Bioresource Technology 174 (2014) 108–117
Contents lists available at ScienceDirect
Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Changes of the microbial population structure in an overloaded fed-batch biogas reactor digesting maize silage Kristina Kampmann a, Stefan Ratering a,⇑, Rita Geißler-Plaum a, Michael Schmidt b, Walter Zerr b, Sylvia Schnell a a b
Institute of Applied Microbiology, Justus-Liebig University, Gießen, Germany Landesbetrieb Hessisches Landeslabor (LHL), Standort Bad Hersfeld, Bad Hersfeld, Germany
h i g h l i g h t s Shift of communities caused by overloading was detectable prior pH decrease. Overloading caused a decreased diversity of bacterial groups. Methanosarcina thermophila-related species appeared before overloading. Certain bacteria became dominant before and during acidification. Bacteria became dominant may be suitable as indicator organisms for acidification.
a r t i c l e
i n f o
Article history: Received 15 July 2014 Received in revised form 19 September 2014 Accepted 23 September 2014 Available online 7 October 2014 Keywords: Biogas Digester overload Microbial community structure Acidification Indicator microorganisms
a b s t r a c t Two parallel, stable operating biogas reactors were fed with increasing amounts of maize silage to monitor microbial community changes caused by overloading. Changes of microorganisms diversity revealed by SSCP (single strand conformation polymorphism) indicating an acidification before and during the pHvalue decrease. The earliest indicator was the appearance of a Methanosarcina thermophila-related species. Diversity of dominant fermenting bacteria within Bacteroidetes, Firmicutes and other Bacteria decreased upon overloading. Some species became dominant directly before and during acidification and thus could be suitable as possible indicator organisms for detection of futurity acidification. Those bacteria were related to Prolixibacter bellariivorans and Streptococcus infantarius subsp. infantarius. An early detection of community shifts will allow better feeding management for optimal biogas production. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction The production of biogas has recently become one of the most promising technologies for overcoming future energy shortages which might occur when the availability of fossil fuels will decline. By the end of 2012, about 7515 biogas plants were being operated in Germany (German Biogas Assoziation, 2013). The anaerobic digestion leading to the production of biogas is influenced by different parameters like temperature, pH value, the concentrations of volatile fatty acids (VFA) and ammonia and the availability of macro- and micronutrients (Weiland, 2010). One of the most severe problems biogas plants confront is acidification of the reactors resulting in process instability (Burgstaler et al., 2010). ⇑ Corresponding author. Tel.: +49 641 9937353. E-mail address: [email protected] (S. Ratering). http://dx.doi.org/10.1016/j.biortech.2014.09.150 0960-8524/Ó 2014 Elsevier Ltd. All rights reserved.
Acidification of biogas reactors leads to lower biogas yields and a loss in biogas quality as trace gases like ammonia, nitrous oxide and hydrogen sulphide are produced. This causes severe technical problems in combined heat and power units when energy is recovered from gas mixtures (Burgstaler et al., 2011). It is therefore important to keep the pH value during anaerobic digestion in the range of 6.0–8.5 as the formation of methane by methanogenic Archaea is inhibited beyond those pH values (Weiland, 2010). During the acidification the archaeal community is changing as previous study showed. Blume et al. (2010) investigated the methanogenic community in a mesophilic, continuously stirred tank reactor of 10 l volume inoculated with liquid pig manure and fed with maize silage. It was suggested that the absence of Methanosaetaceae might be an indicator for the instability of the biogas process since at lower organic loading rates (OLR) acetoclastic Methanosaetaceae dominated while at OLRs above 3.7 g dry organic
K. Kampmann et al. / Bioresource Technology 174 (2014) 108–117
mass l 1 d 1, hydrogenotrophic Methanobacteriales were dominant. Hori et al. (2006) investigated the stability of microbial communities in a 1.4 l thermophilic bioreactor treating synthetic wastewater. It was observed that the archaeal community was affected by the concentration of volatile fatty acids (VFAs) while the bacterial community was impacted by the pH value. In a study published by Munk et al. (2010) six mesophilic biogas reactors of 28–30 l volume were operated with maize silage as sole substrate. The methanogenic community in these reactors consisted of Methanosaetaceae which were only found at low acetate concentrations and Methanobacteriales and Methanosarcinaceae which were dominating at higher acetate concentrations. Under acidified conditions, Methanomicrobiales like Methanospirillum hungatei or Methanoculleus sp. were most abundant while the overall number of methanogens had decreased (Munk et al., 2010). The study of Chen et al. (2012) evaluated the archaeal population of an animal waste biogas plant before and 350 days after overfeeding when biogas production had recovered by archaeal 16S RNA gene clone libraries. A higher population diversity which developed during the stressful time of overloading could be found. However, most of these studies focus on the methanogenic Archaea while the fermenting bacteria are mostly neglected. To detect an impending acidification of the biogas reactors early enough to counteract the acidification process, it would be helpful to find indicator microorganisms showing the futurity acidification before chemical parameter like pH-value changed. If the pH-value and the biogas yield have already dropped the recovery of stable conditions and high biogas yields need much more time. To find those indicator microorganisms and to prove the hypothesis that a population shift of fermenting bacteria or methanogenic Archaea can be monitored upon overloading before acidification we conducted the present study. The development of the microbial population structure in mesophilic fed-batch biogas reactors which were overloaded with maize silage at on organic loading rate (OLR) of up to 16.8 g VS (volatile solids) l 1 d 1 was investigated via single strand conformation polymorphism (SSCP) analysis in order to identify the community shifts resulting from acidification. Primer pairs detecting methanogenic Archaea, Firmicutes, Bacteroidetes and the whole group Bacteria were used. Firmicutes and Bacteroidetes were chosen since they represent the majority of the fermenting bacteria. In contrast to other studies, 200 l pilot-scale reactors were used including frequent sampling for simultaneous logging of bacterial and methanogenic population and pH-values.
2. Methods 2.1. Reactor operation and sampling Anaerobic reactor experiments were carried out at the Eichhof in Bad Hersfeld, Germany. The laboratory fed-batch biogas reactors were filled with the digestate from a biogas reactor with a volume of 600 m3 and initially consisted of liquid cattle manure (70%) and liquid pig manure (30%) with a dry mass of 3–4%. This reactor was regularly fed with maize silage and bruised grain and was kept at a temperature of 36–38 °C. For the experiments, two parallel running laboratory scale fed-batch reactors of 200 l were used with anchor stirrers that reached the bottom of the tanks. Stirring was conducted for 15 min per hour. Temperature was maintained at 39 °C. Before starting the laboratory experiment, digestate from the 600 m3 main reactor was incubated until no further gas formation could be detected. Then, maize silage was added to the digestate with following organic loading rates: At the beginning 2.55 g VS per l digestate and day was added, after 167 h 5.1 g VS l 1 d 1 and between 372 h and 507 h around 3.4 g VS l 1 d 1 was added until the specific methane yield per kilogram silage was
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stable. Starting with 537 h the organic loading rate was increased to 16.8 g VS l 1 d 1 (Fig. 1). Volatile solids were determined after the standard method and the loss of volatile compounds (alcohols, fatty acids) in the 105 °C step were corrected according to Pietschmann et al. (2012). At different time points (a–j) the samples for SSCP analysis were taken (Fig. 1). A third 200 l reactor was handled as the other but not fed with any substrate and served as control. Per time point one sample from each reactor was taken in sterile 50 ml plastic centrifugation tubes (Falcon, Greiner BioOne, Frickenhausen, Germany) via a valve that was situated at half height of the tank reactor and immediately frozen at 20 °C. They were transported to Giessen, Germany, in a freezing box to prevent thawing and stored again at 20 °C. Gas production was measured in a drum-type gas meter (Type 1/6, Ritter, Bochum, Germany) and gas was collected in bags. Methane contents were determined with a nondispersive infrared sensor (NDIR, Type GS IRM 100, GS Messtechnik GmbH, Ratingen, Germany). The NDIR analyser was placed in the gas flow between reactor and bags and the gas was measured once an hour. Concentrations of volatile fatty acids were determined by injection of 1 ll sample in an Agilent 6890N chromatograph (Agilent Technologies, Böblingen, Germany) with a flame ionization detector (300 °C), a Zebron ZB-WAX-Plus column (30 m 0.25 mm 0.25 lm; Phenomenex, Torrance, USA) and N2 as carrier gas. The sample preparation was done by mixing 20 g homogenised digestate with 10 ml formic acid (96%) and filling up the volume to 100 ml with distilled water. The temperature program of the oven included following steps: 1. 75–130 °C heating up rate 10 °C min 1, 2. 130–220 °C heating rate 80 °C min 1, 3. 220 °C holding time 5.7 min and 4. 75 °C holding time 0.25 min. 2.2. Molecular analysis of manure samples Manure samples were thawed and homogenized for one minute in a StomacherÒ80 Biomaster lab homogenizer (Seward Laboratory Systems Inc., Bohemia, USA). From each sample, 200 mg were used for DNA extraction with the QIAamp Stool minikit (Qiagen, Hilden, Germany) as described previously (Kampmann et al., 2012b). All primers used in this study are listed in Table 1 and were purchased from Eurofins MWG Operon (Ebersberg, Germany). PCR was performed according to Kampmann et al. (2012b). PCR with primers targeting the mcrA gene encoding the alpha subunit of the methyl coenzyme-M reductase which is unique to methanogenic Archaea started with an initial denaturation for 5 min at 95 °C, followed by 35 cycles that included denaturation for 45 s at 95 °C, annealing for 45 s at 50 °C and extension for 60 s at 72 °C. A final extension step of 30 min at 72 °C was added. As a migration marker for SSCP analysis, a mixture of single-stranded rRNA genes from different pure cultures was used and the PCR for generating the SSCP standards was carried out as described in (Kampmann et al., 2012b). For PCR products that were used for single strand digestion, phosphorylated reverse primers for the corresponding PCR reactions were used. PCR products were checked for quality and amount by agarose gel electrophoresis and measurement at 260 nm in a photometer (Thermo Scientific GENESYS 20™, Thermo Fisher Scientific Inc., Waltham, USA). Purification of PCR products, single strand digestion, purification and denaturation of single stranded DNA was performed according to Kampmann et al. (2012b). For every sample, equal amounts (2000 ng) of DNA were applied to the polyacrylamide gel. Single strand conformation polymorphism (SSCP) analysis including electrophoresis, staining of the gels, preparation of migration markers and processing and normalization of gel scans were conducted as described previously (Kampmann et al., 2012b). Intensive DNAbands that appeared or disappeared before or after acidification or were dominant during the entire incubation time were cut out
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K. Kampmann et al. / Bioresource Technology 174 (2014) 108–117
Fig. 1. Organic loading rate (OLR), methane production per fed silage and sampling points (a, b, c. . .) for SSCP analysis during the fermentation with maize silage in 200 l batch reactors. The reactors 1 and 2 were fed with increasing organic loading rates (circles) of up to 16.8 g VS l 1 d 1. Columns indicate the average of duplicates of the specific methane yield generated between the feeding points; error bars show the range of the values.
Table 1 Summary of PCR primers used in this study. Primer
Sequence (5’– >3’)
Target
Reference
Firm350 for Firm814 rev CFB555 for CFB968 rev ML for ML rev M13 for M13 rev Com1 for Com2 rev
GGCAGCAGTRGGGAATCTTC ACACYTAGYACTCATCGTTT CCGGAWTYATTGGGTTTAAAGGG GGTAAGGTTCCTCGCGTA GGTGGTGTMGGATTCACACARTAYGCWACAGC AACTAYCCWAACTAYGCAATGAA CGCCAGGGTTTTCCCAGTCACGAC TCACACAGGAAACAGCTATGAC CAGCAGCCGCGGTAATAC CCGTCAATTCCTTTGAGTTT
Firmicutes Firmicutes Bacteroidetes Bacteroidetes Methanogens Methanogens Vector pGEM-T Vector pGEM-T Bacteria Bacteria
Mühling et al. (2008) Mühling et al. (2008) Mühling et al. (2008) Mühling et al. (2008) Luton et al. (2002) Luton et al. (2002) Promega Promega Schwieger and Tebbe (1998) Schwieger and Tebbe (1998)
with a sterile scalpel. DNA was isolated from the gel as described previously (Kampmann et al., 2012b). Purification of PCR products from the DNA bands, cloning and sequencing of the clones were performed as described previously (Kampmann et al., 2012b). For every DNA band, 2–3 clones were sequenced. This was necessary because in one SSCP DNA-band more than one DNA fragment could be found in complex community due to the presence of comigrating and contaminating DNA (Schwieger and Tebbe, 1998).
help of RDP’s Functional Gene Pipeline and Repository (Fish et al., 2013).
All sequences were deposited in the NCBI GenBank database with the following accession numbers: KC469424–KC469510 and KC517498–KC517510.
2.3. Phylogenetic analyses
3. Results
Quality checks and trimming of sequences were performed using the software package MEGA, version 5.0 (Tamura et al., 2011). Sequences of Firmicutes and Bacteroidetes 16S rRNA genes were analysed for chimera and aligned as described previously (Kampmann et al., 2012b). Bacterial 16S rRNA gene sequences obtained by usage of the Com-primers were analysed for chimera with the Pintail software (Ashelford et al., 2005) and phylogenetic trees of the 16S rRNA genes were constructed with the ARB software package (Ludwig et al., 2004) as described previously (Kampmann et al., 2012b). Phylogenetic trees of McrA amino acid sequences were constructed with MEGA using the Maximum likelihood algorithm (Tamura et al., 2011). Translation of the nucleotide sequences to the amino acid sequences, frame shift corrections and alignments of the sequences were done with the
3.1. Acetate and propionate concentrations, pH value and methane production
2.4. Nucleotide sequence accession numbers
Regarding the chemical parameters, the two replicate reactors performed in the same way over the time course of the fermentation with only reactor 2 producing more acid after 694 h and 718 h, respectively. As shown in Fig. 2A the formation of methane in the reactors 1 and 2 increased with higher feeding amounts of maize silage, especially after 500 h of fermentation. After 693 h when the last feeding step of 16.8 g VS per liter of digestate and day had been performed, methane still accumulated in constant amounts. After 724 h, the methane accumulation curve flattened, but still, methane was produced. The amount of the specific methane yield per fed substrate (Fig. 1) also increased in the first 430 h
K. Kampmann et al. / Bioresource Technology 174 (2014) 108–117
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Fig. 2. Development of methane accumulation (A), pH values (B), acetate concentrations (C) and propionate concentrations (D) in 200 l biogas reactors fed with increasing amounts of up to 19 g dw l 1 maize silage. All three reactors were operated under the same conditions, but to the control reactor, no substrate was added. Data for reactor 1 and 2 are means of duplicates; error bars show the range of the values. The x-axis refers to the incubation time after substrate addition; the y-axis refers to methane accumulation (l), pH and fatty acid concentrations (mM), respectively. The shaded area marks the time frame in which the microbial community changed.
at organic loading rates between 2.55 and 5.13 g VS l 1 d 1, stayed constant between 453 h and 506 h at organic loading rates between 3.4 and 3.5 g VS l 1 d 1 and reached a maximum at 537 h at an organic loading rate of 4.8 g VS l 1 d 1 (Fig. 1). Thereafter the amount of the methane production per fed substrate decreased reaching the lowest value at 694 h. When the experiment was finished after 966 h, ca. 5300 l of methane had been produced in reactor 1, ca. 5100 l in reactor 2 and minor amounts of methane (42 l) in the control reactor which was not fed with any substrate. The pH value in the control reactor was stable, ranging from 7.8 to 7.9 (Fig. 2B). In the reactors 1 and 2, the pH values were lower, ranging from 7.3 to 7.8. In the samples from reactor 1 and 2 which were taken after 694 h and 718 h, the pH value had decreased to 6.65 ± 0.09. Corresponding to these results, the highest concentrations of acetate and propionate were found. The acetate concentrations ranged from 1.4 to 45 mM, reaching up to 65 ± 3 mM after 694 h and up to 79 ± 8 mM after 718 h (Fig. 2C). The propionate concentrations ranged from 0.1 to 6 mM, reaching up to 13 ± 5 mM after 694 h and up to 25 ± 9 mM after 718 h (Fig. 2D). In the control reactor, minor amounts of acetate of 1.5–8 mM were detected as well as minor amounts of propionate of 0.1–1.5 mM.
3.2. Single strand conformation polymorphism analysis and phylogenetic analysis For Bacteroidetes, Firmicutes and the whole group of Bacteria, SSCP analyses revealed communities with a high diversity in the beginning (Fig. 3I, II and IV). In the course of the fermentation, many DNA bands became weaker in intensity until an abrupt change in the community compositions in the time frame between 529 h and 675 h (Fig. 3). The pH-value at this time frame (indicated with shaded area in Fig. 2) was still 7.5, but the diversity of Bacteria, Firmicutes and Bacteroidetes already was considerably reduced. These changes did not appear in the samples from the control reactor which was not fed with maize silage. Among the Bacteroidetes (Fig. 4A), most species were distantly related to Proteiniphilum acetatigenes (CFB-1 (KC469424, KC469425), 90.4 to 90.5% sequence identity; CFB-8 (KC469444, KC46446), 92.1–92.3% sequence identity) and Petrimonas sulfuriphila (CFB-5 (KC469436–KC46338), 95.8–96% sequence identity, CFB-9 (KC469449), 97.2–97.7% sequence identity). Upon overloading CFB-4 DNA band (KC469433–KC469435) appeared (Fig. 3I) which sequence was distantly related to Prolixibacter bellariivorans (87.1–87.4% sequence identity) but highly similar to an uncultured
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Fig. 3. SSCP analyses of microbial population structures in 200 l biogas reactors fed with increasing organic loading rates of up to 16.8 g VS l 1 d 1. All three reactors were operated under the same conditions, but to the control reactor, no substrate was added. The following microbial groups were investigated: Bacteroidetes (I), Firmicutes (II), methanogenic Archaea (III) and Bacteria (IV). Samples were taken at different points of time, before the first feeding step (a) and after 2 h (b); 24.8 h (c); 189.3 h (d); 359.4 h (e); 384.8 h (f); 529.4 (g); 675.1 (h); 694.6 (i) and 718.0 h (j). Dominant DNA-bands (circles) that appeared or disappeared before or after acidification or were dominant during the entire incubation time were cut out. DNA was isolated for cloning and subsequent sequencing. St, standard.
bacterium (GenbankType: Nucleotide CR933139) found in a wastewater treatment plant. At the same time CFB-3 DNA band (KC469430–KC469432) disappeared which sequence was distantly related to Alkaliflexus imshenetskii (94.9–95.8% sequence identity) and other bands CFB-6 (KC469439–KC469441) and CFB-7 (KC469442, KC469443) which also thinned out. Over the total time period the specie distantly related to Petrimonas sulfuriphila CFB-5 (KC469436–KC46338) was found as one dominant member of the Bacteroidetes. Fig. 4B shows a phylogenetic tree of the Firmicutes sequences found during fermentation. Some of them were closely related to Turicibacter sanguinis (Firm-2 (KC469466), 98.1–98.6% sequence identity; Firm-3 (KC469463), 97.5–98.3% sequence identity) and distantly related to Pelotomaculum isophthalicicum (Firm-1 (KC469462, KC469464), 86.2–90.3% sequence identity; Firm-6 (KC469475, KC469476), 95.5–95.6% sequence identity), and Sporomusa aerivorans (Firm-5 (KC469473, KC469474), 90.5–90.7% sequence identity; Firm-8 (KC469480–KC469482), 90.9–91.5% sequence identity). Upon overloading the intensity of two DNA bands Firm-7 (KC469477–KC469479) and Firm-9 (KC469483, KC469485) drastically increased (Fig. 3 II). The sequence of band Firm-7 showed high similarity to Streptococcus infantarius subsp. infantarius (99.2–99.4% sequence identity) whereas band Firm-9 was distantly related to Dehalobacter restrictus (92.5–92.7% sequence identity). The abundance of some species was rather stable like the DNA
band Firm-6 (KC469475, KC469476) demonstrated which sequence was distantly related to Pelotomaculum isophthalicicum (95.5–95.6% sequence identity). Because of the limited specificity of the Firmicutes primer (Kampmann et al., 2012b) not all Firmicutes were detectable but some Firmicutes sequences which were not detected by the Firmicutes primers could be identified with the Bacteria primer (Fig. 5). These were closely related to Clostridium stercorarium (Com-1 (KC469486, KC469487), 96.1–96.3% sequence identity), Clostridium caenicola (one sequence from Com-2 (KC469488), 96.6% sequence identity) and Clostridium isatidis (Com-3 (KC469490–KC469492), 98.5–99.3% sequence identity). Other bacteria detected with the Bacteria primer (Fig. 5), showed similar sequences as detected by the Bacteroidetes primers, for example Com-5 (KC469496) and Com-10 (KC469508–KC469510), but also sequences distantly related to Bacteroides salanitronis (Com-8 (KC469502), 87.2% sequence identity; Com-9 (KC469506, KC469507), 87.4–87.6% sequence identity) which were not detected by the CFB primer. The species distantly related to P. bellariivorans (DNA band Com-7 (KC469499) and Com-10 (KC469508–KC469510); 86.1– 86.4% sequence identity) showing increasing intensity of the DNA band after starting overloading whereas the DNA band Com-9 (KC469506–KC469509) showing a stable intensity over the total incubation time (Fig. 3IV). A phylogenetic tree from McrA amino acid sequences from the methanogenic Archaea is shown in Fig. 6. The DNA band patterns
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Fig. 4. Phylogenetic tree of Bacteroidetes (A) and Firmicutes (B) 16S rRNA partial gene sequences from 200 l biogas reactors fed with maize silage. The trees were constructed using the Maximum Likelihood algorithm for the nearly full length sequences (>1400 bp). Partial clone sequences (
Contents lists available at ScienceDirect
Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Changes of the microbial population structure in an overloaded fed-batch biogas reactor digesting maize silage Kristina Kampmann a, Stefan Ratering a,⇑, Rita Geißler-Plaum a, Michael Schmidt b, Walter Zerr b, Sylvia Schnell a a b
Institute of Applied Microbiology, Justus-Liebig University, Gießen, Germany Landesbetrieb Hessisches Landeslabor (LHL), Standort Bad Hersfeld, Bad Hersfeld, Germany
h i g h l i g h t s Shift of communities caused by overloading was detectable prior pH decrease. Overloading caused a decreased diversity of bacterial groups. Methanosarcina thermophila-related species appeared before overloading. Certain bacteria became dominant before and during acidification. Bacteria became dominant may be suitable as indicator organisms for acidification.
a r t i c l e
i n f o
Article history: Received 15 July 2014 Received in revised form 19 September 2014 Accepted 23 September 2014 Available online 7 October 2014 Keywords: Biogas Digester overload Microbial community structure Acidification Indicator microorganisms
a b s t r a c t Two parallel, stable operating biogas reactors were fed with increasing amounts of maize silage to monitor microbial community changes caused by overloading. Changes of microorganisms diversity revealed by SSCP (single strand conformation polymorphism) indicating an acidification before and during the pHvalue decrease. The earliest indicator was the appearance of a Methanosarcina thermophila-related species. Diversity of dominant fermenting bacteria within Bacteroidetes, Firmicutes and other Bacteria decreased upon overloading. Some species became dominant directly before and during acidification and thus could be suitable as possible indicator organisms for detection of futurity acidification. Those bacteria were related to Prolixibacter bellariivorans and Streptococcus infantarius subsp. infantarius. An early detection of community shifts will allow better feeding management for optimal biogas production. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction The production of biogas has recently become one of the most promising technologies for overcoming future energy shortages which might occur when the availability of fossil fuels will decline. By the end of 2012, about 7515 biogas plants were being operated in Germany (German Biogas Assoziation, 2013). The anaerobic digestion leading to the production of biogas is influenced by different parameters like temperature, pH value, the concentrations of volatile fatty acids (VFA) and ammonia and the availability of macro- and micronutrients (Weiland, 2010). One of the most severe problems biogas plants confront is acidification of the reactors resulting in process instability (Burgstaler et al., 2010). ⇑ Corresponding author. Tel.: +49 641 9937353. E-mail address: [email protected] (S. Ratering). http://dx.doi.org/10.1016/j.biortech.2014.09.150 0960-8524/Ó 2014 Elsevier Ltd. All rights reserved.
Acidification of biogas reactors leads to lower biogas yields and a loss in biogas quality as trace gases like ammonia, nitrous oxide and hydrogen sulphide are produced. This causes severe technical problems in combined heat and power units when energy is recovered from gas mixtures (Burgstaler et al., 2011). It is therefore important to keep the pH value during anaerobic digestion in the range of 6.0–8.5 as the formation of methane by methanogenic Archaea is inhibited beyond those pH values (Weiland, 2010). During the acidification the archaeal community is changing as previous study showed. Blume et al. (2010) investigated the methanogenic community in a mesophilic, continuously stirred tank reactor of 10 l volume inoculated with liquid pig manure and fed with maize silage. It was suggested that the absence of Methanosaetaceae might be an indicator for the instability of the biogas process since at lower organic loading rates (OLR) acetoclastic Methanosaetaceae dominated while at OLRs above 3.7 g dry organic
K. Kampmann et al. / Bioresource Technology 174 (2014) 108–117
mass l 1 d 1, hydrogenotrophic Methanobacteriales were dominant. Hori et al. (2006) investigated the stability of microbial communities in a 1.4 l thermophilic bioreactor treating synthetic wastewater. It was observed that the archaeal community was affected by the concentration of volatile fatty acids (VFAs) while the bacterial community was impacted by the pH value. In a study published by Munk et al. (2010) six mesophilic biogas reactors of 28–30 l volume were operated with maize silage as sole substrate. The methanogenic community in these reactors consisted of Methanosaetaceae which were only found at low acetate concentrations and Methanobacteriales and Methanosarcinaceae which were dominating at higher acetate concentrations. Under acidified conditions, Methanomicrobiales like Methanospirillum hungatei or Methanoculleus sp. were most abundant while the overall number of methanogens had decreased (Munk et al., 2010). The study of Chen et al. (2012) evaluated the archaeal population of an animal waste biogas plant before and 350 days after overfeeding when biogas production had recovered by archaeal 16S RNA gene clone libraries. A higher population diversity which developed during the stressful time of overloading could be found. However, most of these studies focus on the methanogenic Archaea while the fermenting bacteria are mostly neglected. To detect an impending acidification of the biogas reactors early enough to counteract the acidification process, it would be helpful to find indicator microorganisms showing the futurity acidification before chemical parameter like pH-value changed. If the pH-value and the biogas yield have already dropped the recovery of stable conditions and high biogas yields need much more time. To find those indicator microorganisms and to prove the hypothesis that a population shift of fermenting bacteria or methanogenic Archaea can be monitored upon overloading before acidification we conducted the present study. The development of the microbial population structure in mesophilic fed-batch biogas reactors which were overloaded with maize silage at on organic loading rate (OLR) of up to 16.8 g VS (volatile solids) l 1 d 1 was investigated via single strand conformation polymorphism (SSCP) analysis in order to identify the community shifts resulting from acidification. Primer pairs detecting methanogenic Archaea, Firmicutes, Bacteroidetes and the whole group Bacteria were used. Firmicutes and Bacteroidetes were chosen since they represent the majority of the fermenting bacteria. In contrast to other studies, 200 l pilot-scale reactors were used including frequent sampling for simultaneous logging of bacterial and methanogenic population and pH-values.
2. Methods 2.1. Reactor operation and sampling Anaerobic reactor experiments were carried out at the Eichhof in Bad Hersfeld, Germany. The laboratory fed-batch biogas reactors were filled with the digestate from a biogas reactor with a volume of 600 m3 and initially consisted of liquid cattle manure (70%) and liquid pig manure (30%) with a dry mass of 3–4%. This reactor was regularly fed with maize silage and bruised grain and was kept at a temperature of 36–38 °C. For the experiments, two parallel running laboratory scale fed-batch reactors of 200 l were used with anchor stirrers that reached the bottom of the tanks. Stirring was conducted for 15 min per hour. Temperature was maintained at 39 °C. Before starting the laboratory experiment, digestate from the 600 m3 main reactor was incubated until no further gas formation could be detected. Then, maize silage was added to the digestate with following organic loading rates: At the beginning 2.55 g VS per l digestate and day was added, after 167 h 5.1 g VS l 1 d 1 and between 372 h and 507 h around 3.4 g VS l 1 d 1 was added until the specific methane yield per kilogram silage was
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stable. Starting with 537 h the organic loading rate was increased to 16.8 g VS l 1 d 1 (Fig. 1). Volatile solids were determined after the standard method and the loss of volatile compounds (alcohols, fatty acids) in the 105 °C step were corrected according to Pietschmann et al. (2012). At different time points (a–j) the samples for SSCP analysis were taken (Fig. 1). A third 200 l reactor was handled as the other but not fed with any substrate and served as control. Per time point one sample from each reactor was taken in sterile 50 ml plastic centrifugation tubes (Falcon, Greiner BioOne, Frickenhausen, Germany) via a valve that was situated at half height of the tank reactor and immediately frozen at 20 °C. They were transported to Giessen, Germany, in a freezing box to prevent thawing and stored again at 20 °C. Gas production was measured in a drum-type gas meter (Type 1/6, Ritter, Bochum, Germany) and gas was collected in bags. Methane contents were determined with a nondispersive infrared sensor (NDIR, Type GS IRM 100, GS Messtechnik GmbH, Ratingen, Germany). The NDIR analyser was placed in the gas flow between reactor and bags and the gas was measured once an hour. Concentrations of volatile fatty acids were determined by injection of 1 ll sample in an Agilent 6890N chromatograph (Agilent Technologies, Böblingen, Germany) with a flame ionization detector (300 °C), a Zebron ZB-WAX-Plus column (30 m 0.25 mm 0.25 lm; Phenomenex, Torrance, USA) and N2 as carrier gas. The sample preparation was done by mixing 20 g homogenised digestate with 10 ml formic acid (96%) and filling up the volume to 100 ml with distilled water. The temperature program of the oven included following steps: 1. 75–130 °C heating up rate 10 °C min 1, 2. 130–220 °C heating rate 80 °C min 1, 3. 220 °C holding time 5.7 min and 4. 75 °C holding time 0.25 min. 2.2. Molecular analysis of manure samples Manure samples were thawed and homogenized for one minute in a StomacherÒ80 Biomaster lab homogenizer (Seward Laboratory Systems Inc., Bohemia, USA). From each sample, 200 mg were used for DNA extraction with the QIAamp Stool minikit (Qiagen, Hilden, Germany) as described previously (Kampmann et al., 2012b). All primers used in this study are listed in Table 1 and were purchased from Eurofins MWG Operon (Ebersberg, Germany). PCR was performed according to Kampmann et al. (2012b). PCR with primers targeting the mcrA gene encoding the alpha subunit of the methyl coenzyme-M reductase which is unique to methanogenic Archaea started with an initial denaturation for 5 min at 95 °C, followed by 35 cycles that included denaturation for 45 s at 95 °C, annealing for 45 s at 50 °C and extension for 60 s at 72 °C. A final extension step of 30 min at 72 °C was added. As a migration marker for SSCP analysis, a mixture of single-stranded rRNA genes from different pure cultures was used and the PCR for generating the SSCP standards was carried out as described in (Kampmann et al., 2012b). For PCR products that were used for single strand digestion, phosphorylated reverse primers for the corresponding PCR reactions were used. PCR products were checked for quality and amount by agarose gel electrophoresis and measurement at 260 nm in a photometer (Thermo Scientific GENESYS 20™, Thermo Fisher Scientific Inc., Waltham, USA). Purification of PCR products, single strand digestion, purification and denaturation of single stranded DNA was performed according to Kampmann et al. (2012b). For every sample, equal amounts (2000 ng) of DNA were applied to the polyacrylamide gel. Single strand conformation polymorphism (SSCP) analysis including electrophoresis, staining of the gels, preparation of migration markers and processing and normalization of gel scans were conducted as described previously (Kampmann et al., 2012b). Intensive DNAbands that appeared or disappeared before or after acidification or were dominant during the entire incubation time were cut out
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Fig. 1. Organic loading rate (OLR), methane production per fed silage and sampling points (a, b, c. . .) for SSCP analysis during the fermentation with maize silage in 200 l batch reactors. The reactors 1 and 2 were fed with increasing organic loading rates (circles) of up to 16.8 g VS l 1 d 1. Columns indicate the average of duplicates of the specific methane yield generated between the feeding points; error bars show the range of the values.
Table 1 Summary of PCR primers used in this study. Primer
Sequence (5’– >3’)
Target
Reference
Firm350 for Firm814 rev CFB555 for CFB968 rev ML for ML rev M13 for M13 rev Com1 for Com2 rev
GGCAGCAGTRGGGAATCTTC ACACYTAGYACTCATCGTTT CCGGAWTYATTGGGTTTAAAGGG GGTAAGGTTCCTCGCGTA GGTGGTGTMGGATTCACACARTAYGCWACAGC AACTAYCCWAACTAYGCAATGAA CGCCAGGGTTTTCCCAGTCACGAC TCACACAGGAAACAGCTATGAC CAGCAGCCGCGGTAATAC CCGTCAATTCCTTTGAGTTT
Firmicutes Firmicutes Bacteroidetes Bacteroidetes Methanogens Methanogens Vector pGEM-T Vector pGEM-T Bacteria Bacteria
Mühling et al. (2008) Mühling et al. (2008) Mühling et al. (2008) Mühling et al. (2008) Luton et al. (2002) Luton et al. (2002) Promega Promega Schwieger and Tebbe (1998) Schwieger and Tebbe (1998)
with a sterile scalpel. DNA was isolated from the gel as described previously (Kampmann et al., 2012b). Purification of PCR products from the DNA bands, cloning and sequencing of the clones were performed as described previously (Kampmann et al., 2012b). For every DNA band, 2–3 clones were sequenced. This was necessary because in one SSCP DNA-band more than one DNA fragment could be found in complex community due to the presence of comigrating and contaminating DNA (Schwieger and Tebbe, 1998).
help of RDP’s Functional Gene Pipeline and Repository (Fish et al., 2013).
All sequences were deposited in the NCBI GenBank database with the following accession numbers: KC469424–KC469510 and KC517498–KC517510.
2.3. Phylogenetic analyses
3. Results
Quality checks and trimming of sequences were performed using the software package MEGA, version 5.0 (Tamura et al., 2011). Sequences of Firmicutes and Bacteroidetes 16S rRNA genes were analysed for chimera and aligned as described previously (Kampmann et al., 2012b). Bacterial 16S rRNA gene sequences obtained by usage of the Com-primers were analysed for chimera with the Pintail software (Ashelford et al., 2005) and phylogenetic trees of the 16S rRNA genes were constructed with the ARB software package (Ludwig et al., 2004) as described previously (Kampmann et al., 2012b). Phylogenetic trees of McrA amino acid sequences were constructed with MEGA using the Maximum likelihood algorithm (Tamura et al., 2011). Translation of the nucleotide sequences to the amino acid sequences, frame shift corrections and alignments of the sequences were done with the
3.1. Acetate and propionate concentrations, pH value and methane production
2.4. Nucleotide sequence accession numbers
Regarding the chemical parameters, the two replicate reactors performed in the same way over the time course of the fermentation with only reactor 2 producing more acid after 694 h and 718 h, respectively. As shown in Fig. 2A the formation of methane in the reactors 1 and 2 increased with higher feeding amounts of maize silage, especially after 500 h of fermentation. After 693 h when the last feeding step of 16.8 g VS per liter of digestate and day had been performed, methane still accumulated in constant amounts. After 724 h, the methane accumulation curve flattened, but still, methane was produced. The amount of the specific methane yield per fed substrate (Fig. 1) also increased in the first 430 h
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Fig. 2. Development of methane accumulation (A), pH values (B), acetate concentrations (C) and propionate concentrations (D) in 200 l biogas reactors fed with increasing amounts of up to 19 g dw l 1 maize silage. All three reactors were operated under the same conditions, but to the control reactor, no substrate was added. Data for reactor 1 and 2 are means of duplicates; error bars show the range of the values. The x-axis refers to the incubation time after substrate addition; the y-axis refers to methane accumulation (l), pH and fatty acid concentrations (mM), respectively. The shaded area marks the time frame in which the microbial community changed.
at organic loading rates between 2.55 and 5.13 g VS l 1 d 1, stayed constant between 453 h and 506 h at organic loading rates between 3.4 and 3.5 g VS l 1 d 1 and reached a maximum at 537 h at an organic loading rate of 4.8 g VS l 1 d 1 (Fig. 1). Thereafter the amount of the methane production per fed substrate decreased reaching the lowest value at 694 h. When the experiment was finished after 966 h, ca. 5300 l of methane had been produced in reactor 1, ca. 5100 l in reactor 2 and minor amounts of methane (42 l) in the control reactor which was not fed with any substrate. The pH value in the control reactor was stable, ranging from 7.8 to 7.9 (Fig. 2B). In the reactors 1 and 2, the pH values were lower, ranging from 7.3 to 7.8. In the samples from reactor 1 and 2 which were taken after 694 h and 718 h, the pH value had decreased to 6.65 ± 0.09. Corresponding to these results, the highest concentrations of acetate and propionate were found. The acetate concentrations ranged from 1.4 to 45 mM, reaching up to 65 ± 3 mM after 694 h and up to 79 ± 8 mM after 718 h (Fig. 2C). The propionate concentrations ranged from 0.1 to 6 mM, reaching up to 13 ± 5 mM after 694 h and up to 25 ± 9 mM after 718 h (Fig. 2D). In the control reactor, minor amounts of acetate of 1.5–8 mM were detected as well as minor amounts of propionate of 0.1–1.5 mM.
3.2. Single strand conformation polymorphism analysis and phylogenetic analysis For Bacteroidetes, Firmicutes and the whole group of Bacteria, SSCP analyses revealed communities with a high diversity in the beginning (Fig. 3I, II and IV). In the course of the fermentation, many DNA bands became weaker in intensity until an abrupt change in the community compositions in the time frame between 529 h and 675 h (Fig. 3). The pH-value at this time frame (indicated with shaded area in Fig. 2) was still 7.5, but the diversity of Bacteria, Firmicutes and Bacteroidetes already was considerably reduced. These changes did not appear in the samples from the control reactor which was not fed with maize silage. Among the Bacteroidetes (Fig. 4A), most species were distantly related to Proteiniphilum acetatigenes (CFB-1 (KC469424, KC469425), 90.4 to 90.5% sequence identity; CFB-8 (KC469444, KC46446), 92.1–92.3% sequence identity) and Petrimonas sulfuriphila (CFB-5 (KC469436–KC46338), 95.8–96% sequence identity, CFB-9 (KC469449), 97.2–97.7% sequence identity). Upon overloading CFB-4 DNA band (KC469433–KC469435) appeared (Fig. 3I) which sequence was distantly related to Prolixibacter bellariivorans (87.1–87.4% sequence identity) but highly similar to an uncultured
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Fig. 3. SSCP analyses of microbial population structures in 200 l biogas reactors fed with increasing organic loading rates of up to 16.8 g VS l 1 d 1. All three reactors were operated under the same conditions, but to the control reactor, no substrate was added. The following microbial groups were investigated: Bacteroidetes (I), Firmicutes (II), methanogenic Archaea (III) and Bacteria (IV). Samples were taken at different points of time, before the first feeding step (a) and after 2 h (b); 24.8 h (c); 189.3 h (d); 359.4 h (e); 384.8 h (f); 529.4 (g); 675.1 (h); 694.6 (i) and 718.0 h (j). Dominant DNA-bands (circles) that appeared or disappeared before or after acidification or were dominant during the entire incubation time were cut out. DNA was isolated for cloning and subsequent sequencing. St, standard.
bacterium (GenbankType: Nucleotide CR933139) found in a wastewater treatment plant. At the same time CFB-3 DNA band (KC469430–KC469432) disappeared which sequence was distantly related to Alkaliflexus imshenetskii (94.9–95.8% sequence identity) and other bands CFB-6 (KC469439–KC469441) and CFB-7 (KC469442, KC469443) which also thinned out. Over the total time period the specie distantly related to Petrimonas sulfuriphila CFB-5 (KC469436–KC46338) was found as one dominant member of the Bacteroidetes. Fig. 4B shows a phylogenetic tree of the Firmicutes sequences found during fermentation. Some of them were closely related to Turicibacter sanguinis (Firm-2 (KC469466), 98.1–98.6% sequence identity; Firm-3 (KC469463), 97.5–98.3% sequence identity) and distantly related to Pelotomaculum isophthalicicum (Firm-1 (KC469462, KC469464), 86.2–90.3% sequence identity; Firm-6 (KC469475, KC469476), 95.5–95.6% sequence identity), and Sporomusa aerivorans (Firm-5 (KC469473, KC469474), 90.5–90.7% sequence identity; Firm-8 (KC469480–KC469482), 90.9–91.5% sequence identity). Upon overloading the intensity of two DNA bands Firm-7 (KC469477–KC469479) and Firm-9 (KC469483, KC469485) drastically increased (Fig. 3 II). The sequence of band Firm-7 showed high similarity to Streptococcus infantarius subsp. infantarius (99.2–99.4% sequence identity) whereas band Firm-9 was distantly related to Dehalobacter restrictus (92.5–92.7% sequence identity). The abundance of some species was rather stable like the DNA
band Firm-6 (KC469475, KC469476) demonstrated which sequence was distantly related to Pelotomaculum isophthalicicum (95.5–95.6% sequence identity). Because of the limited specificity of the Firmicutes primer (Kampmann et al., 2012b) not all Firmicutes were detectable but some Firmicutes sequences which were not detected by the Firmicutes primers could be identified with the Bacteria primer (Fig. 5). These were closely related to Clostridium stercorarium (Com-1 (KC469486, KC469487), 96.1–96.3% sequence identity), Clostridium caenicola (one sequence from Com-2 (KC469488), 96.6% sequence identity) and Clostridium isatidis (Com-3 (KC469490–KC469492), 98.5–99.3% sequence identity). Other bacteria detected with the Bacteria primer (Fig. 5), showed similar sequences as detected by the Bacteroidetes primers, for example Com-5 (KC469496) and Com-10 (KC469508–KC469510), but also sequences distantly related to Bacteroides salanitronis (Com-8 (KC469502), 87.2% sequence identity; Com-9 (KC469506, KC469507), 87.4–87.6% sequence identity) which were not detected by the CFB primer. The species distantly related to P. bellariivorans (DNA band Com-7 (KC469499) and Com-10 (KC469508–KC469510); 86.1– 86.4% sequence identity) showing increasing intensity of the DNA band after starting overloading whereas the DNA band Com-9 (KC469506–KC469509) showing a stable intensity over the total incubation time (Fig. 3IV). A phylogenetic tree from McrA amino acid sequences from the methanogenic Archaea is shown in Fig. 6. The DNA band patterns
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Fig. 4. Phylogenetic tree of Bacteroidetes (A) and Firmicutes (B) 16S rRNA partial gene sequences from 200 l biogas reactors fed with maize silage. The trees were constructed using the Maximum Likelihood algorithm for the nearly full length sequences (>1400 bp). Partial clone sequences (
