Absolute Quantification of Individual Biomass Concentrations in a Methanogenic Coculture
© Junicke et al.; licensee Springer 2014
Received: 8 January 2014
Accepted: 16 March 2014
Published: 12 April 2014
Identification of individual biomass concentrations is a crucial step towards an improved understanding of anaerobic digestion processes and mixed microbial conversions in general. The knowledge of individual biomass concentrations allows for the calculation of biomass specific conversion rates which form the basis of anaerobic digestion models. Only few attempts addressed the absolute quantification of individual biomass concentrations in methanogenic microbial ecosystems which has so far impaired the calculation of biomass specific conversion rates and thus model validation. This study proposes a quantitative PCR (qPCR) approach for the direct determination of individual biomass concentrations in methanogenic microbial associations by correlating the native qPCR signal (cycle threshold, Ct) to individual biomass concentrations (mg dry matter/L). Unlike existing methods, the proposed approach circumvents error-prone conversion factors that are typically used to convert gene copy numbers or cell concentrations into actual biomass concentrations. The newly developed method was assessed and deemed suitable for the determination of individual biomass concentrations in a defined coculture of Desulfovibrio sp. G11 and Methanospirillum hungatei JF1. The obtained calibration curves showed high accuracy, indicating that the new approach is well suited for any engineering applications where the knowledge of individual biomass concentrations is required.
KeywordsAnaerobic digestion qPCR Individual biomass concentration Biomass specific conversion rates
Many biotechnological processes rely on the combined action of complex microbial consortia (Kleerebezem and van Loosdrecht ). An important example is the anaerobic digestion process which converts organic residues into biogas, a renewable form of energy containing methane as the primary energy carrier (Chen et al. ; Gujer and Zehnder ).
Anaerobic digestion comprises a series of reaction steps each performed by a specific microbial group of the anaerobic ecosystem (Gavala et al. ; Stams and Plugge ). Due to the interdependence of involved reactions, the overall mechanism is kinetically controlled by the rate limiting reaction step (Griffin et al. ; Lyberatos and Skiadas ; Yu et al. ). Therefore, to improve conversion performance and process control major importance lies in the identification of the factors that govern a well-balanced reaction mechanism (Chen et al. ; Griffin et al. ; Rittmann and McCarty ). To investigate these factors mathematical models such as the Anaerobic Digestion Model No.1 (ADM1) have been developed (Batstone et al. ; Gavala et al. ). Unfortunately, validation of ADM1 and similar models is yet hampered by the lacking information on individual biomass concentrations i.e., the biomass concentrations of individual species or different functional groups contained in the microbial community. Only by knowing individual biomass concentrations it is possible to calculate biomass specific rates which form the basis of these models and whose determination is hence required for their evaluation.
Only limited research has focused on the determination of individual biomass concentrations in mixed microbial communities. Seitz et al. () determined individual biomass concentrations of an anaerobic coculture by phase-contrast microscopy assisted manual cell counting. Nevertheless, this approach is time-intensive and suffers from low accuracy since morphologically similar microorganisms and aggregated cells can hardly be distinguished (Manz et al. ; Wagner et al. ). Emerging molecular techniques such as qPCR, quantitative fluorescence in situ hybridization (qFISH) or pyrosequencing promise to be much faster and more accurate (Coskuner et al. ; Ronaghi and Elahi ; Wagner et al. ; Zhang and Fang ).
Previous studies have used qPCR to analyze microbial community structures and population dynamics in a range of samples from wastewater treatment plants (Hall et al. ; Harms et al. ; Winkler et al. ), anaerobic bioreactors (Shin et al. ; Yu et al. ), activated sludge processes (Hall et al. ; Kim et al. ) and natural habitats (Schippers and Neretin ). Similar applications were covered by qFISH (Albertsen et al. ; Egli et al. ; Juretschko et al. ; Kragelund et al. ) and pyrosequencing (Jaenicke et al. ; Kröber et al. ; Kwon et al. ; Schlüter et al. ; Zhang et al. ). However, only a few attempts addressed the absolute quantification of individual biomass concentrations or the calculation of biomass specific conversion rates.
Harms et al. () quantified nitrite-oxidizing bacteria (NOB) and ammonia-oxidizing bacteria (AOB) by means of qPCR and derived cell-specific conversion rates in activated sludge. The native qPCR results (DNA copies/L) were converted to cells/L, cells/g, and percent of biomass using several assumptions. Ahn et al. (), Cho et al. () and Kindaichi et al. () used qPCR to determine maximum biomass specific growth rates in nitrifying communities based on the abundance of DNA copy numbers, and assuming a constant correlation factor between DNA content and biomass. Cho et al. () determined DNA specific growth yields (DNA copy numbers/mg-N) but did not express results in terms of biomass. Ahn et al. () used additional conversion factors to derive biomass growth yields (mg-COD biomass/mg-N) from growth yields expressed in terms of DNA. The prevalent use of DNA copy numbers (Cheng et al. ; Chon et al. ) or cell numbers (Coskuner et al. ), rather than actual biomass, renders these results inconvenient for most engineering purposes. Error-prone conversion factors (e.g. gene copies/genome, genomes/cell, cells/g dry matter, DNA extraction efficiency) add to the problematic of this approach.
Several studies employed FISH to quantify cells of AOB and NOB, and to determine cell-specific ammonium and nitrite oxidation rates (Altmann et al. ; Daims et al. ; Gieseke et al. ; Wagner et al. ). In a direct comparison of FISH and qPCR for the quantification of cells in nitrifying biofilms Kindaichi et al. () found that both methods yielded comparable results but qPCR was more favorable due to higher sensitivity and faster handling. Low sample concentrations (<105 cells/mL), autofluorescence, non-specific binding and low signal intensity can become limiting factors for FISH analysis (Kindaichi et al. ; Konuma et al. ; Rittmann et al. ; Zhang and Fang ). The quantification of individual biomass concentrations by means of pyrosequencing remains challenging due to the semi-quantitative nature of the method (Amend et al. ). Purely quantitative applications of pyrosequencing remain scarce as of yet. Lastly, biomass concentrations were estimated from observed substrate transformation rates, metabolite ratios and individual biomass growth yields (Jiang et al. ; Lopez-Vazquez et al. ; Rittmann et al. ). These indirect methods are based on the measurement of commonly used analytical variables (e.g. substrate and product concentrations, lumped biomass concentration) without requiring molecular techniques (Lopez-Vazquez et al. ). However, assumptions of reaction stoichiometry or maximum biomass specific conversion rates are inherent to these indirect methods and pose a major source of inaccuracy. In view of the previous, qPCR is regarded the most suited molecular method for the quantification of individual biomass concentrations in complex microbial ecosystems, and it stands out due to its high sensitivity (< 5 gene copies), high reproducibility (standard deviation < 2%) and high specificity (Kim et al. ).
Here it is aimed to derive individual biomass concentrations, expressed in gram dry matter per liter, directly from the qPCR signal of a given sample. No such approach has been reported so far, despite a few key advantages: Firstly, the result can readily be used in mathematical models and engineering applications. Secondly, several limitations of existing methods can be avoided, including unnecessary assumptions or erroneous conversion factors.
Material and methods
A defined coculture of Desulfovibrio sp. G11 and Methanospirillum hungatei JF1 was used to evaluate the applicability of a qPCR approach for the determination of specific biomass concentrations.
Cultivation of microorganisms
Pure cultures of Desulfovibrio sp. strain G11 (DSM 7057) and Methanospirillum hungatei type strain JF1 (DSM 864) were obtained from the Laboratory of Microbiology, Wageningen University, The Netherlands, and cultivated in 2 L Schott bottles in the absence of oxygen and under sterile conditions. The basic medium was prepared according to Plugge (). Culture medium for Desulfovibrio sp. G11 contained 20 mM sodium lactate, as the sole carbon source, and 10 mM sodium sulphate as electron acceptor. Basic medium for Methanospirillum hungatei JF1 was supplemented with 2 mM sodium acetate and 4 mM cysteine hydrochloride. While Desulfovibrio sp. G11 was kept under 80%/20% N2/CO2 atmosphere, Methanospirillum hungatei JF1 was grown under 80%/20% H2/CO2. The headspace of the methanogenic culture was exchanged every other day. The pH was maintained between 7.0 and 7.2. All cultures were incubated at a temperature of 37 ºC and constantly shaken at 150 rpm.
Centrifugation efficiency test
The centrifugation efficiency was tested for a biomass concentration of 21.0 mg/L (Desulfovibrio sp. G11) and 179.9 mg/L (Methanospirillum hungatei JF1) and four further two-fold dilutions, respectively. A three-step centrifugation procedure using a cell suspension volume of 2 mL (13000 rpm, 21,000 × g, 4ºC, Heraeus, Biofuge fresco) was used. The duration of the first step amounted to 5 min. The resulting supernatant was again centrifuged for 3 min. Supernatant of the second step was centrifuged for 10 min. Three pellets resulting from the previous centrifugation steps were combined for DNA extraction.
The UltraClean microbial DNA isolation kit (Mo Bio, Carlsbad, USA) was used for DNA extraction in triplicates. Instead of horizontal vortex mixing for 10 min, the Mini Bead Beater 16 (BioSpec Products, Bartlesville, USA) was used for 5 min. In order to improve DNA elution efficiency herring-sperm DNA (HS-DNA) was added prior to the bead-beating step.
Primer sets and sequences used for the amplification of the partial 16S rDNA sequences by qPCR
Primer sequence (5’-3’)
DSVsp G11 201f
Desulfovibrio sp. strain G11
DSVsp G11 471r
CTG GTT GAT CCT GCC AG
Mariakakis et al. ()
Methanospirillum hungatei JF1
CAG ACT CAT CCT GAA GCG AC
Worm et al. ()
Specific primers for Desulfovibrio sp. G11 were designed using the ARB software program (Ludwig et al. ). The optimum annealing temperature was determined using qPCR with a temperature gradient ranging from 50°C to 65°C. Primer efficiency was tested at the optimum annealing temperature using dilution series of the extracted genomic DNA in the range of 10−1 to 10−6. Each sample was analysed by qPCR in triplicate. From the exponential behaviour of the Ct value as a function of the DNA starting concentration, the primer efficiency was deduced. A combination of the specific primer MH236r and the general archaeal primer Arch25f was used as a primer set for Methanospirillum hungatei JF1. The optimum annealing temperature was determined using qPCR with a temperature gradient ranging from 52°C to 65°C. The qPCR amplification for Desulfovibrio sp. G11 proceeded according to the following scheme: 95°C (5 min), for the next 40 cycles 95°C (30 s), 62°C (40 s), 72°C (40 s), 80°C (25 s). The amplification for Methanospirillum hungatei JF1 was as follows: 95°C (5 min), for the next 40 cycles 95°C (30 s), 57°C (30 s), 72°C (15 s), 80°C (25 s). The bacterial reaction mixture consisted of 10 μl 2x iCycler mix (Bio-Rad, Hercules, USA), 0.1 μl DSVsp G11 201f primer (50 μM stock), 0.1 μl DSVsp G11 471r primer (50 μM stock), and 2.5 μl template DNA (5.0 ng/μl). In contrast, 0.2 μl Arch25f primer (50 μM stock) and 0.2 μl MH236r primer (50 μM stock) were used for the archaeal assay. Reaction mixtures were filled up to 20 μl with PCR-H20.
To assess primer cross-sensitivity, qPCR was performed with the following primer/DNA mixtures: (1) DSVsp G11 201f and DSVsp G11 471r primer/HS-DNA and DNA of Methanospirillum hungatei JF1; (2) Arch25f and MH236r primer/HS-DNA and DNA of Desulfovibrio sp. G11.
Determination of dry weight
Total suspended solids (TSS) of both pure cultures were determined according to standard filtration methods (Taras et al. ). A Supor® 200 PES membrane filter with a pore size of 0.2 μm (PALL, Port Washington, USA) was used. The ash content was determined according to ESS Method 340.2 (WSLH ). All measurements were conducted in triplicate. By subtracting the ash content from the TSS concentration, the concentration of volatile suspended solids (VSS) was obtained.
Pure cultures of Desulfovibrio sp. G11 and Methanospirillum hungatei JF1 were first diluted to a biomass concentration of 1.1 mg/L (Desulfovibrio sp. G11) and 9.0 mg/L (Methanospirillum hungatei JF1) and then combined to obtain co-cultures of known biomass ratios. The cell suspensions were taken from the late exponential/early stationary state. The optical density was measured at 660 nm (DR 2800, Hach-Lange, Tiel, The Netherlands) and was in the range of 0.150 and 0.250 for both cultures. Coculture samples (2 mL) of known biomass mixing ratios were centrifuged and the DNA was extracted according to the described procedures. At low DNA concentration of the sample, a volume of 5 μl of 10-fold diluted HS-DNA (10 μg/μl, Sigma) was added to improve the elution efficiency during DNA extraction. HS-DNA was added just before using the mini bead beater. To obtain a calibration curve relating the Ct value to the biomass concentration in the sample for each of the two species, qPCR was performed either with primers specific for Desulfovibrio sp. G11 or Methanospirillum hungatei JF1. All measurements were performed in triplicates. Instead of 2.5 μl of 5 ng/μl DNA template, 2.5 μl of 60-fold diluted DNA template was used for the qPCR.
A low centrifugation efficiency leads to cell loss, thereby limiting the total amount of DNA available for qPCR, and fluctuations in centrifugation efficiency are reflected in the qPCR calibration curve. A three-step centrifugation procedure yielded best cell recovery with a centrifugation efficiency consistently above 97%. A standard error of 5% was derived from triplicate measurement.
DNA extraction efficiency
HS-DNA was added during the DNA extraction step to enhance DNA elution from the filter membrane of the used DNA extraction kit. Leaving all other steps of the calibration procedure unchanged, the addition of HS-DNA during DNA extraction yielded significant improvements of the calibration curves. Largest improvements were obtained with Desulfovibrio sp. G11, as can be seen in Figure 1 (without HS-DNA) and Figure 2 (with HS-DNA). Calibration data obtained for Desulfovibrio sp. G11 in the presence of HS-DNA resulted in a more accurate regression line (R2 = 0.994) compared to the case without addition of HS-DNA (R2 = 0.134).
To confirm a constant DNA extraction efficiency, DNA was extracted from biomass samples of different concentrations and subjected to qPCR. Taking primer efficiency and the dilution factor between samples into account the expected theoretical difference in Ct values with regard to the undiluted sample can be calculated. It was found that experimental and theoretical Ct values differed on average by less than 0.9% for Methanospirillum hungatei JF1, and less than 1.2% for Desulfovibrio sp. G11 in the presence of HS-DNA. In contrast, the same difference amounted to 7.5% for Desulfovibrio sp. G11 in the absence of HS-DNA. These results confirm that a constant DNA extraction efficiency was achieved for both species irrespective of the biomass starting concentration due to the addition of HS-DNA.
A primer efficiency of 93.4% was obtained at an optimum annealing temperature of 57°C for Methanospirillum hungatei JF1. The primer efficiency for Desulfovibrio sp. G11 was 75.9% at 62°C. Primer efficiency was constant and remained unaffected by the presence of HS-DNA.
No significant primer cross-sensitivities were observed in the calibrated biomass concentration range. Cross-sensitivities occurred at biomass concentrations 100-fold lower than the minimum biomass concentrations used for the preparation of the calibration lines.
The Ct values obtained from qPCR triplicates of Methanospirillum hungatei JF1 showed a standard deviation of 1.6% and 2.7% for Desulfovibrio sp. G11.
Determination of dry weight
The measurement error of the VSS determination is 1.8% (Methanospirillum hungatei JF1) and 0.6% (Desulfovibrio sp. G11).
Seitz et al. () made the first attempt to determine individual biomass concentrations in methanogenic microbial cocultures by manual cell counting. This approach is not only tedious but is also affected by varying cell morphologies and the occurrence of cell aggregates which renders cell counting less accurate than DNA-based techniques. In this study qPCR was investigated for the determination of individual biomass concentrations because of its reported higher accuracy, sensitivity and reproducibility compared to other quantification methods such as quantitative FISH or pyrosequencing (Amend et al. ; Kim et al. ).
The present study successfully demonstrated the suitability of a newly developed qPCR approach for the quantification of individual biomass concentrations in a defined methanogenic coculture of Desulfovibrio sp. G11 and Methanospirillum hungatei JF1. Calibration curves correlating the native qPCR signal (Ct value) directly with absolute biomass concentrations (mg dry matter/L) were obtained with high accuracy. A biomass calibration curve was established in a concentration range of 1.1 – 9.0 mg/L for Methanospirillum hungatei JF1 and 0.1 – 1.1 mg/L for Desulfovibrio sp. G11. Dilution or pre-concentration of samples can be used to ensure that biomass concentrations are within the calibrated regime. The calibrated biomass concentration range can possibly be extended much further into both directions given that qPCR has a reported dynamic range of more than 6 orders of magnitude (Kim et al. ). However, in practice, saturation of the DNA extraction kit (at about 109 cells/mL) may pose an upper detection limit for the presented approach and sample dilution will be required (data not shown). For this reason it is advisable to use 10 mg/L (Desulfovibrio sp. G11) and 100 mg/L (for Methanospirillum hungatei JF1) as the respective upper limits (data not shown) which roughly equals 108 cells/mL. The lower detection limit is most likely determined by DNA elution efficiency and primer cross-sensitivity. Addition of HS-DNA during the DNA extraction step increased the DNA elution efficiency significantly, most notably for Desulfovibrio sp. G11, while showing no noticeable cross-sensitivities with the employed primer pairs.
Many qPCR approaches for the quantification of microbial communities rely on the knowledge of the DNA extraction efficiency, the number of gene copies per genome and the number of genome copies per organism. However, such data is not always available in genomic databases and may show broad variation (Kim et al. ; Malandrin et al. ; Mileyko et al. ). Another factor of uncertainty is a varying DNA extraction efficiency. As a result, the conversion of DNA copy numbers to actual biomass concentrations is often a vague procedure. The here presented quantification method overcomes these limitations because the native qPCR signal is directly linked to the biomass concentration of the species of interest. The aforementioned conversion factors are not required and the DNA extraction efficiency needs not to be known explicitly. Nevertheless, it must be ensured that DNA extraction efficiency is constant between replicates. A constant extraction efficiency was confirmed for the target organisms in this study.
Suitability of the presented qPCR approach for the determination of individual biomass concentrations in a defined coculture of Desulfovibrio sp. G11 and Methanospirillum hungatei JF1 was successfully demonstrated in this study. Feasibility of the proposed method in non-defined environmental samples was not yet examined. High primer specificity is required in non-defined samples. The newly designed primer set for Desulfovibrio sp. G11 certainly meets that criterion. In contrast, the primer set used for DNA amplification of Methanospirillum hungatei JF1 consists of an archaea-specific forward primer and a reverse primer highly specific for Methanospirillum hungatei JF1. The analysis of non-defined environmental samples may require a species specific reverse primer to ensure only Methanospirillum hungatei JF1 is detected.
Group-specific determination of biomass concentrations remains challenging with this method. In principle all species belonging to the microbial group in question have to be grown in pure culture for the preparation of single-species calibration curves. Summation of species-specific biomass concentrations of an environmental sample, derived from the respective calibration curves, yields the biomass concentration of a desired group consisting of the different species previously assigned to it. This procedure is very time-consuming but it remains a one-time activity. Apart from that, in non-defined microbial communities target organisms need to be identified first (e.g. by denaturing gradient gel electrophoresis) to assign them to the microbial group of interest.
Future research should aim for a comparison of the presented qPCR approach with other existing methods such as qFISH or pyrosequencing, especially with regard to environmental samples. While the authors believe that the presented qPCR approach shows its specific qualities in the accurate quantification of individual biomass concentrations of single species, it is recognized that FISH and pyrosequencing can serve quite complementary purposes: Solely FISH is capable to identify cell distribution and cell interaction in-situ (Kindaichi et al. ; Okabe et al. ), while pyrosequencing is regarded most suited for the high-throughput analysis of complex non-defined microbial communities (Amend et al. ; Ronaghi and Elahi ).
The proposed quantification method poses a major improvement over prevailing approaches because no error-prone conversion factors and assumptions are needed to obtain absolute biomass concentrations. The method developed is therefore ideally suited for engineering applications and for providing model input. The gained knowledge on individual biomass concentrations in defined or non-defined microbial communities opens up the opportunity to calculate biomass specific conversion rates which enables the validation of models for anaerobic digestion processes and other mixed microbial conversions.
The financial support of STW (project number 11603) and Veolia Water is gratefully acknowledged.
- Ahn JH, Yu R, Chandran K: Distinctive microbial ecology and biokinetics of autotrophic ammonia and nitrite oxidation in a partial nitrification Bioreactor. Biotechnol Bioeng 2008, 100(6):1078–1087.PubMedGoogle Scholar
- Albertsen M, Hansen LBS, Saunders AM, Nielsen PH, Nielsen KL: A metagenome of a full-scale microbial community carrying out enhanced biological phosphorus removal. Isme J 2012, 6(6):1094–1106.PubMed CentralPubMedGoogle Scholar
- Altmann D, Stief P, Amann R, de Beer D, Schramm A: In situ distribution and activity of nitrifying bacteria in freshwater sediment. Environ Microbiol 2003, 5(9):798–803.PubMedGoogle Scholar
- Amend AS, Seifert KA, Bruns TD: Quantifying microbial communities with 454 pyrosequencing: does read abundance count? Mol Ecol 2010, 19(24):5555–5565.PubMedGoogle Scholar
- Batstone DJ, Keller J, Angelidaki I, Kalyuzhnyi SV, Pavlostathis SG, Rozzi A, Sanders WTM, Siegrist H, Vavilin VA: The IWA Anaerobic Digestion Model No 1 (ADM1). Water Sci Technol 2002, 45(10):65–73.PubMedGoogle Scholar
- Chen Y, Cheng JJ, Creamer KS: Inhibition of anaerobic digestion process: A review. Bioresource Technol 2008, 99(10):4044–4064.Google Scholar
- Cheng CH, Hsu SC, Wu CH, Chang PW, Lin CY, Hung CH: Quantitative analysis of microorganism composition in a pilot-scale fermentative biohydrogen production system. Int J Hydrogen Energ 2011, 36(21):14153–14161.Google Scholar
- Cho K, Nguyen DX, Lee S, Hwang S: Use of real-time QPCR in biokinetics and modeling of two different ammonia-oxidizing bacteria growing simultaneously. J Ind Microbiol Biot 2013, 40(9):1015–1022.Google Scholar
- Chon K, Chang JS, Lee E, Lee J, Ryu J, Cho J: Abundance of denitrifying genes coding for nitrate (narG), nitrite (nirS), and nitrous oxide (nosZ) reductases in estuarine versus wastewater effluent-fed constructed wetlands. Ecol Eng 2011, 37(1):64–69.Google Scholar
- Coskuner G, Ballinger SJ, Davenport RJ, Pickering RL, Solera R, Head IM, Curtis TP: Agreement between theory and measurement in quantification of ammonia-oxidizing bacteria. Appl Environ Microb 2005, 71(10):6325–6334.Google Scholar
- Daims H, Ramsing NB, Schleifer KH, Wagner M: Cultivation-independent, semiautomatic determination of absolute bacterial cell numbers in environmental samples by fluorescence in situ hybridization. Appl Environ Microb 2001, 67(12):5810–5818.Google Scholar
- Egli K, Langer C, Siegrist HR, Zehnder AJB, Wagner M, van der Meer JR: Community analysis of ammonia and nitrite oxidizers during start-up of nitritation reactors. Appl Environ Microb 2003, 69(6):3213–3222.Google Scholar
- Gavala HN, Angelidaki I, Ahring BK: Kinetics and modeling of anaerobic digestion process. In Biomethanation I. Edited by: Ahring BK. Springer, Berlin/Heidelberg; 2003.Google Scholar
- Gieseke A, Nielsen JL, Amann R, Nielsen PH, de Beer D: In situ substrate conversion and assimilation by nitrifying bacteria in a model biofilm. Environ Microbiol 2005, 7(9):1392–1404.PubMedGoogle Scholar
- Griffin ME, McMahon KD, Mackie RI, Raskin L: Methanogenic population dynamics during start-up of anaerobic digesters treating municipal solid waste and biosolids. Biotechnol Bioeng 1998, 57(3):342–355.PubMedGoogle Scholar
- Gujer W, Zehnder AJB: Conversion Processes in Anaerobic-Digestion. Water Sci Technol 1983, 15(8–9):127–167.Google Scholar
- Hall SJ, Hugenholtz P, Siyambalapitiya N, Keller J, Blackall LL: The development and use of real-time PCR for the quantification of nitrifiers in activated sludge. Water Sci Technol 2002, 46(1–2):267–272.PubMedGoogle Scholar
- Harms G, Layton AC, Dionisi HM, Gregory IR, Garrett VM, Hawkins SA, Robinson KG, Sayler GS: Real-time PCR quantification of nitrifying bacteria in a municipal wastewater treatment plant. Environ Sci Technol 2003, 37(2):343–351. doi:10.1021/Es0257164PubMedGoogle Scholar
- Heijnen JJ: Mass Balances, Rates, and Experiments. In The metabolic pathway engineering handbook: Fundamentals. Edited by: Smolke CD. CRC Press/Taylor & Francis, Boca Raton; 2010.Google Scholar
- Jaenicke S, Ander C, Bekel T, Bisdorf R, Droge M, Gartemann KH, Junemann S, Kaiser O, Krause L, Tille F, Zakrzewski M, Puhler A, Schluter A, Goesmann A: Comparative and Joint Analysis of Two Metagenomic Datasets from a Biogas Fermenter Obtained by 454-Pyrosequencing. Plos One 2011, 6(1):ᅟ.Google Scholar
- Jiang Y, Marang L, Kleerebezem R, Muyzer G, van Loosdrecht MCM: Polyhydroxybutyrate Production From Lactate Using a Mixed Microbial Culture. Biotechnol Bioeng 2011, 108(9):2022–2035.PubMedGoogle Scholar
- Juretschko S, Loy A, Lehner A, Wagner M: The microbial community composition of a nitrifying-denitrifying activated sludge from an industrial sewage treatment plant analyzed by the full-cycle rRNA approach. Syst Appl Microbiol 2002, 25(1):84–99.PubMedGoogle Scholar
- Kim YM, Lee DS, Park C, Park D, Park JM: Effects of free cyanide on microbial communities and biological carbon and nitrogen removal performance in the industrial activated sludge process. Water Res 2011, 45(3):1267–1279.PubMedGoogle Scholar
- Kim J, Lim J, Lee C: Quantitative real-time PCR approaches for microbial community studies in wastewater treatment systems: Applications and considerations. Biotechnology advances 2013, 31(8):1358–1373.PubMedGoogle Scholar
- Kindaichi T, Kawano Y, Ito T, Satoh H, Okabe S: Population dynamics and in situ kinetics of nitrifying bacteria in autotrophic nitrifying biofilms as determined by real-time quantitative PCR. Biotechnol Bioeng 2006, 94(6):1111–1121.PubMedGoogle Scholar
- Kindaichi T, Tsushima I, Ogasawara Y, Shimokawa M, Ozaki N, Satoh H, Okabe S: In situ activity and spatial organization of anaerobic ammonium-oxidizing (anammox) bacteria in biofilms. Appl Environ Microb 2007, 73(15):4931–4939.Google Scholar
- Kleerebezem R, van Loosdrecht MCM: Mixed culture biotechnology for bioenergy production. Curr Opin Biotech 2007, 18(3):207–212.PubMedGoogle Scholar
- Konuma S, Satoh H, Mino T, Matsuo T: Comparison of enumeration methods for ammonia-oxidizing bacteria. Water Science & Technology 2000, 43(1):107–114.Google Scholar
- Kragelund C, Thomsen TR, Mielczarek AT, Nielsen PH: Eikelboom's morphotype 0803 in activated sludge belongs to the genus Caldilinea in the phylum Chloroflexi . Fems Microbiol Ecol 2011, 76(3):451–462.PubMedGoogle Scholar
- Kröber M, Bekel T, Diaz NN, Goesmann A, Jaenicke S, Krause L, Miller D, Runte KJ, Viehover P, Puhler A, Schluter A: Phylogenetic characterization of a biogas plant microbial community integrating clone library 16S-rDNA sequences and metagenome sequence data obtained by 454-pyrosequencing. J Biotechnol 2009, 142(1):38–49.PubMedGoogle Scholar
- Kwon S, Kim TS, Yu GH, Jung JH, Park HD: Bacterial Community Composition and Diversity of a Full-Scale Integrated Fixed-Film Activated Sludge System as Investigated by Pyrosequencing. J Microbiol Biotechn 2010, 20(12):1717–1723.Google Scholar
- Lopez-Vazquez CM, Hooijmans CM, Brdjanovic D, Gijzen HJ, van Loosdrecht MCM: A practical method for quantification of phosphorus- and glycogen-accumulating organism populations in activated sludge systems. Water Environ Res 2007, 79(13):2487–2498.PubMedGoogle Scholar
- Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar A, Yadhukumar A, Buchner A, Lai T, Steppi S, Jobb G, Forster W, Brettske I, Gerber S, Ginhart AW, Gross O, Grumann S, Hermann S, Jost R, Konig A, Liss T, Lussmann R, May M, Nonhoff B, Reichel B, Strehlow R, Stamatakis A, Stuckmann N, Vilbig A, Lenke M, Ludwig T, Bode A, Schleifer KH: ARB: a software environment for sequence data. Nucleic Acids Res 2004, 32(4):1363–1371.PubMed CentralPubMedGoogle Scholar
- Lyberatos G, Skiadas IV: Modelling of anaerobic digestion–a review. Global Nest Int J 1999, 1: 63–76.Google Scholar
- Malandrin L, Huber H, Bernander R: Nucleoid structure and partition in Methanococcus jannaschii : An Archaeon with multiple copies of the chromosome. Genetics 1999, 152(4):1315–1323.PubMed CentralPubMedGoogle Scholar
- Manz W, Wagner M, Amann R, Schleifer K-H: In situ characterization of the microbial consortia active in two wastewater treatment plants. Water Res 1994, 28(8):1715–1723.Google Scholar
- Mariakakis I, Bischoff P, Krampe J, Meyer C, Steinmetz H: Effect of organic loading rate and solids retention time on microbial population during bio-hydrogen production by dark fermentation in large lab-scale. Int J Hydrogen Energ 2011, 36(17):10690–10700.Google Scholar
- Mileyko Y, Joh RI, Weitz JS: Small-scale copy number variation and large-scale changes in gene expression. P Natl Acad Sci USA 2008, 105(43):16659–16664.Google Scholar
- Okabe S, Itoh T, Satoh H, Watanabe Y: Analyses of spatial distributions of sulfate-reducing bacteria and their activity in aerobic wastewater biofilms. Appl Environ Microb 1999, 65(11):5107–5116.Google Scholar
- Plugge CM: Anoxic media design, preparation, and considerations. Environ Microbiol 2005, 397: 3–16.Google Scholar
- Rittmann BE, McCarty PL: Environmental biotechnology. McGraw-Hill, New York; 2001.Google Scholar
- Rittmann BE, Laspidou CS, Flax J, Stahl DA, Urbain V, Harduin H, van der Waarde JJ, Geurkink B, Henssen MJC, Brouwer H, Klapwijk A, Wetterauw M: Molecular and modeling analyses of the structure and function of nitrifying activated sludge. Water Sci Technol 1999, 39(1):51–59.Google Scholar
- Ronaghi M, Elahi E: Pyrosequencing for microbial typing. J Chromatogr B 2002, 782(1–2):67–72.Google Scholar
- Schippers A, Neretin LN: Quantification of microbial communities in near-surface and deeply buried marine sediments on the Peru continental margin using real-time PCR. Environ Microbiol 2006, 8(7):1251–1260.PubMedGoogle Scholar
- Schlüter A, Bekel T, Diaz NN, Dondrup M, Eichenlaub R, Gartemann KH, Krahn I, Krause L, Kromeke H, Kruse O, Mussgnug JH, Neuweger H, Niehaus K, Puhler A, Runte KJ, Szczepanowski R, Tauch A, Tilker A, Viehover P, Goesmann A: The metagenome of a biogas-producing microbial community of a production-scale biogas plant fermenter analysed by the 454-pyrosequencing technology. J Biotechnol 2008, 136(1–2):77–90.PubMedGoogle Scholar
- Seitz HJ, Schink B, Pfennig N, Conrad R: Energetics of Syntrophic Ethanol Oxidation in Defined Chemostat Cocultures .2. Energy Sharing in Biomass Production. Arch Microbiol 1990, 155(1):89–93.Google Scholar
- Shin SG, Zhou BW, Lee S, Kim W, Hwang S: Variations in methanogenic population structure under overloading of pre-acidified high-strength organic wastewaters. Process Biochem 2011, 46(4):1035–1038.Google Scholar
- Stams AJM, Plugge CM: Electron transfer in syntrophic communities of anaerobic bacteria and archaea. Nat Rev Microbiol 2009, 7(8):568–577.PubMedGoogle Scholar
- Taras MJ, Greenberg AE, Hoak RD, Rand MC: Standard methods for the examination of water and waste water. American Public Health Association, New York; 1971.Google Scholar
- Wagner M, Rath G, Amann R, Koops HP, Schleifer KH: In-Situ Identification of Ammonia-Oxidizing Bacteria. Syst Appl Microbiol 1995, 18(2):251–264.Google Scholar
- Wagner M, Horn M, Daims H: Fluorescence in situ hybridisation for the identification and characterisation of prokaryotes. Curr Opin Microbiol 2003, 6(3):302–309.PubMedGoogle Scholar
- Winkler MKH, Bassin JP, Kleerebezem R, Sorokin DY, van Loosdrecht MCM: Unravelling the reasons for disproportion in the ratio of AOB and NOB in aerobic granular sludge. Appl Microbiol Biot 2012, 94(6):1657–1666.Google Scholar
- Worm P, Stams AJM, Cheng X, Plugge CM: Growth- and substrate-dependent transcription of formate dehydrogenase and hydrogenase coding genes in Syntrophobacter fumaroxidans and Methanospirillum hungatei . Microbiol-Sgm 2011, 157: 280–289.Google Scholar
- WSLH (1993) Total suspended solids, mass balance, volatile suspended solids. Wisconsin State Laboratory of Hygiene, Environmental Sciences Section Inorganic Chemistry UnitGoogle Scholar
- Yu Y, Lee C, Kim J, Hwang S: Group-specific primer and probe sets to detect methanogenic communities using quantitative real-time polymerase chain reaction. Biotechnol Bioeng 2005, 89(6):670–679.PubMedGoogle Scholar
- Yu Y, Kim J, Hwang S: Use of real-time PCR for group-specific quantification of aceticlastic methanogens in anaerobic processes: Population dynamics and community structures. Biotechnol Bioeng 2006, 93(3):424–433.PubMedGoogle Scholar
- Zhang T, Fang HHP: Applications of real-time polymerase chain reaction for quantification of microorganisms in environmental samples. Appl Microbiol Biot 2006, 70(3):281–289.Google Scholar
- Zhang T, Shao MF, Ye L: 454 Pyrosequencing reveals bacterial diversity of activated sludge from 14 sewage treatment plants. Isme J 2012, 6(6):1137–1147.PubMed CentralPubMedGoogle Scholar
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