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toxins
Article
Carbon Dioxide Mediates the Response to
Temperature and Water Activity Levels in
Aspergillus flavus during Infection of Maize Kernels
Matthew K. Gilbert 1, *, Angel Medina 2, Brian M. Mack 1, Matthew D. Lebar 1,
Alicia Rodríguez 3, Deepak Bhatnagar 1, Naresh Magan 2, Gregory Obrian 4and Gary Payne 4
1USDA/Agricultural Research Service, 1100 Robert E Lee Blvd., New Orleans, LA 70124, USA;
brian.mack@ars.usda.gov (B.M.M.); matthew.lebar@ars.usda.gov (M.D.L.);
deepak.bhatnagar@ars.usda.gov (D.B.)
2Applied Mycology Group, Biotechnology Centre, Cranfield University, Silsoe, Bedford MK45 4DT, UK;
a.medinavaya@cranfield.ac.uk (A.M.); n.magan@cranfield.ac.uk (N.M.)
3Food Hygiene and Safety, Meat and Meat products Research Institute, University of Extremadura,
10003 Caceres, Spain; aliciarj@unex.es
4Department of Entomology and Plant Pathology, 223 Partners III, P.O. Box 7567, North Carolina State
University, Raleigh, NC 27695, USA; grobrian@ncsu.edu (G.O.); gaplab@ncsu.edu (G.P.)
*Correspondence: matthew.gilbert@ars.usda.gov
Received: 26 September 2017; Accepted: 14 December 2017; Published: 22 December 2017
Abstract:
Aspergillus flavus is a saprophytic fungus that may colonize several important crops,
including cotton, maize, peanuts and tree nuts. Concomitant with A. flavus colonization is its potential
to secrete mycotoxins, of which the most prominent is aflatoxin. Temperature, water activity (a
w
) and
carbon dioxide (CO
2
) are three environmental factors shown to influence the fungus-plant interaction,
which are predicted to undergo significant changes in the next century. In this study, we used
RNA sequencing to better understand the transcriptomic response of the fungus to a
w
, temperature,
and elevated CO
2
levels. We demonstrate that aflatoxin (AFB
1
) production on maize grain was altered
by water availability, temperature and CO
2
. RNA-Sequencing data indicated that several genes, and in
particular those involved in the biosynthesis of secondary metabolites, exhibit different responses
to water availability or temperature stress depending on the atmospheric CO
2
content. Other gene
categories affected by CO
2
levels alone (350 ppm vs. 1000 ppm at 30
◦
C/0.99 a
w
), included amino acid
metabolism and folate biosynthesis. Finally, we identified two gene networks significantly influenced
by changes in CO
2
levels that contain several genes related to cellular replication and transcription.
These results demonstrate that changes in atmospheric CO
2
under climate change scenarios greatly
influences the response of A. flavus to water and temperature when colonizing maize grain.
Keywords: aflatoxin; Zea mays; climate change; secondary metabolites; RNA-seq
1. Introduction
Aspergillus flavus is a saprophytic fungus that infects several crops of agronomic importance,
including corn, cotton, peanuts and tree nuts. Prior to harvest, A. flavus may infect the fruiting bodies
or seeds in crops and produce several toxic secondary metabolites, including the polyketide derived
aflatoxins (AFs), cyclopiazonic acid and aflatrem. In the USA and other industrialized countries,
the establishment of contamination thresholds by regulatory agencies and close monitoring of crops
have minimized the direct impact on human health. However, economic losses remain significant.
Regarding AF contamination in the USA alone, estimates of losses are between $163 million for maize
crops to $500 million annually for maize, peanuts and other crops [
1
,
2
]. In lower middle income
countries (LMCs) where the regulatory controls either do not exist or are not enforced, especially in
Toxins 2018,10, 5; doi:10.3390/toxins10010005 www.mdpi.com/journal/toxins
Toxins 2018,10, 5 2 of 16
sub-Saharan Africa, the consumption of food contaminated with AFs are directly linked to liver
disease, tumor development, stunted development in children and other medical defects (reviewed
in [
3
]). The AF compounds produced by A. flavus include the structurally similar forms B
1
, B
2
, G
1
and G
2
. Most A. flavus strains produce only the B forms of AF, however other related species, such as
A. parasiticus and A. nomius, produce both B and G AFs [4,5].
Mitigation strategies to minimize contamination of maize have involved the implementation
of different approaches. These include the development of genetically modified (GM) maize
crops [
6
,
7
], which are resistant to pests and minimize damage that provides entry points for A. flavus;
classic breeding for resistance [
8
], and pre-harvest biocontrol by using naturally non-aflatoxigenic
strains of A. flavus to outcompete the toxigenic strains [
9
]. The latter approach has resulted in several
commercial biocontrol products currently being used in maize, groundnuts and cotton.
Environmental factors, including water activity (a
w
), temperature, light, as well as their
interactions, have been demonstrated to have a significant influence on germination, growth and AFs
production by strains of A. flavus [
10
–
13
]. Recent studies by Medina et al. (2015) demonstrated that
water activity had a statistically significant effect on growth rates, whereas the effect of temperature
and CO
2
was negligible under the conditions tested. Further studies by Medina et al. (2017) examined
the impact of a
w
and temperature interactions on global transcriptomic changes in A. flavus and how
this relates to AFB1production in maize-based matrices.
There has been interest in the resilience of mycotoxigenic fungi in relation to potential future
climate change (CC) scenarios and whether interactions between a
w
,increased temperature andelevated
CO
2
might impact mycotoxin production at a fundamental genomic level and phenotypic toxin
production. By the year 2100 temperatures could increase by 4
◦
C, and CO
2
levels are anticipated to
reach approximately 1000 ppm, depending on mitigation efforts [
14
]. Medina et al. (2015) showed that
when A. flavus was exposed
in vitro
to these three-way interacting CC factors growth was unaffected,
while expression of biosynthetic genes (aflD;aflR) and phenotypic AFB
1
production were significantly
increased. This is likely to result in economic impacts. For example, it has been suggested that CC could
increase economic losses in the maize industry by $50 million to up to $1.7 billion if AF production
increases under such scenarios [
15
]. A recent review suggested that food security, especially in LMCs,
could be profoundly impacted on due to such increases in mycotoxin contamination of staple food
commodities [16].
There has been interest in applying current technologies, including functional genomics,
transcriptomics, and proteomics to measure the impact of interacting CC-related abiotic parameters
on fungal growth, the regulation of the AF biosynthetic cluster, and toxin production. A recent study
examined the impact of interactions between a
w
and temperature in the transcriptome of A. flavus
when colonizing maize [
17
]. It was determined that a
w
(0.99, 0.91) and temperature (30, 37
◦
C)
influenced the colonization of maize grain by A. flavus with a significant effect on AFB
1
production
at 0.91 a
w
and 37
◦
C. Both environmental factors affected biological processes and the numbers of
up- and downregulated genes. The interacting environmental factors influenced the functioning
of the secondary metabolite clusters for AFs and CPA such that an elevated number of genes were
co-regulated by both a
w
and temperature. For example, an interaction effect for 4 of the 25 AFB
1
genes,
including regulatory and transcription activators occurred. For CPA, all 5 biosynthetic genes were
affected by awstress, regardless of temperature.
The objectives of the present study were to examine, for the first time, the impact of interactions
between a
w
(0.99, 0.91), temperature (30, 37
◦
C) and CO
2
(350, 650 and 1000 ppm) on A. flavus
(NRRL 3357) colonization of stored maize grain to evaluate the impacts on (1) the whole genome,
including the aflatoxin biosynthesis gene cluster using RNA-seq, (2) effects on key biosynthetic genes
using q-PCR, and (3) effects on AFB
1
production. We used functional genomics to better understand
the transcriptomic response of the fungus to CC parameters of a
w
, temperature and CO
2
levels,
with a view towards predicting changes in fungal infection and toxin production associated with
resilience to such climatic stress factors.
Toxins 2018,10, 5 3 of 16
2. Results
2.1. The Interaction of Water, Temperature, and CO2Impact AFB1Production in Maize Grain
Overall, AFB
1
production showed a positive correlation with water availability and CO
2
levels.
AFB
1
was extracted and quantified after 10 days incubation at 30
◦
C or 37
◦
C, 0.91 or 0.99 a
w
, and 350,
650, or 1000 ppm CO
2
. At low a
w
(Figure 1A) the quantity of AF produced was 5- to 10-fold lower
than at high a
w
levels (Figure 1B). However, the effect of CO
2
was positively correlated with AFB
1
production at both high and low a
w
levels. With an elevated CO
2
level of 650 ppm, there was
an interaction effect with temperature and a
w
. At 0.91 a
w
, AFB
1
production was highest at 37
◦
C.
Conversely, at 0.99 a
w
the temperature condition of 30
◦
C exhibited higher toxin levels. The trends
observed indicate that CO
2
affects toxin production. The maximum quantity of toxin was observed
at elevated levels of CO
2
(650 ppm) with low water activity and at 30
◦
C, however, no additional
accumulation of AFB1was observed at 1000 ppm.
Toxins 2018, 10, 5 3 of 15
on AFB1 production. We used functional genomics to better understand the transcriptomic response of the
fungus to CC parameters of aw, temperature and CO2 levels, with a view towards predicting changes in
fungal infection and toxin production associated with resilience to such climatic stress factors.
2. Results
2.1. The Interaction of Water, Temperature, and CO2 Impact AFB1 Production in Maize Grain
Overall, AFB1 production showed a positive correlation with water availability and CO2 levels. AFB1
was extracted and quantified after 10 days incubation at 30 °C or 37 °C, 0.91 or 0.99 aw, and 350, 650, or 1000
ppm CO2. At low aw (Figure 1A) the quantity of AF produced was 5- to 10-fold lower than at high aw levels
(Figure 1B). However, the effect of CO2 was positively correlated with AFB1 production at both high and
low aw levels. With an elevated CO2 level of 650 ppm, there was an interaction effect with temperature and
aw. At 0.91 aw, AFB1 production was highest at 37 °C. Conversely, at 0.99 aw the temperature condition of
30 °C exhibited higher toxin levels. The trends observed indicate that CO2 affects toxin production. The
maximum quantity of toxin was observed at elevated levels of CO2 (650 ppm) with low water activity and
at 30 °C, however, no additional accumulation of AFB1 was observed at 1000 ppm.
Figure 1. Aflatoxin B1 (AFB1) production by A. flavus under different combinations of environmental
conditions. (A) At 30 °C and under low water activity levels of 0.91 aw (“30/91”), the effects of CO2 are
minimal; however, at 37 °C (“37/91”) there are increases in AFB1 production correlating with higher CO2
levels. (B) At high water activity levels the biosynthesis of AFB1 was significantly elevated at both 30 °C
(“30/99”) and 37 °C (“37/99”). ANOVA: analysis of variance statistical analysis. *p ≤ 0.05.
2.2. Effect of Three-Way Interacting CC Conditions on Gene Expression
RNA-seq of A. flavus genes showed a large global effect with water and temperature stress, but a
limited effect with elevated CO2 levels. Between 6.22 × 105 and 3.37 × 107 reads mapped to exogenic regions
of A. flavus strain 3357 (Table 1). Principle component analysis (PCA) indicated that aw caused the largest
variance observed (Figure 2A), accounting for 67% of the variance, however samples with similar water
activity cluster together on PC1, indicating there little sample variation for this metric. The second principle
component indicated that a change in temperature was the second most important factor, but only for
samples stored with freely available water (0.99 aw). All samples stored under water stress (0.91 aw)
clustered together and showed relatively little inter-sample variance. At high aw, a change in temperature
resulted in a high variance in gene expression. The total number of genes affected under the various
conditions are illustrated in Figure 2B. For each data point in Figure 2B, the unidentified variable (aw level,
temperature or CO2 level) is the baseline condition (30 °C, 0.99 aw, or 350 ppm CO2) for those samples being
Figure 1.
Aflatoxin B
1
(AFB
1
) production by A. flavus under different combinations of environmental
conditions. (
A
) At 30
◦
C and under low water activity levels of 0.91 a
w
(“30/91”), the effects of CO
2
are
minimal; however, at 37
◦
C (“37/91”) there are increases in AFB
1
production correlating with higher
CO
2
levels. (
B
) At high water activity levels the biosynthesis of AFB
1
was significantly elevated at both
30 ◦C (“30/99”) and 37 ◦C (“37/99”). ANOVA: analysis of variance statistical analysis. * p≤0.05.
2.2. Effect of Three-Way Interacting CC Conditions on Gene Expression
RNA-seq of A. flavus genes showed a large global effect with water and temperature stress,
but a limited effect with elevated CO
2
levels. Between 6.22
×
10
5
and 3.37
×
10
7
reads mapped to
exogenic regions of A. flavus strain 3357 (Table 1). Principle component analysis (PCA) indicated that a
w
caused the largest variance observed (Figure 2A), accounting for 67% of the variance, however samples
with similar water activity cluster together on PC1, indicating there little sample variation for this
metric. The second principle component indicated that a change in temperature was the second most
important factor, but only for samples stored with freely available water (0.99 a
w
). All samples stored
under water stress (0.91 a
w
) clustered together and showed relatively little inter-sample variance.
At high a
w
, a change in temperature resulted in a high variance in gene expression. The total number of
genes affected under the various conditions are illustrated in Figure 2B. For each data point in Figure 2B,
the unidentified variable (a
w
level, temperature or CO
2
level) is the baseline condition (30
◦
C, 0.99 a
w
,
or 350 ppm CO
2
) for those samples being compared. Of note, ~10-fold more genes responded to
temperature and CO
2
stress at high a
w
levels (Figure 2B, upper graph). Likewise, ~3x more genes were
affected by temperature and ~25x more genes were affected by CO
2
in the non-stressed temperature
conditions (30
◦
C; Figure 2B, middle graph). High CO
2
levels (1000 ppm) had a significant impact on
the number of genes expressed, decreasing the number of genes affected by water and temperature by
~3 fold (Figure 2B bottom graph). In summary, elevated temperature, water stress, and increased CO
2
decreased the number of differentially expressed genes when compared to the non-stressed conditions.
Toxins 2018,10, 5 4 of 16
Table 1. Number of reads mapping to exons for all conditions tested.
30 ◦C 37 ◦C
0.91 aw
350 ppm 3.37 ×1072.13 ×107
650 ppm 2.64 ×1073.00 ×106
1000 ppm 3.04 ×1076.84 ×106
0.99 aw
350 ppm 1.04 ×1078.01 ×105
650 ppm 4.69 ×1062.24 ×106
1000 ppm 6.22 ×1051.03 ×106
Toxins 2018, 10, 5 4 of 15
compared. Of note, ~10-fold more genes responded to temperature and CO2 stress at high aw levels (Figure
2B, upper graph). Likewise, ~3x more genes were affected by temperature and ~25x more genes were
affected by CO2 in the non-stressed temperature conditions (30 °C; Figure 2B, middle graph). High CO2
levels (1000 ppm) had a significant impact on the number of genes expressed, decreasing the number of
genes affected by water and temperature by ~3 fold (Figure 2B bottom graph). In summary, elevated
temperature, water stress, and increased CO2 decreased the number of differentially expressed genes when
compared to the non-stressed conditions.
Table 1. Number of reads mapping to exons for all conditions tested.
30 °C 37 °C
0.91 aw
350 ppm 3.37 × 107 2.13 × 107
650 ppm 2.64 × 107 3.00 × 106
1000 ppm 3.04 × 107 6.84 × 106
0.99 aw
350 ppm 1.04 × 1078.01 × 105
650 ppm 4.69 × 106 2.24 × 106
1000 ppm 6.22 × 105 1.03 × 106
Figure 2. (A) Principle component analysis and (B) total gene counts of differentially expressed genes
indicate that of the three environmental variables tested, water activity is the primary driver of
transcriptional changes, with temperature also having a measurable impact. Carbon dioxide levels (1000
ppm vs. 350 ppm) has the largest impact at 0.99 aw (B, top) and at 30 °C (B, middle). High carbon dioxide
levels (1000 ppm) reduces the level of genes affected by water activity and temperature by approximately
1/3 (B, bottom).
A significantly larger number of genes were affected by water stress, whereas approximately equal
numbers were affected by temperature and CO2 (Figure 3, left and center). Most of the genes that were
affected by changing temperature and CO2 levels were also affected by low aw, with 472 genes being
upregulated by all three environmental conditions and 564 genes being downregulated. Figure 3 illustrates
Figure 2.
(
A
) Principle component analysis and (
B
) total gene counts of differentially expressed
genes indicate that of the three environmental variables tested, water activity is the primary driver of
transcriptional changes, with temperature also having a measurable impact. Carbon dioxide levels
(1000 ppm vs. 350 ppm) has the largest impact at 0.99 a
w
(
B
,
top
) and at 30
◦
C (
B
,
middle
). High carbon
dioxide levels (1000 ppm) reduces the level of genes affected by water activity and temperature by
approximately 1/3 (B,bottom).
A significantly larger number of genes were affected by water stress, whereas approximately
equal numbers were affected by temperature and CO
2
(Figure 3, left and center). Most of the genes that
were affected by changing temperature and CO
2
levels were also affected by low a
w
, with 472 genes
being upregulated by all three environmental conditions and 564 genes being downregulated. Figure 3
illustrates how changing all three environmental variables simultaneously resulted in a significantly
larger number of genes (4853) being affected than changing variables independently. In other words,
there is a cumulative effect of changing environmental conditions on global gene expression. The group
labeled “Genes affected individually” refers to genes that are affected by just a
w
, temperature, or CO
2
levels when examined separately. Sixty-nine genes were affected by each of the three conditions
individually, but not affected when all three conditions are applied simultaneously.
Toxins 2018,10, 5 5 of 16
Toxins 2018, 10, 5 5 of 15
how changing all three environmental variables simultaneously resulted in a significantly larger number
of genes (4853) being affected than changing variables independently. In other words, there is a cumulative
effect of changing environmental conditions on global gene expression. The group labeled “Genes affected
individually” refers to genes that are affected by just a
w
, temperature, or CO
2
levels when examined
separately. Sixty-nine genes were affected by each of the three conditions individually, but not affected
when all three conditions are applied simultaneously.
Figure 3. Venn Diagrams illustrate the number of genes up regulated (left) and down regulated (middle)
by individual conditions. For each comparison described, control conditions are assumed (30 °C, 0.99 a
w
and
350 ppm CO
2
). When all three conditions are changed simultaneously, 5820 genes are affected, however
when only one environmental condition is changed, only 967 of these genes are affected, indicating a
significant difference between the cumulative and individual effects of environmental changes (right).
2.3. Effect of Three-Way Interacting CC Conditions on Biological Processes
After 10 days A. flavus colonization of the maize kernels, quantitative PCR was conducted on two AF
cluster genes: aflR, the regulating transcription factor, and aflD, a reductase (Figure 4A). Gene expression
levels are shown relative to a control condition of 30 °C, 0.99 a
w
, and 350 ppm CO
2
. Four important
observations were made: (1) at 30 °C/0.99 a
w
there were decreases in aflR and aflD expression, even at
elevated CO
2
levels (650 ppm; 1000 ppm) relative to the control (350 ppm). This is in contrast to what was
observed at 37 °C, where aflD is activated at higher levels in the 650 and 1000 ppm CO
2
treatments at 0.99
a
w
; (2) in two separate stressed conditions 30 °C/0.91 a
w
(water stressed conditions) and 37 °C/0.99 a
w
(high
temperature), there are sharp differences in aflR expression levels between 650 and 1000 ppm, indicating
that some CO
2
level between the two may represent a threshold; (3) at low a
w
and high temperature, there
were higher levels of activity than at 0.99 a
w
/350 ppm; (4) at 37 °C/0.99 a
w
, a dose response in relation to
CO
2
was observed.
Figure 3.
Venn Diagrams illustrate the number of genes up regulated (
left
) and down regulated
(
middle
) by individual conditions. For each comparison described, control conditions are assumed
(30
◦
C, 0.99 a
w
and 350 ppm CO
2
). When all three conditions are changed simultaneously, 5820 genes
are affected, however when only one environmental condition is changed, only 967 of these genes
are affected, indicating a significant difference between the cumulative and individual effects of
environmental changes (right).
2.3. Effect of Three-Way Interacting CC Conditions on Biological Processes
After 10 days A. flavus colonization of the maize kernels, quantitative PCR was conducted on
two AF cluster genes: aflR, the regulating transcription factor, and aflD, a reductase (Figure 4A).
Gene expression levels are shown relative to a control condition of 30
◦
C, 0.99 a
w
, and 350 ppm
CO
2
. Four important observations were made: (1) at 30
◦
C/0.99 a
w
there were decreases in aflR and
aflD expression, even at elevated CO
2
levels (650 ppm; 1000 ppm) relative to the control (350 ppm).
This is in contrast to what was observed at 37
◦
C, where aflD is activated at higher levels in the
650 and 1000 ppm CO
2
treatments at 0.99 a
w
; (2) in two separate stressed conditions 30
◦
C/0.91 a
w
(water stressed conditions) and 37
◦
C/0.99 a
w
(high temperature), there are sharp differences in aflR
expression levels between 650 and 1000 ppm, indicating that some CO
2
level between the two may
represent a threshold; (3) at low a
w
and high temperature, there were higher levels of activity than at
0.99 aw/350 ppm; (4) at 37 ◦C/0.99 aw, a dose response in relation to CO2was observed.
Toxins 2018,10, 5 6 of 16
Toxins 2018, 10, 5 6 of 15
Figure 4. Gene Expression of the aflatoxin gene cluster. (A) Quantitative PCR analysis shows the expression
of aflR and aflD, an aflatoxin cluster transcription factor and structural gene, respectively. After 10 days of
incubation on maize kernels gene levels at 30 °C generally decrease, however the effects of high CO2 (1000
ppm) levels at 0.91 aw indicate decrease values (left), possibly in response to elevated AFB1 levels. At 37 °C
gene levels remain high, however, again at 1000 ppm CO2 the transcription factor aflR is decreased. (B) The
heat map of regularized log transformed counts indicate hierarchal clustering associated with water activity
levels. The clustering also indicates expression patterns that suggest early genes in the pathway may be
responsive to high CO2 levels (See Results).
The heat map (Figure 4B) indicates relative expression levels for all genes in the AF gene cluster
obtained from RNA-seq. Hierarchical clustering of sample conditions (top brackets) and genes (left side)
are indicated. The AF cluster genes aflD and aflR are clustered relatively close together (genes 3 and 6,
respectively), indicating similar expression profiles, which is in agreement with qPCR results where 9 of
the 12 samples are similarly expressed. Hierarchical clustering of the samples indicated water availability
as the primary determinant of expression patterns. After 10 days, the 30 °C/0.99 aw/350 ppm CO2 condition
exhibited higher overall expression levels, distinct from any of the stressed conditions. Notably, expression
at 30 °C/0.99 aw/350 ppm is higher than at 650 ppm and 1000 ppm CO2. Three of the genes in the AF
biosynthesis pathway, fatty acid synthases aflA and aflB, and the polyketide synthase aflC, all of which are
responsible for the synthesis of norsolorinic acid, were enriched in all samples. The reductases coded by
aflD and aflF, responsible for the reduction of norsolorinic acid, were also enriched at relatively high levels;
however, a third reductase, aflE, exhibited the highest enrichment level in the most stressed condition
examined (37 °C/0.91 aw and 1000 ppm CO2).
Enrichment analysis was conducted to identify KEGG biological processes with an over-
representation of genes being up- or downregulated. Table 2 lists the KEGG categories affected by changes
in conditions, both individually and combined, from which several important observations can be made.
Primarily, energy-related metabolic processes such as glycolysis/gluconeogenesis, purine/pyrimidine
metabolism, and starch/sucrose metabolism are prominent among conditions analyzed. Most genes in this
category that are essential for glycolysis, including triosephosphate isomerase (AFLA_094630), fructose bis
phosphate aldolase (AFLA_030930), and pyruvate kinase (AFLA_087900) are all downregulated in all the
Figure 4.
Gene Expression of the aflatoxin gene cluster. (
A
) Quantitative PCR analysis shows the
expression of aflR and aflD, an aflatoxin cluster transcription factor and structural gene, respectively.
After 10 days of incubation on maize kernels gene levels at 30
◦
C generally decrease, however the
effects of high CO
2
(1000 ppm) levels at 0.91 a
w
indicate decrease values (
left
), possibly in response
to elevated AFB
1
levels. At 37
◦
C gene levels remain high, however, again at 1000 ppm CO
2
the
transcription factor aflR is decreased. (
B
) The heat map of regularized log transformed counts indicate
hierarchal clustering associated with water activity levels. The clustering also indicates expression
patterns that suggest early genes in the pathway may be responsive to high CO2levels (See Results).
The heat map (Figure 4B) indicates relative expression levels for all genes in the AF gene cluster
obtained from RNA-seq. Hierarchical clustering of sample conditions (top brackets) and genes
(left side) are indicated. The AF cluster genes aflD and aflR are clustered relatively close together
(genes 3 and 6, respectively), indicating similar expression profiles, which is in agreement with
qPCR results where 9 of the 12 samples are similarly expressed. Hierarchical clustering of the
samples indicated water availability as the primary determinant of expression patterns. After 10 days,
the 30
◦
C/0.99 a
w
/ 350 ppm CO
2
condition exhibited higher overall expression levels, distinct from
any of the stressed conditions. Notably, expression at 30
◦
C/0.99 a
w
/350 ppm is higher than at 650 ppm
and 1000 ppm CO
2
. Three of the genes in the AF biosynthesis pathway, fatty acid synthases aflA and
aflB, and the polyketide synthase aflC, all of which are responsible for the synthesis of norsolorinic acid,
were enriched in all samples. The reductases coded by aflD and aflF, responsible for the reduction
of norsolorinic acid, were also enriched at relatively high levels; however, a third reductase, aflE,
exhibited the highest enrichment level in the most stressed condition examined (37
◦
C/0.91 a
w
and
1000 ppm CO2).
Enrichment analysis was conducted to identify KEGG biological processes with an over-representation
of genes being up- or downregulated. Table 2lists the KEGG categories affected by changes in
conditions, both individually and combined, from which several important observations can be made.
Primarily, energy-related metabolic processes such as glycolysis/gluconeogenesis, purine/pyrimidine
metabolism, and starch/sucrose metabolism are prominent among conditions analyzed. Most genes in
this category that are essential for glycolysis, including triosephosphate isomerase (AFLA_094630),
fructose bis phosphate aldolase (AFLA_030930), and pyruvate kinase (AFLA_087900) are all
downregulated in all the conditions examined. The KEGG categories affected, that are unique to
changes in CO
2
concentration (30
◦
C/0.99 a
w
/350 ppm CO
2
vs. 30
◦
C/0.99 a
w
/1000 ppm CO
2
),
are cysteine and methionine metabolism (p= 0.009) and folate biosynthesis (p= 0.030). Three categories
of KEGG were affected only when all three environmental conditions were changed simultaneously
Toxins 2018,10, 5 7 of 16
(37
◦
C/0.91 a
w
/1000 ppm CO
2
vs. 30
◦
C/0.99 a
w
/350 ppm CO
2
). These were taurine and hypotaurine
metabolism (p= 0.014), cyanoamino acid metabolism (p= 0.017), and ether lipid metabolism (p= 0.049).
Table 2.
KEGG Categories with a statistically significant overrepresentation of genes that are
differentially expressed and their associated pvalues.
Carbon Dioxide Temperature
(30 ◦C/0.99 aw/350 ppm vs. 30 ◦C/0.99 aw/1000 ppm) (37 ◦C/0.99 aw/350 ppm vs. 30 ◦C/0.99 aw/350 ppm)
KEGG Category p-Value KEGG Category p-Value
Glycolysis/Gluconeogenesis 0.003 Glycolysis/Gluconeogenesis 0.000
Purine metabolism 0.008 Starch and sucrose metabolism 0.002
Cysteine and methionine metabolism 0.009 Methane metabolism 0.017
Fructose and mannose metabolism 0.011 Riboflavin metabolism 0.024
Pyrimidine metabolism 0.025 Glutathione metabolism 0.024
Folate biosynthesis 0.030 Glycosphingolipid biosynthesis - globo series 0.025
Carbon fixation in photosynthetic organisms 0.041 Fructose and mannose metabolism 0.029
Inositol phosphate metabolism 0.049 Pentose phosphate pathway 0.043
Water Activity Combined
(30 ◦C/0.91 aw/350 ppm vs. 30 ◦C/0.99 aw/350 ppm) (37 ◦C/0.91 aw/1000 ppm vs. 30 ◦C/0.99 aw/350 ppm)
KEGG Category p-Value KEGG Category p-Value
Riboflavin metabolism 0.001 Glycolysis/Gluconeogenesis 0.000
Inositol phosphate metabolism 0.002 Methane metabolism 0.002
Glyoxylate and dicarboxylate metabolism 0.011 Riboflavin metabolism 0.005
Purine metabolism 0.012 Inositol phosphate metabolism 0.009
Methane metabolism 0.019 Taurine and hypotaurine metabolism 0.014
Starch and sucrose metabolism 0.020 Fructose and mannose metabolism 0.014
Glycolysis/Gluconeogenesis 0.023 Starch and sucrose metabolism 0.015
Cyanoamino acid metabolism 0.031 Cyanoamino acid metabolism 0.017
Fructose and mannose metabolism 0.039 Purine metabolism 0.019
Pyrimidine metabolism 0.022
Ether lipid metabolism 0.049
2.4. Effect of CO2and Interactions with Other Abiotic Factors on Secondary Metabolite Gene Clusters
To identify secondary metabolic gene clusters that were affected by the interacting environmental
conditions tested according to the RNA-seq results, a Secondary Metabolite Unique Regions Finder
(SMURF) analysis of the A. flavus genome was conducted. Table 3lists several SMURF-identified
secondary metabolic gene clusters labeled according to Georgianna, et al. [
18
] and their corresponding
relative expression values (log
2
fold change). The full list of secondary metabolite-associated genes
identified by SMURF is provided in Supplementary Table S1.
Many of the genes listed in Table 3have high sequence identity to previously characterized genes
(in black). Of note, four of the genes (shown in red) are affected by both of the elevated CO
2
levels
(650 ppm and 1000 ppm). These consist of two dimethylallyl tryptophan synthases (DMTS), a terpene
cyclase, and the hybrid NRPS/PKS shown to be responsible for leporin biosynthesis (see Discussion).
Toxins 2018,10, 5 8 of 16
Table 3.
Known or putative gene clusters in Aspergillus flavus identified by the gene for their primary backbone enzyme. The fold change values indicate upregulation
(positive number) or downregulation (negative number) according to RNA sequencing results. Clusters in bold indicate the gene is affected by both carbon dioxide
levels tested.
Effect of Carbon Dioxide Effect of Water Effect of Temperature
At 30 ◦C/0.99 aw: At 30 ◦C, 0.91 awvs. 0.99 aw: At 0.99 aw, 37 ◦C vs. 30 ◦C:
#A. flavus 3357 Name SM* Product 650 ppm/350 ppm 1000 ppm/350 ppm 350 ppm 1000 ppm 350 ppm 1000 ppm
5 AFLA_006170 polyketide synthetase (PksP) naphthopyrone - 3.37 5.01 1.96 1.86 -
10 AFLA_016140 scytalone dehydratase (Arp1)
(conidial pigment biosynthesis)
conidial pigment
1,8-dihydroxynaphthalene-melanin
- - −3.54 −2.21 - -
15 AFLA_045490 dimethylallyl tryptophan
synthase, putative aflatrem, ATM2 2.50 - - - - -
19 AFLA_060680 dimethylallyl tryptophan synthase Unknown −3.38 −4.09 −4.17 - - 2.68
20 AFLA_062860 polyketide synthase (PkfA)
3-(2,4-dihydroxy-6-methylbenzyl)-O
rsellinaldehyde 1.35 - - −1.87 2.42 -
21 AFLA_064240 nonribosomal peptide
synthase (wykN)WYK peptidase inhibitor - 2.23 - −2.14 1.31 −2.19
23 AFLA_066840 hybrid NRPS/PKS enzyme Leporins 1.59 2.45 - −3.27 - −3.40
35 AFLA_101700 NRPS enzyme (lnaA) piperazines - 2.15 - −1.94 2.66 -
36 AFLA_104210 PKS-like enzyme, putative dihydrocurvularin - - −3.04 - −2.94 -
39 AFLA_108550 polyketide synthase monodictylphenone - - - - - -
41 AFLA_114820 polyketide synthase (fluP) (pksL2) 6-MSAi - 1.55 - −2.17 - -
44 AFLA_116890 polyketide synthase (PkiA) 6-hydroxy-7-methyl-3-
nonylisoquinoline-5,8-dione -−2.52 −3.49 - - -
54 AFLA_139410 polyketide synthase
(aflC/pksA/pksL1)aflatoxin - 1.20 0.95 - - −2.05
55 AFLA_139490 hybrid PKS/NRPS enzyme cyclopiazonic acid - - −2.75 −5.39 - −3.09
AFLA_125760 Class 2 Terpene Cyclase Unknown 1.99 4.65 3.98 - 3.48 -
*Putative product based on identity to characterized enzymes.
Toxins 2018,10, 5 9 of 16
2.5. Identification of Gene Networks Affected by CO2Levels
Gene co-expression networks were determined by analyzing all RNA sequencing results using
WGNCA. The results were then visualized using Cytoscape. This analysis revealed two prominent
gene networks with significant numbers of genes affected by CO2levels (Figure 5).
Toxins 2018, 10, 5 9 of 15
2.5. Identification of Gene Networks Affected by CO
2
Levels
Gene co-expression networks were determined by analyzing all RNA sequencing results using
WGNCA. The results were then visualized using Cytoscape. This analysis revealed two prominent
gene networks with significant numbers of genes affected by CO
2
levels (Figure 5).
Figure 5. Weighted Gene Network Co-expression Analysis followed by visualization in Cytoscape
shows two unidentified networks heavily influenced by increased carbon dioxide levels (1000 ppm
CO
2
vs. 350 ppm CO
2
) at 30 °C and 0.99 a
w
. The color of the node indicates the log
2
fold change values.
(A) 268 out of 905 genes in the network are differentially expressed, with most showing increased
levels. (B) 114 genes out of 415 genes in the network show altered expression levels. See Results for
description of the genes in the network.
Genes with altered differential expression at 30 °C, 0.99 a
w
and 1000 ppm CO
2
, compared with
350 ppm, are colored and genes not differentially expressed are shown in grey. The network
illustrated in Figure 5A shows 905 genes interacting in total, with 268 genes differentially expressed.
The network has three distinct clusters on the left, right, and center, and the center cluster is co-
expressing with genes in the outer clusters (indicated by black lines (edges)). The left and right
clusters (95 and 67 genes, respectively) have edges connecting to the 45 genes in the center cluster.
Gene ontology (GO) enrichment analysis of the individual clusters indicated the molecular function
of genes on the left involve primarily ATP-binding protein kinases (e.g., GO:0005524, ATP binding;
GO:0004672, protein kinase activity) (See Supplemental Table S2). The right cluster is enriched in
structural and secretory-related genes (e.g., GO:0000166, nucleotide binding, Rheb, and Myo5;
GO:0051056 regulation of small GTPase mediated signal transduction, Sar1). The center of the
network contained no enriched GO categories, however it does contain several transcription factors
and transcriptional regulatory elements (AFLA_029620, AFLA_114920, AFLA_003630). Figure 5B is
a second network (network 2) identified by WGNCA analysis and Cytoscape rendering. It shows 415
genes in the network, 114 of which are significantly affected by changing CO
2
levels. Network 2
consists of a significantly enriched number of ribosomal-related genes (GO:0003735, structural
constituent of ribosome). Other non-ribosomal genes include and Hsp90-binding chaperone sba1
(AFLA_095590), a mycelial catalase cat1 (AFLA_090690), and the AF cluster gene aflT (AFLA_139420)
(Supplementary Table S2).
3. Discussion
To date, the majority of research pertaining to environmental effects on AF production, fungal
growth, and plant pathogenicity have focused on the effects of water availability and temperature.
Figure 5.
Weighted Gene Network Co-expression Analysis followed by visualization in Cytoscape
shows two unidentified networks heavily influenced by increased carbon dioxide levels (1000 ppm
CO
2
vs. 350 ppm CO
2
) at 30
◦
C and 0.99 a
w
. The color of the node indicates the log
2
fold change values.
(
A
) 268 out of 905 genes in the network are differentially expressed, with most showing increased
levels. (
B
) 114 genes out of 415 genes in the network show altered expression levels. See Results for
description of the genes in the network.
Genes with altered differential expression at 30
◦
C, 0.99 a
w
and 1000 ppm CO
2
, compared with
350 ppm, are colored and genes not differentially expressed are shown in grey. The network illustrated
in Figure 5A shows 905 genes interacting in total, with 268 genes differentially expressed. The network
has three distinct clusters on the left, right, and center, and the center cluster is co-expressing with
genes in the outer clusters (indicated by black lines (edges)). The left and right clusters (95 and
67 genes, respectively) have edges connecting to the 45 genes in the center cluster. Gene ontology
(GO) enrichment analysis of the individual clusters indicated the molecular function of genes on
the left involve primarily ATP-binding protein kinases (e.g., GO:0005524, ATP binding; GO:0004672,
protein kinase activity) (See Supplemental Table S2). The right cluster is enriched in structural and
secretory-related genes (e.g., GO:0000166, nucleotide binding, Rheb, and Myo5; GO:0051056 regulation of
small GTPase mediated signal transduction, Sar1). The center of the network contained no enriched GO
categories, however it does contain several transcription factors and transcriptional regulatory elements
(AFLA_029620, AFLA_114920, AFLA_003630). Figure 5B is a second network (network 2) identified
by WGNCA analysis and Cytoscape rendering. It shows 415 genes in the network, 114 of which are
significantly affected by changing CO
2
levels. Network 2 consists of a significantly enriched number of
ribosomal-related genes (GO:0003735, structural constituent of ribosome). Other non-ribosomal genes
include and Hsp90-binding chaperone sba1 (AFLA_095590), a mycelial catalase cat1 (AFLA_090690),
and the AF cluster gene aflT (AFLA_139420) (Supplementary Table S2).
Toxins 2018,10, 5 10 of 16
3. Discussion
To date, the majority of research pertaining to environmental effects on AF production,
fungal growth, and plant pathogenicity have focused on the effects of water availability and
temperature. Work by Medina et al. [
19
,
20
] was among the first attempts to examine the effects
of a
w
and temperature in the context of higher CO
2
levels. They found that the interactive effect of
water stress, high temperature and high CO
2
increased both gene expression of the AF biosynthesis
genes aflR and aflD, and AFB
1
production in maize kernels. While the experimental procedure here
permitted us to observe changes in gene expression and AF production after 10 days of growth, it is
limited in that we cannot observe a temporal response of gene activation followed immediately by
AF biosynthesis. However, we further expand on previous findings, allowing us to characterize
putative secondary metabolic gene clusters, important developmental genes, and identify networks of
co-expressed genes affected by CO2levels.
Most evidence involving fungal carbon metabolism involves the release of CO
2
via cellular
respiration, however CO
2
serves other functions. Hall et al. [
21
] reported that CO
2
serves as an intra-colony
signaling molecule important for colonization and pathogenesis in the fungus Candida albicans. It has also
been shown that insects, in symbiosis with fungi, demonstrate preferences for certain ranges of elevated
CO
2
in which to conduct their fungal-rearing [
22
]. Furthermore, elevated CO
2
present in the ambient
environment of soil samples containing a mixed fungal population decreases respiration activity [
23
]
and decreases AF accumulation in A. parasiticus [
24
]. The data here demonstrates that while marked
effects in transcription (and by consequence growth and toxin production) are primarily a result of
water availability, and secondarily temperature, changing CO
2
levels altered fungal response to both
water and temperature changes. This is made apparent by the total numbers of genes affected and by
our observing that changes brought on by low water or high temperature stresses can vary depending
on CO
2
availability. Furthermore, evidence indicates that the combination of environmental stressors,
including high CO2, have a compounding effect compared to that of individual stressors.
The production of secondary metabolites under different environmental conditions is of
concern due to potential shifts in toxigenic potential. The biosynthetic gene cluster 19 in A. flavus
contains a dimethylallyl tryptophan synthase (DMAT), which prenylates tryptophan or other
aromatic substrates, however the secondary metabolite biosynthesized has yet to be identified.
While the expression profile of individual cluster genes does not necessarily correlate with metabolite
biosynthesis, DMATS genes are involved in the biosynthesis of several toxic metabolites produced
by fungi that infect and contaminate crops such as cyclopiazonic acid [
25
] in A. flavus and
ergotamine in Claviceps purpurea [
26
]. Toxic metabolites can serve offensive or defensive functions,
increasing virulence of the fungus toward its host, or protecting the fungus from threats by other
microorganisms or predation. The backbone gene in cluster 23, a hybrid Non-ribosomal Peptide
Synthase/Polyketide Synthase (NRPS/PKS), is upregulated with increasing CO
2
levels. This cluster
is responsible for the biosynthesis of leporins [
27
]. Leporin A has anti-insectan properties [
28
],
while leporin B [
29
] can chelate iron, forming a trimer with Fe
3+
[
27
]. Siderophore biosynthesis
and siderophore-mediated iron uptake have been found to increase under hypoxic conditions in
A. fumigatus [
30
]. AFLA_125760, which was also upregulated at elevated CO
2
levels, encodes for
a terpene cyclase flanked by a putative P450 alkane hydroxylase (AFLA_125750) and a steroid alpha
reductase (AFLA_125740). These types of cyclases convert the linear triterpene squalene into cyclized
products under hypoxic conditions. Cyclized triterpenes, such as fungal ergosterol and bacterial
hopenoids, provide cell membrane structural integrity and fluidity. Production of ergosterol, the most
prevalent cyclized triterpene found in fungal cell membranes, is affected by mechanical and oxidative
stress [
30
,
31
]. Other cyclic triterpenes may also be expressed under hypoxic and stressful conditions to
improve cell membrane integrity.
Here we have demonstrated for the first time transcriptome-wide changes that occur in
a pathogenic fungus while colonizing maize kernels under these interacting CC-related environmental
conditions. AF biosynthesis and genes from the AF biosynthetic cluster responded to elevated CO
2
Toxins 2018,10, 5 11 of 16
levels, as did several other identified secondary-metabolic gene clusters. Further, there is a global
change in the transcriptome in response to water and temperature stress under high CO
2
conditions.
This works lays a solid foundation for further research to establish which genes and gene networks
should be targeted for fungal inhibition in future CC scenarios.
4. Materials and Methods
4.1. Fungal Strain
Aspergillus flavus strain NRRL 3357 (ATCC 200026, GenBank assembly accession:
GCA_000006275.2) [
32
,
33
] was obtained from the Southern Regional Research Center, New Orleans, LA.
This strain has been characterized extensively in several areas, including metabolite production [
34
,
35
],
genome profiling, [
36
], for the development of biological control [
37
], molecular ecology [
38
] and
many others. Spore stocks were stored at 4
◦
C or sub-cultured on Malt Extract agar (MEA; CM59,
Oxoid Ltd., Basingstoke, UK) when needed.
4.2. Sample Preparation and Treatment
Undamaged French feed maize kernels were used in this study. Initially a standard curve
was conducted whereby known amounts of water were added to multiple 10 g samples of kernels,
incubated at 4
◦
C for 48 h, and the resulting a
w
of the kernels was determined using an Aqualab 4 TE
water activity meter (Decagon Devices, Pullman, WA, USA).
Based on a
w
standard curve results, a volume of water was added to each 10 g sample of maize
kernels to obtain a
w
levels of 0.99 and 0.91 (minus 200
µ
L for the later additions of A. flavus spore
suspension). The maize kernels were then placed in glass culture vessels containing a microporous
lid, which allows for moisture and air exchange (Magenta, Sigma Ltd., Castleford, UK). Subsequently,
200
µ
L of spore suspension (approx. 10
6
spores/mL) were added to make up the predetermined
amounts of water required and thoroughly mixed. The inoculated vessels for each treatment were
placed in enclosed environmental chambers. To control humidity levels, 2 glass jars (500 mL) containing
glycerol-water solutions, appropriate to maintaining the equilibrium relative humidity at the target a
w
level, were placed in each chamber. The chambers were incubated at 30 and 37
◦
C for 10 days. To control
CO
2
and maintain humidity, specialty certified CO
2
gas cylinders (British Oxygen Company, Jierfude,
UK) containing either air, 650 ppm CO
2
or 1000 ppm CO
2
were used for flushing the environmental
chambers. The gases were bubbled through a glycerol:water solution of the required a
w
level before
flushing through the chambers and the valves closed. CO
2
flushing was performed every day as
described previously [
13
]. The glycerol-water solutions in the chambers were replaced with fresh
solutions every 2 days during the incubation period. Three replicates per treatment were used in all
cases. At the end of the incubation period, samples were snap frozen using liquid N
2
and kept at
−
80
◦
C until a portion of each sample could be used for RNA extraction and purification, or dried for
AFB1extraction and clean-up prior to quantification using HPLC analysis.
4.3. Aflatoxin Analysis
AFB
1
extraction was performed using AflaStar
™®
—Immunoaffinity Columns (IAC, Romer Labs
Inc., St. Louis, MO, USA), following the manufacturer’s instructions. Briefly, 5 g of the sample were
dried overnight at 80
◦
C and stored at room temperature. The samples were ground and 4 g was
placed into a 50 mL Falcon tube, to which 16 mL of a methanol:water (60:40 v:v) solution was added.
The samples were shaken for 1 h at room temperature, and then filtered through qualitative filter
paper (QL 110, Fisher Scientific UK Ltd., Loughborough, UK). The extract (1 mL) was diluted in
a 15 mL Falcon tube with 9 mL of PBS buffer (0.05 M/0.15 M NaCl, pH 7.4, Fisher Bioreagents
®
,
Fisher Scientific UK Ltd., Loughborough, UK), and pH was checked with pH strips. The diluted extract
was applied to the IAC, and allowed to drip through. After further cleaning, 3 mL of Methanol (HPLC
grade) was used to elute the AFs. The eluent was dried and standards were prepared using 200
µ
L
Toxins 2018,10, 5 12 of 16
AF (R-Biopharm Rhône Ltd., Darmstadt, Germany) stock solution comprised of 1 ng/uL AFB
1
. The
stock solution was pipetted into 2 mL Eppendorf tubes and left to evaporate to dryness overnight
inside a fume hood. For quantification of AFs, 200
µ
L hexane was added to the residue followed by
the addition of 50
µ
L triflouroacetic acid (TFA). The mixture was then vortexed for 30 s and then left
for 5 min. Thereafter, a mixture of water:acetonitrile (9:1, v:v) was added and the entire contents of
the tube were vortexed for 30 s, after which the mixture was left for 10 min to allow for thorough
separation of layers. The hexane layer was discarded and the aqueous layer filtered through nylon
syringe filters (13 mm
×
0.22
µ
m; Jaytee Biosciences Ltd., Herne Bay, UK) directly into amber salinized
2 mL HPLC.
A reversed-phase Agilent 1200 series HPLC system with fluorescence detection was used to
confirm the identity and quantify AFB
1
. This consisted of an in-line degasser, auto sampler, binary
pump and a fluorescence detector (excitation and emission wavelength of 360 and 440 nm, respectively).
Separation was achieved through the use of a C
18
column (Agilent Zorbax Eclipse plus C
18
4.6 mm
×
150 mm, 3.5
µ
m particle size; Agilent, Berks, UK) preceded by a guard cartridge with the same
packing material. Isocratic elution, with a mobile phase that included methanol:water:acetonitrile
(30:60:10, v:v:v), was performed at a flow rate of 1.0 mL/min. The injection volume was 20
µ
L. A set
of standards was injected (1 to 5 ng AFB
1
, B
2
, G
1
and G
2
per injection) and standard curves were
generated by plotting the area underneath the peaks against the amounts of AFB
1
standard injected.
Linear regression was performed in order to establish a correlation relationship (correlation coefficient,
R2= 0.99).
4.4. Total RNA Extraction
For RNA-seq, tissue harvest was performed after 10 days using three replicates. This time
frame was chosen because previous studies with both A. flavus and A. parasiticus suggested that gene
expression of many of the biosynthetic genes was optimal after 8–10 days growth, although there does
appear to be a sequential expression of groups of AF biosynthetic genes [
39
–
41
]. Studies on stored
maize grain have also shown optimum AFB
1
production at between 5–10 days at 0.98 a
w
and 10 days
at 0.95 a
w
[
42
]. We have compromised and used 10 days in this study so that we can obtain molecular
information and relevant toxin data. After 10 days of kernel infection (see above), 1 g of frozen milled
maize was ground to powder using a mortar and pestle in the presence of liquid nitrogen, and then
placed into a 2 mL extraction tube for isolation of total RNA. Total RNA was extracted using the
RNAeasy Plant Mini kit (Qiagen, Hilden, Germany). One hundred milligram of the resulting powder
was used for isolation of total RNA. The powder was resuspended in 1 mL lysis buffer supplemented
with 10
µ
L
β
-mercaptoethanol in a 2 mL RNase free micro reaction tube. After vortexing, the tube was
quickly frozen in liquid nitrogen. The sample was then thawed on ice. All further procedures were
essentially the same as recommended by the manufacturer’s protocol.
4.5. RNA Sequencing
Library preparation for RNA sequencing was conducted using the NEB Ultra Directional
RNA Library Prep Kit. Sequencing was performed on an Illumina HiSeq 2000 instrument.
All samples had three biological replicates except the following samples: 30
◦
C/0.99 a
w
/350 ppm,
30
◦
C/0.99 a
w
/650 ppm and 37
◦
C/0.99 a
w
/650 ppm, which had two replicates, and 30
◦
C/
0.99 a
w
/1000 ppm, 37
◦
C/0.99 a
w
/1000 ppm and 37
◦
C/0.99 a
w
/350 ppm which consisted of
one replicate. Untrimmed sequencing reads were mapped to the A. flavus NRRL3357 (assembly
JCVI-afl1-v2.0, http://www.ncbi.nlm.nih.gov/genome/360?genome_assembly_id=28730) reference
sequence using GSNAP [
43
,
44
]. Reads (www.ncbi.nih.gov, accession ID: PRJNA380582) mapping
to exons were counted using featureCounts [
45
] followed by differential expression testing with
DESeq2 [
46
]. Genes were considered differentially expressed if they had an adjusted p-value < 0.05.
Gene ontology and KEGG term enrichment was done using the GOSeq R Bioconductor package.
The gene co-expression network was made using WGCNA (Weighted Gene Network Co-expression
Toxins 2018,10, 5 13 of 16
Analysis) with a signed network, the bi-weight mid-correlation method, and a soft-thresholding
power of 10. Variance stabilized counts from DESeq2 were used as input to WGNCA. Genes that had
an average read count of less than 10 across all samples were removed prior to WGNCA analysis.
Principle Component Analysis plots and heat maps were generated using R. Venn Diagrams were
created by BioVenn [
47
]. Gene ontology term enrichment analysis for WGCNA clusters was conducted
using Fungifun2 [
48
]. Rendering of Gene Networks was performed using Cytoscape v3.4.0 [
49
].
KEGG annotation was produced through the BlastKOALA [50] service.
4.6. RNA Isolation, cDNA Synthesis and Quantitative PCR
RNA for qPCR was isolated using the RNeasy Plant Mini Kit (Qiagen) from infected maize kernels
(see above). cDNA was synthesized according to the Omniscript RT kit protocol (Qiagen) using 500 ng
of total RNA. The Bio-Rad CFX96 Real Time PCR Detection System (Bio-Rad, Watford, UK) was used
for qPCR with TaqMan probes targeting two AF cluster genes, aflD and aflR, and the housekeeping gene
β
-tubulin gene [
20
,
38
]. Primer and probe sequences were obtained from Medina et al. [
17
]. Reactions from
three biological replicates were prepared in 12.5
µ
L reaction mixtures in MicroAmp optical 96-well reaction
plates and sealed with optical adhesive covers (Bio-Rad). The optimal thermal cycling conditions included
an initial step of 10 min at 95
◦
C and all 45 cycles at 95
◦
C for 15 s, 55
◦
C for 20 s and 72
◦
C for 30 s.
Quantification cycle (Cq) determinations were automatically performed by the instrument using default
parameters. The expression ratio was calculated using the 2−∆∆Ctmethod [51].
Supplementary Materials:
The following are available online at www.mdpi.com/2072-6651/10/1/5/s1, Table S1:
SMURF. SMURF-designated secondary metabolic gene clusters in A. flavus and their associated log
2
expression
values, Table S2: Network Analysis. Genes identified as co-expressed by gene cluster network analysis and their
corresponding differential expression values.
Acknowledgments:
We thank Geromy Moore, Christopher Mattison and Jinyoung Barnaby for critical reading of
the manuscript. We also thank the staff at North Carolina State University Genomic Sciences Laboratory for their
technical services with RNA-sequencing. The United States Department of Agriculture, ARS, provided funding
for this research.
Author Contributions:
M.K.G. and N.M. wrote the manuscript and conducted bioinformatics data analysis.
A.M. and A.R. prepared samples for RNA-seq, conducted HPLC and qPCR. G.O. assisted with preparing RNA-seq
samples. B.M.M. and M.D.L. conducted bioinformatics data analysis. D.B., N.M. and G.P. conducted data analysis
and conceived and designed the experiment.
Conflicts of Interest:
Mention of trade names or commercial products in this publication is solely for the purpose
of providing specific information and does not imply recommendation or endorsement by the U.S. Department of
Agriculture. USDA is an equal opportunity provider and employer. The authors declare there are no conflicts
of interest.
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