Evaluation of different storage methods to characterize the fecal bacterial communities of captive western lowland gorillas (Gorilla gorilla gorilla).

Abstract

Freezing is considered to be the best method for long-term storage of bacterial DNA from feces; however this method cannot be usually applied for samples of wild primates collected in the challenging conditions of the tropical forest. In order to find an alternative conservation method of fecal samples from wild great apes, we compared freezing with other fixation methods. Fecal samples from 11 captive gorillas (Gorilla gorilla gorilla) from three Czech Zoos were stored using freezing, RNA Stabilization Reagent (RNAlater), and 96% ethanol. Subsequently, the samples were examined using culture-independent methods (PCR-DGGE, and Real-time PCR) to qualitatively and quantitatively assess fecal microbiota composition and to compare differences among the storage methods. Noticeably, freezing samples resulted in the highest recoveries of DNA. No significant differences in DNA recovery were found between freezing and using RNAlater; however, significantly lower DNA concentrations were recovered from samples stored in 96% ethanol. Using PCR-DGGE we found that either 96% ethanol, RNAlater or freezing were suitable for preserving bacterial DNA; however fingerprints obtained from RNAlater storage were more similar to those obtained from the frozen method; in comparison to the patterns resulting from storing samples in ethanol. Using qPCR, frozen samples yielded the highest values of bacterial counts, with the exception of Enterobacteriaceae, which showed the highest numbers using samples stored in ethanol. Sequences of amplicons obtained from PCR-DGGE belonged to the families Clostridiaceae, Lactobacillaceae, Staphylococcaceae, and Lachnospiraceae, phylum Firmicutes; however most amplicons showed sequence similarity to previously uncultured microorganisms. Bacteria belonging to the phylum Firmicutes were the most frequently identified species in the fecal bacterial communities of captive western gorillas. The study showed that RNAlater is an optimal storage method when freezing is not possible.

Full text

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Evaluation of different storage methods to characterize the fecal bacterial
communities of captive western lowland gorillas (Gorilla gorilla gorilla)
Klára Vlčkováa,⁎, Jakub Mrázekb, Jan Kopečnýb, Klára J. Petrželkovác,d,e,f
aDepartment of Botany and Zoology, Masaryk University, Brno, Czech Republic
bInstitute of Animal Physiology and Genetics, The Academy of Sciences of the Czech Republic, Praha, Czech Republic
cInstitute of Vertebrate Biology, The Academy of Sciences of the Czech Republic, Brno, Czech Republic
dLiberec Zoo, Liberec, Czech Republic
eUniversity of Veterinary and Pharmaceutical Sciences, Brno, Czech Republic
fInstitute of Parasitology, The Academy of Sciences of the Czech Republic, České Budějovice, Czech Republic
a b s t r a c ta r t i c l ei n f o
Article history:
Received 1 February 2012
Received in revised form 16 July 2012
Accepted 16 July 2012
Available online 22 July 2012
Keywords:
Storage method
Bacterial DNA
Fecal sample
Western lowland gorilla
PCR-DGGE
Real-time PCR
Freezing is considered to be the best method for long-term storage of bacterial DNA from feces; however this
method cannot be usually applied for samples of wild primates collected in the challenging conditions of the
tropical forest. In order to find an alternative conservation method of fecal samples from wild great apes, we
compared freezing with other fixation methods. Fecal samples from 11 captive gorillas (Gorilla gorilla gorilla)
from three Czech Zoos were stored using freezing, RNA Stabilization Reagent (RNAlater), and 96% ethanol. Sub-
sequently, the samples were examined using culture-independent methods (PCR-DGGE, and Real-time PCR) to
qualitatively and quantitatively assess fecal microbiota composition and to compare differences among the stor-
agemethods.Noticeably,freezingsamplesresultedinthehighestrecoveriesofDNA.Nosignificantdifferencesin
DNA recovery were found between freezing and using RNAlater; however, significantly lower DNA concentra-
tions were recovered from samples stored in 96% ethanol. Using PCR-DGGE we found that either 96% ethanol,
RNAlater or freezing were suitable for preserving bacterial DNA; however fingerprints obtained from RNAlater
storage were more similar to those obtained from the frozen method; in comparison to the patterns resulting
from storing samples in ethanol. Using qPCR, frozen samples yielded the highest values of bacterial counts,
with the exception of Enterobacteriaceae, which showed the highest numbers using samples stored in ethanol.
Sequences of amplicons obtained from PCR-DGGE belonged to the families Clostridiaceae, Lactobacillaceae,
Staphylococcaceae, and Lachnospiraceae, phylum Firmicutes; however most amplicons showed sequence similar-
ity to previously uncultured microorganisms. Bacteria belonging to the phylum Firmicutes were the most fre-
quently identified species in the fecal bacterial communities of captive western gorillas. The study showed
that RNAlater is an optimal storage method when freezing is not possible.
© 2012 Published by Elsevier B.V.
1. Introduction
Bacterial DNA is a fragile molecule that is easily degraded by
different types of endonucleases (Lindahl, 1993). The reaction can
be slowed down by decreasing the reaction temperature or by dena-
turing the enzymes. Maintaining low temperatures at the time of
fecal sample collection and storage helps to slow this process; this
implies that freezing is an optimal storage method to recover DNA
from feces (Wu et al., 2010). However, in studies were samples
need to be collected in the wild; the possibility of freezing the
samples is rather limited, especially in primates living in the challeng-
ing conditions of tropical forests. In previous studies, several storage
methods have been used to preserve nucleic acids from fecal microbial
communities: 70% ethanol for storing samples from wild chimpanzees
(Uenishi et al., 2007); 96% ethanol for samples from howler monkeys
(Nakamura et al., 2011); and RNAlater (RNA Stabilization Reagent,
Qiagen, Germany) for storing human (Kato et al., 2010) and various
great ape samples (Ochman et al., 2010; Szekely et al., 2010). For the
preservation of host epithelial cell DNA in feces, samples from ursids
were dried with silica beads (Wasser et al., 1997) whilst fecal samples
from gorillas and chimpanzees were stored either in RNAlater or
ethanol with subsequent desiccation using silica (Nsubuga et al.,
2004). Several studies have compared methods for preservation of
host's DNA in feces (Nsubuga et al., 2004; Soto-Calderón et al., 2009;
Bubb et al., 2011) and plant and fungal DNA (Bainard et al., 2010),
while Nechvatal et al. (2008) tested selected methods for storage of
bacterial DNA in fecal samples of humans finding that the highest
DNA yields tended to be obtained using either RNAlater or Paxgene.
Journal of Microbiological Methods 91 (2012) 45–51
⁎ Corresponding author. Tel.: +420 543210450; fax: +420 543211346.
E-mail addresses: klari.vlckova@gmail.com (K. Vlčková), mrazek@iapg.cas.cz
(J. Mrázek), kopecnyj@iapg.cas.cz (J. Kopečný), petrzelkova@ivb.cz (K.J. Petrželková).
0167-7012/$ – see front matter © 2012 Published by Elsevier B.V.
doi:10.1016/j.mimet.2012.07.015
Contents lists available at SciVerse ScienceDirect
Journal of Microbiological Methods
journal homepage: www.elsevier.com/locate/jmicmeth

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In contrast to the extensive literature on the gastrointestinal
bacterial composition of humans, little is known about the normal in-
testinal microbiota of great apes, even though great apes have been
characterized as our closest living evolutionary relatives (Kumar
et al., 2005). In this regard, few studies on the microbiota of great
apes in the wild and captivity have been published in recent years
(Frey et al., 2006; Uenishi et al., 2007; Ley et al., 2008; Kišidayová
et al., 2009; Ochman et al., 2010; Szekely et al., 2010).
Herein, we analyze alternative methods of storage of bacterial
DNA from fecal samples of great apes, in situations where freezing
is not available. Specifically, we aim at: (i) comparing the effect of
three preservative methods (freezing, RNAlater, 96% ethanol) on
the quality of bacterial DNA in the fecal samples of captive gorillas
(Gorilla gorilla gorilla); (ii) using PCR-DGGE and Real-time PCR to
describe, quantifyandcompareselectedbacterialtaxapresentingorilla
feces preserved by methods mentioned above, and (iii) comparing the
bacterial fingerprints obtained with results from previous studies on
the gastrointestinal microbiota of African great apes.
2. Materials and methods
2.1. Fecal sample collection
Fresh fecal samples from 11 captive western lowland gorillas
(Gorilla gorilla gorilla) were obtained from three Czech Zoos (Prague
Zoo: adult male — Richard, adult females: Kijivu, Kamba, Shinda 2,
infants: male Tatu and female Moja; Zlín-Lešná Zoo: male — Bosso,
female — Judita; Dvůr Králové Zoo: male — Tadao and two females —
Shinda, Messy) from March to August 2009. Fecal samples were
collected by Zoo keepers during the routine daily cleaning of cages.
Each fecal sample was divided in three fractions; each preserved
using three different methods: freezing, 96% ethanol, and RNAlater.
We fixed approximately 1 g of feces into 10 ml of either ethanol or
RNAlater. One gram of feces was frozen at −30 °C without any
chemical additives. Freezing/fixing of fecal samples was done within 2
hours after defecation. All fecal samples were processed at the Institute
of Animal Physiology and Genetics, Academy of Sciences of the Czech
Republic. The fecal collection was non-invasive and did not include
any disturbance of the animals. Research adhered to the legal require-
ments of the Czech Republic.
2.2. DNA isolation
Bacterial DNA was isolated using the ZR Fecal Kit™ (Zymo
Research, U.S.A.) according to the manufacturer's protocol. DNA isola-
tion was performed 2–6 months after sampling. The quality of isolated
DNA was defined by measuring its concentration by absorbance at
260 nm and 280 nm (measuring for protein contamination) using a
NanoDrop Spectrophotometer.
2.3. PCR-DGGE
Eubacterial DNA amplification targeting the 16 S rRNA gene was
performed using the universal bacterial primers 338GC (5′-CGC CCG
CCG CGC CCC GCG CCC GGC CCG CCG CCG CCG CCG CAC TCC TAC
GGG AGG CAG CAG-3′) and RP534 (5′-ATT ACC GCG GCT GCT GG-3’).
PCR assays were performed using the amplification protocol described
by Muyzer et al. (1993). The PCR mixture contained 2 μl of DNA
template, 0.5 μl of each primer (10 μM), 15 μl of ReadyMix™ Taq PCR
Reaction Mix (Sigma-Aldrich, Germany), and 12 μl of sterile H2O. All
30 μl of the PCR mixture was used in the DGGE analysis.
Productsfrom PCRwerethenprocessedbyDGGEusingtheDCode™
Universal Mutation Detection System (Biorad, USA) on 9% polyacryl-
amide gel with 35–60% denaturing gradient. The electrophoresis was
performed as described by Fischer and Lerman (1983). Gels were
stained in 50 ml of 1× TAE with SYBR Green I dye (0.001%) for
30 min and visualized by UV light using the Vilber Lourmat System
(France). DNA from pure isolates obtained from culture collections
wasusedtoprepareDGGEstandardsandforproperaligningofseparate
DGGE gels.
Ampliconsofinterest wereexcised from the stained polyacrylamide
gel using a sterile scalpel blade. DNA was eluted by the addition of
100 μl of sterile dH2O followed by centrifugation at 10,000 rpm for
10 min. Then 1 μl of this solution was used for amplification with
primers FP341 (CCT ACG GGA GGC AGC AG) and RP534 under
PCR-DGGE program (Muyzer et al., 1993). The resulting PCR products
were cleaned with QIAquick PCR purification kit (Qiagen, Germany)
and sequenced using ABI PRISM® BigDye® Terminator v3.1 Cycle
Sequencing Kit (Applied Biosystems, USA) and a PCR thermocycler
T-personel Combi (Biometra, Germany). Sequenced products were
purified using BigDye purification kit (Applied Biosystems, USA) and
analyzed using a 3100 Avant Genetic Analyser (Applied Biosystems,
USA) in the Institute of Animal Science sequencing facility (Prague,
Czech Republic). The resulting sequences were compared to those in
the GenBank database using the BLASTn algorithm (Maidak et al.,
1994). The sequences and nearest BLAST database hits were aligned
by Muscle algorithm and a phylogenetic tree was constructed by
maximum likelihood method using a Tai-Nemura substitution model.
The tree was verified by bootstrap method with 1000 replicates. A
MEGA software was used for this analysis (Tamura et al., 2011).
2.4. Real-time PCR
MX3005P QPCR System (Stratagene, U.S.A.) was used for quantifi-
cation of bacterial DNA. The target organisms were quantified by
qPCR using specific primers. We used 10 μl qPCR 2x SYBR Master
Mix (Top-Bio, Czech Republic), 1 μl forward primer (10 μM), 1 μl
reverse primer (10 μM), 0.3 μl Reference Dye (includes ROX dye,
Stratagene, USA), 1 μl DNA and 6.7 μl dH2O for one amplification reac-
tion. Each reaction was carried out in a 20 μl volume. Amplifications
were performed using the temperature profiles according to the
authors: all eubacteria (Nadkarni et al., 2002; Bartosch et al., 2004),
Bifidobacterium group (Temmerman et al., 2003), Bacteroides group
(Bartosch et al., 2004), Lactobacillus group (Walter et al., 2001; Heilig
et al., 2002), Enterobacteriaceae (Bartosch et al., 2004), and Clostridium
leptum group (Salzman et al., 2009).
In order to verify the qPCR specificity for all investigated groups,
except eubacteria, melting curves were also estimated. Serially diluted
DNA, isolated from the known number of bacterial cells, was used for
constructing calibration curves. The following bacterial species were
used: C. leptum (assessment of all eubacteria and C. leptum group),
Bifidobacterium breve (Bifidobacterium group), Bacteroides vulgatus
(Bacteroides group), Lactobacillus breve (Lactobacillus group), and
Escherichia coli (Enterobacteriaceae). The qPCR results obtained were
expressed as number of bacterial cells per gram of wet fecal sample
(bacterial counts).
2.5. Statistical analyses
General Linear Model (GLM) repeated measures ANOVA were used
to compare DNA concentrations, protein contamination and the counts
for each quantified bacteria group in aliquots from the same fecal sam-
ples stored by different ways (frozen sample, stored in 96% ethanol,
RNAlater).AfterwardsTukeyposthoctestswereusedforpairwisecom-
parisons. Data were analyzed using STATISTICA, version 8.0 (StatSoft
Inc., Tulsa, OK).
The obtained DGGE microbial fingerprints from the fecal samples
stored using different methods were normalized and analyzed using
the BioNumerics software version 6.6. (Applied Maths, Sint-Martens-
Latem, Belgium). During this process, all lanes (samples) were normal-
ized, and a correlationmatrixwascalculatedbasedonthe presence and
absence of bands (Operational taxonomic units) as well as their
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concentration. Dendrogram was constructed using the Ward clustering
algorithm. As a parameter for the structural diversity of the microbial
community, the Shannon-Weaver index (H) of general diversity was
calculated (Eichner et al., 1999). GLM repeated measures ANOVA was
used for comparison of Shannon-Weaver indexes calculated from all
samples stored using the three methods.
3. Results
We detected a significant difference in DNA concentrations
recovered from samples stored using the three different methods
(GLM repeated measures ANOVA; F2,20=3.92, pbb0.04) (Table 1).
The highest DNA yield was obtained from frozen samples. Tukey
pos-hoc tests showed that the DNA concentration in frozen samples
was significantly higher than obtained from samples stored in ethanol
(Tukey post-hoc test, p=0.03), but no significant differences were
found between frozen and RNAlater samples (Tukey post-hoc test,
p=0.22) or RNAlater and ethanol samples (Tukey post-hoc test,
p=0.58). There was a statistically significant difference in protein
contamination (A260/A280 ratios) using the three storing methods
(GLM repeated measures ANOVA; F2,20=7.20, pbb0.01). DNA from
frozen samples had the lowest protein contamination (highest,
A260/A280 ratio), with significant differences found compared
to samples stored in 96% ethanol (Tukey post-hoc test, p=0.03).
However, no significant differences were found between the frozen
and RNAlater samples (Tukey post-hoc test, p=0.15) or RNAlater
and ethanol samples (Tukey post-hoc test, p=0.18) (Table 1).
DGGE profiles of microbiota from the 11 sampled animals are
shown in Fig. 1. Microbial profiles of samples stored by freezing and
samples stored in RNAlater were much more similar in several cases
than samples stored by freezing and samples stored in ethanol
(Fig. 2). No significant differences among Shannon-Weaver indexes
(H) obtained from samples stored by three different ways were
found (GLM repeated measures ANOVA; F2,20=1.4, p=0.27).
Identification of DNA amplicons by sequencing was possible
from samples preserved using all three storage methods. Twenty
amplicons were excised from the DDGE gels; only 7 of them could
be identified to a genera level, others were identified as uncultured
bacterial clones (Fig. 3).
The Real-time PCR results showed that the C. leptum group was
the most abundant taxon, followed, in order, by Bifidobacterium
group,
Lactobacillus
and
Bacteroides
showed the lowest counts (Table 2).
Significant differences in the measured counts of bacteria from
frozen, RNAlater and 96% ethanol samples were recorded for all
measured groups, except for Bacteroides group (GLM repeated mea-
sures ANOVA; all eubacteria: F2,20=11.14, pbb0.01; Bifidobacterium
group: F2,20=6.03, pbb0.01; Bacteroides group: F2,20=3.27, p=
0.06; Lactobacillus group: F2,20=12.66, pbb0.01; C. leptum group:
F2,20=5.87, pbb0.01; Enterobacteriaceae: F2,20=5.44, p=0.01). In all
measured groups, bacterial counts reached significantly higher values
in the frozen samples in comparison to samples stored in RNAlater or
ethanol, with the exception of Bacteroides group and Enterobacteriaceae
(see below); however no significant differences were found between
the samples stored in RNAlater and 96% ethanol (Tukey post-hoc
tests, data not shown, Table 2). Enterobacteriaceae counts in ethanol
samples were significantly higher than those found in samples stored
in RNAlater (Tukey post-hoc test, p=0.01, Table 2), but there were
no statistical differences in Enterobacteriaceae counts between frozen
and RNAlater stored samples, or between frozen and ethanol stored
samples (Tukey post-hoc tests, data not shown, Table 2).
groups.
Enterobacteriaceae
Table 1
Concentration (c) and quality (degree of contamination with proteins as determined
with A260/A280 ratios) of DNA isolated from fecal samples of captive western lowland
gorillas using three different storage methods. Data are presented as mean values with
standard deviations (n=11).
sample storagec (ng/μl) A260/280
Frozen
RNAlater
Ethanol
70.64±47.33
47.08±22.79
33.15±16.63
1.50±0.17
1.36±0.16
1.23±0.26
Fig. 1. Denaturant Gradient Gel Electrophoresis (DGGE) of fecal samples from captive western lowland gorillas. Each lane represents a sample stored using three different methods
(F — frozen, R — RNAlater, E — 96% ethanol). S is the standard. Excised and successfully sequenced bands are identified with numbers (1–20).
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4. Discussion
Freezing, RNAlater and 96% ethanol were used for the long term
storage of bacterial DNA in fecal samples from captive western
gorillas. We confirmed the ability of both chemical storage methods
(RNAlater and ethanol) to slow down bacterial DNA degradation
like freezing, which is consistent to other reports that consider
freezing to be the best method for long-term storage of bacterial
DNA (Röder et al., 2010). Although the DNA yields from chemically
treated samples were lower than those obtained from frozen
methods (especially for samples preserved in ethanol), there was
sufficient DNA for PCR based analyses to be performed. Still, the esti-
mated numbers of bacteria obtained by qPCR were lower for samples
stored in RNAlater and ethanol samples compared to the numbers
obtained from frozen samples (Tables 1, 2). Also Nechvatal et al.
(2008), obtained the highest concentrations of bacterial DNA in
RNAlater treated samples in comparison to samples stored in Paxgene
or after drying over silica gel or Watman FTA cards. Surprisingly, in
our study, ethanol was found to be the best storage method for
Enterobacteriaceae group, even more suitable than freezing.
Three storage methods gave very similar DGGE fingerprints for
Tatu, Richard, Bosso, and Judita, but the fingerprints obtained when
these samples were stored in RNAlater were more similar to those
obtained when the samples were frozen, even more than the profiles
obtained with ethanol-stored samples, with the exception of Tatu
(Fig. 2). All dominant amplicons were present in the DGGE profiles
from the three methods, although with variable band intensities.
For other samples, the storage method had more significant effects.
For example, although the frozen and RNAlater samples from Tadao
yielded very similar fingerprints, its ethanol sample presented a
different clustering pattern in the DGGE dendrogram (Fig. 2). The
same was observed for Kamba, Moja, and Shinda 2. Only in the case
of Shinda, the microbial profile from its frozen sample resembled
that obtained using ethanol storage methods. For Messy and Kamba,
the samples stored in ethanol and RNAlater clustered together, in
contrast to the frozen samples (Fig. 2). Based on cluster analysis, it
seems that samples stored in RNAlater and frozen yield much more
similar microbial profiles compared to samples stored in ethanol.
However, these observations were not supported by Shannon–
Weaver diversity index analyses (H), no differences in H among
storage methods were detected. Nonetheless, these results should
be taken with care taking into account the limitations of PCR-DGGE
(Kisand and Pikner, 2003), possible minor variations in bacterial
distribution in the fecal sample or technical error as only one DNA
isolation was performed for each sample.
PCR-DGGE showed that Moja, Kijivu, Shinda 2 and Kamba from
the Prague Zoo had very similar major amplicons. Kijivu and Shinda
2 share the same sire, but different dams and they belonged to the
same group even before they were transferred to the Prague Zoo in
2003. Moja is Kijivu's daughter and she was born at the Prague Zoo.
Kamba was born in the wild and subsequently transferred to three
different Zoos before her arrival to Prague in 2005. However, the
DGGE profiles from Tatu (an offspring of Kijivu) and his father
Richard, a silverback clustered together apart from other Prague
gorillas. Three non-related gorillas from the Dvůr Králové Zoo
(Messy, Shinda and Tadao) had similar DGGE profiles. Tadao and
Messy were born wild and probably come from different natal groups,
but they were brought from Africa to Europe within the same trans-
port and spent some time together as infants which might explain
their similar bacteria profiles. Shinda was born captive and came to
the Dvůr Králové group in 2008. In humans, microbial colonization
of the gastrointestinal tract starts during birth with transmission
from the mother's vaginal and fecal flora (Palmer et al., 2007). After
birth an infant's microflora is further shaped by that from the mother
and the environment surroundings and its diversity increases after
weaning (Stark and Lee, 1982; Sakata et al., 1985; Wang et al.,
2004). Significant similarities of fecal microbiota between mothers
and their offspring have been shown in our study and also in both
wild and captive chimpanzees (Ushida, 2009; Szekely et al., 2010).
Our results are also consistent to those of Ushida (2009) who
observed a significant effect of housing in DGGE bacterial profiles of
captive chimpanzees from a group kept in the Primate Research Insti-
tute group in Inuyama, Japan, and in wild chimpanzees from a commu-
nity in Bossou. The profiles of captive chimpanzees were relatively
similar, probably due to close relation of animals (mother-offspring
pairs), late weaning of babies, and identical dietary conditions
(Ushida, 2009). A different situation was observed in the Zlín-Lešná
Zoo. Two gorillas(Bosso and Judita) that were housed together showed
considerable variation in DGGE banding patterns; both gorillas were
born wild at different localities and housed in different Zoos until
1992. We hypothesize that there are multiple factors triggering
Fig. 2. Clustering analysis of DGGE banding profiles of fecal samples from captive
western lowland gorillas. Each lane represents a sample stored using three different
methods (F — frozen, R — RNAlater, E — 96% ethanol). The dendrogram was calculated
using the Wards method from a Pearson correlation matrix. The numbers on the nodes
indicate the bootstrap value expressed as percentage from 1000 replications.
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changes in the fecal microflora of great apes; not only contact with
members of natal group, but also housing conditions, diet and probably
also contacts with humans/Zoo keepers. In humans, there is high
inter-individual variation in the intestinal microflora (Franks et al.,
1998), which is consistent with a number of environmental factors
shaping the human intestinal microbiota (Fanaro et al., 2003). In a
study of the fecal microbiota involved in the fermentation of fiber in
captive chimpanzees, Kišidayová et al. (2009) demonstrated a clear
diet induced shift in the composition of both archaeal and eubacterial
communities as well as possible transmission of bacteria from Zoo
keepers.
In our study, Firmicutes, particularly clostridia, were the most
common bacteria in the fecal microbiota of captive western lowland
gorillas. High counts of the C. leptum group reflect the herbivorous
diet of gorillas. Our results conformed to Frey et al. (2006) and Ley
et al. (2008), who described the bacterial diversity of western (G. goril-
la) and eastern gorillas (G. beringei) in both captivity and thewild, even
though different methods were used (16 S rRNA clone library, T-RFLP).
Using pyrosequencing of the 16 S rRNA, Ochman et al. (2010) found
that Firmicutes is still a dominant phylum in both species of gorillas,
but it was less prevalent than Proteobacteria and Bacteroidetes.
Firmicutes were detected as the predominant bacterial group also in
wild and captive chimpanzees (Ushida, 2009; Szekely et al., 2010;
Ochman et al., 2010). Our results indicated that lactobacili were quite
common in the studied gorillas. However, in wild mountain gorillas Ba-
cilli (including lactobacilli) comprised only 2% of detected Firmicutes,
whilst clostridia up to 52% (Frey et al., 2006). We did not detect
Mollicutes, which was indicated as the second most common group
within the Firmicutes in mountain gorillas (Frey et al., 2006).
Bifidobacteria, from the phyllum Actinobacteria, were found to be the
second most predominant group in our samples, while in other studies
on both gorilla species and chimpanzees Actinobacteria were rather in-
frequent (Frey et al., 2006; Ley et al., 2008; Ochman et al., 2010;
Ushida, 2009). Bacteroides composed a minor part of the microbiota
Fig. 3. Phylogenetic tree of sequenced bands (Seq01-20) from PCR-DGGE analysis with most similar database hits. The tree was inferred by maximum likelihood method using a
Tai-Nemura substitution model. The numbers on nods represent bootstrap values expressed as percentage from 1000 replikates.
Table 2
Fecal bacterial composition of captive western lowland gorillas from samples obtained using three different storage methods. The data are expressed as log10of numbers of bacterial
cells per gram of wet weight as determined by qPCR (mean values with standard deviations for counts of tested bacterial group).
Fixation methodAll eubacteria
Bifidobacterium group
Bacteroides group
Lactobacillus group
C. leptum group
Enterobacteriaceae
Frozen
RNAlater
Ethanol
11.21±0.46
10.17±0.42
9.40±0.30
10.29±0.60
9.12±1.37
7.20±0.91
8.74±0.43
7.84±1.24
6.97±0.89
9.36±0.57
8.68±0.46
7.80±0.59
10.64±0.41
9.92±0.56
9.38±0.60
3.89±0.34
3.58±0.28
4.02±0.13
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in our samples; results are consistent with those of Frey et al. (2006) in
mountain gorillas, in whichclones belongingto Bacteroidetes only com-
prised 1% of the clone library. On the contrary, Ochman et al. (2010)
and Ley et al. (2008) found this phylum to be the most dominant in
both western and eastern gorillas in the wild and in captivity.
Bacteriodetes are also frequent in humans (Eckburg et al., 2005) and
chimpanzees (Ushida, 2009; Szekely et al., 2010). Observed differences
in studies of gorilla microbiota can be explained by several factors
including different methods used for analyses of microbiota, origin of
investigated animals, or the effect of environment and diet. Hence the
comparison must be interpreted with caution.
Our results and also other studies on the fecal microbiota of great
apes showed that only 30-50% of bacterial 16 S rRNA clones from
gorillas samples could be identified at more than 96 % identity level
to sequences found in the GenBak database (Uenishi et al., 2007; Ley
et al., 2008; Szekely et al., 2010). In depth characterization of the
gastrointestinal microbiomes of great apes is therefore still warranted,
technical approaches such as next generation, high throughput se-
quencing will be of great value on characterizing these bacterial
communities in more detail (Ochman et al., 2010; Yildirim et al.,
2010). Furthermore, the use of larger datasets from both wild and cap-
tive animals from different localities will help explain the sources of
variation and forces that shape the gastrointestinal microbiomes of
great apes, with important implications for their health, conservation
and management.
Based on the results from this study, we suggest using RNAlater
solution to preserve fecal samples for analyses of the bacterial
community composition of great apes, if freezing of samples is not
possible or problematic. However, if the samples are to be used solely
for analyses focused on Enterobacteriaceae, 96% ethanol should be
considered as the best and most efficient storage medium. For further
studies, we suggest investigating the effect of storage method on
other bacterial groups that were not the focus of our research.
Acknowledgements
We are grateful to Zoo keepers and primate curators from the
Prague Zoo (Pavel Brandl, Gabriela Kopecká, Iveta Běličáková, Šárka
Hanzálková, Veronika Svobodová, Zdeněk Pišl), Dvůr Králové Zoo
(Zdeňka Jeřábková, Naděžda Humlová, Alice Ryšavá), and Zlín-Lešná
Zoo (Roman Horský, Marcela Čechová, Lucie Aronová) for their
assistance during collection of fecal samples of captive gorillas. We
would also like to express gratitude to Sabine Podmirseg (University
of Innsbruck, Austria) for analysis of DGGE electroforeograms. We
would like to express our sincere thanks to Bryan A. White and
Andres Gomez from University of Illinois, U.S.A. for their comments,
language corrections and improvements.
Appendix A. Supplementary data
Supplementary data to this article can be found online at http://
dx.doi.org/10.1016/j.mimet.2012.07.015.
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