Shaping Reactor Microbiomes to Produce

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.
■
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-butyratehere 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
© XXXX 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 productmethanewith 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: June 14, 2012
Revised: August 15, 2012
Accepted: August 15, 2012
A
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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.
■
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%
B
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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
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Table 2. Expansion of Operating Condition-Constrained Community Structure Models with Performance Parameters
base constraints →
operating conditions
added constraint
explained inertia
(%)
VIF
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
54.1
55.6
54.8
54.8
54.7
73.5
63.7
60.8
1.30
1.60
1.75
2.25
9.76a
2.29
11.40a
3.27a
operating conditions/history
ANOVA (Pr > F)
explained inertia
(%)
VIF
ANOVA (Pr > F)
0.01a
0.16
0.10
0.25
0.31
0.02a
0.03a
0.03a
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.
■
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).
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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
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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
replicatedRhisA 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
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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 rateboth; n-butyrate production rate
only pretreatment/operating conditions; n-caproate production
rateonly history; total undissociated, short-chain carboxylic
acid concentrationboth (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
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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
■
ASSOCIATED CONTENT
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.
■
AUTHOR INFORMATION
Corresponding Author
*E-mail: [email protected].
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The authors declare no competing financial interest.
■
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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