treatment with antimicrobial growth promoter, tylosin
Hyeun Bum Kima, Klaudyna Borewicza, Bryan A. Whiteb, Randall S. Singera, Srinand Sreevatsana, Zheng Jin Tuc,
and Richard E. Isaacsona,1
aDepartment of Veterinary and Biomedical Sciences, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN 55108;bDepartment of Animal
Sciences, University of Illinois, Urbana, IL 61801; andcMinnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455
Edited* by Harley W. Moon, Veterinary Medical Research Institute, Ames, IA, and approved August 13, 2012 (received for review March 27, 2012)
Antimicrobials have been used extensively as growth promoters
(AGPs) in agricultural animal production. However, the specific
mechanism of action for AGPs has not yet been determined. The
work presented here was to determine and characterize the
microbiome of pigs receiving one AGP, tylosin, compared with
untreated pigs. We hypothesized that AGPs exerted their growth
promoting effect by altering gut microbial population composi-
tion. We determined the fecal microbiome of pigs receiving tylosin
compared with untreated pigs using pyrosequencing of 16S rRNA
gene libraries. The data showed microbial population shifts
representing both microbial succession and changes in response
to the use of tylosin. Quantitative and qualitative analyses of
sequences showed that tylosin caused microbial population shifts
in both abundant and less abundant species. Our results estab-
lished a baseline upon which mechanisms of AGPs in regulation of
health and growth of animals can be investigated. Furthermore,
the data will aid in the identification of alternative strategies to
improve animal health and consequently production.
intestinal microbiota|microbiome maturation|metagenomics
cycline exhibited enhanced growth (1). Since the 1950s, anti-
microbials have been used in agricultural animal production for
the treatment, control, and prevention of infectious diseases, as
well as for growth promotion (2, 3). The National Swine Survey
showed that 41% of feeds included one or more individual
antimicrobials and 19% included combinations of antimicrobials.
Tylosin and bacitracin were most often used in feeds for grower
and finisher pigs (4, 5). Because the use of antimicrobials leads to
the emergence of resistance to antimicrobials (6, 7), efforts to
reduce their use with the goal of preserving the efficacy of cur-
rent antimicrobials are being implemented.
The specific mechanisms whereby antimicrobials act as growth
promoters (AGPs) are unclear. Early studies demonstrated that
oral antimicrobials did not have growth-promoting effects when
given to germ-free animals (8). Therefore, it is likely that growth
promotion is based on changes to the gut microbiota. Several
mechanisms of how growth promoters act have been postulated
including the prevention of subclinical infections and the re-
duction in microbial use of nutrients (2, 9). AGPs also could act
by reducing the presence of opportunistic pathogens in animals
fed AGPs. Increased immune mediators such as interleukin-1
induced by gut bacteria have been shown to reduce feed con-
version in animals with a conventional microflora (10), which
illustrate that the host’s response to the indigenous microflora
could be a factor limiting growth efficiency. Direct effects of
AGPs on the gut microflora might result in decreased competi-
tion for nutrients and a reduction in microbial metabolites that
depress animal growth (9). Certain bacteria in the gut are known
to be metabolically important for animal growth. For example,
cellulolytic bacteria in ruminants digest cellulose into ferment-
able glucose that serves as a substrate for further microbial
fermentation and for use by the animal. Cellulose would not
n the 1940s, it was observed that animals fed dried mycelia of
Streptomyces aureofaciens that naturally contained chlortetra-
otherwise be used by the host. Therefore, it is highly likely that
growth promotion could be mediated by selection of specific
bacterial populations that contribute to the metabolism of the
animal thereby enhancing feed conversion.
It is our hypothesis that AGPs cause alterations of the gut
microflora, and these changes bring increased feed efficiency to
animals resulting in growth promotion. A first step to un-
derstanding the process of growth promotion is to identify and
describe alterations in the gut microflora of the pig. Recently
there have been three reports on microbial shifts in response to
the use of AGPs using culture independent assessments (11–13).
Overall, these studies were small in scale and used animals held
in infectious disease isolation facilities and did not replicate
conditions comparable to how commercial pigs are reared. The
work presented here was designed to better understand the
effects that a single growth promoter had on the microbiome of
pig feces using a longitudinal study design that used pigs in
typical commercial settings. Tylosin, a member of the macrolide
family of antimicrobials, was selected for this study because it is
thought to be the most commonly used AGP. The results pre-
sented here demonstrate that tylosin produces consistent and
specific alterations of the distal intestinal microflora of the pig.
DNA Sequence Data and Quality Control. The studies described
were designed to detect changes in the fecal microbiome of pigs
being raised in conventional production units over time. The
study design used two groups of pigs (10 pigs per group). One
group received tylosin in their feed during the entire experi-
mental period, whereas the second group did not receive tylosin.
The protocol was performed using two independent farms. DNA
sequences in each group and farm were analyzed as pooled
groups based on our previous results that demonstrated strong
similarities between the fecal microbiomes of pigs within the
same pen (14). However, because samples from each pig had
a bar code identifier, we were able to quantify the number of
sequence reads per animal. A total of 504,204 and 1,543,479
DNA sequence reads were generated from farm 1 and farm 2,
respectively. Over 86% and 94% of the total number of sequence
reads from farm 1 and farm 2 passed the quality control imple-
mented in this study, resulting in 435,225 and 1,445,371
sequences for farm 1 and farm 2. The total number of sequence
reads from farm 2 was 3.3 times larger than that of farm 1 be-
cause of the improved sequencing chemistry used with samples
Author contributions: H.B.K., B.A.W., R.S.S., S.S., and R.E.I. designed research; H.B.K., K.B.,
and R.E.I. performed research; H.B.K. contributed new reagents/analytic tools; H.B.K.,
B.A.W., R.S.S., S.S., Z.J.T., and R.E.I. analyzed data; and H.B.K. and R.E.I. wrote the paper.
The authors declare no conflict of interest.
*This Direct Submission article had a prearranged editor.
Data deposition: The sequences used in this paper are publicly available at Galaxy, https://
1To whom correspondence should be addressed. E-mail: firstname.lastname@example.org.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
| September 18, 2012
| vol. 109
| no. 38
from farm 2 (GS-FLX chemistry for farm 1 and titanium
chemistry for farm 2). The average number of sequence reads
generated per pig was 4,352 for farm 1 and 14,454 for farm 2.
The median sequence read length for both the tylosin and no-
tylosin groups on farm 1 and farm 2 was 137–138 bases with no
ambiguous bases. The range of sequence reads was 81–196 bases
and, when a homopolymer run was detected, the median length
of the homopolymer was 5 bases (Table S1).
Microbial Diversity. The diversity of microbial communities using
pooled sequence reads from all ten pigs in each group was
measured using Shannon–Weaver and Simpson (1-D) diversity
indices (15). The diversity indices used represent how many dif-
ferent taxa are present in a sample, and higher numbers indicate
higher diversity. The average Shannon–Weaver and Simpson (1-
D) index values showed highly diverse microbial communities
with mean values per group of 4.87 and 0.96 for farm 1, and 5.28
and 0.97 for farm 2. The range of these calculated values over the
five sampling times (3-wk intervals starting at 10 wk of age and
ending 22 wk of age) was 4.39–5.50 for Shannon–Weaver and
0.95–0.98 for Simpson (1-D). Both diversity indices were greater
in younger animals than in older animals (Table S2).
Microbial Shifts in Response to the Use of Tylosin as an AGP: Taxon-
Based Analysis. A taxon-dependent analysis using the Ribosomal
Database Project (RDP) classifier was conducted to describe the
composition of the fecal microbiome between animals receiving
tylosin and those that did not receive tylosin and how it changed
over time (16). The results of phylum and class distributions are
shown in Fig. 1. Two phyla, Firmicutes and Bacteroidetes, were
the most dominant in the fecal samples regardless of age of pigs
or treatment group, and comprised more than 92% of the total
sequences. The proportion of sequences that could not be
assigned to a phylum using RDP classifier ranged from 1.61% to
6.37%. Bacteria in the phylum Spirochaetes increased over time
ranging from 0.07% to 1.85%. The other phyla represented less
than 1% of the total at all time points. The proportion of bac-
teria in the phyla Firmicutes, Spirochaetes, and unclassified
phyla increased as the pigs aged, whereas the proportion of
bacteria in the phyla Bacteroidetes, Actinobacteria, and Pro-
teobacteria decreased. These changes were consistently observed
in both farms. When the relative abundance of bacteria at the
phylum level was compared between tylosin and no-tylosin
groups of the same age, there were no significant differences
(Fig. 1 A and B).
Bacteria in the classes Clostridia, Erysipelotrichi, Bacilli,
Bacteroidia, Spirochaetes, and unclassified classes made up
95.58% of the total bacteria at all times. Bacteria in other classes
represented less than 1% at most time points. Two classes,
Clostridia and Bacilli represented the most common classes
within the phylum Firmicutes. The proportion of Clostridia in-
creased as pigs got older, whereas the proportion of Bacilli
pigs in each group were used. (A) RDP classification of the sequence reads from farm 1 at phylum level. (B) RDP classification of the sequence reads from farm
2 at phylum level. (C) RDP classification of the sequence reads from farm 1 at class level. (D) RDP classification of the sequence reads from farm 2 at class level.
RDP classification of the sequences at phylum and class levels. RDP classifier was used with a bootstrap cutoff of 50. Pooled sequence reads from all 10
| www.pnas.org/cgi/doi/10.1073/pnas.1205147109Kim et al.
decreased. The increase in the proportion of Clostridia was
greater than the decrease in the proportion of Bacilli resulting in
an overall increase in the population of Firmicutes over time.
The class Bacteroidia was the major class in the phylum Bac-
teroidetes, and the proportion of Bacteroidia decreased over
time (Fig. 1 C and D).
A total of 158 and 187 genera were identified from farm 1 and
farm 2, respectively. Of the total number of genera identified
in both farms, 16 abundant genera were detected. The abun-
dant genera were defined as having more than 1% of the total
DNA sequences. The 16 most abundant genera were: Anaero-
bacter, Prevotella, Streptococcus, Lactobacillus, Coprococcus, Spor-
acetigenium, Megasphaera, Blautia, Oscillibacter, Subdoligranulum,
Faecalibacterium, Dialister, Pseudobutyrivibrio, Roseburia, Sarcina,
and Butyricicoccus. These 16 genera plus the unclassified genera
accounted for 90.3% and 90.5% of the total sequences from
farms 1 and 2, respectively. One genus, Prevotella, was a member
of phylum Bacteroidetes; the other 15 genera belonged to the
phylum Firmicutes. Among the 16 abundant genera, the de-
tection frequencies of the genera Anaerobacter, Streptococcus,
Coprococcus, Sporacetigenium, Oscillibacter, Roseburia, and Sarcina
showed increases over time, whereas those in the genera Prevotella,
Lactobacillus, Megasphaera, Blautia, Subdoligranulum, Faecalibacte-
rium, Dialister, Pseudobutyrivibrio, and Butyricicoccus decreased
(Tables S3, S4, and S5).
To compare how the composition of the fecal bacteria differed
between treatment groups, Metastats was used to identify differ-
entially abundant genera. Although there were no differences
between treatment groups at the phylum level, a total of 12 dif-
ferentially abundant genera were identified from both farms at the
genus level (Fig. 2 A and B). These included six abundant (>1% of
the total sequences) and six less abundant genera. The six abun-
dant genera consisted of Prevotella, Lactobacillus, Sporacetigenium,
Megasphaera, Blautia, and Sarcina, and the less abundant
obacterium, Anaerosporobacter, Succinivibrio, and Eggerthella.
Eight of the 12 differentially abundant genera belonged to the
phyla Firmicutes and two of 12 belonged to Bacteroidetes.
Each of the other two genera was in the phyla Proteobacteria,
and Actinobacteria. Lactobacillus, Sporacetigenium, Acetanaer-
obacterium, and Eggerthella were detected more frequently in the
tylosin group than in no-tylosin group. The others were present
more frequently in the no-tylosin group than the tylosin group.
Unclassified bacteria at the genus level also were differentially
represented in pigs from both farms and were observed more
frequently in the no-tylosin group. The same pattern of pop-
ulation distribution of the differentially abundant genera was
observed from both farms at 22 wk. The population distribution
patterns of differentially abundant genera at 22 wk began to
emerge from 16 wk and continuing through 19 wk of age (Fig. 2
A and B).
consisted of Barnesiella,Mitsuokella,Acetanaer-
Microbial Shifts in Response to the Use of Tylosin as an AGP:
Operational Taxonomic Unit (OTU)-Based Analysis. Although most
sequences were classified at the phylum and class levels, a high
proportion of sequences could not be classified at the genus level.
Therefore, a taxon-independent analysis using OTUs as the unit
of analysis was conducted for the in depth ecological analysis of
the bacterial communities. We defined an OTU as having 95%
sequence identity. Compared with a total of 158 and 187 genera
identified in farm 1 and farm 2, a total of 5,499 and 12,637 OTUs
were identified in farm 1 and farm 2, respectively. Of those
OTUs, 421 and 870 OTUs on farm 1 and farm 2, respectively,
were defined in our study as core OTUs because they existed in
all groups at all five time points in each farm. Although core
OTUs comprised only 7.68% and 6.88% of the total OTUs in
farms 1 and 2, these core OTUs contained the majority of the
sequences, 82.4% and 85.4% of the total sequences from farms
tylosin and no-tylosin groups. Pigs in the tylosin
groups received tylosin in their feed during the whole
experimental period (10–22 wk of age), whereas pigs
in the no-tylosin groups did not receive tylosin. The
heat map was created using genus based results after
normalization. Yellow indicates abundant genera and
black indicates less abundant genera. Each column
represents groups and each row indicates genus. F1
and F2 indicate farm 1 and farm 2, respectively, and T
and NT represent each tylosin group and no-tylosin
group. (A) Differentially abundant genera were in-
dicated by arrows and color-coded. Genus names in-
dicate by the color their relationship to the phylum
level. (B) Twelve differentially abundant genera and
Differentially abundant genera between
Kim et al.PNAS
| September 18, 2012
| vol. 109
| no. 38
1 and 2. These results indicate that animals share a core set
of bacteria that are the most prevalent microbes. This core
represents a small number of species regardless of their ages.
Therefore, the major variation of bacterial populations is mainly
a result of the less dominant species present in different individ-
uals. Representative sequences for each core OTU were retrieved
and subjected to a taxonomic analysis using RDP classifier. As
would be expected, most of the core OTUs belonged to the phyla
Firmicutes and Bacteroidetes, accounting for 86.22% and 87.01%
of the total core OTUs in farms 1 and 2, respectively.
Venn diagrams were generated to compare OTUs between
groups at the same time points (Fig. S1). An average of 66.48%
and 68.60% of the total OTUs were shared between groups at
the same age, and an average of 97.89% and 98.47% of the total
sequence reads belonged to the shared OTUs in farm 1 and farm
2, respectively. Unique OTUs, which accounted for an average of
33.52% and 31.40% of the total OTUs at the same age, con-
tained just 2.11% and 1.53% of the total sequence reads in farm
1 and farm 2 (Fig. S1). Although shared OTUs were comprised
of both abundant and less abundant OTUs, unique OTUs to
each group were mainly less abundant OTUs. Sequence reads
belonging to unique OTUs at different time points were sub-
jected to analysis using RDP classifier. RDP classifier is a web
based program that assigns 16S rRNA sequences to phyloge-
netically consistent bacterial taxonomy. Unclassified bacteria at
the genus level were the most abundant at 64.11% and 63.89% of
the total unique sequence reads to each group in farms 1 and
farm 2. Among the OTUs classified at the genus level, Prevotella
was the most abundant genus accounting for 7.67% and 9.04% of
the total unique sequence reads in farms 1 and 2 (Table S6).
To visualize the distribution of the different OTUs in the two
treatment groups, a heat map was prepared (Fig. 3). The OTUs
were ordered by abundance with the most abundant sequences
being at the top of the heat map. Abundant OTUs were red
color-coded and white blanks indicated missing OTUs. Two
regions of the heat map, labeled A and B, contained close to 40%
of the total OTU sequences. These regions were highlighted
because of the apparent differences in their distributions between
the pigs in the two treatment groups. The fecal microbiomes of
pigs that were 10 wk of age had nearly identical heat map pat-
terns regardless of treatment group. This finding would be
expected because pigs only began to receive tylosin at this time.
However, by 16 wk and continuing through 19 wk of age, there
was an obvious shift in microbial content in the feces in pigs
treated with tylosin with the depletion of OTUs in bracket B and
an increase in the OTUs in bracket A. When pigs reached 22 wk
of age, a similar shift was found in pigs that did not receive tylosin
(Fig. 3). When the same process was used to analyze pigs from
farm 2, a more complicated pattern emerged (Fig. 4) that did not
mimic what was observed with farm 1. We did detect differences
in the compositions of the fecal microbiomes between the two
treatment groups. However, the ordering of the OTUs did not
result in the same heat map patterns in the regions designated A
and B. This could result because the depth of sequence coverage
was much greater for farm 2 and thus the ordering of the se-
quence abundance was different for farm two, the two farms
could not be directly compared using heat maps for visualization.
Dendrograms were prepared by using the Bray–Curtis index to
compare the similarity of the bacterial communities between
groups. Whereas microbial population shifts in both abundant
and less abundant genera in pig intestines were detected, the
samples clustered based on sampling time point (Fig. S2).
We hypothesized that the beneficial effects of tylosin would be
mediated by compositional changes of the pig gut bacterial
communities (2, 11, 17, 18). This hypothesis is partly based on
the understanding that AGPs lack growth promoting effects in
germ-free animals (8, 19) and because many antimicrobials with
different modes of action promote animal growth. The work
presented here was designed to better understand the effects that
a single AGP, tylosin, has on the fecal microbiome of the pig. We
showed that tylosin consistently and specifically altered the
microbiome of feces of pigs.
Using 16S rRNA gene sequencing we previously showed
profound changes in the pig fecal microbiome over time (14).
Here we showed that tylosin results in significant changes in
specific genera and OTUs and that some of these changes occur
at unique times within the growth of pigs (Figs. 2 and 3). What
was most striking were the changes in the less abundant OTUs in
pigs from farm 1 over time (see groups bracketed with A and B
in Fig. 3). Before exposure to tylosin, the fecal microbiomes of
pigs in the two groups were quite similar. However, there was
a pronounced shift in the distribution and quantity of microbes in
regions A and B with treatment with tylosin being correlated
heat map for farm 1 was created using OTUs with an OTU definition at
a similarity cutoff of 95%. Each column represents groups and each row
indicates OTUs. OTUs were sorted with the most abundant OTUs displayed at
the top and the least abundant OTUs at the bottom. T and NT indicate
tylosin and no-tylosin group, respectively, and numbers indicates weeks of
age. Abundant OTUs were red color-coded and white blanks indicate miss-
Distribution of OTUs in the two treatment groups in farm 1. The
| www.pnas.org/cgi/doi/10.1073/pnas.1205147109Kim et al.
with a shift of OTUs in the B region of the heat map to the A
region (Fig. 3). As the untreated pigs reached maturity, their
fecal microbiomes, particularly in the A and B regions become
more like pigs in the tylosin treatment group. Our interpretation
of these results is that tylosin speeds the development or matu-
ration of the unique “adult-like” fecal microbiome but that
eventually pigs in the nontreatment groups finally catch up.
However, the same pattern of redistribution of OTUs was not
overtly seen when a similar heat map of OTUs in pigs from farm
2 was observed (Fig. 4). This difference could be the result of
several possibilities. The first possibility is a technical issue that is
based on the fact that much deeper sequencing was performed
for samples from farm 2. This resulted from the shift to “tita-
nium” chemistry use during sequencing of samples from farm 2.
Thus, because of the difference in coverage the actual ordering
of the OTUs in the two heat maps could be different. A second
possibility is that, because the data were obtained from pigs from
two different farms, their microbiomes were not identical and
the compositional changes caused by tylosin therefore were dif-
ferent. However, the genus level analysis (Fig. 2) did show
compositional changes in common to both farms when pigs
treated with tylosin were compared with nontreated pigs. A third
possibility is that our interpretation based on farm 1 that tylosin
speeds up the maturation of the gut microbiome is not correct
and that the actual compositional changes identified through the
genus level analysis are microbes closely linked to feeding of
tylosin. To distinguish these possibilities additional sampling
from additional farms will be necessary.
There was no significant difference in the proportion of tax-
onomic groupings of the fecal microbiomes at the phylum level
regardless of treatment group at all sampling times. However,
there were statistical differences when measured at the genus
level. A total of twelve differentially abundant genera were ob-
served in both farms at the genus level (Fig. 2 A and B). Lac-
tobacillus, Sporacetigenium, Acetanaerobacterium, and Eggerthella
were detected more frequently in the tylosin group than in no-
tylosin group, and the others were present more frequently in the
no-tylosin group than the tylosin group (Fig. 2B).
In previous studies using pigs as an animal model, certain
species of Lactobacillus have been shown to increase in con-
centration in AGP-treated pigs (11, 12, 20). In this study we also
detected a relative increase in the concentration of the genus
Lactobacillus. A positive relation between the increase in abun-
dance of Lactobacillus spp. population and an increase in weight
gain has been shown in other animal models (18, 21).
Sporacetigenium, Acetanaerobacterium, and Eggerthella were
more abundant in pigs with tylosin treatment. In vitro studies
showed that Sporacetigenium and Acetanaerobacterium spp fer-
ment mono, di-, or oligosaccharides into acetic acid, ethanol,
hydrogen and carbon dioxide (22, 23). Sporacetigenium spp also
has been identified as one of common bacterial population in
fermentation reactors where the main volatile fatty acids were
acetic acid and propionic acid (24). It should be noted that about
5–20% of the total energy of the pigs is provided by fermentation
end products including acetic and propionic acids in the cecum
and colon (9). Eggerthella spp is a Gram-positive bacteria, which
is known to be involved in metabolism of catechin and lignan that
are plant origin (25, 26). However, physiological and functional
roles of Eggerthella in the intestines of food animals are unknown.
In comparing the results reported here with those from other
studies using AGPs, differences in the microbial population
shifts did occur (11, 12, 20). It is unclear why these differences
occurred, but is likely related to differences in the AGP used.
The AGP used in our study was tylosin, whereas the other three
studies used combination of chlortetracycline with different
AGPs. Likewise, we elected to use feces as the collected sample
so that we could repeatedly sample the same pigs, whereas
Rettedal et al. and Collier et al. collected tissue or digesta in
their studies. We also elected to use commercial pigs in a pro-
duction setting instead of experimental isolation facilities where
access to different sets of microbes in different environments
might be responsible for this inconsistency. Furthermore, in our
study two farms were used and the microbial shifts between the
two farms were quite similar.
Venn diagrams were generated to make qualitative compar-
isons between tylosin and no-tylosin groups at the same age. It
should be noted that almost all of the sequences were shared
between tylosin and no-tylosin groups at the same age. An
heat map for the farm 2 was created using OTUs with an OTU definition at
a similarity cutoff of 93%. Each column represents groups and each row indi-
cates OTUs. OTUs were sorted with the most abundant OTUs displayed at the
top and the least abundant OTUs at the bottom. T and NT indicate tylosin and
no-tylosin group, respectively, and numbers indicates weeks of age. Abundant
OTUs were red color-coded and white blanks indicate missing OTUs.
Distribution of OTUs in the two treatment groups in the farm 2. The
Kim et al. PNAS
| September 18, 2012
| vol. 109
| no. 38
average of 97.89% and 98.47% of the total bacterial sequences
belonged to the shared OTUs. These OTUs represented an av-
erage of 66.48% and 68.60% of the total OTUs in farm 1 and
farm 2, respectively (Fig. S1). This indicates that unique OTUs
to each group were more likely to be found as less abundant
OTUs because unique OTUs that accounted for average of
33.52% and 31.40% of the total OTUs at the same age contained
just 2.11% and 1.53% of the bacterial sequences in farm 1 and
farm 2 (Fig. S1). This result is consistent with the data visuali-
zation based on heat maps where the greatest number of dif-
ferences appear toward the bottom of the heat maps.
Given that unique OTUs are less abundant OTUs, abundant
and beneficial functions from less abundant microbes can be
considered (27, 28). Sequence reads belonging to OTUs unique
to each tylosin and no-tylosin groups were subjected to the RDP
classifier analysis. RDP classifier analysis indicated that the
majority of unique OTUs were unclassified bacteria at genus
level, accounting for 64.11% and 63.89% of the total unique
sequence reads in farm 1 and farm 2 (Table S6). However, it
remains unknown at this point whether unclassified bacteria
contributed to fundamental metabolic functions within gastro-
intestinal tract of the animals.
Materials and Methods
Full protocols are available in SI Materials and Methods.
Animals and Sample Collection. Two independent commercial farms were
tagged for identification. Pigs in one pen received tylosin [40 ppm (40 g/ton)]
in their feed, whereas pigs in the other pen did not. Fresh fecal samples were
individually collected from the rectum of each of the ear tagged animals at
3-wk intervals starting when the pigs were 10 wk old.
Isolation of DNA. Total DNA containing the fecal microbial communities was
extracted fromindividualfecal samplesusinganestablished method (29).The
protocol was modified by including 1,000 μL of ASL buffer and InhibitEX
tablets. Both reagents are components of the QIAgen DNA Mini Stool kit
(QIAGEN). DNA quantity was measured using a NanoDrop 1000 spectro-
photometer (Thermo Fisher Scientific).
PCR Amplicon Production and Sequencing. PCR primers that flanked the V3
hypervariable region of bacterial 16S rRNA genes were designed. The primer
sequences were 5′-(Roche A)-10-base barcode-CCTACGGGAGGCAGCAG-3′
(forward) and 5′-(Roche B)-ATTACCGCGGCTGCTGG-3′ (reverse) (14, 30, 31).
(The forward primers were composed of the Roche A sequence followed by
a unique 10 base pair barcode and then the region flanking V3.) The PCR
amplicon products were generated by 20 cycles of PCR reactions. Only PCR
products without primer dimers and contaminant bands were used for
pyrosequencing. The PCR products from different pigs in the same time
group were pooled together in equimolar ratios. The pooled PCR amplicons
were sequenced at the Biomedical Genomic Center at the University of
Minnesota using a Roche 454 GS-FLX sequencer (454 Life Sciences).
Data Analysis. Only high-quality sequences obtained after quality control
analysis were used in our analysis (full protocols are described in SI Materials
and Methods). Phylogenetic assessments were performed using RDP classi-
fier with a bootstrap cutoff of 50% (16). Metastats (32) was used to detect
differentially abundant genera in samples after each sequence read was
assigned to a taxon by RDP classifier. Richness and diversity indices were
generated using Mothur (version 1.21.1) with an OTU definition at an
identity cutoff of 95% after having implemented a pseudosingle linkage
algorithm (15, 33, 34). Heat maps were generated using Mothur (version
1.21.1) and Java TreeView (15, 35).
ACKNOWLEDGMENTS. We thank Dr. Russ Bey and Dr. Keith Wilson from
Newport Laboratories (Worthington, MN) and Dr. Kwang-Soo Lyoo for their
support and help. We thank Minnesota Supercomputing Institute of the
University of Minnesota for their technical support. H.B.K. was supported by
a Doctoral Dissertation Fellowship provided by the University of Minnesota.
This work was supported by US Department of Agriculture/National Re-
search Initative Grant 2007-35212-18046.
1. Jones FT, Ricke SC (2003) Observations on the history of the development of anti-
microbials and their use in poultry feeds. Poult Sci 82:613–617.
2. Dibner JJ, Richards JD (2005) Antibiotic growth promoters in agriculture: History and
mode of action. Poult Sci 84:634–643.
3. Niewold TA (2007) The nonantibiotic anti-inflammatory effect of antimicrobial
growth promoters, the real mode of action? A hypothesis. Poult Sci 86:605–609.
4. McEwen SA, Fedorka-Cray PJ (2002) Antimicrobial use and resistance in animals. Clin
Infect Dis 34(Suppl 3):S93–S106.
5. Dewey CE, Cox BD, Straw BE, Bush EJ (1999) Use of antimicrobials in swine feeds in the
United States. Swine Health and Production 7:19–25.
6. Schwarz S, Chaslus-Dancla E (2001) Use of antimicrobials in veterinary medicine and
mechanisms of resistance. Vet Res 32:201–225.
7. Phillips I, et al. (2004) Does the use of antibiotics in food animals pose a risk to human
health? A critical review of published data. J Antimicrob Chemother 53:28–52.
8. Coates ME, Fuller R, Harrison GF, Lev M, Suffolk SF (1963) A comparison of the growth
of chicks in the Gustafsson germ-free apparatus and in a conventional environment,
with and without dietary supplements of penicillin. Br J Nutr 17:141–150.
9. Gaskins HR, Collier CT, Anderson DB (2002) Antibiotics as growth promotants: Mode
of action. Anim Biotechnol 13:29–42.
10. Roura E, Homedes J, Klasing KC (1992) Prevention of immunologic stress contributes
to the growth-permitting ability of dietary antibiotics in chicks. J Nutr 122:2383–2390.
11. Looft T, et al. (2012) In-feed antibiotic effects on the swine intestinal microbiome.
Proc Natl Acad Sci USA 109:1691–1696.
12. Rettedal E, et al. (2009) Alteration of the ileal microbiota of weanling piglets by the
growth-promoting antibiotic chlortetracycline. Appl Environ Microbiol 75:5489–5495.
13. Allen HK, et al. (2011) Antibiotics in feed induce prophages in swine fecal micro-
biomes. MBio 2, 2: e00260-11.
14. Kim HB, et al. (2011) Longitudinal investigation of the age-related bacterial diversity
in the feces of commercial pigs. Vet Microbiol 153:124–133.
15. Schloss PD, et al. (2009) Introducing mothur: Open-source, platform-independent,
community-supported software for describing and comparing microbial communities.
Appl Environ Microbiol 75:7537–7541.
16. Cole JR, et al. (2009) The Ribosomal Database Project: Improved alignments and new
tools for rRNA analysis. Nucleic Acids Res 37(Database issue):D141–D145.
17. Chapman HD, Johnson ZB (2002) Use of antibiotics and roxarsone in broiler chickens
in the USA: analysis for the years 1995 to 2000. Poult Sci 81:356–364.
18. Lin J (2011) Effect of antibiotic growth promoters on intestinal microbiota in food
animals: A novel model for studying the relationship between gut microbiota and
human obesity? Front Microbiol 2:53.
19. Coates ME, Davies MK, Kon SK (1955) The effect of antibiotics on the intestine of the
chick. Br J Nutr 9:110–119.
20. Collier CT, et al. (2003) Molecular ecological analysis of porcine ileal microbiota re-
sponses to antimicrobial growth promoters. J Anim Sci 81:3035–3045.
21. Dumonceaux TJ, Hill JE, Hemmingsen SM, Van Kessel AG (2006) Characterization of
intestinal microbiota and response to dietary virginiamycin supplementation in the
broiler chicken. Appl Environ Microbiol 72:2815–2823.
22. Chen S, Song L, Dong X (2006) Sporacetigenium mesophilum gen. nov., sp. nov.,
isolated from an anaerobic digester treating municipal solid waste and sewage. Int J
Syst Evol Microbiol 56:721–725.
23. Chen S, Dong X (2004) Acetanaerobacterium elongatum gen. nov., sp. nov., from
paper mill waste water. Int J Syst Evol Microbiol 54:2257–2262.
24. Ren N, et al. (2007) Microbial community structure of ethanol type fermentation in
bio-hydrogen production. Environ Microbiol 9:1112–1125.
25. Mabrok HB, et al. (2012) Lignan transformation by gut bacteria lowers tumor burden
in a gnotobiotic rat model of breast cancer. Carcinogenesis 33:203–208.
26. Kutschera M, Engst W, Blaut M, Braune A (2011) Isolation of catechin-converting
human intestinal bacteria. J Appl Microbiol 111:165–175.
27. Walker A (2007) Say hello to our little friends. Nat Rev Microbiol 5:572–573.
28. Arumugam M, et al.; MetaHIT Consortium (2011) Enterotypes of the human gut
microbiome. Nature 473:174–180.
29. Yu Z, Morrison M (2004) Improved extraction of PCR-quality community DNA from
digesta and fecal samples. Biotechniques 36:808–812.
30. Muyzer G, de Waal EC, Uitterlinden AG (1993) Profiling of complex microbial pop-
ulations by denaturing gradient gel electrophoresis analysis of polymerase chain re-
action-amplified genes coding for 16S rRNA. Appl Environ Microbiol 59:695–700.
31. Parameswaran P, et al. (2007) A pyrosequencing-tailored nucleotide barcode design
unveils opportunities for large-scale sample multiplexing. Nucleic Acids Res 35:e130.
32. White JR, Nagarajan N, Pop M (2009) Statistical methods for detecting differentially
abundant features in clinical metagenomic samples. PLOS Comput Biol 5:e1000352.
33. Schloss PD, Handelsman J (2005) Introducing DOTUR, a computer program for de-
fining operational taxonomic units and estimating species richness. Appl Environ
34. Huse SM, Welch DM, Morrison HG, Sogin ML (2010) Ironing out the wrinkles in the
rare biosphere through improved OTU clustering. Environ Microbiol 12:1889–1898.
35. Saldanha AJ (2004) Java Treeview—extensible visualization of microarray data.
| www.pnas.org/cgi/doi/10.1073/pnas.1205147109 Kim et al.