Metabolic and microbial community dynamics during the hydrolytic and acidogenic fermentation in a leach-bed process
© Sträuber et al.; licensee Springer. 2012
Received: 26 June 2012
Accepted: 16 July 2012
Published: 16 July 2012
Biogas production from lignocellulosic feedstock not competing with food production can contribute to a sustainable bioenergy system. The hydrolysis is the rate-limiting step in the anaerobic digestion of solid substrates such as straw. Hence, a detailed understanding of the metabolic processes during the steps of hydrolysis and acidogenesis is required to improve process control strategies.
The fermentation products formed during the acidogenic fermentation of maize silage as a model substrate in a leach-bed process were determined by gas and liquid chromatography. The bacterial community dynamics was monitored by terminal restriction fragment length polymorphism analysis. The community profiles were correlated with the process data using multivariate statistics.
The batch process comprised three metabolic phases characterized by different fermentation products. The bacterial community dynamics correlated with the production of the respective metabolites. In phase 1, lactic and acetic acid fermentations dominated. Accordingly, bacteria of the genera Lactobacillus and Acetobacter were detected. In phase 2, the metabolic pathways shifted to butyric acid fermentation, accompanied by the production of hydrogen and carbon dioxide and a dominance of the genus Clostridium. In phase 3, phylotypes affiliated with Ruminococcaceae and Lachnospiraceae prevailed, accompanied by the formation of caproic and acetic acids, and a high gas production rate.
A clostridial butyric type of fermentation was predominant in the acidogenic fermentation of maize silage, whereas propionic type fermentation was marginal. As the metabolite composition resulting from acidogenesis affects the subsequent methanogenic performance, process control should focus on hydrolysis/acidogenesis when solid substrates are digested.
KeywordsBiogas Anaerobic digestion Maize silage Hydrolysis Acidogenesis Solid state fermentation Bacterial 16 S rRNA genes T-RFLP fingerprinting
Biogas, a mixture of mainly methane and carbon dioxide, is produced during the anaerobic digestion of biomass by a complex microbial network. Due to its high methane yield per hectare, maize is the most widely used energy crop in Germany for biogas production . Usually, whole plants are harvested, chopped and ensiled for conservation. Ensilage also serves as a pre-treatment measure for enhanced biogas production. The production of maize silage is a complex biochemical process, where bacteria produce a number of organic acids and alcohols from the maize plant material which is rich in carbohydrates, mainly starch, cellulose and hemicellulose. Several chemical and microbial silage additives are used to control the ensilage process and prevent undesirable kinds of silage fermentation. To stimulate the ensilage process, homofermentative and/or heterofermentative consortia or single strains of lactic acid bacteria are used. The homofermentative bacterial metabolism results in the production of lactic acid, whereas the heterofermentative one produces a mixture of lactic acid, acetic acid, ethanol and carbon dioxide. The different fermentation pathways are accompanied by different losses of total solids (TS) during ensilaging , whereas the content of volatile solids (VS) is only affected marginally . The chemical composition of plant biomass modified by the ensilage process influences the subsequent anaerobic digestion process. Whereas the crude protein and crude fat contents of the substrate do not change during this treatment, the fiber content decreases to 15%, dependent on the fermentation conditions . The digestion of the ensiled maize plants results in higher biogas yields as a direct effect of the decomposition of fibers compared to the untreated maize . Furthermore, storage of the silage is possible for about 1 year. Within this time, properly ensiled plants can be used without any significant loss in methane production.
The biogas process comprises four stages, i.e., hydrolysis, acidogenesis, acetogenesis, and methanogenesis , which are catalyzed by different and specialized microorganisms. Parts of the metabolic network have been investigated on different levels to understand the key processes. The metabolic pathways involved in the final stage - the formation of methane by the archaea - have been intensively studied [5–7], whereas the preceding metabolic pathways catalyzed by different bacterial groups are less understood. One of the reasons is the lower diversity of methanogenic archaea involved in the biogas process compared to that of the various functional groups of bacteria [8, 9]. Furthermore, methanogenesis is often the rate-limiting step, especially when wastewater is treated . However, when solid substrates such as complex organic substances of plants are digested, the hydrolysis is the rate-limiting step [11, 12]. Thus, to enhance the overall production rate in such processes, it is necessary to understand the primary degradation steps, i.e., hydrolysis and acidogenesis, for the control and optimization of the whole process. Although the use of maize as an energy crop is coming more and more under criticism for its negative effects on the agro-ecosystem, maize silage is a suitable model substrate to engineer solid-state fermentation processes and develop strategies for process control.
The hydrolysis of plant material is often inefficient under anaerobic conditions. The process occurs primarily through the activity of extracellular enzymes secreted by hydrolytic bacteria attached to polymeric substrates . However, the hydrolytic bacteria do not gain any energy from this reaction. Hence, the same organisms perform the following acidogenesis steps by uptaking and fermenting the hydrolysis products. The range of products formed during this primary fermentation comprises various volatile fatty acids (VFA), alcohols, hydrogen and carbon dioxide. However, the ratios of the respective components can differ significantly, dependent on the process conditions such as hydraulic retention time, organic loading rate, substrate concentration, temperature, and pH [14–16]. Process imbalances and overloading are often accompanied by an accumulation of propionic acid [17, 18]. It is generally accepted that the propionic acid concentration should be kept below 1.5 g L−1 for proper process operation , and the ratio of propionic/acetic acid was suggested to be a sufficient indicator of a digester failure . However, in rare cases, propionic acid was not a reliable indicator of process imbalances .
It is known that the rate of ethanol and butyric acid production accompanied by hydrogen production is relatively higher than that of propionic acid production ; thus, propionic acid is considered as an inferior metabolite. The metabolic background of propionic acid accumulation is not yet completely clear. Some researchers found a correlation of a high hydrogen partial pressure and an increased propionic acid production [22, 23]. It is assumed that the hydrogen partial pressure regulates the metabolic reactions, as the hydrogen content determines the ratio of the oxidized NAD+ to the reduced NADH within the bacterial cells . However, the production of propionic acid was not always found to be related to a high hydrogen partial pressure, but this effect seems to be dependent on the pH value [25, 26].
There are many open questions regarding the complex and functionally redundant hydrolytic and acidogenic metabolic pathways. Knowledge of the biological catalysts, i.e., the hydrolytic and fermenting bacteria, is sparse. Thus, our research is focused on the investigation of the dynamics of acidogenic fermentations, on the one hand, and the investigation of how the formation of fermentation products is reflected by the dynamics of the bacterial community composition, on the other. Correlations of the process data and the community composition have revealed both the key players involved in the process and the decisive process parameters shaping the acidogenic community. We used a solid-state leach-bed reactor as this reactor type is not only suitable for energy crops but also for more sustainable feedstocks such as straw.
Batch reactor design, operation and sampling
Analysis of process parameters and calculations
To determine the TS and VS contents of the substrate or the solid digestate, respectively, samples were dried at 105 °C for at least 12 h. The TS value was calculated from the difference in the weight of the fresh and the cooled, dried sample. The VS value was measured as the loss of ignition when treating the dried samples in a muffle furnace at 550 °C for 2 h. The VS value was calculated from the difference of the weight between the dried and the incinerated sample.
where msc is the mass of the component in the substrate (in grams), and mdc is the mass of the component in the solid digestate (in grams).
The concentrations of VFA (acetic, propionic, n-butyric, iso-butyric, n-valeric, iso-valeric and caproic acids) in the percolate were determined using a 5890 series II gas chromatograph (Hewlett Packard Company, CA, USA) equipped with a HS40 automatic headspace sampler (Perkin Elmer, MA, USA), a HP-FFAP column (film thickness, 0.25 μm; inside diameter, 0.32 mm; length, 30 m; Agilent Technologies, Inc. CA, USA) and a flame ionization detector. Nitrogen was the carrier gas with a flow rate of 29 mL min−1. The chromatographic conditions were as follows: injector temperature, 220 °C (split/splitless); detector temperature, 250 °C; and an oven temperature program initiating at 60 °C, followed by three sequenced temperature increases (i) at a rate of 20 K min−1 up to 100 °C, (ii) 5 K min−1 up to 140 °C and, finally, (iii) 40 K min−1 until 200 °C was reached. One milliliter of the supernatant of a liquid sample was diluted 1:3 in distilled water (final volume, 3 mL) and filled into a 20-mL glass vial. 500 μL of 42.5% phosphoric acid and 100 μl internal standard (2-ethylbutyric acid) were added to each vial. The vials were incubated for 35 min at 80 °C before injection.
Lactic acid was analyzed using a high performance liquid chromatograph (Shimadzu Corporation, Nakagyo-ku, Kyoto, Japan) equipped with a refractive index detector RID-6A and a Nukleogel ION 300 OA column with a pre-column (Macherey-Nagel GmbH & Co. KG, Düren, Germany). The oven temperature was 70 °C. Sulfuric acid (0.01 N) was used as the liquid phase at a flow rate of 0.6 mL min−1. Liquid samples of the percolate were centrifuged (10 min at 10,000·g and 10 °C), and the supernatant was filtered using syringe filter units with cellulose acetate membranes (0.2 μm in pore size) before measurement.
Milligascounters MGC-1 V3.0 (Ritter Apparatebau GmbH and Co., Bochum, Germany) were used for the determination of the volume of the hydrolysis gas produced during the batch process. The gas amounts were monitored every day. The hydrolysis gas produced during the last 5 days was collected in gastight bags (produced on-site using thermoplastic coated aluminum foil) and analyzed in duplicate regarding H2, N2 and CO2 at the end of the batch experiments. For the measurement, a HP 5890 Series II gas chromatograph (Hewlett Packard) equipped with a thermal conductivity detector and a Caboxen-1000 column (length, 4.57 m; inner diameter, 2.1 mm; Supelco, Sigma-Aldrich Corporation, MO, USA) was employed. Helium served as the carrier gas at a constant pressure of 105 kPa. The chromatographic conditions were as follows: detector temperature, 220 °C; injector temperature, 180 °C (split/splitless) and an oven temperature program starting with 5 min at 45 °C, followed by a temperature increase at a rate of 20 K min−1 up to 225 °C, and this temperature was then kept for 10.5 min. The gas sample was filled into a 280-μL loop by connecting the gas bags to the gas chromatograph before injection. All three gasses were detected in significant amounts. Since nitrogen was used as a cover gas in the reactor to ensure anoxic conditions and was not microbially produced during the process, the detected concentrations of hydrogen plus carbon dioxide were set to 100%.
Molecular community analysis
Total DNA was extracted from frozen cell pellets using a FastDNA® SPIN Kit for soil (MP Biomedicals LLC, Illkirch, France). DNA quantity and purity were determined photometrically using a NanoDrop® ND-1000 UV–vis spectral photometer (Thermo Fisher Scientific Inc., PA, USA) and by agarose gel electrophoresis. Bacterial 16 S rRNA gene fragments were polymerase chain reaction (PCR)-amplified using the primers 27 F and 1492R , and cloned as described previously . Screening of the clone library, partial sequencing of representative clones and sequence analysis were performed as described by Ziganshin et al. . The BLASTN tool [30, 31] was used to search for similar sequences in the GenBank database, and the RDP Classifier [32, 33] was used for taxonomic assignment. The determined 16 S rRNA gene sequences were deposited in the GenBank database under accession numbers JX099788-099852.
For community profiling using the T-RFLP, the forward primer 27 F was labeled at the 5′-end with 6-carboxyfluorescein (FAM). PCR products were purified using SureClean (Bioline GmbH, Luckenwalde, Germany) and quantified after gel electrophoresis using the GeneTools program (Syngene, Cambridge, UK). The purified PCR products were then digested with the restriction endonucleases Mse I or Msp I, respectively (New England Biolabs, MA, USA), using 10 U of the respective enzyme for digesting 10 ng PCR product. The samples were incubated at 37 °C overnight and then precipitated with 0.1 volumes of 3 M sodium acetate (pH 5.5) and 2.5 volumes of absolute ethanol. The dried DNA samples were resuspended in 20 μL HiDi formamide (Applied Biosystems, Life Technologies Corporation, CA, USA) containing 1.5% (v/v) MapMarker® 1000 (Eurogentec S.A., Seraing, Belgium) labeled with 5-carboxy-X-rhodamine. The samples were denatured at 95 °C for 5 min and chilled on ice. The fragments were separated by means of capillary electrophoresis on an ABI PRISM 3130xl Genetic Analyzer (Applied Biosystems). The lengths of the fluorescent terminal restriction fragments (T-RFs) were determined using the GeneMapper V3.7 software (Applied Biosystems). The fluorescence signals of T-RFs in the range of 50 to 1,000 bp were extracted. Noise removal, peak binning to account for inter-run differences in T-RF size and normalization of signal intensity were performed using an R script (R version 2.12.2; ) according to . The relative peak areas were determined by dividing the individual T-RF area by the total area of peaks within the range of 50 to 1,000 bp. The theoretical T-RF values of the representative phylotypes represented in the clone library were calculated using the NEB cutter  and confirmed experimentally by T-RFLP analysis using the corresponding clones as templates. The relative T-RF abundances of representative phylotypes were determined based on the relative peak areas of the corresponding T-RF.
A multivariate statistical analysis of the normalized sample-peak tables was performed by means of the R package ‘vegan’ . Non-metric multidimensional scaling (NMDS) analyses applying the Bray-Curtis similarity index (regarding the presence and relative abundance of T-RFs) were used to plot the rank order of similarity of T-RFLP profiles in a way that allows distances to be exactly expressed on a two-dimensional sheet (greater distances represent greater dissimilarities). The major process parameters correlating with the community composition as well as with single T-RFs were fitted using the ‘envfit’ algorithm provided with the ‘vegan’ package. The significance of single process parameters for the NMDS results was tested by means of a Monte Carlo test with 1,000 permutations.
Results and discussion
The anaerobic digestion of maize silage in a solid-state fermentation reactor with percolation was monitored for 8 days. In the following, the results of column A are shown, whereas the results of the replicate batch process (column B) are presented as additional files. The results of partial sequencing of cloned 16 S rRNA amplicons and the corresponding T-RF values are listed in Additional file 1.
During the first 2 days of fermentation (phase 1), acetic and lactic acids were found to be the main constituents of the percolate. Both substances originated from the respective substrate in considerable concentrations (lactic acid, about 5.3 g L−1; acetic acid, about 1.2 g L−1; both substance concentrations measured in the percolate). These organic acids are typical products of the ensilage procedure. In phase 1, the characteristic fermentation processes of the ensiling continued as both substances increased in their concentrations. Bacterial communities catalyzing the ensilage process are expected to be predominated by lactic acid-producing bacteria. Accordingly, phylotypes affiliated to the genus Lactobacillus were detected at the beginning of the acidogenic fermentation (Figure 3b, day 0). Lactobacilli produce lactic acid as the major fermentation product from sugars . They belong to the Firmicutes and have a high acid tolerance, surviving pH values of 5 and lower. Therefore, they have a selective advantage over the other more acid-sensitive bacteria. As presented in Figure 4, the transition phase during the first day of fermentation (from inoculation to day 1) was characterized by a significant correlation of the community composition with the lactic acid concentration and the occurrence of several Lactobacillus spp. represented by the T-RFs 179, 497, 571 and 579. During the acidogenic fermentation, the bacteria continued the ensilage by producing a slightly higher concentration of both lactic and acetic acids during the first 2 days of fermentation (Figure 3a). Concomitantly, the community composition changed to the dominance of other Lactobacillus phylotypes, favored by the current fermentation conditions and members of the genus Acetobacter until day 2 (Figure 3b). Acetobacter species are Alphaproteobacteria forming acetic acid under aerobic conditions, indicating that oxygen was still present in the system. Despite becoming overgrown by other bacteria, both the Lactobacillus and the Acetobacter related phylotypes remained present in minor proportions during the entire experimental time. This might be explained by the fact that the community composition was analyzed based on DNA, which does not necessarily reflect the actual activity of the organisms. However, based on the community shifts and the increase of other community members, the strong community dynamics became obvious.
After phase 1 during the 1st interphase, the metabolic performance of the system changed. Lactic and acetic acids were no longer produced but consumed in the 1st interphase and at the beginning of phase 2, whereas, simultaneously, butyric acid and hydrolysis gas were produced at a high rate (Figure 3a). As soon as the lactic acid was depleted, production rates of gas and butyric acid decreased drastically, pointing to a direct correlation of lactic acid degradation and butyric acid production. The production of acetic acid started again during phase 2, and the concentration of caproic acid increased slowly. The altered community composition reflected these metabolic shifts between phases 1 and 2 (Figure 3b). After day 3, the Lactobacillus and Acetobacter strains were gradually replaced by phylotypes affiliated to the genus Clostridium. The clostridial phylotype with the T-RF 518, which emerged on day 2, became the dominant community member on days 3 and 4. The clostridia are strict anaerobes and represent one of the most prevalent bacterial groups in biogas reactors. C. thermocellum and C. stercorarium were identified as the major players in the hydrolysis of plant biomass , whereas C. thermopalmarium was found to be the main butyric acid producer in a wastewater treatment system . The clostridia represent the majority of the light-independent fermentative bacteria which have the ability to produce hydrogen .
In the 2nd interphase between phases 2 and 3, the formation of fermentation products accelerated. Hydrolysis gas as well as acetic and caproic acids were produced, whereas the concentration of butyric acid increased only marginally (Figure 3a). During phase 3, this metabolic behavior continued as reflected by significantly increased concentrations of acetic and caproic acids, accompanied by a comparably high gas production rate of up to 1.5 L d−1. However, butyric acid production decreased slowly. On day 6, lactic acid was produced again in minor amounts but degraded during the following day, reflecting the ongoing dynamics of the fermentation process. The community composition on day 6 was most significantly correlated with gas production and formation of iso-valeric acid, whereas on day 7, a significant correlation with iso- butyric and n- valeric acid concentrations was visible (Figure 4). During the 2nd interphase, the Clostridium strains represented by the T-RFs 518 and 520 were overgrown by phylotypes affiliated to the Ruminococcaceae and Lachnospiraceae (Figure 3b). As shown in Figure 4, the decisive phylotype correlated with day 6 was T-RF 280 which represents a member of the Ruminococcaceae. The Ruminococcaceae and Lachnospiraceae belong to the order Clostridiales. The Ruminococcaceae can hydrolyze a variety of polysaccharides by different mechanisms, e.g., the production of a cellulosome enzyme complex and cellulose adhesion proteins . Moreover, they are able to ferment hexoses as well as pentoses. The production of hydrogen by Ruminococcus albus from sweet sorghum was reported by Ntaikou et al. . Various genera of Lachnospiraceae are known to produce large amounts of n-butyric acid, acetic acid and carbon dioxide through the fermentation of carbohydrates .
At the end of the acidogenic batch fermentation, a VFA concentration of 11.24 g L−1 was achieved, consisting of 3.34 g L−1 acetic acid, 0.28 g L−1 propionic acid, 0.36 g L−1iso-butyric acid, 3.98 g L−1n-butyric acid, 0.11 g L−1iso-valeric acid, 0.24 g L−1n-valeric acid, 2.77 g L−1 caproic acid, and 0.18 g L−1 lactic acid. In total, 4.37 L hydrolysis gas composed of 35.2% hydrogen and 68.8% carbon dioxide was produced.
Extended Weende forage analysis of maize silage and solid digestate after 8 days of acidogenic fermentation
Degree of conversion (%)
Fresh mass (g)
TS (%fresh mass)
VS (%fresh mass)
Crude protein (g/kgTS)
Crude lipid (g/kgTS)
NfE, including starch (g/kgTS)
A mixture of acetic, n-butyric, caproic and lactic acids developed as metabolites which are characteristic of clostridial fermentation. Propionic, iso-butyric and n-valeric acids were produced only in minor amounts. This result indicates that butyric-type fermentation was dominant, whereas propionic-type fermentation characterized by the production of propionic, acetic and some valeric acids without a significant gas production  was marginal. Lactic acid was observed to be an intermediate fermentation product as it was firstly produced and subsequently metabolized during the process. This type of fermentation is certainly a characteristic of the digestion of silages, as active lactic acid producing bacterial strains are inoculated in a considerable amount along with the substrate. However, the appearance of lactic acid was also observed with other carbohydrate-rich substrates  and garbage .
The performance of the acidogenic fermentation strongly depends on the process conditions. Contradictory results were reported regarding the effect of the pH on the product composition, which was shown to be negligible in the range of 5 to 7 [38, 51], while other researchers detected a pronounced influence [15, 16, 26, 52, 53]. Veeken et al.  observed that the hydrolysis rate during the anaerobic digestion of organic solid waste was not related to the total or undissociated VFA concentrations but was found to be pH dependent. The cellulase system of C. thermocellum works with a smaller hydrolysis rate at pH values below 6.5 . Most of the studies were carried out using wastewater treatment systems. Therefore, little is known about the pH impact on the acidogenic fermentation of energy crops and the molecular mechanisms of pH effects. Evidently, different pH optima do not exist for metabolic pathways but for the microorganisms which carry out these reactions. They do not only catalyze the desired fermentations but also grow by increasing cell size and performing cell divisions at a species-specific rate. The composition of an operating bacterial community is determined by the composition of the inoculum. Depending on the environmental conditions and the distinct sensitivities of the persisting bacteria, the community will develop.
The composition of the bacterial products of the acidogenic fermentation determines the rates and performance of the subsequent metabolic steps, i.e., acetogenesis and methanogenesis. Acetic acid can directly be used by the acetoclastic methanogens for biogas production. In single-stage biogas processes, all metabolic steps occur in one reactor simultaneously. Organic acids are detected as intermediate products only in minor amounts, and the accumulation of VFA and the lowering of the pH are known to lead to the suppression of the methanogenic activity and to a process failure in single-stage reactors. Two-stage processes are characterized by separated hydrolysis/acidogenesis and acetogenesis/methanogenesis . Numerous advantages of two-stage processes over the conventional biogas production have been described [55, 56]. These include increased process stability, control and efficiency, as well as a high tolerance to overloading. In two-stage processes, the production of bio-products (VFA or lactic acid) for industrial use and biogas for covering energy demands can be combined [49, 53, 57]. In such systems and other reactors with separate hydrolysis, e.g., plug-flow reactors, the control of the acidogenic reactions is of special interest, as different metabolite compositions lead to a different methanogenic performance. For example, the rate of butyric acid conversion has been found to be higher than that of the other VFA . Propionic acid degradation is largely inhibited during periods of high activity of the butyric acid-converting bacteria, whereas acetic acid exerts a weaker influence on the conversion of propionic acid . However, high activity single-stage fermenters are commonly used in the biogas industry. In these full-scale reactors, high-performance hydrolysis and optimal methanogenesis do not exclude each other when running in parallel within one reactor. Nevertheless, further research could help in the engineering of the first phase with the objective of obtaining desirable fermentation products and enhanced biogas production rates.
Batch acidogenic fermentation of maize silage occurs in three metabolic phases characterized by the production of distinct primary fermentation products and correlating with the respective bacterial key players. The clostridial butyric-type fermentation predominates, whereas the propionic-type fermentation is marginal. The composition of the inoculum seems to influence the performance of the hydrolysis and acidogenesis steps. Further studies should reveal the metabolic dynamics and community composition when using both a continuous fermentation regime and solid substrates other than maize silage.
As the metabolite composition of the acidogenesis affects the subsequent methanogenic performance, process control and optimization should focus on the first two phases, i.e., hydrolysis and acidogenesis of the biogas production when solid substrates are digested. Especially in plug-flow digesters or digesters with a separated hydrolysis (two-stage systems), the control of the acidogenic reactions is important. More detailed analyses of the hydrolysis and acidogenesis steps in solid-state fermentation are needed for the efficient exploitation of more sustainable feedstocks such as straw or energy crops other than maize.
This publication is dedicated to Prof. Wolfgang Babel on the occasion of his 75th birthday.
non-metric multidimensional scaling
polymerase chain reaction
total Kjeldahl nitrogen content
terminal restriction fragment
terminal restriction fragment length polymorphism
volatile fatty acids
This work was supported by the Initiative and Networking Fund of the Helmholtz Association. We would like to thank our collaboration partners from the Department of Biochemical Conversion of the Deutsches Biomasseforschungszentrum (DBFZ) for contributing to the analytical measurements. Furthermore, we would like to thank Ute Lohse for her technical assistance with molecular analyses.
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