs from Whole Community Fingerprint. The phylogenetic background of the investigated biogas community organisms cannot be determined by using community flow cytometry. Molecular fingerprinting or deep sequencing methods need to be used for this purpose.2,10 The CyBar-based functional heat map can be helpful in detecting subcommunities of interest that can be sorted, to perform molecular analysis and obtain metabolic capacity information. We started with fingerprinting of the whole community for comparison reasons using T-RFLP. The phylogenetic community composition hardly altered during the biogas experiment (Supporting Figure S4). The same approach was applied to sorted subcommunities that showed different phylogenetic compositions. Cell sorting was performed before and after the 2-fold SO and at the climax of the 4-fold SO. Gates with higher and lower cell number as well as stable and variating gates regarding the CyBar result were chosen, cell number between 105−106 (Supporting Table S6) sorted, and T-RFLP analysis performed. The first six bars in Figure 3 represent the T-RFLP results for the whole community of the particular days (day 162 (phase S2), 256 (phase S3), and 263 (phase 4)). The result was similar to the whole community fingerprint (Supporting Figure S4). The sorted gates G20 and G1 represented gates with very high average cell abundances in the CyBar plot with about 14% of total cell number in average. Gate G20 represented a similar composition as the whole community (Supporting Figure S4) with Bacteroidetes (11 to 19%) and Firmicutes (24 to 39%) as main phylotypes. The situation was different for G1, only a very small part of the found T-RFs could be identified. G2, G10, and G12 were also sorted and found to be different from the phylogenetic composition of G20. These gates represented low abundant subcommunities with on average 5.23, 6.79, and 1.52% of all cells, respectively. G12 was characterized by cells with high FSC and low to medium DNA content. It can be assumed that the main phylotype of G12 was exceptional, because the main fragment was found to have only very small, if any, contribution to other gates with T-RFLP. It needs to be pointed out that most subcommunities remained their phylogenetic compositions over the investigated time period. Contrarily, the CyBar showed partly dramatic changes in certain subcommunity cell abundances. For example, the cell abundance in G8 was doubled without effecting its phylogenetic composition (see also Supporting Information S1). Therefore, we concluded that structural community changes (increases in abundances of cells in certain subcommunities) enabled the community to cope with the applied perturbations. Although it is known that other phylotypes can overtake missing functions during perturbation,17,36 cell abundance changes were the main driver for resilience in our biogas community. All in all, 38 different T-RFs were found using whole community T-RFLP, while this number was extended to 51 TRFs using a combination of cell sorting and T-RFLP. To disclose potential biochemical traits of e.g. index gate cells in addition to the provided phylogenetic information higher resolving sequencing techniques3,37 would be most informative at this point of the workflow. Nevertheless, it can already be stated that cell sorting documented the different phylogenetic composition of the respective gates with higher resolution in comparison to the whole community T-RFLP analysis.

■

ASSOCIATED CONTENT

S Supporting Information *

Additional information on experimental details, abiotic measurements, and correlation data sets. This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*Phone: ++49 341 235 1318. Fax: ++49 341 235 1351. E-mail: [email protected]. Corresponding author address: Department of Environmental Microbiology, UFZ − Helmholtz Centre for Environmental Research, Permoserstrasse 15, D04318 Leipzig, Germany. Present Address

Stockholm Resilience Centre − Stockholm University, Stockholm, Sweden. Notes

The authors declare no competing financial interest. 1758



■

Article

Li, C.; Maechler, M.; Rossini, A. J.; Sawitzki, G.; Smith, C.; Smyth, G.; Tierney, L.; Yang, J. Y. H.; Zhang, Z. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5, R80. (16) Demirel, B.; Scherer, P. The roles of acetotrophic and hydrogenotrophic methanogens during anaerobic conversion of biomass to methane: a review. Rev. Environ. Sci. Biotechnol. 2008, 7, 173−190. (17) Fernandez, A. S.; Hashsham, S. A.; Dollhopf, S. L.; Raskin, L.; Glagoleva, O.; Dazzo, F. B.; Hickey, R. F.; Criddle, C. S.; Tiedje, J. M. Flexible community structure correlates with stable community function in methanogenic bioreactor communities perturbed by glucose. Appl. Environ. Microbiol. 2000, 66, 4058−4067. (18) Ziganshin, A.; Schmidt, T.; Scholwin, F.; Il’inskaya, O. N.; Harms, H.; Kleinsteuber, S. Bacteria and archaea involved in anaerobic digestion of distillers grains with solubles. Appl. Microbiol. Biotechnol. 2011, 89, 2039−2052. (19) Schlüter, A.; Bekel, T.; Diaz, N. N.; Dondrup, M.; Eichenlaub, R.; Gartemann, K. H.; Krahn, I.; Krause, L.; Krömeke, H.; Kruse, O.; Mussgnug, J. H.; Neuweger, H.; Niehaus, K.; Pühler, A.; Runte, K. J.; Szczepanowski, R.; Tauch, A.; Tilker, A.; Viehöver, P.; Goesmann, A. The metagenome of a biogas-producing microbial community of a production-scale biogas plant fermenter analysed by the 454pyrosequencing technology. J. Biotechnol. 2008, 136, 77−90. (20) Koch, C.; Günther, S.; Desta, A. F.; Hübschmann, T.; Müller, S. Cytometric fingerprinting for analysing microbial intra-community structure variation and identifying sub-community function. Nat. Protoc. In press. (21) Kleinsteuber, S.; Riis, V.; Fetzer, I.; Harms, H.; Müller, S. Population dynamics within a microbial consortium during growth on diesel fuel in saline environments. Appl. Environ. Microbiol. 2006, 72, 3531−3542. (22) Müller, S.; Harms, H.; Bley, T. Origin and analysis of microbial population heterogeneity in bioprocesses. Curr. Opin. Biotechnol. 2010, 21, 100−113. (23) Müller, S. Modes of cytometric bacterial DNA pattern: a tool for pursuing growth. Cell Prolif. 2007, 40, 621−639. (24) Bombach, P.; et al. Resolution of natural microbial community dynamics by community fingerprinting, flow cytometry, and trend interpretation analysis. In High Resolution Microbial Single Cell Analytics. Advances in Biochemical Engineering and Biotechnology 124; Müller, S., Bley, T., Eds.; Springer: Berlin, Heidelberg, 2011; p 151. (25) Karhadkar, P. P.; Audic, J.-M.; Faup, G. M.; Khanna, P. Sulfide and sulphate inhibition of methanogenesis. Water Res. 1987, 21, 1061−1066. (26) Pobeheim, H.; Munk, B.; Lindorfer, H.; Guebitz, G. M. Impact of nickel and cobalt on biogas production and process stability during semi-continuous anaerobic fermentation of a model substrate for maize silage. Water Res. 2011, 45, 781−787. (27) Demirel, B.; Scherer, P. Trace element requirements of agricultural biogas digesters during biological conversion of renewable biomass to methane. Biomass Bioenergy 2001, 35, 992−998. (28) Sundh, I.; Carlsson, H.; Nordberg, Å.; Hansson, M.; Mathisen, B. Effects of glucose overloading on microbial community structure and biogas production in a laboratory-scale anaerobic digester. Bioresour. Technol. 2003, 89, 237−243. (29) Girvan, M. S.; Campbell, C. D.; Killham, K.; Prosser, J. I.; Glover, L. A. Bacterial diversity promotes community stability and functional resilience after perturbation. Environ. Microbiol. 2005, 7, 301−313. (30) Gunderson, L. H. Ecological resilience − in theory and application. Annu. Rev. Ecol. Syst. 2000, 31, 425−439. (31) Scheffer, M.; Carpenter, S. R. Catastrophic regime shifts in ecosystems: linking theory to observation. Trends Ecol. Evol. 2003, 18, 648−656. (32) Schröder, A.; Persson, L.; De Roos, A. M. Direct experimental evidence for alternative stable states: a review. Oikos 2005, 110, 3−19.

ACKNOWLEDGMENTS We thank Thomas Hübschmann, Birke Brumme, Elisabeth Scholz, and Mareen Morawe for their technical assistance. The work of C.K. was kindly supported by Helmholtz Impulse and Networking Fund through Helmholtz Interdisciplinary Graduate School for Environmental Research (HIGRADE). The work was integrated in the internal research and development program of the UFZ and the CITE program (Chemicals in the environment).

■

REFERENCES

(1) Turnbaugh, P. J.; Ley, R. E.; Hamady, M.; Fraser-Liggett, C. M.; Knight, R.; Gordon, J. I. The human microbiome project. Nature 2007, 449, 804−810. (2) Talbot, G.; Topp, E.; Palin, M. F.; Massé, D. I. Evaluation of molecular methods used for establishing the interactions and functions of microorganisms in anaerobic bioreactors. Water Res. 2008, 42, 513− 537. (3) Morales, S. E.; Holben, W. E. Linking bacterial identities and ecosystem processes: can ’omic’ analyses be more than the sum of their parts? FEMS Microbiol. Ecol. 2011, 75, 2−16. (4) Günther, S.; Trutnau, M.; Kleinsteuber, S.; Hause, G.; Bley, T.; Rö ske, I.; Harms, H.; Müller, S. Dynamics of polyphosphateaccumulating bacteria in wastewater treatment plant microbial communities detected via DAPI (4′,6′-diamidino-2-phenylindole) and tetracycline labeling. Appl. Environ. Microbiol. 2009, 75, 2111− 2121. (5) Amann, R.; Fuchs, B. M. Single-cell identification in microbial communities by improved fluorescence in situ hybridization techniques. Nat. Rev. Microbiol. 2008, 6, 339−348. (6) Müller, S.; Nebe-von-Caron, G. Functional single-cell analyses: flow cytometry and cell sorting of microbial populations and communities. FEMS Microbiol. Rev. 2010, 34, 554−587. (7) Lebaron, P.; Servais, P.; Agogué, H.; Courties, C.; Joux, F. Does the high nucleic acid content of individual bacterial cells allow us to discriminate between active cells and inactive cells in aquatic systems? Appl. Environ. Microbiol. 2001, 67, 1775−1782. (8) Wang, Y.; Hammes, F.; De Roy, K.; Verstraete, W.; Boon, N. Past, present and future applications of flow cytometry in aquatic microbiology. Trends Biotechnol. 2010, 28, 416−424. (9) Kuczynski, J.; Lauber, C. L.; Walters, W. A.; Wegener Parfrey, L.; Clemente, J. C.; Gevers, D.; Knight, R. Experimental and analytical tools for studying the human microbiome. Nat. Rev. Genet. 2012, 13, 47−58. (10) Hamady, M.; Knight, R. Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res. 2009, 19, 1141−1152. (11) Werner, J. J.; Knights, D.; Garcia, M. L.; Scalfone, N. B.; Smith, S.; Yarasheski, K.; Cummings, T. A.; Beers, A. R.; Knight, R.; Angenent, L. T. Bacterial community structures are unique and resilient in full-scale bioenergy systems. Proc. Natl. Acad. Sci. U. S. A. 2011, 108, 4158−4163. (12) Günther, S.; Koch, C.; Hübschmann, T.; Röske, I.; Müller, R. A.; Bley, T.; Harms, H.; Müller, S. Correlation of community dynamics and process parameters as a tool for the prediction of the stability of wastewater treatment. Environ. Sci. Technol. 2012, 46, 84−92. (13) R Development Core Team R: a language and environment for statistical computing. R Foundation for Statistical Computing; Vienna, Austria, 2011. http://www.R-project.org/. (14) Gregory, R. W.; Bolker, B.; Bonebakker, L.; Gentleman, R.; Huber, W.; Liaw, A.; Lumley, T.; Maechler, M.; Magnusson, A.; Moeller, S.; Schwartz, M.; Venables, B. Gplots: various R programming tools for plotting data. R package version 2.10.1; 2011. http://CRAN.Rproject.org/package=gplots. (15) Gentleman, R. C.; Carey, V. J.; Bates, D. M.; Bolstad, B.; Dettling, M.; Dudoit, S.; Ellis, B.; Gautier, L.; Ge, Y.; Gentry, J.; Hornik, K.; Hothorn, T.; Huber, W.; Iacus, S.; Irizarry, R.; Leisch, F.; 1759



Article

(33) Schnürer, A.; Zellner, G.; Svensson, B. H. Mesophilic syntrophic acetate oxidation during methane formation in biogas reactors. FEMS Microbiol. Ecol. 1999, 29, 249−261. (34) Xing, J.; Criddle, C.; Hickey, R. Long-term adaptive shifts in anaerobic community structure in response to a sustained cyclic substrate perturbation. Microb. Ecol. 1997, 33, 58−58. (35) Hattori, S. Syntrophic acetate-oxidizing microbes in methanogenic environments. Microbes Environ. 2008, 23, 118−127. (36) Fernández, A.; Huang, S.; Seston, S.; Xing, J.; Hickey, R.; Criddle, C.; Tiedje, J. How stable is stable? Function versus community composition. Appl. Environ. Microbiol. 1999, 65, 3697− 3704. (37) Martinez-Garcia, M.; Swan, B. K.; Poulton, N. J.; Gomez, M. L.; Masland, D.; Sieracki, M. E.; Stepanauskas, R. High-throughput singlecell sequencing identifies photoheterotrophs and chemoautotrophs in freshwater bacterioplankton. ISME J. 2012, 6, 113−123. (38) Fuhrmann, J. A. Microbial community structure and its functional implications. Nature 2009, 459, 193−199. (39) Pedros-Alio, C. Marine microbial diversity: can it be determined? Trends Microbiol. 2006, 14, 257−263. (40) Koch, C.; Fetzer, I.; Harms, H.; Müller, S. CHIC − an automated approach for the detection of dynamic variations in complex microbial communities. Manuscript in revision.

1760


Monitoring Functions in Managed Microbial Systems by Cytometric

Recommend Documents