Appl Microbiol Biotechnol (2014) 98:2015–2027 DOI 10.1007/s00253-013-5378-8

BIOTECHNOLOGICAL PRODUCTS AND PROCESS ENGINEERING

Influence of transitional states on the microbial ecology of anaerobic digesters treating solid wastes Leticia Regueiro & Patricia Veiga & Mónica Figueroa & Juan M. Lema & Marta Carballa

Received: 6 August 2013 / Revised: 30 October 2013 / Accepted: 2 November 2013 / Published online: 30 November 2013 # Springer-Verlag Berlin Heidelberg 2013

Abstract A better understanding of the microbial ecology of anaerobic processes during transitional states is important to achieve a long-term efficient reactor operation. Five wastes (pig manure, biodiesel residues, ethanol stillage, molasses residues, and fish canning waste) were treated in five anaerobic reactors under the same operational conditions. The influence of the type of substrate and the effect of modifying feeding composition on the microbial community structure was evaluated. The highest biomethanation efficiency was observed in reactors fed with fish canning waste, which also presented the highest active archaeal population and the most diverse microbial communities. Only two Bacteria populations could be directly related to a particular substrate: Ilyobacter with biodiesel residues and Trichococcus with molasses residues. Results showed that the time to achieve steady-state performance after these transitional states was not dependent on the substrate treated. But reactors needed more time to handle the stress conditions derived from the start-up compared to the adaptation to a new feeding. Cluster analyses showed that the type of substrate had a clear influence on the microbiology of the reactors, and that segregation was related to the reactors performance. Finally, we conclude that the previous inoculum history treating solid waste and higher values of active Archaea population are important factors to face a successful change in substrate not entailing stability failure.

Electronic supplementary material The online version of this article (doi:10.1007/s00253-013-5378-8) contains supplementary material, which is available to authorized users. L. Regueiro (*) : P. Veiga : M. Figueroa : J. M. Lema : M. Carballa Department of Chemical Engineering, Institute of Technology, University of Santiago de Compostela, Rúa Lope Gómez de Marzoa s/n, 15782 Santiago de Compostela, Galicia, Spain e-mail: [email protected]

Keywords Denaturing gradient gel electrophoresis (DGGE) . Feeding characteristics influence . Ilyobacter . Inoculum history . Microbial community organization . Start-up

Introduction Huge quantities of organic wastes are generated daily by human activities and they could represent a useful raw material for energy production and by-products recovery. Anaerobic digestion (AD) is a biotechnological process that has been widely used to reduce and stabilize these abundant residues and to convert them in renewable energy-methane and biosolids (Verstraete et al. 2000). Despite AD process is already well established, the lack of knowledge about the microbial consortia involved in the process implies that the design of full-scale bioreactors has been mostly empirical and generally focused on applying low organic loading rates (OLR) and long residence times (HRT) to avoid collapse (Hori et al. 2006). Nevertheless, recent research on microbial diversity in bioreactors and its physiological requirements has been an interesting tool to increase the efficiency of biogas plants (Briones and Raskin 2003; Ward et al. 2008). Trying to go beyond, biomolecular tools could be useful to predict operational problems, and consequently, avoid process failures in advance (Talbot et al. 2008). Only few studies have followed community dynamics during different stages of AD process aiming at answering a basic question: who lives with whom and why? However, the results are not always consistent. For example, Dearman et al. (2006) showed that it is not necessary a great microbial diversity to operate successfully anaerobic reactors. In contrast, other studies related positively the microbial diversity with the good performance of anaerobic reactors (Fernández et al. 1999). Furthermore, microbial community dynamics is poorly understood. Some authors (Carballa et al. 2011; Pycke

2016

et al. 2011) have reported highly dynamic bacterial communities during stable performance of microbial processes, while Werner et al. (2011) suggested that bacterial populations are stable, resilient, and specific in steady-state full-scale anaerobic reactors. Most works have been performed under steady-state conditions. However, studies in transitional (non-steady) states, such as start-up or changes in operational conditions, would be useful to analyze the response of microbial community to these situations. This information could enable to identify the proper microbial communities providing the desirable physiological functions for a successful start-up or to tackle eventual instability problems in critical steps, such as changes in feeding composition. The main objective of this work was to evaluate the microbial community dynamics in five anaerobic reactors treating different wastes during two transitional states: start-up, examining the influence of substrate characteristics on the anaerobic community structure, and variation in feeding composition, analyzing the response of the microbial community to an alteration in operational conditions. Denaturing gradient gel electrophoresis (DGGE) was used to study the microbial community dynamics, since it is one of the most wellestablished molecular tools in microbial ecology (Ueno et al. 2001) and the generated data can be integrated with statistical analysis to facilitate their interpretation. Fluorescence in situ hybridization (FISH) was also applied to identify and semiquantify the major microbial players.

Materials and methods Substrates Five agro-industrial wastes were chosen according to their different physico-chemical characteristics: pig manure (PM), biodiesel residue (BR, mainly glycerin), ethanol stillage (ES), molasses residue (MR), and fish canning waste (CW). After collection, all wastes were stored at 4 °C to minimize decomposition. Anaerobic reactors Five continuously stirred tank reactors with a working volume of 2 L were used. The reactors were operated during 160 days under mesophilic conditions (37±2 °C) and at the same OLR and HRT, i.e. 1.0±0.2 g chemical oxygen demand (COD) L−1 day−1 and 20 days, respectively. They were inoculated with a mixture of six biomasses from full-scale anaerobic reactors described elsewhere (Regueiro et al., 2012) in order to obtain a greater initial microbial diversity. Biogas flow rate was measured daily by liquid displacement. Samples of the supernatant were taken twice a week for

Appl Microbiol Biotechnol (2014) 98:2015–2027

pH, alkalinity, COD, N-NH4+, and volatile fatty acids (VFAs) determinations. Once a week, a sample of the anaerobic biomass was taken to characterize the microbial community. FISH samples were fixed on the same day of sampling, while samples for DGGE were stored at −20 °C until DNA extraction. Experimental periods Two experiments with different objectives were performed: experiment 1 (days 0–80), aiming at evaluating the influence of the type of substrate in a reactor start-up, and experiment 2 (days 81–160), aiming at studying the effect of modifying feeding composition on reactor performance. In the latter experiment, the change was conducted in such a way that the second substrate displayed fewer physico-chemical characteristics in common with the first substrate (Table 1). Table 2 shows the substrates fed in each experiment. In this way, R1, working with PM in experiment 1, was fed with CW in experiment 2 to analyze the influence of feeding fat. R2, working with BR in experiment 1, was fed with CW in the second one to analyze the effect of fat and proteins. R3, working with ES in the first experiment, was fed with MR in experiment 2 to study the increase in nitrogen content. R4, treating MR in experiment 1, was fed with CW in experiment 2 to evaluate the effect of fat increase and protein decrease. R5, working with CW in the first experiment, was fed with PM in experiment 2 to observe the response to a decrease in fat and protein content. Analytical methods Biogas composition was determined by gas chromatography (HP, 5890 Series II). VFA concentration was analyzed by gas chromatography (HP, 5890A) equipped with a flame ionization detector (HP, 7673A). pH measurements were conducted using an electrode Ingold U-455 model, connected to a pH meter (Crison 506). COD, total solids (TS), volatile solids (VS), total Kjeldahl nitrogen (N-TKN), N-NH4+, total alkalinity (TA), partial alkalinity (PA), and lipids were determined according to standard methods (APHA, 1998). Molecular techniques DNA extraction DNA was extracted using the PowerSoil DNA soil extraction kit (MoBio Laboratories, Inc., Solano Beach, CA) following the manufacturer’s instructions. Total nucleic acid diluted in 50 μL of sigma water was quantified and checked for purity at A260/280 and A260/230 nm with Nanodrop (Thermo Scientific 2000c).

Appl Microbiol Biotechnol (2014) 98:2015–2027

2017

Table 1 Physico-chemical characterization of the five substrates Substrates Parameter

PM

BR

ES

MR

CW

TS (g kg−1) VS (g kg−1) N-NH4+ (g N kg−1) N-TKN (g N kg−1) COD (g O2 kg−1) PA (g CaCO3 kg−1) TA (g CaCO3 kg−1) Lipids (g kg−1)

20 10 2.2 3.3 14 4.5 9.4 0.0

593 557 0.0 0.0 1,679 1.1 2.0 2.2

438 422 0.4 2.8 374 0.0 0.0 0.0

835 707 14.8 57.0 723 0.0 0.0 0.0

304 282 0.7 19.0 567 0.0 0.0 35.1

PM pig manure, BR biodiesel residues, ES ethanol stillage, MR molasses residues, CW canning waste, TS total solids, VS volatile solids, N-NH 4 + ammonium, N-TKN total Kjeldahl nitrogen, COD chemical oxygen demand, PA partial alkalinity, TA total alkalinity

Denaturing gradient gel electrophoresis and sequencing Genomic DNA was subjected to DGGE analysis as previously described by Regueiro et al. (2012). The 16S rRNA gene hypervariable regions of Bacteria and Archaea were amplified by PCR using primers U968-f and L1401-r for Bacteria and primers A109(T)-f and 515-r for Archaea. Primers U968-f and 515-r included a GC clamp at the 5′ end. DGGE gels were run at 60 °C for 16 h at 100 V and then stained with SYBR-gold (Molecular Probes, Inc., Eugene, OR, USA) for 30 min. The denaturing gradients used to separate the amplified Bacteria and Archaea rDNA were 40–70 and 30–70 % urea–formamide gradient, respectively, in a 6 % of polyacrylamide gel. DGGE bands were excised from the gels, eluted, and

sequenced as described earlier (Regueiro et al. 2012). Details about the primers sequence are previously explained by Sousa et al. (2007). Twenty-two sequences (12 for Bacteria and ten for Archaea) of 16S rRNA gene of DGGE band were deposited in the GenBank database (NCBI, National Center for Biotechnology Information, http://ncbi.nlm.nih.gov) under the accession numbers KF500550-KF500561 for Bacteria (Table 3) and KF500562-KF500571 for Archaea (Table 4). Fluorescent in situ hybridization FISH was performed according to the procedure described by Regueiro et al. (2012) and the probes used were: Eub338mix (Bacteria ), ALF1b (Alphaproteobacteria ), BET42a (Betaproteobacteria ), GAM42a (Gammaproteobacteria ), DELTA495a (Deltaproteobacteria), CFX1223 (Chloroflexi), Arc915 (Archaea ), Ms821 (Methanosarcina ), Mx825 (Methanosaeta), and MB1174 (Methanobacteriales). All the details of each probe (formamide percentage, sequence, and target organism) can be found in the probeBase database (Loy et al. 2007). DAIME program was used to make the semiquantification of the populations with FISH images. At least 15–20 photos for each fixed sample were taken to apply the program. Statistical analysis of DGGE patterns and parameters estimation Gel images were analyzed and normalized with Bionumerics v2.1 software (Applied Maths, Belgium). Only those bands with more than 1 % intensity were taken into account.

Table 2 Semi-quantitative percentages of Archaea/Bacteria ratio and of different bacterial and archaeal communities determined by FISH in the startup (day 0) and at end of experiments 1 and 2 (days 80 and 160, respectively) Microorganism

Day 0

Experiment 1 (day 80)

Experiment 2 (day 160)

R1 PM

R2 BR

R3 ES

R4 MR

R5 CW

R1 CW

R2 CW

R3 MR

R4 CW

R5 PM

Bacteria a Archaea a Betaproteobacteria b

50 50 10

77 23 –

70 30 4

80 20 –

80 20 2

55 45 10

58 42 8

50 50 10

80 20 5

55 45 5

85 15 –

Gammaproteobacteria b Chloroflexi b Methanosaeta c Methanosarcina c Methanobacteriales c

4 – 90 1 1

– – 85 – –

5 5 70 – 2

– 10 75 – –

2 8 60 20 4

8 10 75 5 2

5 8 75 8 2

8 10 75 5 1

4 6 40 30 5

8 10 70 10 4

– – 80 – –

The table also includes the substrate treated in each reactor in each experiment a

Referred to total active microorganisms

b

Referred to total active Bacteria

c

Referred to total active Archaea

PM pig manure, BR biodiesel residues, ES ethanol stillage, MR molasses residues, CW canning waste, − not detected

2018

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Table 3 Phylogenetic affiliation of the gene sequences from bacterial DGGE Accession number Phylogenetic affiliation

GeneBank accession Similarity (%) Substrate/othersa number

KF500550

CU919401

96.0

CW/ALL

FJ497632

94.6

MR/CW

AB529540

97.4

MR

CU926035

99.4

BR/ALL

EU498386

94.9

CW/BR, MR

FR733681 EF123525

99.0 96.6

BR PM/ALL

EU370417 EU634804

99.0 95.2

MR/CW CW/MR, BR

EU289054

96.5

ES/PM

FJ440027

96.2

BR/ALL

JN680653

97.2

CW

KF500551 KF500552 KF500553 KF500554 KF500555 KF500556 KF500557 KF500558 KF500559 KF500560 KF500561

Uncultured Firmicutes bacterium 16S rRNA gene from clone QEDV1CC10 Uncultured gamma proteobacterium clone VS_CL-380 16S ribosomal RNA gene, partial sequence Uncultured Trichococcus sp. gene for 16S rRNA, partial sequence, clone: TCE-2 Uncultured Firmicutes bacterium 16S rRNA gene from clone QEDN2DB01 Clostridia bacterium enrichment culture clone D2CL_Bac_16S_Clone20 16 Ilyobacter delafieldii partial 16S rRNA gene, type strain DSM5704T Uncultured Firmicutes bacterium clone WA_24b 16S ribosomal RNA gene, partial sequence Pseudomonas sp. HY-24 16S ribosomal RNA gene, partial sequence Uncultured Methylobacter sp. clone DDM2W1u04 16S ribosomal RNA gene, partial sequence Uncultured Firmicutes bacterium clone 8837-D0-C-8A 16S ribosomal RNA gene, partial sequence Uncultured Clostridiales bacterium clone ABXD_AB23 16S ribosomal RNA gene, partial sequence Uncultured Lachnospiraceae bacterium clone SL76 16S ribosomal RNA gene, partial sequence

a

Substrate/others: the substrate corresponds to the substrate fed in the reactor where this band was excised. Others are the reactors that presented this band in the same position

For the clustering analysis, the DGGE tracks were converted into a binary matrix in which the digits 0 and 1 indicated the absence and presence, respectively, of the band. This matrix, called similarity matrix, was used to construct the dendrogram with the UPGMA algorithm. Principal component analysis (PCA) was applied to the resulting abundances matrix obtained with Bionumerics for all reactors during the first

experiment, using the Excel statistical package (XLSTAT) to show the evolution of the samples with time. The statistical analysis of the DGGE gels was done based on the parameters described by Marzorati et al. (2008) under the microbial resource management concept. The rangeweighted richness (Rr) was calculated as the total number of bands multiplied by the percentage of denaturing gradient

Table 4 Phylogenetic affiliation of the gene sequences from archaeal DGGE Accession number

Phylogenetic affiliation

GeneBank accession number

Similarity (%)

Substrate/ othersa

KF500562 KF500563 KF500564 KF500565 KF500566

Uncultured ArcI archaeon 16S rRNA gene from clone QEEK1CE021 Uncultured ArcI archaeon 16S rRNA gene from clone QEEC1BF051 Uncultured Methanolinea sp. gene for 16S rRNA, clone: SMS-sludge-2 Methanobacterium petrolearium gene for 16S ribosomal RNA Uncultured Methanolinea sp. gene for 16S rRNA, clone: SMS-T-Pro-2, partial sequence Methanosaeta sp. enrichment culture clone A14120 16S ribosomal RNA gene Methanosaeta concilii GP-6, complete genome Uncultured ArcI archaeon 16S rRNA gene from clone QEEE1CA061 Uncultured Methanosarcinales archaeon 16S rRNA gene from clone QEEH1DA081 Methanobacterium beijingense strain 8-2 16S ribosomal RNA gene, partial sequence

CU916036 CU917392 AB479393 AB542742 AB479405

100 100 98.6 91.6 96.9

CW/MR,BR BR/CW PM/ALL BR MR/ALL

HQ133099

95.2

CW/MR,BR

CP002565 CU917052 CU916348

95.5 96.1 98.1

CWBR ES/ALL MR/ALL

DQ649303

99.9

PM/ALL

KF500567 KF500568 KF500569 KF500570 KF500571 a

Substrate/others: the substrate corresponds to the substrate fed in the reactor where this band was excised. Others are the reactors that presented this band in the same position

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where these bands are located (Rr=N 2 ×D g). Community dynamics (Dy) was calculated as the percentage of change between consecutive DGGE profiles (% change is obtained as: 100 %—similarity percentage based on moving window analysis). The community organization coefficient (Co) was calculated as the percentage of the Gini coefficient (Wittebolle et al. 2009).

2019

supplementary material), and concomitantly, the biogas production dropped in the last operational days (Fig. 1d). R5, treating CW, also showed a high methane yield, around 82± 6 % (Fig. 1e). In addition, methane content in the biogas of this reactor was a bit higher (55 %) than in the other reactors (between 45 and 52 %). Influence of substrate characteristics on microbial community structure

Results Substrates characterization Table 1 summarizes the results of the physico-chemical characterization of the five agro-industrial wastes. BR presented the highest COD content, while PM had the lowest. CW was the only substrate that showed a medium-lipid content and MR had the highest total nitrogen concentration. ES displayed medium values of all the parameters (nitrogen, solids, COD…) excluding lipids and alkalinity, whose values were under the detection limit. Influence of substrate characteristics on reactor performance (experiment 1) pH and alkalinity remained constant during experiment 1 in the five reactors with values varying between 7.0–7.8 and 2, 000–4,500 mg CaCO3 L−1, respectively (data not shown). Only in R2, the pH value decreased to 6.7 during a short period of VFAs inhibition. Similarly, biomass concentrations were kept at around 12–15 g VSS L−1 and the ammonium concentrations ranged from 500 to 2,000 mg L−1, except in R4 (around 3,500 mg L−1; Fig. S1, supplementary material). Figure 1 shows the performance of the five anaerobic digesters in terms of methane production and OLR applied during the whole experimental period. Although the aim was to apply a fixed OLR (~1 g COD L−1 day−1) in all reactors, the need of diluting the substrates with tap water to attain the proper OLR caused some variations in this parameter, mainly during the first 2 weeks. R1, treating PM, achieved a biomethanation conversion (calculated from days 40 to 80) of 53±6 % (Fig. 1a). R2 presented a better result (close to 77±5 %, Fig. 1b) working with BR, despite the VFA accumulation event occurring around day 20 (biogas production dropped close to zero), which was overcome by discontinuing feeding for 3 days. R3, treating ES, showed the worst methane yield, and only 46 ±6 % of the total COD was converted into methane (Fig. 1c). R4, fed with MR, showed the best result (86 ± 4 % of biomethanation conversion, Fig. 1d) till day 60. However, from day 65 on, the ammonium that had been accumulating slowly in this reactor caused a rise in the VFAs levels up to 1.8 g HAc L −1 by the end of experiment (Fig. S2,

The microbial diversity and the shifts in bacterial and archaeal communities during the entire operation were tracked by DGGE analysis. Results revealed that there was no clear relationship between the dominant phyla found in each reactor and the substrate fed since the DNA retrieved from the bands was affiliated with Firmicutes and Proteobacteria, regardless the substrate fed (data not shown). Firmicutes phylum was represented by the Clostridia class, while Pseudomonadales was the dominant order in Proteobacteria . Interestingly, Ilyobacter and Trichococcus appeared related to BR and MR, respectively. The sequencing results for Archaea indicated that the most intense bands in all the reactors (data not shown) corresponded to the two main archaeal groups: h y dr o ge n o t r op h i c ( M e t ha n o m i c ro b i a l e s , m ai n ly Methanolinea, and also Methanobacterium and Arc1) and acetoclastic (Methanosarcinales). Regardless the type of substrate treated, the community structure changed over time, likely related to biomass adaptation to each specific substrate, and this fact was confirmed by both the cluster analysis showing a segregation of samples according to the sampling time (Figs. 2 and 3) and the PCA (Fig. S3). Thus, it was observed that the populations changed in all reactors but, in order to extract some conclusions relevant to the relationship between microbial community dynamics and reactors efficiency, three questions were attempted to be answered: how, when, and how much did it change? The Dy (% change of the microbial communities) was clearly higher in the bacterial community (Fig. S4) than in the archaeal one (Fig. S5). This trend could be also observed in the cluster analysis, since only three or a maximum of four clusters could be distinguished in the archaeal population in all reactors (Fig. 3), while more than six or seven clusters were detected for Bacteria in all cases (Fig. 2). Whatever the substrate treated, the archaeal population reached the steadystate performance (defined as the period when two or more consecutive samples have a 100 % of similarity) in all reactors around days 30 to 40 (Fig. 3, Fig. S5) whereas the bacterial one did not achieve it in reactors treating ES and MR (Fig. 2c and d). Bacteria needed only 7 days to differentiate clearly from the original inoculum in all the reactors, whereas Archaea needed 14, 21, or even more days to change from the initial inoculum. Yet, archaeal population was more similar to the initial community in the reactors (70–80 % similarity)

2020

OLR, CH4 (g COD L-1 d-1)

Experiment 1 1.5

OLR, CH4 (g COD L-1 d-1) OLR, CH4 (g COD L-1 d-1)

Fig. 1 Performance of anaerobic reactors in terms of methane produced (white triangles) and organic loading rate applied (black squares) (a R1, b R2, c R3, d R4, and e R5)

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1.5

Experiment 2

A

1.2 0.9 0.6 0.3 0.0

B

1.2 0.9 0.6 0.3 0.0 1.5

C

1.2 0.9 0.6 0.3

OLR, CH4 (g COD L-1 d-1) OLR, CH4 (g COD L-1 d-1)

0.0 1.5

D 1.2 0.9 0.6 0.3 0.0 1.5

E

1.2 0.9 0.6 0.3 0.0 0

20

40

60

80

100

120

140

160

Time (d)

than the bacterial population (15 to 50 % similarity). The PCA results for Bacteria (Fig. S3, A–E) and Archaea (data not shown) populations corroborated these results. The PCA gathered more than 75 % of the total variability in the datasets of all the reactors. It seems clear that the big change corresponds to the first days of operation. Conversely, the substrate characteristics clearly influenced the microbial communities present in the reactors. Analyzing

the microbial community structure by the cluster analysis on steady-state conditions (day 80, Fig. 4a), two different groups can be observed: PM and ES, similar to each other by 30 %, and CW and BR similar to each other by 40 %. Reactor treating MR differed from the others, most probably due to the high ammonium concentration detected in this reactor (≈4 g N-NH4+ L−1). A similar grouping was observed for the Archaea population (Fig. 4b). PCA data (Fig. S3) confirmed

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2021

% Similarity

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Fig. 2 Cluster analyses of Bacteria in experiment 1 (a R1, b R2, c R3, d R4, and e R5). Similar results were obtained in experiment 2 (data not shown)

these results with R4 being segregated from the other reactors, but it should be noticed that, in this case, the analysis represented only around 40 % of the total variation. Figure 5 shows the Rr and Co values for Bacteria in the five different reactors along the experimental period. Regardless the type of substrate, Rr followed the same trend, and its value decreased from 35 to a value that was dependent on feeding characteristics since reactors treating BR, CW, and MR showed higher Rr values (20) than ES and PM digesters (Rr of 10). A similar pattern was observed for Archaea (data not shown). Co values remained constant and, overall, bacterial communities were more uneven (average Co values of 30–80) than the archaeal ones (average Co values of 10–60) during this first experiment. Only R3, treating ES (Fig. 5c),

showed a different trend from day 50 on and the average Co decreased by the end of the experiment. FISH was used to identify and semi-quantify the relative abundances of Bacteria, Archaea, and other anaerobic populations. The average error that we committed using the DAIME program to quantify was up to 35 %. Table 2 summarizes the positive hybridization results of different populations at the end (steady-state conditions) of both experiments, and also in the initial inoculum. Figure 6 shows the evolution of the bacterial and archaeal population during the first experimental period. It seems clear that the change in reactors from an equilibrated proportion of the two communities to a Bacteria-dominated one was more abrupt at the beginning of the start-up period. This trend was similar in all reactors

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Appl Microbiol Biotechnol (2014) 98:2015–2027

A

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% Similarity

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Day 44

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Fig. 3 Cluster analyses of Archaea in experiment 1 (a R1, b R2, c R3, d R4, and e R5). Similar results were obtained in experiment 2 (data not shown)

regardless the type of substrate fed, though the final proportion of the different species was dependent on the substrate treated. The highest Archaea presence was detected in R5 that worked with CW (Table 2). Due to the high diversity, it was very difficult to identify and also quantify the entire bacterial community. The main microorganisms detected by DGGE were analyzed with FISH probes. Results indicated that Alphaproteobacteria and Deltaproteobacteria species were

A

not active in any reactor and Betaproteobacteria and Gammaproteobacteria values were very low, ranging from 0 (PM and ES reactors) to 8–10 % of bacterial cell counts (Table 2). Chloroflexi was the other bacterial population presenting positive FISH results (5–10 % of active bacterial population), except in reactors treating PM. Regrettably, Firmicutes population was not followed with FISH, since as gram-positive Bacteria have a different fixation protocol. In

B

Fig. 4 Cluster analyses of Bacteria (a) and Archaea (b) at day 80 (experiment 1)

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A

2023

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100

Exp 2

Exp 1

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Fig. 5 Range-weighted richness (Rr, black squares) and community organization (Co, black triangles) of Bacteria in the five reactors (a R1, b R2, c R3, d R4, and e R5)

the archaeal domain was the aceticlastic Methanosaetaceae the most abundant family in all the reactors (Table 2) while Methanosarcina did not seem relevant under the applied operational conditions. The latter only played a minimum role in the reactor fed with CW (5 %), and a more important one in reactor treating MR (20 %). Effect of changes in the feeding composition on reactors performance (experiment 2) Physico-chemical parameters (pH, alkalinity, volatile solids…) stayed in the normal range during this second period

(81–160 days), except the ammonium concentration in the reactor working with MR as substrate (R3), which reached higher values, close to 4 g N-NH4+ L−1. The three reactors treating CW (R1, Fig. 1a; R2, Fig. 1b; and R4, Fig. 1e) presented better results (methane yield around 88±4 % (R1), 89±6 % (R2), and 77±6 % (R4) calculated from day 120 on) than the other two reactors fed with PM (57±5 %) and MR (40±10 %), respectively. Regardless of their performance during experiment 1, R1 and R2 responded similarly to the change of substrate (CW) and reached high biomethanation percentages. R4 behave slightly different, because during the first days of this experiment, the

2024 100

% of Active Population

Fig. 6 Evolution of active bacterial (green bars) and archaeal (red bars) population percentages in the first experiment based on FISH results

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R2-BR

R3-ES

R4-MR

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R5-CW

80

60

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biomethane yield did not achieve the same values as R1 and R2 and its value decreased, probably due to the deteriorated performance by the end of experiment 1 (high VFAs and ammonium levels, Figs. S1 and 2). R3, treating MR, displayed a decreasing biogas production along the experiment (Fig. 1c), which could be explained by the rise of ammonium concentrations from 500 to 3,500 mg L−1 during the first 20 days (Fig. S1) resulting in VFA levels close to 2 g L−1 (Fig. S2), mainly propionate. R5 adapted its performance to a low biomethanation yield substrate (PM), without exhibiting any inhibition problem. Effect of changes in the feeding composition on microbial community structure Taking into account the DGGE results, the same populations appeared in all the reactors during experiment 2 (data not shown). Once again, Trichococcus was detected in the reactor treating MR. In this case, Aeromonas instead of Pseudomonas (both belonging to Pseudomonadaceae family) was the dominant species within Proteobacteria class in reactors treating CW. Again, the samples were grouped according to the sampling time, but in this case both bacterial and archaeal domains reached steady-state conditions in only 40 and 10–15 days, respectively. Bacterial population was also more dynamic (more number of clusters) than the archaeal one, but the latter was slightly more dynamic than in the first experiment (data not shown). Bacteria was more diverse (Rr values around 5–40, Fig. 5) than Archaea population (Rr values of 2–10; data not shown), and Rr values were similar to those achieved in the first experiment, except in R1 that had a higher value (Fig. 5). Co values followed the same trend (medium Co values) as in

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the first experiment and again R3 presented the poorest value for bacterial population, around 30. FISH results showed that the proportion of archaeal population increased in reactors 1, 2, and 4 that worked with CW (Table 2), reaching values close to 50 % at the end of this experimental part. R3 and R5, treating MR and PM, respectively, only achieved values of 20 and 15 %, respectively. Methanosaeta was also the most abundant in the archaeal domain and, again, Chloroflexi was not detected in reactor treating PM. In addition, it was interesting to observe what happened when two substrates were exchanged. The DGGE tracks (Fig. S6) showed similar patterns in reactors 1 and 5 when they were fed with the same substrate in both experiments for bacterial community. Besides FISH results confirm this trend for the archaeal community in reactors 1 and 5 at the end of both experiments (Fig. S7). It can be seen that the presence of CW (Fig. S7b and c) was linked with high archaeal percentages, while the opposite occurred in reactors fed with PM (Fig. S7a and d).

Discussion Shortening start-up period and/or recovery time after modifying operational parameters, such as the type of substrate treated, are quite interesting points that would allow improving the economical competitiveness of anaerobic digestion processes. Therefore, a better knowledge of the microbial communities enrolled in these transitional states could serve to assess the optimization of the digesters during these events. The highest biomethanation percentages were obtained treating the same substrates, BR and CW, regardless the transitional state studied. Probably their characteristics,

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specially the high content in soluble organic matter and lipids in BR and CW, respectively, were the reason of their greater efficiency. On the contrary, the anaerobic digestion of ES and PM derived in lower biogas yields, which could be related to the presence of non-biodegradable solids and ammonia, respectively. The anaerobic digestion of MR exhibited a clear inhibition derived from the high nitrogen content, resulting in high ammonium and VFA levels. Therefore, the type of substrate clearly determined the reactors performance, as previously showed by Gavala and Lyberatos (2001). But also the type of substrate fed conditioned the microbial communities present in the anaerobic reactors. The microbial clusters at steady-state conditions (Fig. 4a and b) are clearly connected with the feeding and the environmental parameters. It is important to note that microorganisms incoming with the feeding could also affect the microbial communities developed in each reactor; however, all the substrates were analyzed by DGGE (data not shown) and only few bands (one for Archaea and two for Bacteria ) were detected in the pig manure. This indicated that the physico-chemical characteristics were the main driving forces to lead changes in microbial communities. MR displayed a separate cluster from the other substrates probably due to the VFAs and ammonium levels, while those with better (BR and CW) and worse (ES and PM) operation efficiency were grouped together. FISH and statistical parameters corroborated this segregation. FISH results underlined that the highest the archaeal percentage (Table 2), the better the reactors performance in terms of biogas production. Besides, the higher the microbial diversity, the higher the biomethanation percentage. Hence, high Rr values may be an important indicator of a well-functioning anaerobic digester (Carballa et al. 2011; Wang et al. 2010) since the richer the community, the greater the possibility of finding a pathway in the metabolic network to degrade the substrate, and thereby the possibility of converting COD into methane could be increased. Does the performance efficiency of the reactor in the stress moment (i.e. when the feeding was changed) matter? In this sense, reactors 1 and 2 underwent the same behavior when CW was introduced in experiment 2, though the biomethanation percentages in the first experiment were completely different. Therefore, it appears that the previous operational efficiency in terms of methane production is not affecting the response against this transitional state. But what about the environmental conditions in the reactor in the stress moment? R4 was also fed with CW in experiment 2, but despite reaching the same methane yields as R1 and R2 (Fig. 1) by the end of the experiment, this reactor needed more time. A possible explanation is the inhibition by VFAs and ammonium observed at the end of experiment 1, so the previous environmental conditions before changing the feeding seem to be an important factor to handle properly this alteration.

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Meanwhile, the time to achieve steady state in both transitory states was not affected by the type of substrate fed, neither at biogas production nor at microbiology level. But the startup was more critical (more time required to stabilize the operation) because the previous reactors acclimation treating solid waste in experiment 1 served to reduce the adaptation time to a new solid substrate in experiment 2. Several studies have indeed suggested that an appropriate inoculum selection could reduce significantly the accommodation period both in anaerobic biodegradability tests (Obaja et al. 2003) and in continuous anaerobic reactors (Oz et al. 2012). From our results, we can suggest that the initial inoculum, coming mainly from the treatment of liquid wastes, found more difficult the adaptation period to treat solid waste than the solid waste-adapted inoculum the change of solid substrate. Therefore, the previous history of the inoculum might determine how to cope with the entry of a new substrate in any anaerobic reactor. Another aspect to be discussed is the path to the steady state, which was clearly different between the two domains. Bacterial population reached the steady state later than the reactors established stable biogas production. This is in agreement with Fernández et al. (1999), who observed changes in the microbial community structure without apparent changes in reactor performance. Briones and Raskin (2003) also stated that the bacterial community is very dynamic with no effects in the reactor operation due to the functional redundancy among diverse bacterial groups. In the archaeal domain and in both experiments, the microbial steady state was achieved at the same time as digesters regular operation (approximately 30–40 days in experiment 1 and 10–15 days in the second one). It seems that once established the proper archaeal community, a stable operation in terms of biogas production is reached as well. Collins et al. (2006) related the shifts in the archaeal populations with the changes in reactor performance and therefore a stable archaeal community is indicative of a steady operating digester. Moreover, archaeal population was more similar to the initial inoculum than the bacterial one, which is explained by the fact that methanogenic populations can use only a limited range of substrates (acetate, hydrogen, and carbon dioxide), regardless the type of waste treated (Ahring 2003). Until now, it was demonstrated that the type of substrate fed affected the microbial community structure established in the reactors and determined their operation. But trying to go beyond, can a direct relationship between the substrate fed and the microbial community be established? This seems difficult since the majority of the sequences belonged to uncultured populations, and moreover, the DGGE technique has clear limitations, since it does not reflect the community composition in a complete and unbiased way. Only two species correlated to the substrate fed in the reactors were identified:

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Ilyobacter, related with the biodiesel waste digestion (Bouvet et al. 1995), and Trichococcus, linked to the fermentation of carbohydrates present in molasses residues (Scheff et al. 1984). Recent studies have pointed out that the role of this population in AD processes has not been elucidated yet (Chouari et al. 2005; Nelson et al. 2011), but the fact of being a filamentous Bacteria may relate this phylum with the hydrolytic step. As PM was the substrate with the lowest solid content (Table 1), a high active hydrolytic bacterial population was not required for its biomethanation. The most abundant bacterial populations in the five reactors were Firmicutes and Proteobacteria, both considered as the two “major” phyla in anaerobic digesters (Nelson et al. 2011). Also, the presence of Pseudomonadaceae family in both experiments and in all the reactors, except in those treating PM, seems to be related with the treatment of solid wastes. Pseudomonales were found in lab-scale digesters treating household solid wastes (CardinaliRezende et al. 2009) and Duran et al. (2006) used Pseudomonas (commercial product) in order to bioaugment the anaerobic digestion of biosolids. Concerning the archaeal population, FISH results displayed that the entry of CW and MR as substrates determined the emergence of Methanosarcina species. They probably appeared in these anaerobic digesters to deal with the salinity (associated with CW) and with the ammonium and VFA accumulation (associated with MR). Karakashev et al. (2005) reported that the concentrations of ammonia and VFA seemed to have the most influence on the dominant Archaea population in anaerobic reactors and De Vrieze et al. (2012) showed that Methanosarcina was tolerant at high salt and ammonium concentrations. Summarizing, the main outcomes of this research are: (a) the length of the transitional states studied (start-up and change in feeding composition) was not dependent on the type of substrate fed, (b) the type of substrate clearly influence the microbial community structure, (c) the previous inoculum experience seems to be important to generate a flexible response to operational perturbations, (d) Ilyobacter and Trichococcus are directly related with the anaerobic biodegradation of BR and MR, respectively, and (e) the steady state at macroscopic level is related with the steady state in the archaeal domain. Although this study is a step forward in the attempt of linking the ecological role of the microbial communities with the operational and environment parameters, more research is needed and the new generation of molecular techniques will definitely help increasing the knowledge in this field.

Acknowledgments This research was supported by the Ministry of Economy and Competitiveness through NOVEDAR_Consolider (CSD2007-00055) and COMDIGEST (CTM2010-17196) projects and by the Xunta de Galicia through GRC2010/37 and MicroDAN (EM2012/ 087) projects and the postdoctoral contract (IPP-08-37) to Dr. Marta Carballa.

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