Article pubs.acs.org/est
Shaping Reactor Microbiomes to Produce the Fuel Precursor n‑Butyrate from Pretreated Cellulosic Hydrolysates Matthew T. Agler,† Jeffrey J. Werner,†,‡ Loren B. Iten,§ Arjan Dekker,† Michael A. Cotta,§ Bruce S. Dien,§ and Largus T. Angenent†,* †
Department of Biological and Environmental Engineering, Cornell University, Ithaca, New York 14853, United States Chemistry Department, SUNY Cortland, Bowers Hall, PO Box 2000, Cortland, New York 13045, United States § United States Department of Agriculture, Agricultural Research Service, Peoria, Illinois 61604, United States ‡
S Supporting Information *
ABSTRACT: To maximize the production of carboxylic acids with open cultures of microbial consortia (reactor microbiomes), we performed experiments to understand which factors affect the community dynamics and performance parameters. We operated six thermophilic (55 °C) bioreactors to test how the factors: (i) biomass pretreatment; (ii) bioreactor operating conditions; and (iii) bioreactor history (after perturbations during the operating period) affected total fermentation product and n-butyrate performance parameters with corn fiber as the cellulosic biomass waste. We observed a maximum total fermentation product yield of 39%, a n-butyrate yield of 23% (both on a COD basis), a maximum total fermentation production rate of 0.74 g COD l−1 d−1 and nbutyrate production rate of 0.47 g COD l−1 d−1 in bioreactors that were fed with dilute-acid pretreated corn fiber at a pH of 5.5. Pyrosequencing of 16S rRNA genes with constrained ordination and other statistical methods showed that changes in operating conditions to enable dilution of toxic carboxylic acid products, which lead to these maximum performance parameters, also altered the composition of the microbiome, and that the microbiome, in turn, affected the performance. Operating conditions are an important factor (tool for operators) to shape reactor microbiomes, but other factors, such as substrate composition after biomass pretreatment and bioreactor history are also important. Further optimization of operating conditions must relieve the toxicity of carboxylic acids at acidic bioreactor pH levels even more, and this can, for example, be accomplished by extracting the product from the bioreactor solutions.
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INTRODUCTION The carboxylate platform consists of a bioprocessing step that hydrolyzes and ferments biomass to short-chain carboxylates with reactor microbiomes (i.e., open cultures of mixed microbial consortia) under anaerobic conditions.1−3 Reactor microbiomes are advantageous compared to pure or definedmixed cultures when a complex, variable, and nonsterile substrate stream is utilized because they maintain functionality for years.4 For example, the circumvention of operating steps, such as sterilization, is advantageous to process operators in terms of economics and feasibility. We have grouped microbial pathways within the complex food web of the microbiome into hydrolysis and primary and secondary fermentation reactions.1 During primary fermentation, the substrate is converted to mixtures of mostly short-chain carboxylates (i.e., acetate, lactate, propionate, n-butyratehere we use the terminology of carboxylates to refer to both the undissociated and dissociated chemical species) and small concentrations of ethanol plus hydrogen and carbon dioxide off gases.1 Further microbial reactions can occur within the microbiome as © 2012 American Chemical Society
secondary fermentation reactions to form various products, including methane, but such reactions may also be inhibited when only primary fermentation products are desired.1 Anaerobic digestion has been the most successful application to date of the carboxylate platform because it converts cellulosic feedstocks to a single end productmethanewith a high product yield (ratio of product to substrate) and product specificity (ratio of product to all fermentation products) by combining hydrolysis and primary and secondary fermentations into one bioprocessing step.4,5 Because of the low economic value of methane, researchers are now exploring production of carboxylates as either primary or secondary fermentation end products (e.g., n-butyrate and n-caproate) as precursors for biofuels or industrial chemicals.1,6−8 n-Butyrate is a versatile carboxylate product that can be reduced to the biofuel nReceived: Revised: Accepted: Published: 10229
June 14, 2012 August 15, 2012 August 15, 2012 August 15, 2012 dx.doi.org/10.1021/es302352c | Environ. Sci. Technol. 2012, 46, 10229−10238
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Table 1. Composition of Corn Fiber Hydrolysate before and after Pretreatmenta treatment
TS [g l−1]
VS [g l−1]
TCOD [g l−1]
SCOD [g l−1]
glucose [mM]
xylose [mM]
arabinose [mM]
dilute acid
51.77 ± 3.71 (n = 15) 63.69 ± 6.66 (n = 15) 56.39 ± 8.68 (n = 15) b 67.50
50.63 ± 3.15 (n = 15) 56.06 ± 6.60 (n = 15) 55.68 ± 9.24 (n = 15) b 66.99
84.86 ± 6.52 (n = 9)
44.50 ± 2.51 (n = 6) 37.50 ± 5.53 (n = 6) 26.00 ± 1.92 (n = 6) 5.40 ± 2.42 (n = 4)
53.79 ± 3.06 (n = 2) 0.67 ± 0 (n = 2)
52.45 ± 1.74 (n = 2) 0.27 ± 0.09 (n = 2) 6.06 ± 0.47 (n = 2) NA
29.17 ± 3.39 (n = 2) 1.03 ± 0.14 (n = 2) 15.15 ± 1.84 (n = 2) NA
dilute alkali hot water none
93.87 ± 26.89 (n = 15) 80.60 ± 15.09 (n = 13) c 96.22 ± 12.41 (n = 8)
1.44 ± 0 (n = 2) NA
The value following ± represents the standard deviations, and n = represents the number of replicates. TS, VS, TCOD, and SCOD measurements were performed once or twice for each batch of substrate, depending on variability in replicates. Glucose, xylose, arabinose, and lactate were only performed for the first two batches of substrate. bNo standard deviation is provided for the unpretreated TS and VS because it was calculated based on how much corn fiber was added to water. cUnpretreated TCOD is calculated based on the COD of dry corn fiber and addition of 67.5 dry g TS to 1 L of water. a
butanol,9,10 incorporated into food and fragrance esters,11 and used directly as an antimicrobial.12 Abundant and low-value agricultural feedstocks, such as corn fiber (mostly pericarp [outer skin] of corn grain), wheat straw, manure, and corn stover (mostly stalks and leaves of the corn plant), are currently used in anaerobic digestion for methane production, and therefore are good candidates for conversion to higher-value carboxylates. These feedstocks are already collected with existing technology and they do not compete with human food production.13 Production of carboxylates, such as nbutyrate from agricultural feedstocks, however, has not been widely adapted because of important concerns that contribute to low product yields and production rates. The first important concern that specifically limits the product yield and rate is biomass recalcitrance to microbial hydrolysis of carbohydrates that are present within the plant cell-wall matrix. Biomass recalcitrance can be overcome using chemical/physical pretreatment strategies that improve microbial and enzymatic degradation rates by opening up the cellwall matrix.14,15 Dilute-acid,16 dilute-alkaline,17 and hot-water 18 pretreatment strategies have all been reported as effective for hemicellulose-rich feedstocks, such as corn fiber. Dilute-acid pretreatment can hydrolyze the hemicellulose all the way to monosaccharides, thereby exposing the cellulose fibers.19 Dilute-alkali pretreatment removes hemicellulose by extraction.20 Finally, hot-water pretreatment can be used instead of dilute-acid pretreatment to circumvent the need for chemicals, but hydrolysis of hemicellulose all the way to monosaccharides is limited.14 For each of these pretreatment technologies it is also important to monitor the toxicological effects of side products, such as for furfural during dilute-acid pretreatment, which can inhibit downstream bioprocessing. Reactor microbiomes can overcome such toxicity by degrading the inhibiting compounds because of their vast metabolic versatility.15,21 Once biomass recalcitrance is overcome and substrate can be converted to fermentation products, accumulation of carboxylates is the second important concern that limits product yield and rates by, for example, inhibiting hydrolysis of cellulose.22 Researchers usually maintain a lower pH to promote carboxylate specificity by inhibiting secondary fermentation pathways that lead to methane,23 but a lower pH increases the fraction of undissociated carboxylic acids, which are the inhibiting species. This toxicity can also inhibit community members that perform preferred secondary fermentation reactions that have the potential to increase product specificity. The third important concern that limits efficient product yield and rates is a lack of understanding of the relationships
between bioreactor operating conditions, microbial community structure, and bioreactor performance. Despite the presence of thousands of stable and well-functioning anaerobic digesters, reactor microbiomes are often still regarded as inefficient and unpredictable. However, this is not what we observed with computational ecology methods, including constrained ordination and machine learning; a clear link between community structure and function emerged in a time-series study with multiple anaerobic digesters,4 and this suggests that microbiomes could be shaped for a specific function. However, which effective tools are available to operators to intentionally shape microbiomes? And even when these tools are known, then: (i) are they economically feasible at a large industrial scale; and (ii) will they disturb positive qualities of microbiomes that are also pertinent? These positive qualities include their robustness and evenness,4,24 their ability to degrade toxic side products,15,21 and their vast metabolic versatility. The latter is important for degradation of lignocellulose feedstocks that are rich in hemicelluloses, and thus pentose polysaccharides. These pentoses are easily metabolized by versatile microbiomes, but not with pure yeast cultures or defined mixed cultures with a narrow metabolic palate.25 The objective of this study was to investigate the factors that control structure and function of reactor microbiomes that produced n-butyrate. Specifically, we targeted factors of which we were in control, so that they could be tools for operators to shape microbiomes. For this study, we found that these tools consisted of the following: (i) pretreating feedstock to change the substrate composition; (ii) controlling operating conditions, such as a pH; and (iii) manipulating the history of the microbiome by temporarily imposing perturbations, such as oxygen addition and heat shock. The objective of this study is, thus, not to achieve the maximum n-butyrate concentration that can be produced with, for example, easy-to-degrade substrates. Rather, we successfully used computational ecology methods to precisely link environmental and performance gradients with the community dynamics of the reactor microbiomes.
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MATERIALS AND METHODS Corn Fiber Pretreatment, Bioreactor Setup, And Bioreactor Operating Conditions. We received raw corn fiber substrate (Aventine ethanol wet-milling plant [Pekin, IL]) and pretreated it to release sugars that are bound in the lignocellulosic matrix and to reduce the recalcitrance to microbial degradation. The fiber was treated in fluidized sand bath reactors at 160 °C for 20 min in either dilute acid (0.5% 10230
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Laboratories, Inc., Carlsbad, CA), representing (i) inoculum; (ii) four time points with bioreactor samples (mixed liquor samples) during startup; (iii) at least three time points with bioreactor samples for each of the Periods 1−4 from Racid, Rbase, and Rheat; and (iv) a total of 8 time points with bioreactor samples from RhisA and RhisB before and after the heat shock. We subsequently amplified the V1−V2 region of 16S rRNA genes using universal bacterial primers 27F (including 454 primer “A”) and 338R (including 454 primer “B” and a unique barcode for each sample). We quantified the dsDNA in the amplified product, pooled the samples in equimolar concentrations, and sequenced on the Roche 454 pyrosequencing platform using Titanium chemistry (Engencore, Columbia, SC) (SI). Nucleotides and MIMARKS-compliant metadata were submitted to MG-RAST through the QIIME webportal. We used the QIIME 1.2.1 pipeline 27 to denoise, quality filter, split sequences into the proper samples, and pick operational taxonomy units (OTUs) at 97% sequence identity. This process resulted in 1063 OTUs with at least one read. For determining α diversity (the mean species diversity at the sample community level), we assigned taxonomy to the OTUs according to the GreenGenes database.28 We also used QIIME to determine the Gini coefficient (i.e., community evenness) and between-sample weighted UniFrac distances (i.e., β diversity [the differentiation between sample communities] by quantifying pairwise phylogenetic community dissimilarities). We calculated unweighted UniFrac distances but we only report weighted distances here because sample clustering was more informative. Redundancy Analysis of Community Structure and OTU Network Analysis. To identify β diversity, we used the principal coordinates analysis that is included in QIIME to graphically display as much of the weighted UniFrac similarities as possible in two dimensions. We performed this analysis twice; first, using only samples from Racid, Rbase, and Rheat, and second, using samples from Racid, Rbase, Rheat, RhisA, and RhisB. To statistically analyze the relationship between substrate composition (pretreatment), operating conditions, community structure, and performance, we used constrained redundancy analysis in the Vegan community ecology package for R.29 Constrained redundancy analysis recreates as much of the β diversity as possible in the original principal coordinates plots using bioreactor metadata (i.e., the constraints: pretreatment, operating conditions, and performance data). If the constrained analysis results in a β diversity model resembling the unconstrained redundancy (weighted UniFrac), then the community structures of the samples are correlated to and probably dependent on those constraints. ANOVA analysis determines whether each constraint added a significant amount of information to the constrained model (if p < 0.05), or if it could be left out (if p > 0.05). In addition to ANOVA, we used the variance inflation factor (VIF) to determine whether constraints describe the same β diversity (i.e., constraints are redundant in the model when VIF is large). We created two base constrained models for the UniFrac principal coordinates: (i) constrained by only pretreatment and operating conditions (constraint 1: pretreatment, pH, and HRT); and (ii) constrained by pretreatment/operating conditions and bioreactor history, where Racid, Rbase, Rheat = history 1 (constraint 2); RhisA and RhisB after air exposure = history 2 (constraint 3); and RhisA and RhisB after heat shock = history 3 (constraint 4). In both models, all constraints were statistically significant and nonredundant, indicating that all
w/w H2SO4), dilute alkali (1:10 Ca(OH)2 to dry biomass), or distilled water (Table 1 and Supporting Information, SI, Table S1). Because of the complex effects of bulk biomass pretreatment, we focused on the overall effect of pretreatments on the microbiota and only discussed individual effects when pertinent. Chemical analysis of the pretreated biomass is provided in Table 1 and SI Table S1. Four identical thermophilic (55 °C) anaerobic sequencing batch reactors (ASBRs) that were controlled at a pH of 5.5 were inoculated with a mix of inoculum from three sources (SI). Three of the reactors (Racid, Rbase, and Rheat) were fed dilute-acid, dilutealkali, or hot-water pretreated corn-fiber hydrolysate, respectively, and were operated at a constant volatile solids (VS) and chemical oxygen demand (COD) loading rate for 419 days (SI Table S2; Figure S1). The fourth thermophilic bioreactor was fed unpretreated corn fiber, but was discontinued after 100 days due to poor performance (SI). We divided the 419 days into four periods by adjusting the operating conditions in each sequential period to decrease carboxylic acid toxicity by the following: (i) shortening the hydraulic retention time (HRT) to dilute substrates and products; and (ii) increasing the pH to reduce the concentrations of undissociated carboxylic acids (SI Table S2). During the first operating period (Period 1) from day 1 to 163, a 25-d HRT and pH of 5.5 was maintained. During the second operating period (Period 2) from day 164 to 243, a shorter HRT of 20 days was applied while a pH of 5.5 was maintained. Similarly, during the third operating period (Period 3) from day 244 to 337, an even shorter HRT of 15 days was applied at a pH of 5.5. Because our loading rates were constant throughout the entire operating period, we accomplished shorter HRTs by adding more water to the pretreated corn fiber substrate solution during Period 2 and 3. During the final period (Period 4) from day 338 to 419, we increased the pH to 5.8 and maintained the 15-d HRT. The fifth and sixth bioreactors (RhisA and RhisB) were fed dilute-acid pretreated corn-fiber hydrolysate, which is a similar substrate as fed to Racid. Therefore, these bioreactors were inoculated from biomass out of Racid during Period 3 and were operated for 200 days at a pH of 5.5 and a 15-d HRT (Period 3 conditions). We used the replicate RhisA and RhisB to test the effects of bioreactor history by perturbing the microbiome via: (1) exposing to oxygen on day 0; and then (2) subjecting to heat shock on day 28 of the operating period. To cause oxygen exposure, we mixed the bioreactor contents vigorously in a bucket that was open to the atmosphere for five min. A heat shock was implemented by increasing the temperature of the water heating jackets of RhisA and RhisB to 70 °C for 24 h (SI). Chemical Analysis. All measurements were performed according to Standard Methods,26 unless otherwise indicated. We measured biogas production, ambient temperature, and ambient pressure daily. Every week, we evaluated the hydrogen content of the biogas by GC. Other weekly measurements of the effluent were total solids (TS) and VS, short-chain carboxylates, alcohols, and soluble and total COD (SCOD and TCOD). Monthly, we measured the concentration of effluent soluble carbohydrates. After day 163, we also characterized the mixed liquor VS, TS, and sludge volume index (SVI) every week (SI). DNA Extraction, Amplification, And Data Preparation. We surveyed microbiome composition via high-throughput sequencing of bacterial 16S rRNA gene amplicons. We extracted DNA from 64 microbiome samples using the MoBio PowerSoil 96-well gDNA isolation kit (MoBio 10231
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Table 2. Expansion of Operating Condition-Constrained Community Structure Models with Performance Parameters base constraints → added constraint none (base constraints) ethanol production rate H2 production rate H2 pressure n-butyrate production rate n-caproate production rate undissociated, short-chain carboxylic acid concentration acetate production rate
operating conditions explained inertia (%) 54.1 55.6 54.8 54.8 54.7 73.5 63.7 60.8
VIF 1.30 1.60 1.75 2.25 9.76a 2.29 11.40a 3.27a
operating conditions/history
ANOVA (Pr > F) a
0.01 0.16 0.10 0.25 0.31 0.02a 0.03a 0.03a
explained inertia (%)
VIF
ANOVA (Pr > F)
84.1 84.2 84.7 84.4 84.4 84.6 84.3 84.1
2.14 2.23 2.16 2.36 11.59a 5.54a 17.71a 6.11a
0.01a 0.42 0.10 0.22 0.24 0.11 0.17 0.17
a These values are statistically significant, indicating that the performance parameter described some of the same community structure as the base constraints (VIF) or that the performance parameter described some of the community structure not constrained by the base constraints (ANOVA).
Figure 1. Fermentation production rates (as g COD per liter bioreactor volume per day) in Racid, Rbase, Rheat, RhisA, and RhisB: A. Steady-state nbutyrate and total fermentation production rates in Racid, Rbase, and Rheat during Period 1 to Period 4. Error bars represent the standard deviation of three measurements; and B. Steady-state acetate, n-butyrate, n-caproate, and total fermentation production rates in Period 3 for Racid, Rbase, and Rheat, and after air exposure and heat shock for RhisA and RhisB. Time periods signify the following major changes in operating conditions: startup and stable operation (Period 1), decrease in HRT from 25 to 20 d (Period 2), decrease in HRT from 20 to 15 d (Period 3), increase in pH from 5.5 to 5.8 (Period 4). Operating conditions for RhisA and RhisB were identical to Racid during Period 3.
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RESULTS AND DISCUSSION Effect of Pretreatment of Cellulosic Biomass. The three corn-fiber pretreatment strategies tested in this study (diluteacid, dilute-alkali, and hot-water) resulted in very different biomass hydrolysates (Table 1 and SI Table S1) with unique ecotoxicological profiles.21 Dilute-acid pretreatment achieved the largest reduction in TS and VS with corn fiber compared to dilute-alkali and hot-water pretreatments, resulting in the highest SCOD concentration of 45 g L−1 in the dilute-acid hydrolysate. This hydrolysate included the monosaccharides glucose (primarily from starch), and xylose and arabinose (from hemicellulose) at considerable concentrations (Table 1), which were completely fermentable by the microbiome (SI Figure S1). This explains why total fermentation product yields and rates (expressed as rates in Figure 1 and SI Figure S1) were the highest in Racid compared to the other bioreactors during Period 1−3 at a pH of 5.5 (Figure 1A, SI Figure S1 and Table S3). Dilute-alkali pretreatment resulted in a SCOD concentration of 38 g l−1, which was lower than dilute-acid pretreatment, but higher than hot-water treatment. Only low
four constraints were causative for community structure. Next, we used the statistical measurements (ANOVA and VIF) to determine if performance metrics were correlated to some of the same community structure dynamics compared to the following: (i) pretreatment and operating conditions; (ii) history; or (iii) other constraints. We added seven performance metrics (Table 2), each one at a time, to the two base models, creating two new constrained models for each metric. For each model, a significant ANOVA value (p < 0.05) indicates that adding a new metric improved the model, and thus was correlated to previously unconstrained community structure. A significant doubling of VIF indicates redundancy in the model (the added metric was correlated to structure that was already included in the model by another constraint). Both ANOVA and VIF can be simultaneously significant. We used correlation calculating function in R 2.13.2 30 and Cytoscape 2.8.0 31 to build a network (SI). 10232
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levels of monosaccharides were detected in the dilute-alkali hydrolysate (Table 1), and most of the soluble carbohydrates were polysaccharides (most likely xylan; SI), which were not completely fermented in Rbase (SI Figure S1). This resulted in the poorest total fermentation production rates for Rbase compared to the other bioreactors. The hot-water pretreatment strategy resulted in a SCOD concentration of 26 g L−1 with considerable concentrations of xylose and arabinose monosaccharides from hemicellulose (Table 1). The total fermentation production rates for Rheat were between Racid and Rbase because of two reasons: (1) intermediate concentrations of easily fermentable monosaccharides with hot-water vs diluteacid and dilute-alkali methods; and (2) biomass in Rheat settled better than in Racid and Rbase, resulting in the longest solids retention times and the most efficient biological VS removal efficiencies (SI Figure S2). Operating Condition Changes to Reduce Product Toxicity. After an initial start-up period at the beginning of Period 1 (SI), steady-state production rates of short-chain carboxylates and ethanol were achieved for each period in the bioreactors (Figure 1A and SI Figure S1). Our experimental design included dilution of the substrate and carboxylate products by shortening the HRT from 25 to 15 days (with a controlled pH of 5.5) to alleviate toxicity from undissociated carboxylic acids.22,32 Indeed, the total fermentation production rate increased by 15−22% between Period 1 and 3 for all bioreactors (Figure 1A and SI Table S3). The enhanced total fermentation production rates were correlated with an increase in relative abundance of OTUs within the genus Thermoanaerobacterium (family Thermoanaerobacterales) for the three bioreactors (Figure 2A). Thermoanaerobacterium spp. ferments xylose and xylan (a xylose polymer), which are the largest constituents of corn fiber, to short-chain carboxylates.33,34 The high abundance of this bacterial genus suggests it was a very important primary fermenter of pentose or pentose polysaccharides in our system. The effect of dilution was even more pronounced for the n-butyrate production rates (and also nbutyrate specificity) than the total fermentation production rates because dilution increased the n-butyrate production rate by 25−46% between Period 1 and 3 for all bioreactors (Figure 1A and SI Table S3). We had anticipated that an increase in pH from 5.5 to 5.8 during Period 4 would further promote pentose degradation and carboxylate production due to lower undissociated carboxylic acid concentrations, but this did not occur. Even though the undissociated carboxylic acid concentrations did decrease by ∼50% at the end of Period 4 (SI Table S3), the pH increase did not have the preferred outcome because total fermentation production rates and n-butyrate production rates stayed similar or declined between Period 3 and 4 for all bioreactors (SI Table S3). The slight increase in pH caused a decreasing trend in the relative abundance of Thermoanaerobacterium spp. (Figure 2B), likely because of inferior growth rates at a higher pH, which most likely reduced degradation of soluble and insoluble xylan (soluble mono sugars were always completely degraded, SI Figure S1). This suggests that Thermoanaerobacterium OTUs may utilize mechanisms preventing the toxic effects of carboxylic acids, as was shown for other bacteria to optimize growth at conditions of low pH/high carboxylate concentrations.22 These complex interactions between substrate type, population dynamics, environmental conditions, and product inhibition also help explain ambiguities between studies.35 Thus, it is clear that effects of changes in pH
Figure 2. Dynamics of the highly abundant genus Thermoanaerobacterium spp.: A. Thermoanaerobacterium spp. relative abundance corresponded to total fermentation production rates, R2 = 0.36; and B. Thermoanaerobacterium spp. relative abundance trended downward when the pH was increased to 5.8 during Period 4.
are not universal for reactor microbiomes due to the growth optima of different populations for each substrate type. In addition to linking Thermoanaerobacterium spp. (α diversity) to operating conditions and bioreactor performances, β diversity was also investigated. Principal coordinates analysis of the weighted UniFrac community distances enables visualization of 73.4% of all between-sample distances for Racid, Rbase, and Rheat in two dimensions (Figure 3A). Clearly, the reactor microbiomes of the three bioreactors developed uniquely and this resulted in grouping by pretreatment (Figure 3A). In addition, changes in operating conditions from Period 1 to Period 4 resulted in shifts in the bacterial community structure and this resulted in grouping by period (Figure 3A; SI Figure S3). Thus, both the composition of the substrate (after different pretreatment methods) and changes in operating conditions affected the microbiome composition. This also resulted in different bioreactor performance. The maximum total fermentation production rate (0.74 g COD l−1 d−1), the n-butyrate production rate (0.44 g COD l−1 d−1), and the n-butyrate specificity (59%) were the highest for Racid compared to Rbase and Rheat at a pH of 5.5 and a 15-d HRT (Figure 1 and SI Table S3). Under these conditions and based on the before-pretreatment corn fiber VS, the total 10233
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Figure 3. Redundancy analysis of the phylogeny described by weighted UniFrac principal coordinates: (A,B) Principal components analysis showing as much of the UniFrac distance between samples as possible in two dimensions demonstrates the effect on community of operating conditions and bioreactor history. In A, points from steady state samples in the same period are connected to their centroid (average location) by blue lines, and the centroid is labeled with the period name (P1: Period 1, P2: Period 2, P3: Period 3, P4: Period 4); and (C,D) Constrained redundancy analysis with operating conditions or operating conditions and history recreates 0.51 and 0.81, respectively, of the community structure shown by the principal coordinates in B.
fermentation product and n-butyrate yields (based on COD) in Racid were 39% (0.56 g COD/g VS fed) and 23% (0.33 g COD/ g VS fed), respectively. We hypothesize that several reasons exists for the maximum performance of Racid, and all reasons would also affect the microbiome structure. First, the larger availability of rapidly fermentable monosaccharides in diluteacid vs dilute-base and hot-water hydrolysates promoted formation of relatively more reduced fermentation products (n-butyrate vs acetate) in primary fermentation reactions as a mechanism to produce fewer toxic acids and to dispose of reducing equivalents quickly.1,36 Second, Heger et al. 21 found that dilute-acid pretreated corn fiber (from this study) causes acute cellular toxicity in ecotoxicological assays while the toxicity of the dilute-base and hot-water pretreated corn fiber was less pronounced. They observed in their assays that the microbiome in Racid at day 70 of the operating period removed this toxicity by degrading unresolved compounds. We hypothesize that these compounds may, therefore, have affected the metabolism of bacteria in our bioreactors, but follow up work is needed. Third, the interplay of primary and secondary fermentation pathways to produce n-butyrate, and secondary fermentation pathways to remove n-butyrate in the complex food web plays important roles in bioreactor performance. Effect of Bioreactor History on the Microbiome. In addition to differences in pretreatment and operating
conditions, the effect of bioreactor history on microbiome and bioreactor performance was analyzed. Replicate bioreactors RhisA and RhisB were operated similarly to Racid (during Period 3) for 200 days. The two bioreactors reached stable performance after two perturbations (air exposure and heat shock) and performed similarly (p < 0.05), which statistically quantifies the duplicate reactors as replicates (Figure 1B and SI Table S3). These data also show that meaningful data can be derived from Racid, Rbase, and Rheat even though they were not replicatedRhisA and RhisB showed replicated performance and microbiome structure even after two perturbations. The two bioreactors were perturbed with oxygen at day 0 of the operating period and generated total fermentation production rates that were only 6−8% lower than the rate for Racid mostly due to lower acetate production rates. The n-butyrate production rate in RhisA and RhisB was slightly higher than in Racid during Period 3; 13% higher after oxygen exposure compared to Racid and 8% higher after heat shock compared to Racid (Figure 1B and SI Table S3). The highest n-butyrate production rate for this study was on average 0.50 g COD l−1 d−1 after the oxygen perturbation for RhisA and RhisB (Table S3). After adding the microbiome data from RhisA and RhisB to the principal coordinates plot, the visualization showed 67.7% of between-sample distances for all five bioreactors (Figure 3B). We showed a strong effect of bioreactor history because after 10234
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Figure 4. Primary and secondary fermenting microbial communities are revealed via OTU network and taxonomic analysis: (A) Correlation network of relatively abundant OTUs, where the large central network is dominated by primary fermenting Thermoanaerobacterium and green edges are a separate network likely responsible for flux of lactate in secondary fermentation. Nodes are OTUs scaled by highest abundance in any sample, while edges are solid for positive correlation and dashed for negative correlation. Edges are also weighted by strength of the correlation. OTU taxonomy is given at the family level unless taxonomy assignment was not that specific (k: kingdom, p: phyla, c: class, o: order).; (B) The structure of primary fermenting Thermoanaerobacterium OTUs could switch between two general states due to bioreactor history, regardless of operating conditions, indicating functional redundancy in primary fermentation.
exposure to oxygen on day 0 of the operating period the microbiome was different from Racid, and the heat shock on day 28 caused the microbiome to change again to a different state (Figure 3B). Constrained redundancy analysis confirmed our results by demonstrating that pretreatment, operating conditions (HRT and pH), and bioreactor history significantly affected the microbiome. Specifically, constraining the model with only pretreatment and operating conditions recreated between-sample distances for all five bioreactors with a relative ratio of 0.541 (36.6% [in Figure 3C]/67.6% [in Figure 3B]). Including bioreactor history as a fourth constraining variable resulted in a relative ratio of 0.841 for the constrained model (56.9% [in Figure 3D]/67.6% [in Figure 3B]). Thus, if we extrapolate this specifically for our study, over half of the between-sample distances of the five bioreactors were due to different pretreatment and operating conditions (0.541), but at least one-third (0.841−0.541) was due to bioreactor history. Bioreactor history can, therefore, be an important tool for us to shape the microbiomes; that is, when we know the precise effect of each perturbation. Even through pretreatment, operating conditions, and bioreactor history affected the microbiome, those changes did not necessarily cascade to changes in performance parameters. A more in-depth statistical analysis was performed to determine which reactor microbiome changes due to pretreatment, operating conditions and/or history did affect specific performance parameters. Many, but not all, performance parameters were correlated with microbiome alterations. Fluctuations in the ethanol production rate, hydrogen production rate, and hydrogen partial pressure were not associated with microbiome changes (low VIFs and insignificant ANOVA values; Table 2). However, several performance parameters were affected by microbiome changes due to either or both the pretreatment/
operating conditions and history; these are as follows: (i) acetate production rateboth; n-butyrate production rate only pretreatment/operating conditions; n-caproate production rateonly history; total undissociated, short-chain carboxylic acid concentrationboth (Table 2). None of the performance parameters that we tested were affected by microbiome changes due to other factors than pretreatment/operating conditions or history. Therefore, a relative ratio of 0.159 (1−0.841) betweensample distances for all five bioreactors not explained by our constrained model was due to performance-silent, random variations to the microbiome composition or due to measurement errors. Factors That Control Reactor Microbiomes. Several factors were studied that could become tools for operators seeking to improve performance by shaping reactor microbiomes. These factors were (i) pretreatment methods to change substrate composition; (ii) operating conditions; and (iii) bioreactor history. β diversity analyses showed that the reactor microbiomes grouped most clearly based on pretreatment methods, which changed the composition of the substrate. These analyses also showed that changes in the operating conditions changed the reactor microbiome. Finally, it was shown that history can also affect the reactor microbiome dynamics and that pretreatment, operating conditions, and history were linked to most of the microbiome variation (0.841), which was considerably higher than only pretreatment/operating conditions. It is important to understand the complexity of the reactor microbiome because changes to the microbiome do not always affect the performance that is most pertinent. In this study, the statistical analysis showed that pretreatment/operating conditions affected n-butyrate production rates in part via changes to the microbiome. The analysis also indicated that the 10235
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to feeding different corn fiber hydrolysates because the heat shock perturbation caused the replicated reactors RhisA and RhisB to switch from a Racid-like state to a Rbase-like state (this was also observed in β diversity plots [Figure 3B]), even though these two bioreactors were operated the same as Racid (Figure 4B). Because of the placement of Thermoanaerobacterium spp. in the middle of the microbiome food web (acidogenesis, which is a primary fermentation pathway), many other bacteria, including members of the family Ruminococcaceae, were connected, but for most of these bacteria, we do not know if they are upstream or downstream of acidogenesis in this food web. The dominance of the central network may have resulted in relatively uneven microbiota (high Gini coefficient in SI Figure S4C of the SI). This is similar to what was observed with other high-throughput sequencing efforts for thermophilic acidogenic systems.40 For mesophilic systems, robust systems have been correlated to more even microbiota than we observed in our study.4,41 It is possible that a characteristic of thermophilic systems is that robustness occurs at more uneven microbiota compared to mesophilic systems, but more research is needed to elucidate this. One small, independent network of four OTUs was separated from the central network (Figure 4A); and is referred to here as the lactate group, because the taxonomy of three out of four OTUs strongly suggests an involvement in lactate production (primary fermentation) or degradation, including the secondary fermentation pathways of: (i) lactate + acetate → n-butyrate; and (ii) lactate + n-butyrate → n-caproate, which we observed during 48-h cycle analysis (SI Figure S5). The first OTU was closely related to Lactobacillus spp. (family Lactobacillaceae), which likely produced lactate and was negatively correlated with n-butyrate production rates over all samples (data not shown). The second and third OTUs were closely related to a member of the family Porphyromonadaceae, which includes known lactate producers,42 and a species of Selenomonas ruminantium (family Veillonellaceae), which is known to ferment lactate, respectively.43 Another OTU, which was separated from both the central network and the lactate group, was Thermosinus, belonging to the same family of Veillonellaceae compared to S. ruminantium. The relative abundance of Thermosinus spp. was positively correlated with rates of n-caproate formation in all three bioreactors (SI Figure S6). Indeed, Veillonellaceae also includes the mesophilic bacterium Megasphaera elsdenii that can grow on lactate by generating acetate, n-butyrate, and n-caproate.44 These results indicate that OTUs that are interconnected with secondary fermentation pathways may be located outside of the central network. However, more research is needed to elucidate if this is generally also observed for other microbiomes.
microbiome composition that was correlated specifically to the n-butyrate production rates was not affected by bioreactor history. In other words, even when perturbations during our history experiments showed a change in microbiome composition, the n-butyrate production rates stayed similar, indicating functional redundancy. Functional redundancy with the presence of parallel pathways for substrate conversion is important for engineered systems with reactor microbiomes because it guarantees functional stability during upsets.4,24 To develop carboxylate-producing bioreactors at the industrial scale, the operator must choose tools to shape the microbiome carefully, because improving functional redundancy may be as important as maximizing the carboxylate production rates. Other bioreactor sequencing efforts have found stable microbiome compositions in carboxylate-producing bioreactors that were linked to performance.37,38 Our work differs from these studies, though, because we have statistically been able to separate the microbiome composition changes from the performance changes. A simple correlation of OTU relative abundance to n-butyrate production rates would have led to misleading conclusions. Other tools to shape microbiomes, which may be effective, but that were not studied here, are as follows: (i) adding inhibiting compounds, trace elements, and/or reduced substrates to bioreactors; and (ii) extracting the product.1,8 In this study, no additional compounds were added to the bioreactors because of anticipated high costs, which would not be recoverable by producing n-butyrate at an industrial scale. When operators can specifically extract the carboxylate product from the bioreactor solutions with, for example, liquid/liquid extraction,32 then we anticipate that a reactor microbiome would be shaped with the same characteristics, such as functional redundancy, that are responsible for stability of anaerobic digesters. In stable and well-performing digesters, methane is produced with a very high yield and specificity because methane freely bubbles out from solution and is continuously removed, resulting in low byproduct (carboxylate) concentrations. We, therefore, anticipate that product extraction is necessary to further increase the n-butyrate yield and specificity, and that this would release the toxicity pressures on the microbiome (accumulation of n-butyric acid and other carboxylate acids)the results of this study showed that changing such operating conditions would both change the performance and the microbiome composition. The resulting increase in hydrolysis rates may double the n-butyrate yields to the methane yield levels that are currently found in anaerobic digesters for lignocellulose degradation.39 Making Sense of the Microbiome Food Web. To understand how the individual OTUs interacted with each other in the reactor microbiome, we performed a network analysis of OTUs with positive or negative correlations of cooccurrence. Most OTUs, including the abundant ones, grouped into a large, central network that comprised 86% (average) of the relative abundance in bioreactors (Figure 4A). The largest fraction of OTUs in the central network (17/35 OTUs) was assigned to Thermoanaerobacterium spp. (28% of the entire community). The vast connectivity of this abundant group of bacteria shows its importance in the microbiome food web, which is in agreement with the correlation we observed of these OTUs to total fermentation production rates (Figure 2A). In addition, one of two Thermoanaerobacterium spp. OTUs consistently dominated the OTUs in all bioreactors (Figure 4B). These alternative OTUs (i.e., states) were not just related
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S Supporting Information *
16S rRNA gene sequencing data and annotated metadata, publicly available for download via MG-RAST project ID 1596, http://metagenomics.anl.gov/linkin.cgi?project=1596). This material is available free of charge via the Internet at http:// pubs.acs.org.
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*E-mail:
[email protected]. 10236
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Notes
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The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors acknowledge Joseph G. Usack and Dr. Miriam Agler-Rosenbaum for reviewing the manuscript. The project was supported by the National Research Initiative of the USDA Cooperative State Research, Education and Extension Service, Grant No. 2007-35504-18256.
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