Ong et al. Biotechnol Biofuels (2016) 9:237
DOI 10.1186/s13068-016-0657-0
Biotechnology for Biofuels
Open Access
RESEARCH
Inhibition of microbial biofuel
production in drought-stressed switchgrass
hydrolysate
Rebecca Garlock Ong1,2,3*, Alan Higbee4, Scott Bottoms5, Quinn Dickinson5, Dan Xie5, Scott A. Smith6,
Jose Serate5, Edward Pohlmann5, Arthur Daniel Jones6,7,8, Joshua J. Coon4,9,10, Trey K. Sato5, Gregg R. Sanford5,11,
Dustin Eilert5, Lawrence G. Oates5,11, Jeff S. Piotrowski5, Donna M. Bates5, David Cavalier1 and Yaoping Zhang5
Abstract
Background: Interannual variability in precipitation, particularly drought, can affect lignocellulosic crop biomass
yields and composition, and is expected to increase biofuel yield variability. However, the effect of precipitation on
downstream fermentation processes has never been directly characterized. In order to investigate the impact of interannual climate variability on biofuel production, corn stover and switchgrass were collected during 3 years with significantly different precipitation profiles, representing a major drought year (2012) and 2 years with average precipitation
for the entire season (2010 and 2013). All feedstocks were AFEX (ammonia fiber expansion)-pretreated, enzymatically
hydrolyzed, and the hydrolysates separately fermented using xylose-utilizing strains of Saccharomyces cerevisiae and
Zymomonas mobilis. A chemical genomics approach was also used to evaluate the growth of yeast mutants in the
hydrolysates.
Results: While most corn stover and switchgrass hydrolysates were readily fermented, growth of S. cerevisiae was
completely inhibited in hydrolysate generated from drought-stressed switchgrass. Based on chemical genomics
analysis, yeast strains deficient in genes related to protein trafficking within the cell were significantly more resistant to the drought-year switchgrass hydrolysate. Detailed biomass and hydrolysate characterization revealed that
switchgrass accumulated greater concentrations of soluble sugars in response to the drought and these sugars were
subsequently degraded to pyrazines and imidazoles during ammonia-based pretreatment. When added ex situ to
normal switchgrass hydrolysate, imidazoles and pyrazines caused anaerobic growth inhibition of S. cerevisiae.
Conclusions: In response to the osmotic pressures experienced during drought stress, plants accumulate soluble sugars that are susceptible to degradation during chemical pretreatments. For ammonia-based pretreatment,
these sugars degrade to imidazoles and pyrazines. These compounds contribute to S. cerevisiae growth inhibition
in drought-year switchgrass hydrolysate. This work discovered that variation in environmental conditions during
the growth of bioenergy crops could have significant detrimental effects on fermentation organisms during biofuel
production. These findings are relevant to regions where climate change is predicted to cause an increased incidence
of drought and to marginal lands with poor water-holding capacity, where fluctuations in soil moisture may trigger
frequent drought stress response in lignocellulosic feedstocks.
Keywords: Biofuel, Corn stover, Drought, Fermentation inhibition, Lignocellulose, Saccharomyces cerevisiae,
Switchgrass
*Correspondence: rgong1@mtu.edu
3
Department of Chemical Engineering, Michigan Technological
University, Houghton, MI, USA
Full list of author information is available at the end of the article
© The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license,
and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/
publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Ong et al. Biotechnol Biofuels (2016) 9:237
Results
Drought‑year switchgrass hydrolysate is inhibitory
to Saccharomyces cerevisiae growth and fermentation
Corn stover (Pioneer 35H56 and P0448R) and switchgrass (Shawnee and Cave-in-Rock) were harvested from
the Arlington Agricultural Research Station (ARL) in
south central Wisconsin from three growing seasons
(2010, 2012, and 2013) that represent, with respect
to total precipitation, an average year (2010), a major
drought year (2012), and a year that was wet during the
first half of the growing season and dry during the second half (2013) (Fig. 1). Each feedstock was processed
using AFEX pretreatment and subjected to high solid
loading enzymatic hydrolysis [6 and 7% glucan-loading
for AFEX-treated corn stover hydrolysates (ACSH) and
AFEX-treated switchgrass hydrolysates (ASGH), respectively] at previously optimized conditions [16]. Engineered xylose-utilizing ethanologens, S. cerevisiae Y128
[17] and Z. mobilis 2032 [18], were used to compare cell
growth, glucose and xylose utilization, and ethanol production in the hydrolysates produced from corn stover
and switchgrass harvested in different years. Z. mobilis
exhibited similar growth, sugar utilization, and ethanol production for all hydrolysates, with slightly lower
final cell densities but greater xylose consumption in the
switchgrass hydrolysates (Fig. 2; Table 1). Saccharomyces
cerevisiae showed similar growth in all corn stover hydrolysates, but reduced xylose consumption in drought-year
Accumulated growing
degree days (base 10 C)
a
b
Accumulated monthly
precipitation (mm)
Background
Biofuels generated from lignocellulosic materials have
enormous potential to reduce transportation-generated
greenhouse gas emissions [1]. By 2030, the US could be
capable of supplying as much as 1.2 billion dry tons of
agricultural residues and dedicated herbaceous energy
feedstocks, enough to generate 58 billion gallons of ethanol per year [2]. However, biomass production in any
given year is highly dependent on weather conditions.
Soil moisture levels during a growing season are affected
by both past and current levels of precipitation, and are
a major determinant of lignocellulosic biomass yields in
non-irrigated systems [3, 4]. Low levels of precipitation
and soil moisture are particularly detrimental. Plants
grown under water stressed conditions have reduced
photosynthesis and slower growth, which reduces biomass yields [4–6]. Drought stress can also affect plant
chemical composition, often resulting in reduced levels
of structural carbohydrates [7–9] and accumulation of
compounds that protect against osmotic stresses, including soluble sugars and amino acids (e.g., proline) [5, 6].
These changes in plant composition are also predicted to
result in lower ethanol yields from drought-stressed feedstocks [7, 8], although actual fermentations have never
been carried out.
A number of different potential lignocellulosic bioenergy feedstocks are being considered in the US, including agricultural residues such as corn stover (Zea mays
L.), and dedicated energy crops such as switchgrass
(Panicum virgatum L.). Corn stover is currently the
feedstock of choice due to its current widespread availability and economic potential [2, 10]. Switchgrass is a
promising perennial bioenergy crop that can be grown
on marginal lands [11] and provides superior environmental benefits compared to corn, including support for
biological diversity [12], lower nitrous oxide emissions
[13], and improved soil properties [14, 15]. In order to
investigate how interannual variation in precipitation
influences the processing characteristics and microbial
fermentation of these two important biofuel feedstocks,
we compared switchgrass and corn stover that were
harvested following the 2012 Midwestern US drought to
those harvested during two non-drought years with different precipitation patterns (2010 and 2013). In order
to generate fermentable sugars, these materials were
processed using ammonia fiber expansion (AFEX) pretreatment followed by enzymatic hydrolysis. The chemical composition of the feedstocks and hydrolysates
were analyzed and the hydrolysates were fermented
separately by Saccharomyces cerevisiae and Zymomonas
mobilis. We also used a chemical genomics approach to
evaluate the yeast biological response to the different
hydrolysates.
Page 2 of 14
2000
Monthly accumulated GDD
1750
30y Normals (1981 - 2010)
1500
1250
1000
750
500
250
0
A M J J A S O
A M J J A S O
A M J J A S O
2010
2012
2013
250
Monthly accumulated precipitation
30y Normals (1981 - 2010)
200
150
100
50
0
A M J J A S O
A M J J A S O
A M J J A S O
2010
2012
2013
Fig. 1 Interannual weather variation. a Temperature [growing degree
days (GDD)] and b precipitation for 2010, 2012, and 2013, and the
30-year average values at Arlington Research Station in south central
Wisconsin (ARL, 43˚17′45″ N, 89˚22′48″ W, 315 masl)
Ong et al. Biotechnol Biofuels (2016) 9:237
8
80
60
6
40
20
a
2010CS (36H56)
0
0
20
40
8
80
60
6
4
40
2
20
0
0
60
b
0
Time (h)
e
20
40
8
80
60
6
60
6
4
40
4
40
4
2
20
2
20
2
0
0
0
0
60
c
0
Time (h)
2010SG (Shawnee)
60
8
80
6
60
f
2012CS (P0448R)
20
40
60
Time (h)
2012SG (Shawnee)
8
80
6
60
g
2013SG (CIR)
6
40
4
40
4
20
2
20
2
20
2
0
0
0
0
20
40
Time (h)
60
0
20
40
60
Time (h)
8
0
20
40
60
Time (h)
4
0
2013CS (P0448R)
8
40
0
d
0
Cell density (OD 600)
Glucose, xylose and ethanol
concentration (g/L)
80
2012CS (36H56)
Cell density (OD 600)
Glucose, xylose and ethanol
concentration (g/L)
80
Page 3 of 14
OD
Glucose
Xylose
Ethanol
0
0
20
40
60
Time (h)
Fig. 2 Fermentation profiles for Zymomonas mobilis 2032 grown in corn stover and switchgrass hydrolysates from different harvest years. a 2010
ACSH (36H56), b 2012 ACSH (36H56), c 2012 ACSH (P0448R), d 2013 ACSH (P0448R), e 2010 ASGH (Shawnee), f 2012 ASGH (Shawnee), g 2013 ASGH
[Cave-in-Rock (CIR)]. Data points represent the mean ± SD (n = 3). Error bars that are smaller than the individual data points may be hidden from
view
2012 ASCH (P0448R) (Fig. 3a–d; Table 1). In the 2010
and 2013 ASGH, S. cerevisiae grew and consumed xylose
more slowly than in the corn stover hydrolysates harvested in the same years (Fig. 3e, g; Table 1), but completely failed to grow or ferment glucose or xylose in the
drought-year 2012 ASGH (Fig. 3f ). With the exception
of the S. cerevisiae fermentation of 2012 ASGH, all of
the fermentations achieved final ethanol concentrations
of between 30 and 40 g/L and ethanol yields of between
~200 and 300 L/Mg untreated dry biomass (~45–70% of
theoretical maximum) (Table 1).
Chemical genomic analysis of hydrolysates reveals a
distinct pattern for drought‑year switchgrass hydrolysate
Chemical genomic analysis was used to measure the relative fitness of ~3500 single-gene deletion yeast strains
[19] in the hydrolysates compared to synthetic hydrolysate [16] (Additional file 1). This analysis revealed a
growth sensitivity profile of the 2012 ASGH that was
drastically different from all other tested hydrolysates
(Fig. 4a), which displayed profiles similar to those seen
for ACSH and ASGH in a previous study [16]. The two
most resistant mutants to the 2012 ASGH are kex2Δ
and vps5Δ (Fig. 4b): the first of which encodes a protein
residing in the trans-Golgi network [20], and the latter is part of the retromer complex for recycling of proteins from the late endosome to the Golgi apparatus
[21]. Of the mutants that were highly susceptible in at
least one of the hydrolysates (fitness < −2.5), 65 (16%)
were susceptible to all five hydrolysates (Fig. 4c), with
enrichment (p < 0.05) in genes related to amino acid
biosynthesis (Additional file 2: Fig. S1). In contrast, of
the 224 mutants that were highly resistant in at least
one of the hydrolysates, only three were highly resistant
to all five hydrolysates (fitness > 2.5) (Fig. 4c): ygr237cΔ,
ydr474cΔ, and bck1Δ. The contrast between the 2012
ASGH and the other four feedstocks is reflected in the
fact that 57 (14%) and 42 (19%) of highly susceptible and
resistant mutants, respectively, were only highly susceptible or resistant to the 2012 ASGH (Fig. 4c). When the
highly resistant mutants were limited to only those that
had a statistically significant difference compared to the
other four hydrolysates (p < 0.001, n = 42), gene ontology (GO) terms were enriched (p < 0.05) for mutations
related to Golgi/vesicle-mediated/vacuolar/endosomal
transport and ribosome subunits (Additional file 2: Fig.
S2A). The next largest intersection was for mutants that
were highly resistant or susceptible to all hydrolysates
Ong et al. Biotechnol Biofuels (2016) 9:237
Page 4 of 14
Table 1 Summary of hydrolysis and fermentation results
Corn stover
Switchgrass
36H56
2010
P0448R
2012
2012
Shawnee
2013
2010
CIR
2012
2013
75.1 ± 1.0
Enzymatic hydrolysis
Glucose conversion (%)a
92.7 ± 2.0 92.6 ± 1.5 94.4 ± 1.4 99.0 ± 3.0 74.4 ± 0.9
69.6 ± 2.4
Xylose conversion (%)b
67.3 ± 0.9 69.3 ± 1.2 67.0 ± 4.0 77.1 ± 4.2 63.4 ± 0.8
61.7 ± 5.0
69.6 ± 2.7
Glucose concentration (g/L)
64.1 ± 1.4 64.0 ± 1.0 66.5 ± 1.0 67.6 ± 2.0 59.2 ± 0.8
60.3 ± 2.1
59.4 ± 0.8
Xylose concentration (g/L)
27.3 ± 0.4 31.7 ± 0.5 28.9 ± 1.7 30.6 ± 1.7 31.2 ± 0.4
30.7 ± 2.5
35.3 ± 1.4
Final time (h)
53.0
56.5
56.3
53.0
43.7
55.8
55.8
Final xylose concentration (g/L)
7.0 ± 1.1
7.7 ± 1.2
6.6 ± 0.4
6.5 ± 0.6
5.5 ± 0.3
4.9 ± 0.4
4.1 ± 0.6
Final ethanol concentration (g/L)
34.6 ± 0.6 39.4 ± 0.4 36.9 ± 0.7 36.7 ± 0.8 39.0 ± 1.2
38.9 ± 0.8
37.3 ± 0.5
Metabolic yield (%)c
80.4 ± 2.8 87.9 ± 2.1 81.4 ± 2.9 78.5 ± 3.6 90.2 ± 3.1
88.7 ± 4.3
80.6 ± 2.3
Process yield (%)d
74.2 ± 2.4 80.8 ± 1.5 75.7 ± 2.8 73.3 ± 3.4 84.6 ± 3.1
83.9 ± 4.1
77.1 ± 2.1
Ethanol yield (L/Mg untreated dry biomass)
230 ± 4
262 ± 2
255 ± 5
288 ± 6
246 ± 7
214 ± 5
245 ± 3
Max theoretical ethanol yield (L/Mg untreated dry biomass)e
371 ± 2
389 ± 2
401 ± 2
432 ± 1
415 ± 1
382 ± 3
437 ± 2
Ethanol yield (% of maximum)
61.8 ± 1.8 67.3 ± 1.1 63.6 ± 1.9 66.7 ± 2.1 59.4 ± 3.0
56.0 ± 2.3
56.2 ± 1.4
Fermentation: Zymomonas mobilis
Fermentation: Saccharomyces cerevisiae
Final time (h)
56.5
50.8
Final xylose concentration (g/L)
4.7 ± 1.0
7.3 ± 4.5 19.9 ± 1.7
60.8
60.8
57.3
57.3
4.0 ± 0.6 14.2 ± 2.1
55.5
40.1 ± 3.7
25.4 ± 2.0
0.2 ± 0.0
30.6 ± 0.5
Final ethanol concentration (g/L)
36.6 ± 0.9 36.3 ± 2.9 34.0 ± 1.0 39.8 ± 0.3 35.3 ± 0.5
Metabolic yield (%)c
82.8 ± 3.1 80.5 ± 9.5 88.4 ± 4.5 82.9 ± 2.9 90.7 ± 3.3 −5.4 ± 75.2 86.5 ± 4.0
Process yield (%)d
78.6 ± 2.9 74.3 ± 8.0 69.9 ± 3.6 79.4 ± 2.8 76.5 ± 1.6
Ethanol yield (L/Mg untreated dry biomass)
243 ± 6
241 ± 19
236 ± 7
312 ± 2
223 ± 3
1±0
202 ± 3
Max theoretical ethanol yield (L/Mg untreated dry biomass)e
371 ± 2
389 ± 2
401 ± 2
432 ± 1
415 ± 1
382 ± 3
437 ± 2
Ethanol yield (% of maximum)
65.5 ± 2.4 61.9 ± 8.0 58.7 ± 2.9 72.2 ± 0.7 53.7 ± 1.4
0.4 ± 11.6 63.3 ± 2.3
0.3 ± 11.1 46.2 ± 1.6
Values are reported as the mean ± SD (n = 3). Propagation of error was conducted to obtain SD values for all calculated values
a
The glucose conversion was calculated based on the total glucan, soluble glucose, and glucose contributed by sucrose in the untreated biomass using a previously
reported equation [51]
b
The xylose conversion was calculated as Xyl/[BL*Xln(150/132)], where Xyl = hydrolysate xylose concentration (g/L), BL = biomass loading (g/L), Xln = untreated
biomass xylan content (g/g biomass), and 150/132 are the molecular weights of xylose/xylan
c
The metabolic yield is the ratio of sugars (glucose and xylose) consumed during fermentation to ethanol produced assuming 0.51 g ethanol/g sugars as the
theoretical maximum
d
The process yield is the ratio of sugars initially present in the hydrolysate (glucose and xylose) to ethanol produced assuming 0.51 g ethanol/g sugars as the
theoretical maximum
e
The maximum theoretical ethanol yield is calculated based on the complete conversion of all glucose (as glucan, free glucose, or part of sucrose) and xylose (as
xylan) in the untreated biomass to ethanol assuming 0.51 g ethanol/g sugars
except the 2012 ASGH. When limited to highly susceptible mutants that had a statistically significant difference
for the four hydrolysates compared to the 2012 ASGH
(n = 56, p < 0.001), GO terms were enriched (p < 0.05)
related to the mitochondrial-nucleus signaling pathway, and Golgi/vacuolar transport (Additional file 2: Fig.
S2B). No significant terms were found for the mutants
that were only highly susceptible to the 2012 ASGH or
only highly resistant to the other four feedstocks. Gene
set enrichment analysis was used to evaluate whether
any yeast metabolic pathways (using the KEGG pathway collection) were enriched for the mutants that were
significantly different between the 2012 ASGH and the
four other feedstocks (p < 0.001). This analysis revealed
three KEGG pathways (FDR < 0.25) that were dominated
by mutants that were resistant to the 2012 ASGH and
susceptible to the hydrolysates of the other four feedstocks: SNARE interactions in vesicular transport, endocytosis, and the ribosome. For the SNARE pathway, the
gene deletions that conferred greater resistance in 2012
ASGH compared to the other hydrolysates (p < 0.001)
were GOS1, VAM7, and SEC22, which are all involved
in vesicle traffic between the ER, Golgi, endosome, and
vacuole.
Ong et al. Biotechnol Biofuels (2016) 9:237
8
80
60
6
40
20
a
2010CS (36H56)
0
0
20
40
8
80
60
6
4
40
2
20
0
0
60
b
0
Time (h)
e
20
40
8
80
60
6
60
6
4
40
4
40
4
2
20
2
20
2
0
0
0
0
60
c
0
Time (h)
2010SG (Shawnee)
60
8
80
6
60
f
2012CS (P0448R)
20
40
60
Time (h)
2012SG (Shawnee)
8
80
6
60
g
2013SG (CIR)
6
40
4
40
4
20
2
20
2
20
2
0
0
0
0
20
40
Time (h)
60
0
20
40
60
Time (h)
8
0
20
40
60
Time (h)
4
0
2013CS (P0448R)
8
40
0
d
0
Cell density (OD 600)
Glucose, xylose and ethanol
concentration (g/L)
80
2012CS (36H56)
Cell density (OD 600)
Glucose, xylose and ethanol
concentration (g/L)
80
Page 5 of 14
OD
Glucose
Xylose
Ethanol
0
0
20
40
60
Time (h)
Fig. 3 Fermentation profiles for Saccharomyces cerevisiae Y128 grown in AFEX-treated biomass hydrolysates. a 2010 ACSH (36H56), b 2012 ACSH
(36H56), c 2012 ACSH (P0448R), d 2013 ACSH (P0448R), e 2010 ASGH (Shawnee), f 2012 ASGH (Shawnee), g 2013 ASGH [Cave-in-Rock (CIR)]. Data
points represent the mean ± SD (n = 3). Error bars that are smaller than the individual data points may be hidden from view
Imidazoles and pyrazines are present in high
concentrations in drought‑year switchgrass hydrolysate
To identify the cause of severe growth inhibition in
the 2012 ASGH, we compared the compositions of
the untreated biomass (Fig. 5; Additional file 2: Table
S1), hydrolysates (Additional file 2: Tables S2–S4), and
extracts of the pretreated biomass. As is typical for
drought-stressed grasses [7, 22], untreated 2012 switchgrass contained higher total extractives (water- and
ethanol-extractable compounds) and soluble sugars
(Fig. 5a) and lower structural carbohydrates and lignin
compared to the 2010 and 2013 switchgrass (Fig. 5b).
A number of amino acids, metals, and furanic and phenolic compounds were also directly quantified from the
hydrolysates (Additional file 2: Tables S2–S4). With the
exception of the 2010 and 2013 ASGH, which overlapped,
all the hydrolysates were readily distinguishable by principal component analysis (PCA) of their hydrolysate
compositions (Fig. 6). The greatest amount of variation
(31%) was attributed to the difference between plant
species (corn stover vs. switchgrass) (Fig. 6a), followed
by the difference between 2010/2013 and 2012 hydrolysates (22% of variance) (Fig. 6b). Of all the compounds
in the hydrolysate, the amino acid content had the largest
influence on segregation of the 2012 feedstocks (Fig. 6c).
When looking at the compounds individually, compared
to the other hydrolysates, the 2012 ASGH had statistically
higher (p < 0.05) levels of benzamide (10 μM), vanillyl
alcohol (0.8 μM), sulfur (5.4 mM), chloride (96.6 mM—
largely from HCl used to neutralize the hydrolysate),
magnesium (24.4 mM), total nitrogen (307.3 mM), proline (1.46 mM), and tryptophan (42.5 μM).
In order to determine whether any additional compounds were present that might be responsible for the
inhibition, the hydrolysates were extracted with ethyl
acetate and analyzed. These extracts revealed the presence of higher levels of pyrazines in the drought-year
(2012) ASGH compared to the other switchgrass hydrolysates (Fig. 7a). Seven substituted imidazoles and pyrazines were further quantified from acetone extracts of
the untreated and pretreated biomass. These compounds
were found at higher levels in pretreated biomass samples and were either present at very low concentrations
(imidazoles) or absent in the untreated biomass, indicating that they were produced during the AFEX pretreatment process (Fig. 7b). Pretreated switchgrass contained
more pyrazines than pretreated corn stover, and the
drought-year (2012) switchgrass exhibited the highest
concentration of pyrazines. Combined imidazole and
pyrazine levels after pretreatment correlated with the
Ong et al. Biotechnol Biofuels (2016) 9:237
6
KEX2
Relative Fitness
4
VPS5
0
-2
RPN14
MAF1 BTT1 LYS5
LAM1
PBS2
-6 ARG2 HOG1
a
250
42
22
3
ONLY
2012 ASGH*
231
Resistant
Interaction score
57
56
65
0
100
NOT
2012 ASGH*
All feedstocks
200
300
Deletion Mutants
Fig. 4 Chemical genomic analysis of hydrolysate variation. a Fitness
heat map for yeast mutants in corn stover (CS) and switchgrass (SG)
hydrolysates. The genome-wide yeast deletion mutant collection was
grown in fifteen different hydrolysate batches (n = 3 per feedstock)
and a synthetic hydrolysate (SynH2.1) control (n = 3). The abundance
of each mutant was assessed by sequencing the strain-specific
barcodes and a fitness score was determined relative to the synthetic
hydrolysate control. Mutants sensitive to the hydrolysate conditions
are shown in blue and resistant are shown in yellow, compared to
the abundance in the SynH2.1 control. The (3) represents the 36H56
variety and the (P) represents the P0448R variety of corn stover. b
Fitness plot of yeast mutants grown in 2012 ASGH. The most resistant (fitness > 4) and susceptible mutants (fitness < −5) are labeled
and shown in red. c Intersection of yeast mutants that are highly
susceptible or resistant to all hydrolysates, only the 2012 ASGH, or all
hydrolysates except the 2012 ASGH. *The fitness of these mutants
was statistically different (p < 0.001) in the 2012 ASGH versus the
other four hydrolysates [2013 ASGH, 2010 CS (36H56), 2012 CS
(P0448R), 2013 CS (P0448R)]
soluble sugar content of the untreated biomass (Fig. 7c).
The concentrations of imidazoles and pyrazines in the
hydrolysates were estimated based on their concentrations in the pretreated biomass (Table 2). The total estimated concentration of all imidazoles and pyrazines in
the 2012 ASGH was almost twice that of the next highest sample, 2013 ACSH (P0448R) (Table 2), and the concentrations of 2-methylimidazole, 4(5)-methylimidazole,
and 2-methylpyrazine were higher than the majority of
the other aromatic compounds that were characterized
in the 2012 ASGH (Table 2; Additional file 2: Table S2).
Acetamide and four of the top five most abundant low
molecular weight phenolics (coumaroyl amide, feruloyl
amide, coumaric acid, and vanillin) were at higher levels
in the readily fermentable 2012 ACSH (36H56) compared
to the inhibitory 2012 ASGH (Table 2).
b
Component concentration
(mg per g untreated biomass)
Other
157
Highly
Resistant
Other Extractives
Other Sugars
Fructose
Glucose
Sucrose
200
150
100
50
0
3505 Deletion Mutants
c
Highly
Susceptible
Sensitive
YLR374C
2
-4
3505 deletion mutants
MIG1
Component concentration
(mg per g untreated biomass)
b
2010 CS (3)
2013 CS (P)
2012 CS (P)
2013 SG
2012 SG
a
Page 6 of 14
10 12 12 13
36H56 P0448R
Corn Stover
10 12 13
Switchgrass
1000
Lignin
Arabinan
800
Galactan
600
Xylan
Glucan
400
200
0
10 12 12 13
36H56 P0448R
Corn Stover
10 12 13
Switchgrass
Fig. 5 Untreated biomass composition. a Water and ethanol soluble
extractives. b Structural carbohydrates and lignin. Values are reported
as the mean ± SD (n = 3)
Imidazoles and pyrazines contribute to the inhibition of S.
cerevisiae
In order to determine whether elevated imidazoles and
pyrazines contribute to the anaerobic growth inhibition
of S. cerevisiae Y128 in the 2012 ASGH, we added these
compounds into the non-inhibitory 2010 ASGH at the
levels estimated in 2012 ASGH and up to 50 times the
concentration. Prior to supplementation with additional
imidazoles and pyrazines, the 2010 ASGH supported
yeast growth and fermentation (Figs. 3e, 8). While there
was still growth at the concentration of imidazoles and
pyrazines in the 2012 ASGH (1×), growth began to be
delayed at 25 times the concentration (25×), with complete inhibition at 50 times the concentration (50×)
within the fermentation time frame. These results correspond to the IC50 values, where at comparable concentrations the individual imidazoles and pyrazines reduced
growth of S. cerevisiae by 50%, with the imidazoles more
strongly inhibitory (Table 3).
Discussion
During the severe Midwestern drought in 2012, soluble sugars accumulated to significantly higher levels in
switchgrass compared to during two non-drought years
in 2010 and 2013. During ammonia-based pretreatment (AFEX), these soluble sugars underwent Maillard
reactions with ammonia to form aromatic nitrogenous
compounds, imidazoles and pyrazines [23, 24]. Both
classes of compounds can be highly toxic [26] and many
Ong et al. Biotechnol Biofuels (2016) 9:237
a
c
-5
Corn Stover
36H56
PC2 (22.2% explained var.)
0
5
PC1 (31.4% explained var.)
2010 / 2013
Switchgrass
PC2
0
-0.0
-0.1
5
2013
2010
-0.2
0
Fe
0.1
2012
PC2 (22.2% explained var.)
5
Sugars & Short Chain
Acids/Alcohols
Minerals
Amino Acids
Diferulates
0.2
Corn Stover
P0448R
-5
b
Page 7 of 14
Aromatic Aldehydes
Aromatic Amides
Aromatic Acids
Aromatic Ketones
Aromatic Alcohols
3-Hydroxybenzoic acid
Na
Formate Benzoic acid
3,4-Dihydroxybenzoic acid
Acetate
4-Hydroxybenzyl alcohol
Acetosyringone
4-Hydroxyacetophenone
Xylose
Vanillic acid
Mo
Li
Vanillyl alcohol
4-Hydroxybenzaldehyde Cr
Acetamide
Benzamide
Syringaldehyde Co
Cl
Syringic acid
Ferulic acid
Furfural
Mn
Succinate
HMF
Gly
K
Mg
Coumaric acid
Furfuryl alcohol
S
Al
Coumaroyl amide
Syringamide
4-Hydroxybenzamide
Acetovanillone
Ethanol
Vanillin Feruloyl amide
8-O-4-DiFA Ni
Zn
NH4-N
8-8'-DiFA
Glucose
Arg
Sinapic acid
Trp
Lys
P
4-Hydroxybenzoic acid
Ca
8-5'-DiFA
N
Asn
Phe
Ser
Glycerol
B
Vanillamide
Azeliac acid
Tyr
Pro
Cu
Lactate
His Leu
Glu
Thr
Ala
Val
Asp
Ile
2012
-5
-0.3
-0 . 2
-5
0
5
PC1 (31.4% explained var.)
-0 . 1
0.0
0.1
0 .2
Corn Stover PC1 Switchgrass
Fig. 6 Principal component analysis (PCA) of hydrolysate composition data—relationship between principal components 1 and 2. a Hydrolysate
batches grouped by plant variety. b Hydrolysate batches grouped by year. c Correlation score graph showing relative effect of each hydrolysate
component
complex azoles are potent antifungal agents [25]. Our
data suggest that these compounds contributed to inhibition of fermentative yeast growth in drought-stressed
switchgrass (Fig. 9); however, they are most likely not
the sole cause. A previous study predicted reductions of
10–15% in the theoretical ethanol yield from lignocellulosic biomass harvested during a drought year compared
to a non-drought year, largely due to the negative effects
of drought on the biomass structural sugar content [7].
In our study, while in some cases there was a reduction
in the actual ethanol yield for drought-year feedstocks
(−7% for CS-P0448R and SG), this was not always the
case (+12% for CS-36H56 for 2012 vs. 2010) (Table 1).
The actual ethanol yield also varied significantly between
feedstocks (from 46 to 72% of the theoretical maximum)
in a manner that was not obvious based on the untreated
biomass composition. Additionally, the complete inhibition of the yeast growth in the 2012 ASGH, while related
to the biomass composition, was not predictable based
on the current state of knowledge. In order to design
feedstocks and processes that are able to either accommodate or reduce feedstock variability, more studies are
needed that focus on understanding how external factors
influence biomass quality and subsequently affect fermentation performance.
Although the drought had some negative effects on
hydrolysate composition, it also had a number of positive
effects, particularly related to hydrolysate amino acid
concentrations. With the exception of glycine and asparagine, the drought-year hydrolysate for each respective
feedstock had the highest concentration of each amino
acid, and of all hydrolysates the 2012 ASGH had the
highest concentration for both proline and tryptophan
(Additional file 2: Table S4). Plants commonly respond
to drought or other abiotic stresses by accumulating
amino acids [5, 26]. In particular, proline is produced by
drought-stressed plants to help regulate osmotic pressure
[5] and both proline and tryptophan have been reported
at higher levels in drought-stressed grasses compared to
their unstressed counterparts [6, 22]. For pretreatments,
such as AFEX, that do not denature, degrade, or remove
proteins and amino acids, the retention of amino acids
in the hydrolysate provides a beneficial source of nutrients for the microorganism [27]. The importance of these
amino acids to microbial fitness in the hydrolysates is
apparent from the large number of amino acid biosynthetic mutants that were highly susceptible in all of the
five hydrolysates investigated (Additional file 2: Fig. S1).
In our study, the soluble sugars that were present in
the lignocellulosic biomass were degraded to inhibitory
imidazoles and pyrazines following ammonia-based pretreatment. However, for other pretreatment methods, the
soluble sugars that accumulate in drought-stressed biomass can also be degraded to other inhibitory compounds,
Ong et al. Biotechnol Biofuels (2016) 9:237
a
2-methyl
2,6-dimethyl
pyrazine
pyrazine
Acetamide
(6-methyl
pyrazin-2-yl)
methanol
4.0
2010 ASGH
2012 ASGH
2013 ASGH
0
4
5
6
7
6.7
1.0
6.6
5.06
5.08
2.0
10.35
3.0
10.30
Counts
x105
5.0
Page 8 of 14
8
9
10
11
12
13
14
Acquisition Time (min)
b
3.0
2,6-dimethylpyrazine
2,5-dimethylpyrazine
2-methylpyrazine
2,4-dimethylimidazole
4(5)-methylimidazole
2-methylimidazole
1-methylimidazole
Concentration
( mol/g dry biomass)
2.5
2.0
1.5
1.0
0.5
CS
CS
36H56 P0448R
Imidazoles and pyrazines
in pretreated biomass
( mol/g dry biomass)
2013
2012
2010
2013
2012
CS
CS
36H56 P0448R
SG
Untreated
c
2012
2010
2013
2012
2010
2013
2012
2012
2010
0.0
SG
Pretreated
3.5
3.0
y = 0.0076x + 0.0972
r = 0.83
2.5
2.0
1.5
1.0
0.5
0.0
0
100
200
300
400
Soluble sugars in untreated biomass
( mol/g dry biomass)
Fig. 7 Imidazole and pyrazine detection and quantification in AFEXtreated biomass and hydrolysates. a Overlaid mass spectrometric
chromatogram of ethyl acetate extracts of AFEX-treated switchgrass
hydrolysates. Each line represents a replicate batch of hydrolysate
(2012: n = 3; 2010 and 2013: n = 2). b Imidazole and pyrazine content of untreated and AFEX-treated corn stover (CS) and switchgrass
(SG). c Correlation between imidazole and pyrazine content of
AFEX-treated biomass and untreated biomass soluble sugars (sucrose,
glucose fructose, xylose, arabinose, and galactose)
in the case of dilute acid to furfural, 5-hydroxymethylfurfural, levulinic acid, and formic acid [28]. These compounds can cause severe negative effects on microbial
fermentation for both yeast and bacteria [29, 30]. Thus,
degradation of soluble sugars that are present in droughtstressed crops poses a potential problem for lignocellulosic biofuel production regardless of the pretreatment
used. However, it may be possible to overcome the inhibition by adjusting pretreatment conditions to limit formation of harmful compounds, removing soluble sugars
prior to processing, or utilizing more resistant microbial strains. For example, it may be preferable to use the
bacterium Z. mobilis 2032, which was less susceptible to
growth inhibition in the 2012 ASGH compared to the
yeast S. cerevisiae Y128 (Figs. 2, 3).
Analysis of the chemical genomics data indicates that
the 2012 ASGH had an impact on the protein trafficking
system within the yeast cell, particularly in relationship to
the late endosome and retromer, which is responsible for
recycling of certain proteins from the late endosome to
the Golgi apparatus. In yeast, the retromer consists of two
subcomplexes: a trimer consisting of Vps26p, Vps29p, and
Vps35p and a dimer consisting of Vps5p and Vps17p [21].
A number of mutants related to these systems, in particular the three retromer subunits for which we had mutants
(vps35Δ, vps5Δ, and vps17Δ), were highly susceptible to
reduced growth in the four other hydrolysates that were
investigated (2010, 2012-P0448R and 2013 ACSH, and
2013 ASGH) but had greater fitness in the 2012 ASGH. If
the mechanism of inhibition in the 2012 ASGH is related
to the endosomal system and vesicular transport between
the organelles, this could explain the difference observed
with the bacterial ethanologen Z. mobilis, which has neither organelles nor the process of endocytosis, and was
able to grow with no difficulty in the 2012 ASGH.
Plants experience drought stress in response to low
levels of soil moisture. Although there are benefits to
growing dedicated bioenergy crops like switchgrass on
marginal lands to avoid competition with food crop
production [31], some marginal lands are classified as
such because their soil has poor water-holding capacity
[32]. Plants grown on these soils may experience greater
drought stress and produce more osmoprotective soluble
sugars than plants grown on more fertile soils. Climate
change may further aggravate these issues as extreme precipitation events are predicted to increase [33]. Intense
rainfall followed by longer dry spells limits the replenishment of soil moisture [33], and in certain regions this may
negatively influence biomass yields and processing characteristics. Moisture stress will be an issue for bioenergy
production systems that needs to be addressed, not just
because of the impact on crop yields, but also because of
the potential negative impact on biomass quality.
Conclusions
Drought induces the accumulation of high concentrations of soluble sugars in lignocellulosic bioenergy crops.
During ammonia-based pretreatment, these sugars are
degraded to imidazoles and pyrazines that during fermentation contribute to growth inhibition of the yeast S.
cerevisiae, but do not negatively affect the bacterium Z.
mobilis. This is the first study that links compounds generated during the processing of environmentally stressed
lignocellulosic biomass to deleterious impacts on the
microbes during biofuel production. Our findings have
Ong et al. Biotechnol Biofuels (2016) 9:237
Page 9 of 14
Table 2 Concentrations (μM) of imidazoles and pyrazines (estimated) and aromatic degradation products in pretreated
biomass hydrolysates
Corn stover 36H56
Corn stover P0448R
Switchgrass
2010
2012
2010
2012
ND
2013
2012
2013
1-Methylimidazole
2
0
0
1
1
5
2-Methylimidazole
26
42
33
44
45
137
37
4(5)-Methylimidazole
67
86
214
264
76
356
60
2,4-Dimethylimidazole
13
14
36
49
5
6
12
10
2-Methylpyrazine
4
6
11
10
14
114
2,5-Dimethylpyrazine
0
0
1
1
1
9
1
2,6-Dimethylpyrazine
1
1
4
4
3
38
2
Sum of imidazoles and pyrazines
Acetamide
112
150
299
371
144
661
126
7755
7114
8283
8309
8888
11,372
8981
1908
Coumaroyl amide
5034
3152
3763
4502
1622
1751
Feruloyl amide
2044
1529
2181
2186
593
1179
753
Coumaric acid
1328
489
774
937
225
268
257
Benzoic acid
132
168
121
124
304
225
300
Vanillin
181
141
156
146
71
69
86
Feedstock production, harvest, and processing
0.4
2010+ddH2O
Growth (OD595)
0.3
2010+1X PI
2010+10X PI
0.2
2010+25X PI
0.1
2010+37.5X PI
2010+50X PI
0.0
2012
-0.1
0
10
20
30
40
50
60
70
Time (hours)
Fig. 8 Imidazoles and pyrazines found in drought-year AFEX-treated
switchgrass hydrolysate (ASGH) can impair anaerobic yeast growth.
Anaerobic yeast growth in add-back experiment, with various concentrations of pyrazines and imidazoles (P/I) in 2010 ASGH relative to
estimated levels in 2012 ASGH (mean, n = 3). Average cell densities
with standard error of the mean are reported from triplicate samples,
with every twelfth time point plotted (roughly one time point every
2 h)
profound implications for the development of sustainable lignocellulosic biofuel production systems that are
able to tolerate fluctuations in precipitation and water
availability.
Methods
The methods for AFEX pretreatment; high solids enzymatic hydrolysis; chemical analysis of hydrolysate composition; and strains, media, growth and fermentation
conditions are the same as previously reported [16].
Switchgrass and corn stover were cultivated at the
Arlington Agricultural Research Station (ARL, 43°17′45″
N, 89°22′48″ W, 315 masl) in Arlington, Wisconsin. Corn
stover was sourced from Arlington field 744 (ARL-744)
in 2010, ARL-570 in 2012, and ARL-742 in 2013. Switchgrass was sourced from ARL-346 in both 2010 and 2012,
and ARL-115 in 2013. The main soil at ARL is Plano siltloam (fine-silty, mixed, superactive, mesic Typic Argiudoll); a deep (>1 m), well-drained mollisol developed over
glacial till and formed under tallgrass prairie [13]. Mean
annual temperature and precipitation are 6.9 °C and
869 mm, respectively [34, 35].
Pioneer 36H56 and P0448R corn stover (both triple
stacked with Roundup Ready and corn borer and rootworm resistance) were planted on May 3 (2010) and
May 11 (2012) for 36H56, and May 11 (2012) and May
15 (2013) for P0448R. Fertilizer (0-0-50 potassium sulfate) was applied in 2010 after 4 years of alfalfa. In 2012,
both corn varieties received 92 kg N/ha as anhydrous in
April, whereas 2013 corn received 83 kg N/ha as urea
in May. Weed control was attended on ARL-744 with a
pre-emerge (Metolachlor: 1848 mL AI/ha) and postemerge herbicide (Dicamba; Diflufenzopyr: 267 mL AI/
ha) applied on May 10 and June 10, 2010, respectively.
For field ARL-570, a mixed pre-emerge herbicide (2,4-D
LV4 Ester; Glyphosate; Mesotrione; S-Metolachlor:
1264 mL AI/ha) was applied on April 16, 2012 prior to
planting and a mixed post-emerge herbicide (Glyphosate;
Tembotrione; Ammonium Sulfate; Methylated Seed Oil:
852 mL AI/ha) on June 8, 2012. The herbicide treatment
Ong et al. Biotechnol Biofuels (2016) 9:237
Page 10 of 14
Table 3 IC50 values of selected nitrogenous compounds
for Saccharomyces cerevisiae Y128
Compound
IC50 (mM)
2-Methylimidazole
50.9 ± 0.7
4(5)-Methylimidazole
25.2 ± 1.0
2,4-Dimethylimidazole
33.1 ± 10.4
2-Methylpyrazine
>100a
2,3-Dimethylpyrazine
83.9 ± 7.5
2,5-Dimethylpyrazine
80.9 ± 4.8
2,6-Dimethylpyrazine
82.6 ± 3.8
2,3,5-Trimethylpyrazine
66.8 ± 3.5
(5-Methylpyrazin-2-yl)methanol
>80a
IC50 values are reported as the mean ± SEM (n = 3)
a
All replicates had no growth inhibition for the range of concentrations tested
ENVIRONMENTAL
CONDITIONS
ENZYMATIC &
MICROBIAL
CONVERSION
THERMOCHEMICAL
PRETREATMENT
R
N
R N
DROUGHT
N
H
HO
NH 3
N
OH
O
Imidazoles and
Pyrazines
HO
O
HO
O
OH
HO
OH
HO
R
AC
ID
Lignocellulosic
Biomass
O
Osmoprotectants
(Sugars)
O
Furanic
Aldehydes
Fig. 9 Interaction between plant response to environmental conditions and pretreatment chemistry. In lignocellulosic biomass, drought
stress causes an increase in osmoprotectants, including soluble sugars that are degraded to microbial inhibitors during thermochemical
pretreatments
on ARL-742 used Mesotrione: 175 mL AI/ha + S-Metolachlor: 1685 mL AI/ha. Corn stover was collected shortly
after grain harvest in early November of all years using
a combine that had been modified to separate the corn
grain and then chop and bail the corn stover.
Switchgrass (Shawnee variety; 2010 and 2012) was
planted on May 29, 2004 using a Brillion Sure Stand
seeder (Landoll Corporation, Marysville, KS) at a rate
of 16.8 kg/ha. For initial weed control, Quinclorac herbicide (1445 mL Al/ha) was applied 1 day after planting.
A tank mix of Imazethapyr (259 ml Al/ha) and Dicamba
(1445 mL Al/ha) was applied on May 19, 2006 for additional weed control. Each year in April, granular urea
(46-0-0) was top-dressed at a rate of 90 kg/ha. In midOctober 2010, switchgrass was cut and conditioned with
a 4.5-m-wide haybine (John Deere 4990). Switchgrass
sourced in 2013 (Cave-in-Rock) was planted in late June
2008 using a drop spreader (Truax Company, Inc.) with
two culti-pack rollers at a rate of 14 kg/ha. Initial weed
control was accomplished with Glyphosate (700 mL AI/
ha) on June 17, 2008 and again as a pre-emerge treatment
on April 23, 2009 and May 3, 2010. Post-emerge weed
control was applied as 2,4-D (773 mL AI/ha) on June 26,
2009 and May 10, 2010. Starting in 2010, 56 kg/ha (34-0-0
ammonium nitrate) was applied annually, and in 2013 N
was applied on May 30. In mid- to late-September (2010
and 2012) and mid-October (2013), biomass was cut and
windrowed, and then chopped with a self-propelled forage harvester into a dump wagon equipped with load
cells.
Following harvest, each corn stover and switchgrass
material was dried in a 60 °C oven until the dry weight
was stable (~48 h), then milled using a 18-7-301 SchutteBuffalo hammer mill (SchutteBuffalo, Buffalo, NY)
equipped with a 5-mm screen, and stored at room temperature in sealed bags until use.
Chemical genomic analysis of hydrolysates
Chemical genomic analysis of these hydrolysates was
performed as described previously using a collection of
~3500 yeast deletion mutants [19, 36]. 200 µL cultures
of the pooled collection of S. cerevisiae deletion mutants
were grown anaerobically in the different versions of
ACSH and ASGH, or yeast-rich medium (YPD, 20 g/L
peptone, 10 g/L yeast extract, 20 g/L glucose), diluted 1:1
with sterile water, in triplicate for 48 h at 30 °C. Genomic
DNA was extracted from the cells and mutant-specific molecular barcodes were amplified using specially
designed multiplex primers as described previously [19].
The barcodes were sequenced using an Illumina HiSeq
2500 in rapid run mode (Illumina, Inc., San Diego, CA).
The barcode counts for each yeast deletion mutant in
the hydrolysates were normalized against the synthetic
hydrolysate control (SynH2.1) [16] in order to define
sensitivity or resistance of individual strains (chemical
genetic interaction score). The pattern of genetic interaction scores for all mutant strains represents the chemical genomic profile or “biological fingerprint” of a sample
[19, 36]. The clustergram of the chemical genomic profiles was created in Cluster 3.0 [37], and visualized in
Treeview (v1.1.6r4) [38]. The p value for the difference
between 2012 ASGH and all other hydrolysates was
calculated and Bonferroni corrected using the multtest package [39] in R-Studio®. A Bonferroni-corrected
hypergeometric distribution test was used to search for
significant enrichment of GO terms among sets of highly
resistant mutants (fitness > 2.5, n = 224) and highly susceptible mutants (fitness < −2.5, n = 409) [40] using
LAGO [41]. For the highly resistant and susceptible
mutants for only 2012 ASGH or the four feedstocks but
Ong et al. Biotechnol Biofuels (2016) 9:237
not 2012 ASGH, the GO terms were evaluated using only
those terms that had statistically different fitness between
the two groups (p < 0.001). Gene set enrichment analysis (GSEA) [42] was used to compare the enrichment of
the KEGG pathways for S. cerevisiae between the 2012
ASGH and the four other feedstocks for genes that conferred statistically different fitness (p < 0.001).
Untreated biomass composition analysis
The composition of the untreated biomass was analyzed
based on the NREL standard procedures for biomass composition analysis [43–47], with the following deviations.
Samples for composition analysis were milled through a
2-mm screen using a Foss Cyclotec™ mill (Eden Prairie,
MN) and not sieved prior to analysis. The protein content was estimated by multiplying the nitrogen content as
determined by a Skalar Primacs SN Total Nitrogen Analyzer (Breda, The Netherlands) by a conversion factor
(6.25), which assumes that 16% of the protein is nitrogen.
Although the preferred method is to calculate the protein
content based on the amino acid profile of the biomass
[48], this method is complex and so we chose to use an
estimation. Water-soluble oligomeric sugars were determined by hydrolyzing the water extractives using sulfuric
acid [49]. The hydrolyzed water extracts were then neutralized using calcium carbonate and both the hydrolyzed
and non-hydrolyzed water extractives were run through
an Aminex HPX-87P column (Bio-Rad, Hercules, CA)
with attached guard columns [47] and analyzed for their
sucrose, fructose, glucose, xylose, arabinose, galactose, and
mannose concentrations based on calibration standards.
Hydrolysate amino acid composition
Prior to amino acid quantification, 50 µL aliquots of
samples were spiked with stable isotope labeled internal
standards for the 20 common proteinogenic amino acids
(Sigma-Aldrich Cell Free Amino Acid Mixture—13C,15N;
P/N 767964-1EA) and processed by solid-phase extraction (Phenomenex Strata-X-C cartridges; P/N 8B-S029HCH) to remove matrix interferents. SPE-processed
samples underwent vacuum centrifugation before resuspension in 1 mL of Mobile Phase A. Samples were then
analyzed via LC–MS/MS, based on the protocol from Gu
et al. [48], with the following modifications: mobile phase
A was 10 mM instead of 1 mM (to reduce column equilibration time) and an LC gradient of 0.00–1.75 min (98%
A); 1.76–8.00 min (linear ramp to 45% A); 8.01–9.00 min
(10% A); and 9.01–13.00 min (98% A). Response factors
were calculated based on the peak area of the selected
multiple reaction monitoring (MRM) chromatograms
for each compound relative to the area of the MRM
peak for each amino acid’s stable isotope labeled internal
standard.
Page 11 of 14
Statistical analysis of hydrolysate composition
Statistical analysis of the hydrolysate composition was
conducted in R-Studio®, version 0.98.1102 (Boston, MA).
A linear model of each chemical component was developed based on the feedstock (corn stover or switchgrass),
harvest year, and their interaction, with variety nested
within feedstock. The model was evaluated using Tukey’s
HSD test based on 95% confidence intervals (Agricolae package, version 1.2-1 [49]). When a reported value
was below the limit of quantitation (LOQ), the value was
recalculated as LOQ/√2 [50]. These recalculated values
were used to determine the mean, standard deviation,
and statistical differences. The principal component analysis was conducted in R-Studio® and plots were generated using the ggbiplot package.
Quantification of imidazoles and pyrazines in AFEX‑treated
Biomass
AFEX-pretreated biomass was milled through a 2.0-mm
screen using a Foss Cyclotec™ mill (Eden Prairie, MN).
The milled biomass was extracted with acetone using
an Accelerated Solvent Extractor (Dionex™ ASE 200,
Thermo Scientific) and the following conditions: 5 min
heat, 5 min static, 150% flush volume, 120 s purge, two
cycles, 1500 psi, and 70 °C. Standards for the analyzed
compounds were prepared in pure acetone in concentrations ranging from 0.00128 to 20 mg/L. Internal
standards of 4-methylimidazole-d6 (imidazole authentic
standard) and 2-methylpyrazine-d6 (pyrazine authentic
standard) were obtained from C/D/N Isotopes (PointeClaire, Quebec, Canada) and added to each sample,
standard, and blank at a final concentration of 6 mg/L.
Samples were directly analyzed via GC–MS, without
derivatization, based on the protocol from Chundawat
et al. [23], with the following modifications to the GC
temperature program: 40 °C (2 min), from 5 °C/min to
150 °C (1 min hold), 8 °C/min to 200 °C (2 min hold),
20 °C/min to 260 °C (3 min hold). Response factors were
calculated based on the peak area of the selected ion
chromatogram (molecular ion; M+) of each compound
relative to the area of the internal standard peak.
Determination of pyrazines and imidazoles in switchgrass
hydrolysates by RP‑HPLC‑HR/AM‑MS
For each year of ASGH, 1 mL of hydrolysate was
extracted with 0.5 mL ethyl acetate by vortex mixing for
approximately 30 s, then centrifuging at 16×g for 5 min
to separate the layers. The organic (top) phase was collected and the procedure was repeated with another
0.5 mL ethyl acetate and the second organic extract
combined with the first. 0.5-g anhydrous sodium sulfate was added to the ethyl acetate extract, capped, and
allowed to stand overnight before an aliquot was taken
Ong et al. Biotechnol Biofuels (2016) 9:237
for analysis by GC–MS. Sample components were separated by an Agilent 7890A gas chromatograph equipped
with an HP-5 MS column, 30 m × 0.25 mm ID, initially
at 40 °C for 2 min and then heated to 320 °C at 10 °C/
min. Mass spectra were recorded with the interfaced Agilent 5975 MSD from m/z 40 to 750 with an ionization
energy of 70 eV. The GC inlet was set to 265 °C and MS
transfer line temperature was 250 °C. The inlet was operated in spilt mode with a split ratio of 10:1, and helium
carrier gas flow rate through the column was held at
1 mL/min. Mass Hunter GC/MS acquisition software
(Agilent) version B.07.00.1413 was used to control the
instrument and collect the data. Identities of 2-methylpyrazine, 2,6-dimethylpyrazine, 2,3-dimethylpyrazine
(the smaller peak immediately following the 2,6 isomer),
(5-methylpyrazin-2-yl) methanol, and (6-methylpyrazin2-yl) methanol were confirmed by co-chromatography
with authentic reference standards. Known amounts of
authentic reference standards were individually added
to aliquots of a composite mixture of the ethyl acetate
extracts of all three batches of 2012 ASGH. The resulting
chromatograms were compared to the chromatogram of
the composite mixture without added standards. Singlepoint calibration gave a value of 300 µM 2-methylpyrazine in the extract. Further experiments suggested the
actual value was higher due to incomplete extraction.
Pyrazine and imidazole spike‑in experiment
To determine if the detected pyrazines and imidazoles
contributed toward the inhibition of yeast in the 2012
ASGH, we grew yeast in 2010 ASGH supplemented with
similar levels of pyrazines and imidazoles found in the
2012 ASGH. 10 μL of exponentially growing Y128 S. cerevisiae [17] at a cell density of OD600 = 1.0 was inoculated
in 96-well microtiter plates containing 190 μL. We grew
triplicate, 200 µL cultures of S. cerevisiae Y128 aerobically in each of the following hydrolysates: 2012 ASGH
and 2010 ASGH supplemented with 0×, 1×, 10×, 25×,
37.5×, or 50× concentrations of the following pyrazines
and imidazoles from 2012 ASGH dissolved in ddH2O:
1 µM 1-methylimidazole, 140 µM 2-methylimidazole,
360 µM 4-methylimidazole, 6 µM 2,4-dimethylimidazole,
430 µM 2-methylpyrazine, 9 µM 2,5-dimethylpyrazine,
and 38 µM 2,6-dimethylpyrazine (Sigma, USA). Cultures
were incubated at 30 °C for 72 h and read every 11.3 min
using a TECAN M1000 multimode plate reader housed
within in an anaerobic chamber (Coy) maintained with
10% H2, 10% CO2, and 80% N2 gases.
Page 12 of 14
200 µL cultures of S. cerevisiae Y128 in synthetic hydrolysate (SynH2.1) [16] supplemented with a range of
0–10 mg/mL (0, 0.5, 1, 2, 3, 4, etc.) of each of the following
compounds separately: 2-methylimidazole, 4(5)-methylimidazole, 2,4-dimethylimidazole, 2-methylpyrazine,
2,3-dimethylpyrazine, 2,5-dimethylpyrazine, 2,6-dimethylpyrazine, 2,3,5-trimethylpyrazine, and (5-methylpyrazin-2-yl) methanol. We incubated these cultures for 48 h
with OD595 readings taken every 15 min using a TECAN
M500 (TECAN, USA). Biological replicates were conducted in triplicate. IC50 values were estimated using SigmaPlot 12.0 (Systat Software, San Jose, CA) and converted
to molar concentrations for ease of comparison.
Additional files
Additional file 1. Chemical genomics dataset.
Additional file 2. Maps of significant gene ontology terms for chemical
genomics data. Untreated biomass composition. Detailed hydrolysate
composition.
Abbreviations
ACSH: AFEX-treated corn stover hydrolysate; ASGH: AFEX-treated switchgrass
hydrolysate; AFEX: ammonia fiber expansion pretreatment; CS: corn stover; SG:
switchgrass.
Authors’ contributions
RGO designed the project, performed composition analysis and prep work for
pyrazine and imidazole quantification, analyzed data, ran statistical analyses,
analyzed chemical genomics data, and wrote the manuscript with input from
all authors. LGO, DE, and GRS designed the project, coordinated collection and
processing of biomass, analyzed data, and edited the manuscript. YZ designed
the project, led hydrolysate production and fermentation experiments, analyzed data, and edited the manuscript. JS, DX, and EP generated hydrolysate,
conducted fermentation experiments, analyzed data, and edited the manuscript. SAS, AH, ADJ and JJC designed and performed mass spectrometric
analysis of biomass extracts and hydrolysates, analyzed data, and edited the
manuscript. JSP designed the project, conducted chemical genomics experiments, analyzed data, and edited the manuscript. TKS, SB, and QD conducted
chemical genomic experiments, individual compound toxicity tests, and
spike-in experiments, analyzed data, and edited the manuscript. DMB and DC
designed the project, analyzed data, and edited the manuscript. All authors
read and approved the final manuscript.
IC50 of imidazoles and pyrazines
Author details
1
DOE Great Lakes Bioenergy Research Center, Michigan State University,
East Lansing, MI, USA. 2 Department of Chemical Engineering, Michigan
State University, East Lansing, MI, USA. 3 Department of Chemical Engineering, Michigan Technological University, Houghton, MI, USA. 4 Department
of Chemistry, University of Wisconsin-Madison, Madison, WI, USA. 5 DOE Great
Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison,
WI, USA. 6 RTSF Mass Spectrometry & Metabolomics Core, Michigan State
University, East Lansing, MI, USA. 7 Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI, USA. 8 Department
of Chemistry, Michigan State University, East Lansing, MI, USA. 9 Department
of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI,
USA. 10 Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA. 11 Department of Agronomy, University of Wisconsin-Madison,
Madison, WI, USA.
To determine the half maximal inhibitory concentration (IC50) of each pyrazine and imidazole, we created a 12-point dose curve of each compound. We grew
Acknowledgements
We thank K. Keegstra, B. Landick, R. Jackson, J. Ralph, and B. Dale for feedback
during preparation of the manuscript. We also thank Novozymes for providing
Ong et al. Biotechnol Biofuels (2016) 9:237
the enzymes used during enzymatic hydrolysis; J. Sustachek, A. Miller, Z.
Andersen, B. Faust, and J. Tesmer for collection and processing of biomass; C.
Donald Jr. for AFEX pretreatment; M. Kreuger, M. Shabani, and C. Gunawan for
biomass composition analysis; M. Kreuger for prep work for imidazole/pyrazine quantification; M. McGee for HPLC analysis of fermentation products; and
the MSU Mass Spectrometry and Metabolomics Core for mass spectrometric
analysis of biomass extracts and hydrolysates.
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
Correspondence and reasonable requests for data or materials should be
addressed to R.G.O. (rgong1@mtu.edu).
Funding
This work was funded by the DOE Great Lakes Bioenergy Research Center
(DOE BER Office of Science DE-FC02-07ER64494). Additional funding for
L.G.O. is under DOE OBP Office of Energy Efficiency and Renewable Energy
(DE-AC05-76RL01830). AFEX is a trademark of MBI, International (Lansing, MI).
Received: 18 August 2016 Accepted: 25 October 2016
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