Rice straw anaerobic digestion enhancement through bioaugmentation: effect on the microbial community

doi:10.21203/rs.3.rs-2989206/v1

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Rice straw anaerobic digestion enhancement through bioaugmentation: effect on the microbial community

https://doi.org/10.21203/rs.3.rs-2989206/v1

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published 04 Oct, 2023

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The degradation of agricultural residues by anaerobic digestion and their bioconversion to methane is still hampered by the search for pretreatment strategies due to the lignocellulosic content that limits the efficiency of the process. Adding an enriched microbial consortium could be an alternative for the biological treatment of lignocellulosic biomass. During the degradation process, it is necessary to study the dynamics and structure of the microbial community. The objective of this study was to evaluate the addition of an enriched microbial consortium, and its effect on the methane-producing prokaryotic community during the anaerobic digestion of rice straw. The consortium was characterized by diversity, microbial community dynamics, and taxonomic identification. The rice straw anaerobic digestion was bioaugmented using the microbial consortium in 10 L semi-continuous stirred tank reactors (35 ± 2°C) for 70 days of operation at increasing organic loading rates up to 1.8 g VS L^-1 d^-1. Relative to the control reactor, higher and more stable methane production was obtained with the biological treatment strategy. The metagenomic method allowed identification down to the genus and species level of microbial consortium and the prokaryotic community within the reactors. From the knowledge of the diversity and dynamics of the microbial community, possible preferential metabolic pathways were presumed. The enhanced anaerobic degradation of rice straw by the microbial consortium and its effect on the methane-producing microbial community demonstrated that it could be used as a bioproduct for the treatment of agricultural waste for energy purposes.

methane

enriched microbial consortium

biological treatment

lignocellulosic biomass

metagenomic

The energy use of agricultural wastes continues to be an underexplored field, despite its known potential. These resources stand out for their abundance, low cost, and relatively high yield [1, 2]. The unsuitable disposal of these residues causes serious environmental and safety issues. Rice straw is an agricultural waste of special interest in the Cuban context due to its high production volume and energy content [3–5]. The cell wall of rice straw is composed mostly of cellulose and hemicellulose (50–80% of dry matter). Lignin binds to both cellulose and hemicellulose, and provides mechanical strength and chemical resistance to the cell wall. Lignin is however, the main recalcitrant factor of lignocellulosic biomass and constitutes 10–15% of its dry matter [6, 7].

Anaerobic digestion (AD) is a complex biological process in which organic matter is degraded in the absence of oxygen by microbial action. It allows the energy use of the biogas [8] and also provides a stabilized effluent with biofertilizer properties [9]. AD is a promising technology to obtain bioenergy from agricultural residues. However the process is complex due to the structure of this type of lignocellulosic biomass [1, 10–12].

Many types of pretreatments have been described to accelerate the hydrolysis of lignocellulosic biomass (chemical, physical and biological) [13, 14]. All have their advantages and limitations. Biological pretreatments are interesting, and although they require long periods of treatment, simulate what naturally occurs during the degradation process of organic matter, they are simple and consume low amounts of energy and chemicals [15, 16].

Among the biological treatment strategies, the use of enzymes or organic additives (biostimulation) and the addition of ligninolytic bacteria, fungi or microbial consortia (bioaugmentation) have been reported to improve the biodegradation of different agricultural wastes [17–19]. Enzymatic treatment is more expensive than the addition of microorganisms to the degradation process [20]. Some authors state that the use of microbial consortia has certain advantages over the use of single bacterial/fungal species [21–23], because they are a diverse mixture of microorganisms with a high adaptability [24, 25].

Therefore, the use of a microbial consortium as a pre-treatment or bioaugmentation strategy that strengthens the lignocellulolytic activity of the prokaryotic community, could improve the degradation of rice straw and thus the efficiency of bioconversion to methane. Considering also that complex microbial populations are involved in this process and that each of them requires different environmental and nutritional conditions for their growth [26], the characterization of the species involved and their dynamics is essential [16, 27]. To address this, the objective of this study was to evaluate the addition of a microbial consortium, and its effect on the methane-producing prokaryotic community during the anaerobic digestion of rice straw.

Microbial consortium: culture and enrichment

The microbial consortium was obtained from the mixture of efficient microorganisms (EM16), rumen fluid microorganisms, and microorganisms isolated from soils covered with agricultural residues (sugarcane straw, tobacco straw, and bamboo fields). The methodology proposed by Carabeo et al. [28] to prepare EM16 and isolate microbial strains able to degrade lignocellulosic substrates was followed. To obtain an enriched consortium, the isolated microorganisms described above were mixed in three reactors (2L). The culture was kept under micro-aeration conditions for 7 days at 30ºC. Samples of 1.5 ml from these reactors were taken after the incubation period and stored, in triplicate, at -20ºC for molecular analyses.

Rice straw anaerobic digestion: experimental set and operational monitoring parameters

Anaerobic digestion assays were conducted in a semi-continuous regime (in addition-extraction cycles), using two 10 L stirred tank reactors (CSTR), fed with rice straw. The reactors were initially filled with the inoculum up to 6 L. The inoculum was adapted for 6 months to mesophilic conditions (35 ± 2ºC) and after that, the effective volume of each reactor (9 L) was topped up with diluted swine manure (1:3) up to 1-1.5% VS to balance the C/N ratio. One reactor was used as a control, which was fed with rice straw and diluted swine manure (1:3). The second reactor was fed with the same substrates, but bioaugmented with the diluted microbial consortium (10^− 2). At start-up, the initial organic loading rate was 0.5 gVSL^-1d^-1, which was increased every 8 days up to 1.8 gVSL^-1d^-1 for a total of 70 reactor working days.

The methane production was monitored daily by liquid displacement method using a 2.5% NaOH solution, considering atmospheric pressure and temperature according to German Standard Methods [30]. Physicochemical analyses of the initial conditions of the reactors and during anaerobic digestion were performed according to the 23rd Standard Methods for the Examination of Water and Wastewater [29]. Samples of 1.5 mL from each reactor were taken in triplicate at the start point, after 15 days, and at 30 days of the experiment. All samples were stored at -20ºC until used for DNA extraction and subsequent molecular analyses.

Substrate and anaerobic sludge source: physico-chemical analises

Rice straw (rice plant harvest residue) was randomly collected at the "Sur del Jíbaro" Agro-industrial Rice Comapny located in Sancti Spíritus province, Cuba, and it was physically pre-treated by size reduction into 5 cm pieces. Anaerobic sludge from a farmer's biogas plant that treats swine manure was used as inoculum for the anaerobic reactors. Swine manure dilution was colected and parepared from the same farm, and then used as a buffer to equilibrate C/N ratio during rice straw anaerobic digestion.

Rice straw, swine manure and the anaerobic sludge were characterized considering the physicochemical parameters: total solids (TS), volatile solids (VS), ash, pH, alkalinity, and ammonium nitrogen (NH₄⁺-N), according to the 23rd Standard Methods for the Examination of Water and Wastewater [29]. The alkalinity ratio was determined as the ratio of partial and total alkalinity according to the German Standard Methods [30].

DNA extraction and amplification of bacterial and archaeal 16S rDNA gene sequences

Genomic DNA was extracted from each sample (triplicate aliquots of 200 mg) using the FastDNA® SPIN Kit for Soil and the FastPrep® Instrument (MP Biomedicals, USA) according to the manufacturer’s guidelines. The quantity and purity of DNA were determined photometrically using a NanoDrop 2000/2000c (Thermo Scientific, USA) according to the manufacturer’s guidelines. Isolated DNA was stored at -20°C until further processing.

Genes encoding for bacterial 16S rRNA (rrs) were amplified using the PCR primers 27f (5’-AGAGTTTGATCMTGGCTCAG-3’) [31, 32] and 1492r (5’- TACGGYTACCTTGTTACGACTT-3’) [33, 34]. The PCR reaction mix contained 1X Taq buffer, 2 mM MgCl₂, 0.2 mM of each deoxynucleoside triphosphate, 0.4 µM of each primer, 1 µl (10 ng approximately) of template DNA and 1 U of Taq DNA polymerase for a final volume of 25 µl. The amplification was performed with an initial denaturation step for 1 min at 94°C, followed by 34 cycles of denaturation at 94°C for 1 min, annealing at 57°C for 1 min, extension at 72°C for 2 min and a final extension stage at 72°C for 3 min.

The correct length of the amplicons was verified by 1% agarose gel electrophoresis, using the GeneRuler® 1kb Plus DNA Ladder (Thermo Scientific, USA). To minimize the risk of any PCR bias, three parallel PCR reactions were performed for each of the three parallel DNA extracts obtained from each environmental sample. The concentration of the amplified 16S rRNA gene fragments was measured using the Qubit4® fluorometer (Invitrogen, Thermo Scientific, USA).

Real time PCR analysis

Real-time PCR (qPCR) assays were performed to amplify archaeal genes in reactor samples. A methodology previously proposed for similar anaerobic systems was followed [35]. The 16S rRNA gene copy numbers of methanogenic archaea of the orders Methanobacteriales, Methanosarcinales, and Methanomicrobiales, as well as of the family Methanosaetaceae, were quantified. Three replicates were used per DNA sample analyzed. The quantification of gene expression was performed by taking the Ct values shown by the sequencer (inversely proportional to the amount of DNA in the sample).

Bacterial 16S RNAr gene sequencing by Ion Torrent

To amplify the 16S hypervariable regions, the Ion 16S® Metagenomics Kit (Life Technologies; Thermo Scientific) was used, on the Ion Torrent Personal Genome Machine® (PGM, Ion Torrent) platform, following the manufacturer guidelines. The remaining PCR reaction was purified using the PureLink PCR Purification Kit (Life Technologies; Thermo Scientific), and the concentration of the purified amplicons was assessed with the Qubit4 fluorometer and Qubit® dsDNA High Sensitivity Assay Kit (Life Technologies; Thermo Scientific). The DNA library construction, quantification, template preparation and sequencing were developed according to the standard protocol, well-described by Adamiak et al. [36]. Metagenomic data was evaluated using the Ion Reporter® software (Invitrogen, Thermo Scientific, USA), showing the taxonomic distribution of the target microbial community, based on the software database.

Analysis of the diversity and organization of the microbial communities

The Bacteria richness (Rr) considering more than 1% of abundance was calculated by the methodology proposed by Marzorati et al. [37], based on the number of OTUs obtained in each electropherogram or cluster, respectively. The Shannon-Weaver diversity index (H) [38] was used to evaluate and compare this diversity. The evenness index (Pielou index, H/Hmax) of the microbial community in each sample was calculated as the quotient of the Shannon index and the potential number of species in the sample (Hmax). This index describes the uniformity of the distribution of individuals in the community [39].

Substrate characterization

The characteristics of the substrates rice straw and swine manure, are depicted in detail in Table 1 according to the physicochemical parameters evaluated. It was found that the percentage of TS in rice straw was higher than that of swine manure, making it a good carbon source for the digestion of lignocellulosic biomass [12]. The opposite occured with ammonium nitrogen where there was a higher contribution from swine manure, which is an essential nutrient for microorganisms. Therefore, the co-digestion of these substrates could effectively contribute to a better balance in the total C/N ratio, in addition to the contribution of micro and macronutrients, mainly K⁺, Na⁺, Ca²⁺, and Fe²⁺. Nitrogen deficiency could lead to failure of anaerobic digestion, but at high concentrations (> 3.5 g L^− 1) can also lead to inhibition of methanogenic archaeal species [40]. The alkalinity ratio and pH values are in the range previously reported for swine manure [41, 42].

Table 1

Substrate characteristics before anaerobic digestion. Standard deviation in brackets (*n = 3*)
Parameter	Swine manure	Rice straw
^*TS (%)	1.30 (0.06)	89.95 (0.22)
^*VS (% TS)	74.08 (0.80)	80.55 (0.51)
Ammonium Nitrogen (g·L^− 1)	2.07 (0.02)	0.08 (0.03)
Total alkalinity (g CaCO₃·L^− 1)	3.1 (0.15)	n/d^*
Alkalinity ratio (α)	0.53 (0.04)	n/d
pH	7.62 (0.06)	n/d
C/N ratio	1:19	48:1
^*TS: total solids, VS: volatile solids, n/d = not determined

The results shown in Table 1 are consistent with previous studies, that worked with the same substrates [4, 5, 40, 43]. The characteristics of the rice straw substrate, in terms of VS content, demonstrate the importance of the use of this biomass for energy purposes, despite its C/N ratio (48:1) being a little high. Furthermore, swine manure is considered a good substrate for anaerobic digestion, not only due to the variety of nutrients but also because of the high buffering capacity, which favors both bacterial and methanogenic trophic groups [44, 45]. Its combination with rice straw, creates a favorable balance for the C/N ratio, and allows the retention of the buffering capacity since the degradation of the straw can lead to the accumulation of volatile organic acids.

Evaluation of anaerobic digestion of rice straw

At start-up, the reactors showed similar methane yield values (without significant differences) during the first 10 days of the experimental stage (Fig. 1). Subsequently, a slight increase in methane yield values was observed in the bioaugmented reactor compared to the control, with increases on average of 50 units of normalized liters of methane per kg of volatile solid added. After 55 days, starting at an organic load of 1.24 gVSL^− 1d^− 1, this difference became noticeable.

A greater instability was observed in the control reactor decreasing the methane yield, probably due to the effects of the low degradability of the rice straw, influenced by the steric effect of non-degraded straw. In contrast, in the bioaugmented reactor, the methane production increased, obtaining values that doubled those obtained in the control reactor and increasing by 40% (v/v) the methane yield. The statistical analysis with the nonparametric test of multiple comparisons of related samples Mann Whitney (p < 0.05) corroborated these results, where significant differences existed between the control and both treatments, but not when the comparison was made between treatments.

The increase in methane yield obtained from the bioaugmentation strategy demonstrated that this could be a promising method to increase the efficiency of lignocellulosic biomass degradation. These results are in agreement with studies related to the impact of bioaugmentation as a bioremediation technique to increase methane potentials from cellulolytic wastes [18, 46, 47] and specifically with the use of microbial consortia to optimize bioethanol and methane production [23, 48–51].

During the degradation process, the pH ranged between 5.6 and 6.4 in both reactors. This may have been due to the addition of the lignocellulosic substrate rice straw which has a high C/N ratio and can accumulate high levels of volatile fatty acids (VFAs), resulting in a low pH [52]. However, Sun et al, [53] showed that pH might not be reliable enough as a monitoring indicator if the AD process is designed to treat high-strength organic waste, such as manure with much higher buffer capacity. With the increase in volumetric organic load, ammonium nitrogen levels also increased (Fig. 2) due to the addition of swine manure, showing a jump from weeks 9 and 10 (50–65 days of operation) in both reactors, being even higher in the bioaugmented reactor. Despite this, the highest methane yields were obtained in the bioaugmented reactor at this time, which is evidence that the high buffering capacity and the high concentrations of NH₄⁺-N did not greatly affect the methanogenic archaea, although at higher levels (> 2.6 g L^− 1) it can be inhibitory [40, 54]. Another indicator was the ratio between partial and total alkalinity (α), which should range between 0.5 and 0.9 [9]; in this case, values between 0.63 and 0.88 were obtained, which demonstrated the stability of the anaerobic process. The ratio of volatile fatty acids to total alkalinity (VFAs/TA) decreased during reactor start-up and remained in the optimal range for this parameter (0.1–0.35) [55] from weeks 3 to 10 (Fig. 2). After this time, VFAs/TA continued to decrease, which was closely related to alkaline pH values and reflected the acid-base balance of the system [56]. Some authors suggest that it may also be influenced by overloading conditions [9, 53, 57] or the effect of high ammonium concentrations that have their effect with accumulated VFAs, which would cause a decrease in TA. Alkalinity due to bicarbonates does not undergo any such change [56].

Molecular characterization of microbial consortium used

The taxonomic distribution up to the species level from the Bacterial Domain was detected in the microbial consortium through the metagenomic study. According to the consulted literature, there are few studies reporting similar metagenomic analyses in microbial consortia [58–61]. The Phyla Proteobacteria (71.8%), Firmicutes (21.55%), and Bacteroidetes (6.59%) predominated (Fig. 3). The genera Pseudomonas (17.74%), Lactobacillus (17.65%), Azotobacter (13.16%), and Arcobacter (8.15%), were identified with an abundance greater than 1%. Species of the genera Clostridium, Alcaligenes, Novispirillum, Bacteroides, Parabacteroides, Desulfotomaculum, Sedimentibacter, Dechlorospirillum, Aeromonas, Lysinibacillus and Acinetobacter were also identified, but in lower abundance. Of the total consortium community, 7% of species were unidentified, indicating that they were probably species that have not been previously detected in this type of sample by the Ion Torrent technique.

The metagenomic analysis of the microbial consortium is in agreement with the indices used to study the structure and organization of microbial community. A richness of 10 species was identified in the consortium related to the diversity according to Shannon's index (1.97), however, they had an equitable distribution in the community expressed in Pielou's equity index (0.85) since the value is close to 1.

Some of the genera identified in the microbial consortium (Lactobacillus, Lysinibacillus, Clostridium, Ruminococcus, Desulfotomaculum, Sedimentibacter, Pseudomonas, Acinetobacter, Arcobacter, Dechlorospirillum) match with those identified in a previous study of molecular characterization of efficient microorganisms (EM16) and microorganisms isolated from soil with agricultural residues [28], which were used for the development of this microbial consortium. In the previous study, the main metabolic pathways of the most abundant species were described. The presence of hydrolytic and cellulolytic species in the consortium demonstrates that it could positively influence the biodegradability of agricultural residues.

Effect of bioaugmentation on the microbial community dynamics in the reactors

Bacterial dynamics and diversity were analyzed in the reactors at different intervals (initial, 30 and 60 days). More than 95% of the community was identified to the species level through the Ion Torrent metagenomic technique. Of special interest for the analysis of the community in the reactors are those that are represented with a relative abundance higher than 1%, especially those predominant at 60 days, since they turned out to be the most stable in time and those that support the greatest methane formation.

In the control reactor, 31 operational taxonomic units (OTUs) with a relative abundance greater than 1% were found in the initial sample. At 30 and 60 days, 29 and 20 OTUs were obtained, respectively. This indicates that the number of dominant species decreased during reactor operation. The diversity determined using the Shannon index (H) was 3.30 at the initial time, and decreased as the days of operation passed to values of 2.76 at 60 days. The Pielou's index (H/Hmax) in this reactor showed values of 0.96 from the initial moment to 0.92 at 60 days, which elucidated the evenness of the community, which was close to 1.

According to Jaccard's similarity index (J'), 46% of the species found at the initial time were also detected at 30 days, but of these, only 31% were detected at 60 days in the control reactor. A comparison of the samples at 30 and 60 days showed that 63% of the species were similar. Of the microbial groups identified with the highest abundance in this reactor (> 1%), 12 were detected at all three sampling times. However, at 30 days, 10 families were detected that were not detected at the initial time, and at 60 days only 1 family was identified that was not detected either at the initial time or at 30 days. These microbial dynamics suggest adaptation and specialization of the anaerobic sludge as the organic load of the reactor increased over time.

In the bioaugmented reactor, 27 OTUs with a relative abundance above 1% were found in the initial sample. At 30 and 60 days, 30 and 21 OTUs were obtained, respectively. In this reactor at 30 days, the diversity of species increased slightly with respect to the initial sample, but as in the control reactor, the diversity decreased during reactor operating days. According to Shannon's index, this reactor showed values of 3.14 at the initial moment to 2.80 at 60 days, and the Pielou evenness index value of 0.95 from the initial moment to 0.94 at 60 days, which was also close to 1.

According to Jaccard's similarity index (J'), 63% of the species found at the initial time were also detected at 30 days, but of these only 27% were detected at 60 days in the bioaugmented reactor. Comparing the samples at 30 and 60 days, it can be said that 32% of the species were similar (lower similarity index than in the control reactor), which may have been because in this reactor the microbial community was exposed to more significant variability due to the addition of the consortium. Of the microbial groups identified with the highest abundance in this reactor (> 1%), 10 were detected at all three sampling times. However, at 30 days, 7 were detected that were not detected at the initial time and at 60 days, and 4 were detected that were not with a relative abundance greater than 1% at the initial time or at 30 days. It is possible that certain species found initially in the inoculum and substrates may not have been favored by increases in organic load and others that were better adapted to this substrate may have benefited. As in the control reactor, these microbial dynamics suggest a better specialization of the community.

Different microbial populations existed in each of the stages of anaerobic digestion, each with specific metabolic pathways. As observed in the results, there were also differences in microbial diversity and dynamics between different reactor operation times. Therefore, it can be stated that there was a close correlation between the operating time and the microbial diversity of the reactors. In the bioaugmented reactor, with the specialization of the community, the diversity of species decreased, but the abundance of those capable of surviving and degrading these lignocellulosic substrates increased, which had a positive effect on methane production, corroborating the higher yield obtained in the bioaugmented reactor to the control. Similar results have been obtained by other authors [62–65].

Analysis of the bacterial community in the reactors

In the control reactor at the initial time, the following families were detected in the highest abundance: Clostridiaceae (genus Clostridium, 5.72%, Clostridium butyricum, 1.98%, Clostridium sp., 1.08%), Chlorobiaceae (genus Chlorobium, 5.49%), Syntrophaceae (4.13%), Ignavibacteriaceae (3.97%), Synergistaceae (3.73%), Dehalococcoidaceae (3.14%), Prolixibacteraceae (3.12%), Erysipelotrichaceae (Turicibacter sanguinis, 2.87%), Planctomycetaceae (2.67%), Syntrophorhabdaceae (genus Syntrophorhabdus, 2.53%), Marinilabiliaceae (2.51%), Geobacteraceae (2.43%), Cytophagaceae (2.42%), Eubacteriaceae (2.25%), and Anaerolineaceae (2.04%) (Fig. 4).

In the sample taken at 30 days the families Prolixibacteraceae (4.6%), Porphyromonadaceae (Parabacteroides chartae, 4.39%), Synergistaceae (3.61%), Clostridiaceae (genus Clostridium 3, 25% and Clostridium butyricum, 1.43%), Cytophagaceae (3.39%), Dehalococcoidaceae (2.61%), Syntrophaceae (2.55%), Marinilabiliaceae (2.44%), Flavobacteriaceae (2.34%) and Eubacteriaceae (2.08%) were found. In addition, others that were not detected in the initial sample were found, such as Porphyromonadaceae (2.78%), Ruminococcaceae (2.57%), Moraxellaceae (genus Acinetobacter, 2.37%), Oxalobacteraceae (genus Undibacterium, 2.09), Bacteroidaceae (Bacteroides sp., 1.06% and Bacteroides graminisolvens, 1.48%), Paenibacillaceae (1.06%) and Prevotellaceae (1.02%) (Fig. 4).

At 60 days in the control reactor the families Clostridiaceae (8.7%), Ruminococcaceae (6.62%) Prolixibacteraceae (5.84%), Moraxellaceae (genus Acinetobacter, 5.73%), Synergistaceae (3.99%), Cytophagaceae (3.56%) Prevotellaceae (3, 29%), Porphyromonadaceae (3.28%), Marinilabiliaceae (2.52%), Porphyromonadaceae (Parabacteroides chartae, 2.4%), Flavobacteriaceae (2.06%), Dehalococcoidaceae (1.59%) and the previously undetected family Nocardioidaceae (1.31%) were found (Fig. 4).

In each sample evaluated from the control reactor there was a percentage of species that were not detected by the Ion Torrent metagenomic technique, which increased with time (1%, 3% and 5%, at the beginning, 30 and 60 days respectively), indicating that they are probably sequences not previously reported in the NCBI database, which inhabit this type of ecosystems.

In the bioaugmented reactor, at start up, the Clostridiaceae family was detected in highest abundance (17.25%). Other families in high proportion included; Erysipelotrichaceae 11.41% (genus Turicibacter, 5.34%, and Turicibacter sanguinis 6.07%); Ignavibacteriaceae (7.71%); Peptostreptococcaceae (6.27%); Syntrophorhabdaceae (6.21%) (genus Syntrophorhabdus, 3.41%); Syntrophaceae (4.55%); Synergistaceae (3.93%); Marinilabiliaceae (3.86%); Cytophagaceae (3.56%); Eubacteriaceae (3.17%); Geobacteraceae (3.06%), Dehalococcoidaceae (2.35%) and Prolixibacteraceae (2.32%) (Fig. 5). Among these families identified at the initial time it is interesting that some of them were also identified in the microbial consortium, such as: Porphyromonadaceae, Prevotellaceae, Cytophagaceae, Flavobacteriaceae, Sphingobacteriaceae (phylum Bacteroidetes), Bacillaceae, Clostridiaceae, Eubacteriaceae, Lachnospiraceae, Peptococcaceae, Peptostreptococcaceae (phylum Firmicutes), and Alcaligenaceae (phylum Proteobacteria).

At 30 days in the bioaugmented bioreactor, the Clostridiaceae families predominated with 16.04% (genus Clostridium, 5.45%, Clostridium butyricum, 3.82%, Clostridium disporicum, 1.09%, Clostridium sp., 1.49%), Erysipelotrichaceae 9.89% (genus Turicibacter, 4.19%, and Turicibacter sanguinis, 4.75%), Prolixibacteraceae (8.88%), Syntrophorhabdaceae 5.3% (genus Syntrophorhabdus, 2.89%), Porphyromonadaceae (4.91%), Bacteroidaceae 4, 13% (Bacteroides graminisolvens, 1.01%), Peptostreptococcaceae (3.87%), Cytophagaceae (3.25%), Ignavibacteriaceae (2.98%), Ruminocococcaceae (2.83%), Marinilabiliaceae (2.59%), Synergistaceae (2.57%), Flavobacteriaceae (2.33%) and Eubacteriaceae (2.23%) (Fig. 5). The bacterial community at 30 days, had change in composition from the sample taken at the beginning of the experiment. Species that probably came from the microbial consortium were favored, increasing their abundance, such as the families Bacteroidaceae, Porphyromonadaceae, Prevotellaceae, Flavobacteriaceae, Bacillaceae, Paenibacillaceae, Ruminococcaceae, Rhodospirillaceae, Campylobacteraceae, Aeromonadaceae, Moraxellaceae, and Pseudomonadaceae. However, others decreased in abundance such as Clostridiaceae, Eubacteriaceae, and Peptostreptococcaceae. This decrease was not very significant however, because at 30 days these three families represented 22.14% of the total community.

At 60 days the families Clostridiaceae (9.76%), Prolixibacteraceae (8.58%), Prevotellaceae (7.21%), Spirochaetaceae (6.51%), Bacteroidaceae (5.52%), Porphyromonadaceae (5.05%), Ruminococcaceae (5%), Leptospiraceae (4, 33%), Cytophagaceae (3.95%), Syntrophaceae (3.89%), Marinilabiliaceae (2.97%), Erysipelotrichaceae (2.48%) Pseudanabaenaceae (2.37%), Pseudomonadaceae 2.15%, and Flavobacteriaceae (2.11%) were more abundant in the bioaugmented bioreactor (Fig. 5). Many of these families were also identified at baseline, at 30 days and in the microbial consortium. Similar behavior was observed in relation to the sample taken at 30 days, where many of the families from the consortium increased their abundance.

If the two reactors are compared, it can be noted that some of the families that are in the bioaugmented reactor were not present in the control reactor because they came from the addition of the consortium, among these were Porphyromonadaceae, Prevotellaceae, Sphingobacteriaceae, Bacillaceae, Lactobacillaceae, Eubacteriaceae, Lachnospiraceae, Peptococcaceae, Ruminocococcaceae, Peptostreptococcaceae, Acetobacteraceae, Alcaligenaceae, Campylobacteraceae, Aeromonadaceae, Moraxellaceae, and Pseudomonadaceae. In this reactor as in the control, in the 3 sampling moments, some species were not identified by this technique. In this case unknown species were less than 1% in the initial sample, 2% at 30 days, and 3% at 60 days.

In both reactors, bacterial species of the phylum Firmicutes predominated, which is similar to the observations of other authors [9, 66, 67]. Cellulose degrading species were dominant, mainly from the Clostridiaceae family. Other families detected in both reactors with a relative abundance greater than 1% predominated up to 60 days, which could be inferred to be part of the basic structure of the community.

As mentioned above, some of the families from the bioaugmented reactor were also part of the consortium community, so the addition of the consortium to the reactor contributed to strengthening its microbial population. The fact that after 60 days, species from the consortium were maintained in this reactor suggests that they easily adapted to the environmental and nutritional conditions inside the reactor and compete with the species that came from the inoculum or substrates. Therefore, it is likely that they increased the capacity to degrade the lignocellulosic material found in the rice straw, which in turn supported the increase in methane production.

Methanogenic archaea analysis

Real-time amplification of genes coding for the four groups of methanogenic archaea of interest revealed that all groups (acetoclastic and hydrogenotrophic methanogens) were detected in both the control and bioaugmented reactors. However, species of the order Methanosarcinales and the family Methanosaetacea predominated in both reactors (Fig. 6). In the control reactor, no significant differences were found among the three time samples analyzed, where similar profiles of methanogenic species were shown. In the bioaugmented reactor, the total number of methanogenic archaea decreased slightly from the initial time until 60 days.

In the reactors, the methanosarcins and methanosaetas, are favored [68]. Members of this group are frequently isolated from anaerobic reactors under mesophilic conditions, which suggests that the acetoclastic metabolic pathway is favored for methane production [69, 70]. In addition, the presence of species belonging to the orders Methanobacteriales and Methanomicrobiales, although in lower abundance, suggests that the anaerobic process was balanced, and in turn allowed the abundance of species of the order Methanosarcinales responsible for the increase in methane yield [68, 71].

This fact contrasts with Kim et al., [72], who detected a predominance of the orders Methanobacteriales and Methanomicrobiales in batch reactors with swine manure. However, these results are in agreement with those obtained by Jiménez-Hernández et al., [73] and Padmasiri et al., [70], in which these groups of archaea predominate under mesophilic conditions. In general, Metanosarcina ssp. has a higher maximum growth rate but a lower affinity to acetate than Metanosaeta ssp. [71, 74]. Therefore, it is to be expected that when acetate is limited, the Methanosaetaceae family predominates. The coexistence of different groups of methanogens in the same ecosystem has been reported, as they are key players in methane production and preserve the functional stability of the ecosystem [75–77]. However, substrate availability and/or concentration and environmental changes seem to be crucial factors regulating the change of methanogenic communities [76, 78].

The microbial diversity and dynamics of both bacteria and archaea can support the positive effect that the addition of the enriched microbial consortium had on the anaerobic digestion process of rice straw residues. This allowed an increase in the utilization of the methane potential contained in this substrate even when the organic load was increased. This is evidence of the importance of the degradative capacity when microorganisms (bioaugmentation) are applied to anaerobic reactors either because the metabolic pathways of degradation of the species present or those that are added are favored.

The molecular characterization of the microbial consortium allowed in depth understanding of the microbial structure and assumed main metabolic pathways of the identified species, where cellulolytic species predominated. The bioaugmentation of the anaerobic digestion of rice straw led to an increase in the methane yield contained in the substrates. This constitutes an attractive alternative for the maximization of energy recovery from agricultural residues, resulting in increased methane production even at high organic loads. The microbial dynamics of prokaryotic populations during the anaerobic digestion of rice straw were modified and benefited by the addition of the microbial consortium, where cellulolytic microorganisms and acetoclastic methanogenic archaea were favored, supporting the increased methane yield. The Ion Torrent metagenomic technique proved to be effective for monitoring these dynamics. Future studies should also include the profile of fungal species that could be found in the consortium and probably inside the reactor, related to the degradation of the lignocellulosic material.

Funding: This work was supported by the Canadian Bureau for International Education (Emerging Leaders in the Americas Program scholarship) with a research grant awarded to Annerys Carabeo-Pérez [grant number 509].

Competing Interests: The authors have no relevant financial or non-financial interests to disclose.

Author Contributions: Janet Jiménez,María Isabel Sánchez López and Gilda Guerra Rivera contributed to the study conception and design. Deborah Henderson provided the study materials, reagents, laboratory samples, instrumentation, computing resources, and other analysis tools. Material preparation, performing the experiments, data collection and analysis were performed by Annerys Carabeo-Pérez. The first draft of the manuscript was written by Annerys Carabeo-Pérez and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

ACKNOWLEDGMENTS

This research was supported by the Canadian Bureau for International Education (Emerging Leaders in the Americas Program scholarship) with a research grant awarded to Annerys Carabeo-Pérez [grant number 509]. The authors acknowledge the cooperation between the Cuban and Canadian institutions that participated in the results of this research. The authors gratefully acknowledge the support provided by the Research, Development and Innovation Project "Bioaugmentation, biocontrol and biostimulation of two-stage anaerobic digestion to obtain hydrogen and methane", with code PN211LH005-005. We also extend our thanks to all the individuals and organizations who contributed to this work in various ways.

Bhatia SK, Jagtap SS, Bedekar AA, et al (2020) Recent developments in pretreatment technologies on lignocellulosic biomass: Effect of key parameters, technological improvements, and challenges. Bioresour Technol 300:122724. https://doi.org/10.1016/j.biortech.2019.122724
Shrestha S, Fonoll X, Khanal SK, Raskin L (2017) Biological strategies for enhanced hydrolysis of lignocellulosic biomass during anaerobic digestion: Current status and future perspectives. Bioresour Technol 245:1245–1257. https://doi.org/10.1016/j.biortech.2017.08.089
Amarante EB, Schulz RK, Romero OR, et al (2021) Emergy analysis the management of rice straw for energy purposes. Univ Soc 13:404–420
Contreras LM, Pereda Reyes I, Romero Romero O, et al (2014) Energetic, environmental and economic potentialities of the anaerobic treatment of rice straw for the case of the Cuban enterprise ‘Sur del Jíbaro.’ Int J Glob Energy Issues 37:267–281
López-Dávila E, Jiménez Hernández J, López González LM, et al (2022) Biochemical methane potential of agro-wastes as a renewable source alternative for electrical energy production in Cuba. Cienc Tecnol Agropecu 23:. https://doi.org/10.21930/rcta.vol23_num1_art:1890
Chandra R, Takeuchi H, Hasegawa T (2012) Methane production from lignocellulosic agricultural crop wastes: A review in context to second generation of biofuel production. Renew Sustain Energy Rev 16:1462–1476. https://doi.org/10.1016/j.rser.2011.11.035
Kim M, Kim B-C, Nam K, Choi Y (2018) Effect of pretreatment solutions and conditions on decomposition and anaerobic digestion of lignocellulosic biomass in rice straw. Biochem Eng J 140:108–114. https://doi.org/10.1016/j.bej.2018.09.012
Alburquerque JA, de la Fuente C, Ferrer-Costa A, et al (2012) Assessment of the fertiliser potential of digestates from farm and agroindustrial residues. Biomass Bioenergy 40:181–189. https://doi.org/10.1016/j.biombioe.2012.02.018
Jiménez J (2015) Adición de paja de arroz y arcillas residuales a la digestión anaerobia de estiércol porcino. Efecto sobre la comunidad procariota productora de metano. Tesis Doctoral, Universidad de La Habana
Baruah J, Nath BK, Sharma R, et al (2018) Recent Trends in the Pretreatment of Lignocellulosic Biomass for Value-Added Products. Front Energy Res 6:. https://doi.org/10.3389/fenrg.2018.00141
Yan J, Oyedeji O, Leal JH, et al (2020) Characterizing Variability in Lignocellulosic Biomass: A Review. ACS Sustain Chem Eng 8:8059–8085. https://doi.org/10.1021/acssuschemeng.9b06263
Zoghlami A, Paës G (2019) Lignocellulosic Biomass: Understanding Recalcitrance and Predicting Hydrolysis. Front Chem 7:. https://doi.org/10.3389/fchem.2019.00874
Karthikeyan OP, Trably E, Mehariya S, et al (2018) Pretreatment of food waste for methane and hydrogen recovery: A review. Bioresour Technol 249:1025–1039. https://doi.org/10.1016/j.biortech.2017.09.105
Tu W-C, Hallett JP (2019) Recent advances in the pretreatment of lignocellulosic biomass. Curr Opin Green Sustain Chem 20:11–17. https://doi.org/10.1016/j.cogsc.2019.07.004
Ali SS, Abomohra AE-F, Sun J (2017) Effective bio-pretreatment of sawdust waste with a novel microbial consortium for enhanced biomethanation. Bioresour Technol 238:425–432. https://doi.org/10.1016/j.biortech.2017.03.187
Ummalyma SB, Supriya RD, Sindhu R, et al (2019) Chapter 7 - Biological pretreatment of lignocellulosic biomass—Current trends and future perspectives. In: Basile A, Dalena F (eds) Second and Third Generation of Feedstocks. Elsevier, pp 197–212
Calero Hurtado A, Pérez Díaz Y, Quintero Rodríguez E, et al (2019) Effect of the associated application between Rhizobium leguminosarum and efficient microorganisms on common bean production. Cienc Tecnol Agropecu 20:295–322. https://doi.org/10.21930/rcta.vol20_num2_art:1460
Hu Y, Hao X, Wang J, Cao Y (2016) Enhancing anaerobic digestion of lignocellulosic materials in excess sludge by bioaugmentation and pre-treatment. Waste Manag 49:55–63. https://doi.org/10.1016/j.wasman.2015.12.006
Shetty D, Joshi A, Dagar SS, et al (2020) Bioaugmentation of anaerobic fungus Orpinomyces joyonii boosts sustainable biomethanation of rice straw without pretreatment. Biomass Bioenergy 138:105546. https://doi.org/10.1016/j.biombioe.2020.105546
Espinosa-Negrín AM, López-González LM, Casdelo-Gutiérrez NL, et al (2022) Pretratamientos aplicados a biomasas lignocelulósicas: una revisión de los principales métodos analíticos utilizados para su evaluación. Rev Cuba Quím 34:87–110
Li J, Wu Y, Zhao J, et al (2022) Bioaugmented degradation of rice straw combining two novel microbial consortia and lactic acid bacteria for enhancing the methane production. Bioresour Technol 344:126148. https://doi.org/10.1016/j.biortech.2021.126148
Liang J, Fang X, Lin Y, Wang D (2018) A new screened microbial consortium OEM2 for lignocellulosic biomass deconstruction and chlorophenols detoxification. J Hazard Mater 347:341–348. https://doi.org/10.1016/j.jhazmat.2018.01.023
Shanmugam S, Sun C, Chen Z, Wu Y-R (2019) Enhanced bioconversion of hemicellulosic biomass by microbial consortium for biobutanol production with bioaugmentation strategy. Bioresour Technol 279:149–155. https://doi.org/10.1016/j.biortech.2019.01.121
Hernández-Beltrán JU, Hernández-De Lira IO, Cruz-Santos MM, et al (2019) Insight into Pretreatment Methods of Lignocellulosic Biomass to Increase Biogas Yield: Current State, Challenges, and Opportunities. Appl Sci 9:3721. https://doi.org/10.3390/app9183721
Sindhu R, Binod P, Pandey A (2016) Biological pretreatment of lignocellulosic biomass – An overview. Bioresour Technol 199:76–82. https://doi.org/10.1016/j.biortech.2015.08.030
Madigan MT, Bender KS, Buckley DH, et al (2019) Brock Biology Of Microorganisms, 15th ed. Pearson Education
Sabra W, Dietz D, Tjahjasari D, Zeng A-P (2010) Biosystems analysis and engineering of microbial consortia for industrial biotechnology. Eng Life Sci 10:407–421. https://doi.org/10.1002/elsc.201000111
Carabeo A, Jiménez J, Gil Z, et al (2022) Taxonomic identification and diversity of effective soil microorganisms: towards a better understanding of this microbiome. Agron Colomb 40:. https://doi.org/10.15446/agron.colomb.v40n2.101378
Bridgewater LL (2017) Standard methods for the examination of water and wasterwater, 23rd edition. American Public Health Association, Washington, DC
VDI-Fachbereich Energietechnik (2016) VDI 4630 - Fermentation of organic materials - Characterization of the substrate, sampling, collection of material data, fermentation tests. VDI Society Energy and Environment
Lane DJ (1991) 16S/23S rRNA sequencing. In: Nucleic Acid Techniques in Bacterial Systematics. E. Stackebrandt and M. Goodfellow, New York, pp 115–175
Sipos R, Székely AJ, Palatinszky M, et al (2007) Effect of primer mismatch, annealing temperature and PCR cycle number on 16S rRNA gene-targetting bacterial community analysis. FEMS Microbiol Ecol 60:341–350. https://doi.org/10.1111/j.1574-6941.2007.00283.x
Després VR, Nowoisky JF, Klose M, et al (2007) Characterization of primary biogenic aerosol particles in urban, rural, and high-alpine air by DNA sequence and restriction fragment analysis of ribosomal RNA genes. Biogeosciences 4:1127–1141
Weisburg WG, Barns SM, Pelletier DA, Lane DJ (1991) 16S ribosomal DNA amplification for phylogenetic study. J Bacteriol 173:697–703. https://doi.org/10.1128/jb.173.2.697-703.1991
Bergmann I, Mundt K, Sontag M, et al (2010) Influence of DNA isolation on Q-PCR-based quantification of methanogenic Archaea in biogas fermenters. Syst Appl Microbiol 33:78–84. https://doi.org/10.1016/j.syapm.2009.11.004
Adamiak J, Otlewska A, Tafer H, et al (2018) First evaluation of the microbiome of built cultural heritage by using the Ion Torrent next generation sequencing platform. Int Biodeterior Biodegrad 131:11–18. https://doi.org/10.1016/j.ibiod.2017.01.040
Marzorati M, Wittebolle L, Boon N, et al (2008) How to get more out of molecular fingerprints: practical tools for microbial ecology. Environ Microbiol 10:1571–1581. https://doi.org/10.1111/j.1462-2920.2008.01572.x
Shannon CE, Weaver W (1963) The mathematical theory of communication. Urbana: University of Illinois Press.
Pielou EC (1966) The measurement of diversity in different types of biological collections. J Theor Biol 13:131–144. https://doi.org/10.1016/0022-5193(66)90013-0
Jiménez J, Theuerl S, Bergmann I, et al (2016) Prokaryote community dynamics in anaerobic co-digestion of swine manure, rice straw and industrial clay residuals. Water Sci Technol 74:824–835. https://doi.org/10.2166/wst.2016.170
Barret M, Gagnon N, Topp E, et al (2013) Physico-chemical characteristics and methanogen communities in swine and dairy manure storage tanks: Spatio-temporal variations and impact on methanogenic activity. Water Res 47:737–746. https://doi.org/10.1016/j.watres.2012.10.047
Mao C, Zhang T, Wang X, et al (2017) Process performance and methane production optimizing of anaerobic co-digestion of swine manure and corn straw. Sci Rep 7:9379. https://doi.org/10.1038/s41598-017-09977-6
Jiménez J, Cisneros-Ortiz ME, Guardia-Puebla Y, et al (2014) Optimization of the thermophilic anaerobic co-digestion of pig manure, agriculture waste and inorganic additive through specific methanogenic activity. Water Sci Technol 69:2381–2388. https://doi.org/10.2166/wst.2014.109
Chartier C, López D, Vidal G (2013) Anaerobic Technology Influence on Pig Slurry Biofertirrigation: Evaluation of Enteric Bacteria. Water Air Soil Pollut 225:1790. https://doi.org/10.1007/s11270-013-1790-z
Nges IA, Escobar F, Fu X, Björnsson L (2012) Benefits of supplementing an industrial waste anaerobic digester with energy crops for increased biogas production. Waste Manag 32:53–59. https://doi.org/10.1016/j.wasman.2011.09.009
Peng X, Börner RA, Nges IA, Liu J (2014) Impact of bioaugmentation on biochemical methane potential for wheat straw with addition of Clostridium cellulolyticum. Bioresour Technol 152:567–571. https://doi.org/10.1016/j.biortech.2013.11.067
Martin-Ryals A, Schideman L, Li P, et al (2015) Improving anaerobic digestion of a cellulosic waste via routine bioaugmentation with cellulolytic microorganisms. Bioresour Technol 189:62–70. https://doi.org/10.1016/j.biortech.2015.03.069
Zhong C, Wang C, Wang F, et al (2016) Enhanced biogas production from wheat straw with the application of synergistic microbial consortium pretreatment. RSC Adv 6:60187–60195. https://doi.org/10.1039/C5RA27393E
Tantayotai P, Pornwongthong P, Muenmuang C, et al (2017) Effect of Cellulase-producing Microbial Consortium on Biogas Production from Lignocellulosic Biomass. Energy Procedia 141:180–183. https://doi.org/10.1016/j.egypro.2017.11.034
Kong X, Du J, Ye X, et al (2018) Enhanced methane production from wheat straw with the assistance of lignocellulolytic microbial consortium TC-5. Bioresour Technol 263:33–39. https://doi.org/10.1016/j.biortech.2018.04.079
Lin Y, Zheng H, Dong L (2020) Enhanced ethanol production from tree trimmings via microbial consortium pretreatment with selective degradation of lignin. Biomass Bioenergy 142:105787. https://doi.org/10.1016/j.biombioe.2020.105787
Wu D, Li L, Zhao X, et al (2019) Anaerobic digestion: A review on process monitoring. Renew Sustain Energy Rev 103:1–12. https://doi.org/10.1016/j.rser.2018.12.039
Sun H, Ni P, Angelidaki I, et al (2019) Exploring stability indicators for efficient monitoring of anaerobic digestion of pig manure under perturbations. Waste Manag 91:139–146. https://doi.org/10.1016/j.wasman.2019.05.008
Nettmann E, Bergmann I, Pramschüfer S, et al (2010) Polyphasic Analyses of Methanogenic Archaeal Communities in Agricultural Biogas Plants. Appl Environ Microbiol 76:2540–2548. https://doi.org/10.1128/AEM.01423-09
Boursier H, Béline F, Paul E (2005) Piggery wastewater characterisation for biological nitrogen removal process design. Bioresour Technol 96:351–358. https://doi.org/10.1016/j.biortech.2004.03.007
Li L, He Q, Wei Y, et al (2014) Early warning indicators for monitoring the process failure of anaerobic digestion system of food waste. Bioresour Technol 171:491–494. https://doi.org/10.1016/j.biortech.2014.08.089
Campos-Pozuelo E (2001) Optimización de la digestión anaerobia de purines de cerdo mediante codigestión con residuos orgánicos de la industria agroalimentaria. Tesis Doctoral, Universitat de Lleida
Jiménez DJ, Chaves-Moreno D, van Elsas JD (2015) Unveiling the metabolic potential of two soil-derived microbial consortia selected on wheat straw. Sci Rep 5:13845. https://doi.org/10.1038/srep13845
Wang C, Dong D, Wang H, et al (2016) Metagenomic analysis of microbial consortia enriched from compost: new insights into the role of Actinobacteria in lignocellulose decomposition. Biotechnol Biofuels 9:22. https://doi.org/10.1186/s13068-016-0440-2
Choi J, Kim E-S, Ahn Y (2017) Microbial community analysis of bulk sludge/cake layers and biofouling-causing microbial consortia in a full-scale aerobic membrane bioreactor. Bioresour Technol 227:133–141. https://doi.org/10.1016/j.biortech.2016.12.056
Reddington K, Eccles D, O’Grady J, et al (2020) Metagenomic analysis of planktonic riverine microbial consortia using nanopore sequencing reveals insight into river microbe taxonomy and function. GigaScience 9:. https://doi.org/10.1093/gigascience/giaa053
Qu Y, Zhou J, Wang J, et al (2005) Microbial community dynamics in bioaugmented sequencing batch reactors for bromoamine acid removal. FEMS Microbiol Lett 246:143–149. https://doi.org/10.1016/j.femsle.2005.04.006
Shi S, Qu Y, Ma Q, et al (2015) Performance and microbial community dynamics in bioaugmented aerated filter reactor treating with coking wastewater. Bioresour Technol 190:159–166. https://doi.org/10.1016/j.biortech.2015.04.075
Ecem Öner B, Akyol Ç, Bozan M, et al (2018) Bioaugmentation with Clostridium thermocellum to enhance the anaerobic biodegradation of lignocellulosic agricultural residues. Bioresour Technol 249:620–625. https://doi.org/10.1016/j.biortech.2017.10.040
Lebiocka M, Montusiewicz A, Cydzik-Kwiatkowska A (2018) Effect of Bioaugmentation on Biogas Yields and Kinetics in Anaerobic Digestion of Sewage Sludge. Int J Environ Res Public Health 15:1717. https://doi.org/10.3390/ijerph15081717
Lee S-H, Lee H-J, Kim S-J, et al (2010) Identification of airborne bacterial and fungal community structures in an urban area by T-RFLP analysis and quantitative real-time PCR. Sci Total Environ 408:1349–1357. https://doi.org/10.1016/j.scitotenv.2009.10.061
Wirth R, Kovács E, Maróti G, et al (2012) Characterization of a biogas-producing microbial community by short-read next generation DNA sequencing. Biotechnol Biofuels 5:41. https://doi.org/10.1186/1754-6834-5-41
De Vrieze J (2014) Methanosaeta vs. Methanosarcina in anaerobic digestion: the quest for enhanced biogas production. Ghent University
Heeg K, Pohl M, Sontag M, et al (2014) Microbial communities involved in biogas production from wheat straw as the sole substrate within a two-phase solid-state anaerobic digestion. Syst Appl Microbiol 37:590–600. https://doi.org/10.1016/j.syapm.2014.10.002
Padmasiri SI, Zhang J, Fitch M, et al (2007) Methanogenic population dynamics and performance of an anaerobic membrane bioreactor (AnMBR) treating swine manure under high shear conditions. Water Res 41:134–144. https://doi.org/10.1016/j.watres.2006.09.021
Supaphol S, Jenkins SN, Intomo P, et al (2011) Microbial community dynamics in mesophilic anaerobic co-digestion of mixed waste. Bioresour Technol 102:4021–4027. https://doi.org/10.1016/j.biortech.2010.11.124
Kim W, Lee S, Shin SG, et al (2010) Methanogenic community shift in anaerobic batch digesters treating swine wastewater. Water Res 44:4900–4907. https://doi.org/10.1016/j.watres.2010.07.029
Jiménez J, Guerra G, Morgan JM, et al (2014) Caracterización microbiológica y molecular de la comunidad anaerobia durante la co-fermentacion de residuos agropecuarios con adición de arcillas para la obtención de metano. Rev CENIC Cienc Biológicas 45:
Hori T, Haruta S, Ueno Y, et al (2006) Dynamic Transition of a Methanogenic Population in Response to the Concentration of Volatile Fatty Acids in a Thermophilic Anaerobic Digester. Appl Environ Microbiol 72:1623–1630. https://doi.org/10.1128/AEM.72.2.1623-1630.2006
Jing H, Cheung S, Zhou Z, et al (2016) Spatial Variations of the Methanogenic Communities in the Sediments of Tropical Mangroves. PLOS ONE 11:e0161065. https://doi.org/10.1371/journal.pone.0161065
Guermazi-Toumi S, Chouari R, Sghir A (2019) Molecular analysis of methanogen populations and their interactions within anaerobic sludge digesters. Environ Technol 40:2864–2879. https://doi.org/10.1080/09593330.2018.1455747
Dyksma S, Jansen L, Gallert C (2020) Syntrophic acetate oxidation replaces acetoclastic methanogenesis during thermophilic digestion of biowaste. Microbiome 8:105. https://doi.org/10.1186/s40168-020-00862-5
Peng J, Lü Z, Rui J, Lu Y (2008) Dynamics of the Methanogenic Archaeal Community during Plant Residue Decomposition in an Anoxic Rice Field Soil. Appl Environ Microbiol 74:2894–2901. https://doi.org/10.1128/AEM.00070-08

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Rice straw anaerobic digestion enhancement through bioaugmentation: effect on the microbial community

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