Variation in fungal microbiome (mycobiome) and aflatoxins during simulated storage of in-shell peanuts and peanut kernels

Abstract

Internal transcribed spacer 2 (ITS2) sequencing was used to characterize the peanut mycobiome during 90 days storage at five conditions. The fungal diversity in in-shell peanuts was higher with 110 operational taxonomic units (OTUs) and 41 genera than peanut kernels (91 OTUs and 37 genera). This means that the micro-environment in shell is more suitable for maintaining fungal diversity. At 20–30 d, Rhizopus, Eurotium and Wallemia were predominant in in-shell peanuts. In peanut kernels, Rhizopus (>30%) and Eurotium (>20%) were predominant at 10–20 d and 30 d, respectively. The relative abundances of Rhizopus, Eurotium and Wallemia were higher than Aspergillus, because they were xerophilic and grew well on substrates with low water activity (aw). During growth, they released metabolic water, thereby favoring the growth of Aspergillus. Therefore, from 30 to 90 d, the relative abundance of Aspergillus increased while that of Rhizopus, Eurotium and Wallemia decreased. Principal Coordinate Analysis (PCoA) revealed that peanuts stored for 60–90 days and for 10–30 days clustered differently from each other. Due to low aw values (0.34–0.72) and low levels of A. flavus, nine of 51 samples were contaminated with aflatoxins.

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Scientific RepoRts | 6:25930 | DOI: 10.1038/srep25930
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Variation in fungal microbiome
(mycobiome) and aatoxins during
simulated storage of in-shell
peanuts and peanut kernels
Fuguo Xing*, Ning Ding*, Xiao Liu, Jonathan Nimal Selvaraj, Limin Wang, Lu Zhou,
Yueju Zhao, Yan Wang & Yang Liu
Internal transcribed spacer 2 (ITS2) sequencing was used to characterize the peanut mycobiome
during 90 days storage at ve conditions. The fungal diversity in in-shell peanuts was higher with 110
operational taxonomic units (OTUs) and 41 genera than peanut kernels (91 OTUs and 37 genera).
This means that the micro-environment in shell is more suitable for maintaining fungal diversity. At
20–30 d, Rhizopus, Eurotium and Wallemia were predominant in in-shell peanuts. In peanut kernels,
Rhizopus (>30%) and Eurotium (>20%) were predominant at 10–20 d and 30 d, respectively. The
relative abundances of Rhizopus, Eurotium and Wallemia were higher than Aspergillus, because they
were xerophilic and grew well on substrates with low water activity (aw). During growth, they released
metabolic water, thereby favoring the growth of Aspergillus. Therefore, from 30 to 90 d, the relative
abundance of Aspergillus increased while that of Rhizopus, Eurotium and Wallemia decreased. Principal
Coordinate Analysis (PCoA) revealed that peanuts stored for 60–90 days and for 10–30 days clustered
dierently from each other. Due to low aw values (0.34–0.72) and low levels of A. avus, nine of 51
samples were contaminated with aatoxins.
Peanuts are important economic crops in the world and are cultivated on a large scale, with Africa, China and
India being the greatest producers. In 2013, the annual peanut yield in China was 17.0 million tons (http://data.
stats.gov.cn/easyquery.htm?cn= C01), which accounts for more than 45% of the worldwide peanut production
(http://faostat.fao.org). Peanut kernels have high nutritional and commercial value due to the presence of calcium,
carbohydrates, fatty acids, bers, phosphorus, proteins and vitamins. Peanuts are mainly used in the manufacture
of oil, sweets, candies and pastes. More than 60% of the worldwide peanut yield is destined to oil production, with
peanut oil being the h most consumed type of oil around the world1.
e contamination of peanuts with Aspergillus avus and aatoxins is considered to be one of the most serious
safety problems in the world2,3. is fungal pathogen infects peanut kernels before and aer harvest4,5. Pre-harvest
peanut kernels contain mycelia and spores of aatoxigenic fungi, which contribute to signicant economic losses
and aatoxin accumulation during storage6. Contamination with aatoxins compromises the quality of the prod-
uct because aatoxins are the most toxic and carcinogenic compounds among the toxins. Aatoxin B1 (AFB1),
which is the most toxic, has both teratogenic and mutagenic properties and causes toxic hepatitis, hemorrhage,
edema, immunosuppression, and hepatic carcinoma7. AFB1, which poses a health risk in animals and humans,
has been classied as a class I human carcinogen by the International Agency for Research on Cancer8,9. More
than 100 countries and organizations including the National Health and Family Planning Commission P.R. of
China, the European Union and the U.S. Food and Drug Administration, have established limits for total aatox-
ins and AFB1 levels in peanuts10,11.
A. avus growth and subsequently aatoxin production depend on several factors, including geographical
region, season, and the environmental conditions during peanut growth and storage. Tropical and subtropical
regions are the most favorable for A. avus growth and aatoxin production12. A. avus growth and aatoxin
production are markedly aected by environmental factors, especially water activity (aw) and temperature13.
Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences/Key Laboratory of Agro-
Products Processing, Ministry of Agriculture, P.R. China. *These authors contributed equally to this work.
Correspondence and requests for materials should be addressed to Y.L. (email: liuyang01@caas.cn)
Received: 16 February 2016
Accepted: 25 April 2016
Published: 16 May 2016
OPEN

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e Yangtze River zone, especially Hubei province, China, is the major peanut-producing region of the country.
However, this region has a subtropical climate with high temperatures and high relative humidity, which is the
favorable environmental condition for A. avus growth and aatoxins production in stored peanuts. In 2012, the
distribution and toxigenicity of A. avus and A. parasiticus in peanut soils of four agroecological zones in China
(Southeast coastal zone, Yangtze River zone, Yellow River valley and Northeast zone) were investigated in our
laboratory. Previous ndings revealed that Yangtze River zone had the highest concentration of A. avus (2749.3
CFU/g) and the highest toxigenic potential of aatoxin production among the four agroecological zones. It is not
surprising that peanut contamination with aatoxins is frequently reported in this region3,14. However, there are
other fungal population that can aect A. avus growth and aatoxin production. erefore, it is necessary to
characterize the fungal microbiome of peanuts and its variations during storage under the environmental condi-
tions of this region.
High-throughput sequencing technologies have opened new frontiers in microbial community analyses by
providing an economic and ecient means of identifying the microbial phylotypes in samples. Furthermore,
next-generation sequencing techniques have led to a revolution in microbial ecology by providing opportunities
to generate unprecedented numbers of sequences and detect rare or low-abundance organisms15,16. Studies have
revolutionized our understanding of the microbial communities present in our bodies17–21, soils22,23 and deep
seas24. is revolution in sequencing technology, coupled with the development of advanced computational tools
that exploit metadata to relate hundreds of samples to one another in ways that reveal clear biological patterns,
has re-invigorated studies focused on the internal transcribed spacer 2 (ITS2) region of rDNA. e ITS2 region,
which is an excellent phylogenetic marker suitable for fungal taxon assignment25, has been successfully used in
comparative ecology studies where it gives results that are convergent with, if not comparable to, those for other
markers25,26. ITS-based surveys are extremely valuable because they allow the assessment of biodiversity and
ecological characteristics of whole communities or individual microbial taxa. However, alternative techniques,
such as metagenomics can provide insight into all genes and their functions in a given community. ITS2 region
phylogenies tend to match trends in overall gene content; the ability to relate at the species level to the host or the
environmental parameters has proven immensely powerful.
In the present study, the barcoded Illumina paired-end sequencing (BIPES) technique was used to character-
ize the mycobiome and its variations in stored in-shell peanuts and peanut kernels under dierent conditions (i.e.
temperature and relative humidity). is study characterizes the mycobiome and its variation in stored peanuts
using ITS2 sequencing and provides a direct comparison of the peanut mycobiome diversity during simulated
Storage In-shell peanuts Peanut kernels
Temperature/
relative humidity Time
Number
of Reads
Average Read
Length
Number
of Reads
Average Read
Length (bp)
0 48352 324 48352 324
20 °C/70%
10 36868 327 10440 327
20 44551 327 49526 331
30 39340 332 45115 334
60 54865 327 47447 334
90 8694 319 – –
20 °C/75%
10 21355 323 60505 328
20 42635 327 41625 333
30 23883 332 22802 333
60 39909 325 31118 323
90 20617 318 43916 318
25 °C/75%
10 8134 325 – –
20 49697 323 14472 327
30 50917 331 20643 324
60 22340 332 70923 328
90 148647 333 44956 324
30 °C/75%
10 119292 328 31395 328
20 50890 329 75468 328
30 63462 337 13954 332
60 63108 331 56194 346
90 136917 348 49436 363
30 °C/80%
10 19000 307 17193 334
20 90315 334 41494 334
30 65118 334 31678 334
60 42468 328 23112 311
90 163054 340 29730 339
Average 56709 329 38396 331
Table 1. Summary of pyrosequencing analysis.

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storage. e ndings of this study are likely to encourage the implementation and design of mould and aatoxin
management strategies.
Results
Data characteristics. In in-shell peanuts, the average number of raw reads generated per sample was 84,834,
of which 56,709 were retained following ltering and denoising steps, and 54,426 reads were subsequently clus-
tered into dierent operational taxonomic units (OTUs). e average length of each read was 329 bp (range:
307–348 bp) (Table1). Of 245 OTUs, 6 OTUs cannot be identied with other organisms, 23 OTUs represent pea-
nut and other plants, 14 OTUs represent nematode and other animals, and 3 OTUs represent E. coli. Remaining
199 OTUs represent fungi, and of them 18 OTUs represent uncultured fungi (Table S1).
In peanut kernels, the average number of raw reads generated per sample was 61,243, of which 38,396 were
retained following ltering and denoising steps, and 36,718 reads were subsequently clustered into 196 OTUs. e
average length of each reads was 331 bp (range: 311–363 bp) (Table1). Of 196 OTUs, 2 OTUs cannot be identi-
ed with other organisms, 21 OTUs represent peanut and other plants, 13 OTUs represent nematode and other
animals, and 7 OTUs represent bacteria. Remaining 153 OTUs represent fungi, and of them 13 OTUs represent
uncultured fungi (Table S2).
Fungal diversity in in-shell peanuts and peanut kernels. e average number of OTUs detected per
sample was 110 (range: 81–140). On average, each OTU contained 495 reads. OTUs representing 41 genera of
fungi were detected. 61.1% of OTUs and 50.7% of reads belonged to the Ascomycota, 9.4% of OTUs and 13.0%
of reads belonged to Basidiomycota, and 7.1% of OTUs and 23.7% of reads belonged to Zygomycota. Dominant
orders among Ascomycota included Dothideomycetes, Eurotiomycetes, Saccharomycetes and Sordariomycetes.
Dominant Eurotiomycetes groups included the genera Aspergillus (12.8% of OTUs and 12.1% of reads), Eurotium
(4.8% of OTUs and 21.6% of reads) and Penicillium (7.7% of OTUs and 10.1% of reads) genera. Dominant gen-
era among Zygomycota and Basidiomycota were Rhizopus (5.2% of OTUs and 23.5% of reads) and Wallemia
(5.3% of OTUs and 11.3% of reads), respectively (Table2). Aspergillus species that found were Aspergillus acu-
leatus, Aspergillus candidus, Aspergillus avipes, A. avus, Aspergillus gracilis, Aspergillus niger, Aspergillus ochra-
ceus, Aspergillus penicillioides (7.19% of reads), Aspergillus restrictus, Aspergillus tamarii, Aspergillus versicolor,
Aspergillus vitricola and Aspergillus wentii. e predominant species in Eurotium was Eurotium niveoglaucum
(39.78% of reads). e predominant species in Penicillium were Penicillium citrinum, Penicillium pinophilum, and
Penicilliium simplicissium. In Wallemia, Wallemia sebi was the only species. e predominant species in Rhizopus
was Rhizopus oryzae (46.16% of reads) (Table S1).
e average number of OTUs detected per sample was 91 (range: 69–114). On average, each OTU contained
403 reads. OTUs representing 37 genera of fungi were detected. 60.5% of OTUs and 44.2% of reads belonged to
the Ascomycota, 9.7% of OTUs and 13.5% of reads belonged to Basidiomycota, and 7.8% of OTUs and 27.8% of
reads belonged to Zygomycota. Dominant orders among Ascomycota were Dothideomycetes, Eurotiomycetes,
In-shell peanuts Peanut kernels
Taxnomic anity
Percent of
OTUs
Percent of
reads Taxnomic anity
Percent of
OTUs
Percent of
reads
Ascomycota 61.1 50.7 Ascomycota 60.5 44.2
Eurotiomycetes 30.1 44.1 Eurotiomycetes 31.5 39.5
Eurotium 4.8 21.6 Eurotium 6.1 19.6
Aspergillus 12.8 12.1 Aspergillus 14.4 10.1
Penicillium 7.7 10.1 Penicillium 8.4 9.7
Dothideomycetes 8.2 0.8 Dothideomycetes 7.1 1.0
Lasiodiplodia 1.4 0.4 Lasiodiplodia 1.1 0.8
Cladosporium 2.0 0.2 Cladosporium 1.7 0.2
Saccharomycetes 2.1 0.9 Saccharomycetes 2.1 1.3
Candida 1.8 0.9 Candida 1.5 1.3
Sordariomycetes 16.9 4.7 Sordariomycetes 16.5 2.2
Fusarium 3.3 2.4 Fusarium 3.9 0.8
Bionectria 1.3 1.2 Gibberella 2.8 0.2
Zygomycota 7.1 23.7 Zygomycota 7.8 27.8
Mucomycotina 7.1 23.7 Mucomycotina 7.8 27.8
Rhizopus 5.2 23.5 Rhizopus 5.9 27.8
Basidiomycota 9.4 13.0 Basidiomycota 9.7 13.5
Wallemiomycetes 5.3 11.3 Wallemiomycetes 6.8 13.4
Wallemia 5.3 11.3 Wallemia 6.8 13.4
Tremellomycetes 2.4 0.2 Tremellomycetes 1.7 0.1
Cryptococcus 1.1 0.2 Cryptococcus 0.7 0.1
Table 2. Distribution of operational taxonomic units (OTUs) among fungal lineages including the 10 most
abundant genera detected in in-shell peanuts and peanut kernels.

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Saccharomycetes and Sordariomycetes. Dominant Eurotiomycetes groups included the genera Aspergillus (14.4%
of OTUs and 10.1% of reads), Eurotium (6.1% of OTUs and 19.6% of reads) and Penicillium (8.4% of OTUs and
9.7% of reads). Dominant genera among Zygomycota and Basidiomycota were Rhizopus (5.9% of OTUs and
27.8% of reads) and Wallemia (6.8% of OTUs and 13.4% of reads) (Table2). e species in Aspergillus were A.
aculeatus, Aspergillus ellipticus, A. avipes, A. avus, Aspergillus glaucus, A. niger (10.50% of reads), A. ochraceus,
A. penicillioides (2.05% of reads), A. restrictus, A. tamarii, Aspergillus terreus, A. versicolor and A. vitricola. e
predominant species in Eurotium was E. niveoglaucum (34.35% of reads). e predominant species in Penicillium
were P. citrinum, P. pinophilum, and P. simplicissium. In Wallemia, W. s eb i and Walleia sp. F53 were the main spe-
cies. e predominant species in Rhizopus was R. oryzae (48.79% of reads) (Table S1).
Storage time and fungal diversity. ere were signicant variation in per-sample OTUs richness based
on storage conditions and storage time (Fig.1). In in-shell peanuts, all denoised reads were clustered into 245
OTUs using a minimum pair-wise identity of 97%. e average number of OTUs detected per sample in in-shell
peanuts was 110 (range: 81–140). In general, OTU number decreased at 10 d, reached its highest value at 20 d,
and subsequently decreased, except in samples stored at 25 °C with 75% relative humidity. ough there were
dierences among storage conditions, but no signicant dierence in the results.
In peanut kernels, all denoised reads were clustered into 196 OTUs using a minimum pair-wise identity of
97%. e average number of OTUs detected per sample in in-shell peanuts was 91 (range: 69–114). In general,
the number of OTUs decreased with storage time, and the dierences were seen among the storage conditions
but not signicant (p > 0.05).
Fungal community variation in phylum across storage time. ere are obvious variations in the
relative abundance of fungal phyla per sample based on storage conditions and storage time (Fig.2). In in-shell
peanuts, four fungal phyla, i.e., Ascomycota, Basidiomycota, Chytridiomycota, and Zygomycota were identied.
Of them, Ascomycota, Basidiomycota, and Zygomycota were the predominant phyla, with 50.7%, 13.0% and
23.7% relative abundance, respectively. In peanut kernels, the same four fungal phyla were identied. Of them,
Ascomycota, Basidiomycota, and Zygomycota were the main phyla (44.2%, 13.5% and 27.8% relative abundance,
respectively).
Figure 1. Predicted number of operational taxonomic units (OTUs) per sample in in-shell peanuts (A) and
peanut kernels (B) stored for 0–90 d at dierent conditions.

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In in-shell peanuts, the relative abundance of Ascomycota fungi increased from 30 to 90 d at 20 °C with 70%
and 75% relative humidity, and from 20 to 90 d at 30 °C with 80% relative humidity. At 25 °C with 75% relative
humidity, there were obvious oscillations in the relative abundance of Ascomycota fungi during storage. Similarly,
in peanut kernels, there were also obvious oscillations in the relative abundance of Ascomycota fungi, but the
regularity was absent.
Fungal community variation in genus level across storage time. ere were obvious variation in
the relative abundance of fungal genera per-sample based on storage conditions and storage time (Fig.3). In
in-shell peanuts, the average number of clean reads retained aer the ltering and denoising steps was 56,709,
of which 54,426 reads were subsequently clustered into 110 OTUs and 41 fungal genera. Of them, Aspergillus,
Eurotium, Penicillium, Rhizopus and Wallemia were the main genera, with average relative abundances of 12.1%,
21.6%, 10.1%, 33.1% and 11.3%, respectively. Fusarium had a relative abundance of 2.4%. e relative abundance
of Aspergillus fungi at 60 and 90 d was signicantly higher than that at 10, 20 or 30 d under all storage conditions
(p < 0.05). In general, the relative abundance of Aspergillus fungi increased from 30 to 90 d, except in samples
stored at 30 °C with 75% relative humidity. e relative abundance of Aspergillus fungi reached its maximum
value (60.3%) at 30 °C with 80% relative humidity. At 20 and 30 d, Eurotium, Rhizopus and Wallemia were the
main genera, with relative abundance higher than 15%, 20% and 10%, respectively.
In peanut kernels, the average number of clean reads retained aer the ltering and denoising steps was
38,936, of which 36,718 were subsequently clustered into 91 OTUs; 37 fungal genera were identied. Of them,
Aspergillus, Eurotium, Penicillium, Rhizopus and Wallemia were the most predominant genera (19.6%, 10.1%,
9.7%, 27.8% and 13.4% relative abundance, respectively). e relative abundance of Aspergillus fungi at 60 and 90
d was signicantly higher than other days at 10, 20 or 30 d (p < 0.05). e relative abundance of Rhizopus fungi
at 10 and 20 d were high (> 30%) and decreased from 30 to 90 d. e relative abundance of Wallemia fungi was
high at 20 and 30 d but further decreased from 30 to 90 d. e relative abundance of Eurotium fungi reached a
maximum value at 30 d (> 20%) and subsequently decreased from 30 to 90 d except in samples stored at 20 °C
with 75% relative humidity. e relative abundance of Penicillium reached the highest level at 60 d.
Changes in Mycobiome are associated with storage time. To investigate whether there is an asso-
ciation between any of the subject storage parameters and changes in mycobiome, we performed Principal
Coordinate Analysis (PCoA). e results revealed that peanuts stored for 60–90 days and peanuts stored for
10–30 days clustered dierently from each other (Figs4 and 5). In in-shell peanuts, all peanuts at 60 and 90 d
Figure 2. Overall distribution of fungi at phylum level in in-shell peanuts (A) and peanut kernels (B) stored for
0–90 d at dierent conditions. RH: relative humidity.

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clustered in the le and PCoA case scores (Bray Curtis) were less than zero, except for G2.6 (from 60 d at 20 °C
with 75% relative humidity); all samples at 10, 20 and 30 d clustered in the right and PCoA case scores were more
than zero, except for G4.1 (from 10 d at 30 °C with 75% relative humidity) and G5.1 (from 10 d at 30 °C with 80%
relative humidity). In peanuts kernels, all samples at 60 and 90 d clustered in the le and PCoA case scores were
less than zero; all samples at 10, 20 and 30 d clustered in the right and PCoA case scores were more than zero,
except for R2.1 (from 10 d at 20 °C with 75% relative humidity). ese suggest a trend in association between
storage time and the peanuts mycobiome.
Aatoxins in stored peanuts. As shown in Table3, of 25 in-shell peanuts ve (20%) were contaminated
with AFB1 (ranging from 0.34 to 10.40 μ g/kg, four (16%) were contaminated with AFB2 (0.10–1.87 μ g/kg), one
(4%) were contaminated with AFG1 (0.72 μ g/kg), and two (8%) were contaminated with AFG2 (0.15 μ g/kg). Of
25 peanut kernels, four (16%) were contaminated with AFB1 (0.34–68.79 μ g/kg, three (12%) were contaminated
with AFB2 (0.07–6.25 μ g/kg), and one (4%) was contaminated with AFG2 (0.24 μ g/kg).
Discussion
e average number of raw reads, clean reads, and taxon reads in in-shell peanuts were 84,834, 56,709 and 54,426,
respectively, and 61,243, 38,396 and 36,718 in peanut kernels, respectively. is result suggested that the number
of fungi in in-shell peanuts was higher than that in peanut kernels. Furthermore, the total number of fungal
OTUs in in-shell peanuts was 199, which was higher than in peanut kernels (OTUs: 153). e average number of
OTUs detected per sample in in-shell peanuts was 110 (range 81–140), while that in peanut kernels was 91 (range:
69–114). Furthermore, in in-shell peanuts, 41 fungal genera were identied, which was higher than that in peanut
kernels. Fungal diversity of in-shell peanuts was signicantly higher than in peanut kernels during storage. is
indicates that the micro-environment in peanut shell proves favorable for maintaining fungal diversity.
In general, Aspergillus, Eurotium, Penicillium Rhizopus, and Wallemia were predominant genera in both
in-shell peanuts and peanut kernels during storage. is result attributes to the greater adaptation of these fungi
to the substrate, especially during storage27,28. e occurrence of Aspergillus, Penicillium, and Rhizopus agrees with
the ndings of other investigators studying peanut kernels from Brazil27–29 and India30 using traditional isolation,
enumeration and identication methods of the mycoora on the Dichloran Rose Bengal Chloramphenicol agar
(DRBC) or A. avus and A. parasiticus agar (AFPA) media. Eurotium was not detected in these studies because
Eurotium did not grow well on DRBC or AFPA media. Nakai et al.27 found a predominance of Fusarium spp.
(67.7% in hulls and 25.8% in kernels) and Aspergillus spp. (10.3% in hulls and 21.8% in kernels). In the previous
Figure 3. Overall distribution of fungi at genus level in in-shell peanuts (A) and peanut kernels (B) stored for
0–90 d at dierent conditions. RH: relative humidity.

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studies, only eight genera were isolated from peanuts kernels and hulls i.e. Aspergillus, Cladosporium, Drechslera,
Fusarium, Penicillium, Phoma, Rhizopus, and Trichoderma. However, in our study, 41 and 37 fungal genera were
detected in in-shell peanuts and peanut kernels during storage using high-throughput ITS2 sequencing technol-
ogies, respectively. e number of fungal genera reported is about 5-folds increase compared to previous studies.
is is because the studies of evaluated the mycoora in stored peanuts using traditional isolation, enumeration
and identication provided only a limited snap shot of the fungal members of the microbiome. While, the bar-
coded Illumina paired-end sequencing (BIPES) method using in this study31 provided a more in-depth compre-
hensive prole of the mycobiome.
At 20–30 d, the relative abundance of Eurotium, Rhizopus and Wallemia were higher than that of Aspergillus,
because the three genera fungi were xerophilic and grew well on substrates with low aw (Fig.6). During growth,
Eurotium, Rhizopus and Wallemia fungi released metabolic water on substrates with low aw, thereby favoring
the growth of Aspergillus, which are less xerophilic fungi. erefore, from 30 to 90 d, the relative abundance of
Aspergillus increased while that of Eurotium, Rhizopus and Wallemia decreased. Similarly, the results from PCoA
analysis showed some tendency for stored peanuts at 60–90 d to cluster together, and stored peanuts at 10–30 d
to cluster together. ese results suggested that storage time plays a vital role in impacting the variation of myc-
obiome in stored peanuts. However, given the limitation of lesser study samples in the current study, it is di-
cult to draw denite conclusions regarding association of the mycobiome with storage time and/or conditions.
erefore, further conrmations on researching larger population sizes were needed.
Wallemia is a genus of cosmopolitan xerophilic fungi that are present in several environments characterized
by low aw32,33, and is frequently involved in food spoilage. In 2006, Sun et al.34 isolated one W. s eb i isolate from
the surface of apples. is was the rst report of its occurrence in China as a saprophyte on foods. In the present
study, Wallemia was identied in peanuts grown in China. e results conrmed that Wallemia grows well on
substrates with low aw because aw of peanuts is ≤ 0.72 (ranging from 0.36 to 0.72) (Fig.6). In general, the relative
abundance of Wallemia in stored in-shell peanuts and peanut kernels were higher at 20–30 d and subsequently
decreased from 30 to 90 d with the concomitant increase in Aspergillus.
In both in-shell peanuts and peanut kernels during storage, Aspergillus, Eurotium, Penicillium, Rhizopus and
Wallemia were the predominant genera. Of them, Rhizopus was the most abundant genus with a relative abun-
dances > 20% as they could grow well on peanuts since it has a low aw of ≤ 0.72. Rhizopus is common saprobic
fungi on plants and specialized parasites on animals. In general, the relative abundance of Rhizopus in stored
in-shell peanuts and peanut kernels were high at 20–30 d and decreased from 30 to 90 d with the concomitant
increase in Aspergillus. Eurotium is the teleomorph genus associated with Aspergillus, which could grow on sub-
strates with low aw. ese organisms are universally distributed in nature and are usually referred to as halophilic
Figure 4. Principal Coordinate Analysis (PCoA) of distribution of fungi in in-shell peanuts during storage.
G0: raw peanuts; G1.1, G1.2, G1.3, G1.6, G1.9: stored in-shell peanuts at 10, 20, 30, 60, 90 d with 20 °C/70%
RH; G2.1, G2.2, G2.3, G2.6, G2.9: stored in-shell peanuts at 10, 20, 30, 60, 90 d with 20 °C/75% RH; G3.1, G3.2,
G3.3, G3.6, G3.9: stored in-shell peanuts at 10, 20, 30, 60, 90 d with 25 °C/75% RH; G4.1, G4.2, G4.3, G4.6, G4.9:
stored in-shell peanuts at 10, 20, 30, 60, 90 d with 30 °C/75% RH; G5.1, G5.2, G5.3, G5.6, G5.9: stored in-shell
peanuts at 10, 20, 30, 60, 90 d with 30 °C/80% RH. RH: relative humidity.

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or xerophilic. ey cause signicant damage in stored grains, cereals and food products preserved by drying or
salt/sugar addition35–38. More important, Eurotium release metabolic water on substrates with low aw, thereby
creating favorable conditions for less xerophilic fungi (e.g., A. avus and A. niger) that can produce more haz-
ardous mycotoxins (e.g., aatoxins and ochratoxins). e results of this study conrmed the above ndings. e
relative abundance of Aspergillus (with the exception of Eurotium) increased from 30 to 90 d in stored in-shell
peanuts and peanut kernels, i.e 2.6% to 60.3%, and 1.1% to 35%, respectively, especially at 30 °C with 80% relative
humidity. So we could conclude that rapidly grown Eurotium at initial stage could release metabolic water which
in turn increases aw, thereby creating a favorable condition for Aspergillus species growth. Most fungi from grains
grow well on DRBC, and Pitt and Hocking37 reported that DRBC is adequate for the numeration of fungi present
in food and feed. Eurotium growth was observed on DG18 medium rather than on DRBC. erefore, Eurotium in
peanuts should be determined using DG18, which is the medium for xerophilic fungi32.
e aw in this study (0.37–0.72 in in-shell peanuts and 0.34–0.69 in peanut kernels) (Fig.6) was below the
minimum range of 0.78–0.80 established for the growth of A. avus39. e low aw in the stored samples is prob-
ably due to the previously used process of peanut drying. In the present experiment, the temperature ranged
from 20 °C to 30 °C and relative humidity ranged from 70% to 80%. erefore, the temperature was lower than
32–33 °C, which is the optimum temperature for the growth of A. avus40. e relative humidity values in the
samples were lower than those measured by Christensen et al.41, who reported a relative humidity of approxi-
mately 83–85%, which favors the growth of A. avus. Due to the low aw of stored peanuts, temperature and rela-
tive humidity, only nine of the 51 samples were contaminated with aatoxins at levels ranging from 0.34 to 68.79
μ g/kg. And only one sample was contaminated with aatoxins at levels > 20 μ g/kg, which is the maximum level
allowed by the National Health and Family Planning Commission of the P.R. China for AFB1 (http://www.nhfpc.
gov.cn/cmsresources/mohwsjdj/cmsrsdocument/doc11939.pdf). e results of aatoxins are also in accord-
ance with the results of the mycological analysis, because the percentages of A. avus, which are well-known
aatoxin-producing species, are lower than 0.01%.
Conclusions
e results of this study revealed that there were more genera, species and number of fungi in in-shell peanuts
than in peanut kernels, and suggested that the micro-environment in shell was more suitable for maintaining
the fungal biodiversity and resist infection of fungi from outer environment. Aspergillus, Eurotium, Penicillium,
Rhizopus and Wallemia were the predominant genera in both in-shell peanuts and peanut kernels during storage.
At 20 to 30 d, Eurotium, Rhizopus and Wallemia were the main genera; however, from 30 to 90 d, their relative
abundance decreased and that of Aspergillus increased. Due to low aw values (0.34–0.72) of stored peanuts, nine
Figure 5. Principal Coordinate Analysis (PCoA) of distribution of fungi in peanut kernels during storage.
G0: raw peanuts; R1.1, R1.2, R1.3, R1.6, R1.9: stored in-shell peanuts at 10, 20, 30, 60, 90 d with 20 °C/70% RH;
R2.1, R2.2, R2.3, R2.6, R2.9: stored in-shell peanuts at 10, 20, 30, 60, 90 d with 20 °C/75% RH; R3.1, R3.2, R3.3,
R3.6, R3.9: stored in-shell peanuts at 10, 20, 30, 60, 90 d with 25 °C/75% RH; R4.1, R4.2, R4.3, R4.6, R4.9: stored
in-shell peanuts at 10, 20, 30, 60, 90 d with 30 °C/75% RH; R5.1, R5.2, R5.3, R5.6, R5.9: stored in-shell peanuts at
10, 20, 30, 60, 90 d with 30 °C/80% RH. RH: relative humidity.

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of 51 samples were contaminated with aatoxins, and only one sample had AFB1 levels > 20 μ g/kg. is study
identied the mycobiome and its variation in stored peanuts during simulated storage using high-throughput
ITS2 sequencing, and provided the basis for a detailed characterization and identication of mycobiome in stored
peanuts.
Methods
Ethics Statement. Specic permission was not needed for our eld studies. e peanuts variety used in our
eld study was main cultivar name Baisha 1016 in Hubei province. No transgenic or created mutant plant has
been used in our study. Also we conrm that the eld studies did not involve endangered or protected species.
Sample preparation. Peanuts were obtained from Xiangyang City, Hubei province, which is in the center
of Yangtze River valley. Aer harvest, the peanuts were transported to Beijing in 25-kg bags (a total of ve bags)
in 3 d. Half of the peanuts were unshelled (peanut kernels). Both peanut kernels and in-shell peanuts were stored
at similar conditions. According to climatic conditions (temperature and relative humidity) of Xiangyang City
from April to June, both peanut kernels and in-shell peanuts were stored in a ZXMP-A1230 constant temperature
and humidity incubator (Zhicheng, Shanghai, China) at ve storage conditions: 20 °C with 70% relative humidity,
20 °C with 75% relative humidity, 25 °C with 75% relative humidity, 30 °C with 75% relative humidity, and 30 °C
with 80% relative humidity. Samples were collected at 10, 20, 30, 60 and 90 d of storage and analyzed.
Total microbiome genomic DNA extraction. Water was sterilized at 121 °C for 30 min, and then ltered
through 0.22 μ m lters. e sterile water was used as a negative control for the experiment. In this experiment,
peanuts kernels (100 g) were separated from the hulls and washed with 100 ml sterile water. Water samples were
collected and vacuum-ltered through 0.22 μ m lters within 24 hours. Filters containing sample were placed in
50-ml tubes and stored at − 20 °C. Genomic DNA was extracted from the lters using the MoBio PowerWater®
DNA Isolation Kit (MoBio Laboratories, Inc., Carlsbad, CA, USA) according to manufacturer’s recommenda-
tions. e nal DNA elution was performed with sterile deionized water instead of the provided buer. DNA
quality and quantity were measured by spectrophotometric quantication in a Beckman DU800 (Beckman, USA)
and NanoDrop 1000 (ermo Fisher Scientic, USA), and by agarose gel electrophoresis. Extracted DNA was
stored at − 80 °C prior to amplication and sequencing.
Storage In-shell peanuts Aatoxins (μg/kg) Peanut kernels Aatoxins (μg/kg)
Temperature/
relative humidity Time AFB1AFB2AFG1AFG2AFB1AFB2AFG1AFG2
G0 0 – – – –
20 °C/70%
10 10.40 1.87 – 0.15 – – – –
20 – – – – – – – –
30 – – – – – – – –
60 0.88 0.08 – – – – – –
90 0.35 0.07 – – 0.35 0.07 – –
20 °C/75%
10 – – – – – – – –
20 – – – – – –––
30 ––– – ––––
60 0.76 0.10 0.72 0.15 ––––
90 ––– – ––––
25 °C/75%
10 ––– – ––––
20 ––– – ––––
30 – – – – 68.79 6.25 –0.24
60 ––– – ––––
90 ––– -––––
30 °C/75%
10 – – – – 7.65 0.54 – –
20 ––– – ––––
30 ––– – ––––
60 ––– – ––––
90 ––– – ––––
30 °C/80%
10 ––– – ––––
20 ––– – ––––
30 ––– – ––––
60 ––– – –––-
90 0.34 – – – 0.34 –––
Table 3. Mean levels of aatoxins B1, B2, G1 and G2 in stored peanuts.

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PCR amplication and ITS2 sequencing. PCR was used to amplify the ITS2 region of rDNA. e barcoded
primers ITS2F (5′ -GCATCGATGAAGAACGCAGC-3′ ) and ITS2R (5′ -TCCTCCGCTTATTGATATGC-3′ )
were used to amplify fungal ITS2 fragments. PCR reactions were carried out in a total reaction volume of 30 μ l
consisting 15 μ l of Phusion High-Fidelity PCR Master Mix (New England Biolabs Inc. Ipswich, MA, USA), 0.2 μM
of forward and reverse primers, and 10 ng of template DNA. e PCR amplication program consisted of a initial
heating to 98 °C for 1 min, 30 cycles of denaturation at 98 °C for 10 s, annealing at 50 °C for 30 s and extension at
72 °C for 60 s, followed by a 5-min extension at 72 °C prior to storage at 4 °C. Amplied products were cleaned and
puried using the GeneJET Gel Extraction Kit (ermo Scientic, South Logan, UT 84321, USA) according to
the manufacturer’s instructions. Of 51 peanut samples, 49 samples were successfully amplied. Amplicons were
then quantied and sequencing libraries were generated using NEB Next Ultra DNA library Prep Kit for Illumina
(New England Biolabs Inc. USA) according to manufacturer’s recommendations. e amplicon libraries were
subsequently sequenced on an Illumina MiSeq platform at Novogene (Novogene, Beijing, P.R. China).
Bioinformatics analyses. Paired-end reads from the original DNA fragments were merged by using
FLASH42. Sequences were analyzed with the QIIME43 software package using default parameters for each
step. e UCLUST method44,45 was used to cluster the sequences into OTUs at an identity threshold of 97%.
Meanwhile, the RDP Classier46 was used to assign each OTU to a taxonomic level. Other analyses, including rar-
efaction curves, Shannon index, and Good’s coverage, were performed with QIIME. In addition, the OTU table
produced by the QIIME pipeline was imported into MEGAN 4 and mapped on the NCBI taxonomy database47.
Abundance-based comparisons were therefore made solely within selected taxonomic groups such as Aspergillus,
Eurotium, Penicillium, Rhizopus and Wallemia, using an OTU table that was raried in QIIME.
PCoA has been recognized as a simple and straight-forward method to group and separate samples in a data-
set, and has been used in disease-association, gender-association and ethnicity studies19,48,49. In the current study,
PCoA was used to analyze the sequencing results using the Multivariate Statistical Package, MVSP (Kovach,
Wales, UK) and SAS (Cary, NC). e PCoA performs an Eigen analysis on the data matrix using a Brays Curtis
distance metric.
Determination of aatoxins. Aatoxins levels were determined by Chinese standard methods50 and AOAC
method 994.0851 with minor modications. In this experiment, peanut kernels (50 g) were manually de-shelled,
ground, mixed to obtain peanut paste, and stored at − 20 °C until analysis. Finely ground samples (5.0 g)
were extracted with 15 ml of acetonitrile:water (84:16, v/v). Following ultrasonic extraction at 50 °C for 10 min
and ltration through double-layer slow quantitative lter paper, 4 ml of the resulting ltrate was mixed with 2
ml petroleum ether. e mixture was mixed on a vortex for 30 s and allowed to stand for 15–20 min. e lower
Figure 6. Storage time variation of water activity (aw) in in-shell peanuts (up) and peanut kernels (below)
stored for 0–90 d at dierent conditions.

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Scientific RepoRts | 6:25930 | DOI: 10.1038/srep25930
layer (3 ml) was collected, mixed with 8 ml pure water and ltered through a 0.45 μ m organic membrane. Extracts
(8 ml) were passed through immunoanity columns with a ow rate of one droplet per second and eluted with
2 ml of methanol into glass tubes. e eluate was evaporated to dryness under a stream of nitrogen gas at 60 °C.
e puried extract was re-dissolved with 1 ml of acetonitrile : water (15 : 85, v/v). e resulting supernatant was
collected in glass tubes for high-performance liquid chromatography (HPLC) quantitative analysis.
e determination of aatoxin levels was performed by HPLC. HPLC analysis was performed with a Waters
2695 (Waters Corporation, Milford, MA, USA) coupled to a Waters 2475 uorescence detector (λ exc 360 nm;
λ em 440 nm) and a post-column derivation system, and an Agilent TC-C18 column (250 × 4.6 mm, 5 μ m particle
size). e mobile phase (water : methanol : acetonitrile, 4 : 1 : 1) was pumped at a ow rate of 0.5 mL/min. AFB1,
B2, G1 and G2 (Sigma-Aldrich, St. Louis, MO, USA) were used as standards. e mean recovery of the method
used was calculated by spiking peanut kernels at dierent levels ranging from 1 to 100 ng/g of aatoxins and was
estimated at 95.2 ± 8.4%. e lowest detection limit was 1 ng/g.
Statistics. All the experiments results were evaluated using analysis of variance (ANOVA) for multiple com-
parisons followed by the Turkey test. Dierences were considered signicant at p < 0.05.
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Acknowledgements
This work was funded by National Basic Research Program of China (973 program) (2013CB127801,
2013CB127805), Special Fund for Agro-scientic Research in the Public Interest (201203037).
Author Contributions
e experiments were conceived and designed by Y.L. and F.X. e experiments were performed by N.D. and
F.X. e date was analyzed by F.X. e following authors contributed in buying reagents or materials or provided
analysis tools: J.N.S., X.L., L.W., L.Z., Y.Z. and Y.W. Finally, F.X. was responsible for writing the paper.
Additional Information
Supplementary information accompanies this paper at http://www.nature.com/srep
Competing nancial interests: e authors declare no competing nancial interests.
How to cite this article: Xing, F. et al. Variation in fungal microbiome (mycobiome) and aatoxins during
simulated storage of in-shell peanuts and peanut kernels. Sci. Rep. 6, 25930; doi: 10.1038/srep25930 (2016).
is work is licensed under a Creative Commons Attribution 4.0 International License. e images
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