Identification of the core bacteria in rectums of diarrheic and non-diarrheic piglets

Abstract

Porcine diarrhea is a global problem that leads to large economic losses of the porcine industry. There are numerous factors related to piglet diarrhea, and compelling evidence suggests that gut microbiota is vital to host health. However, the key bacterial differences between non-diarrheic and diarrheic piglets are not well understood. In the present study, a total of 85 commercial piglets at three pig farms in Sichuan Province and Chongqing Municipality, China were investigated. To accomplish this, anal swab samples were collected from piglets during the lactation (0–19 days old in this study), weaning (20–21 days old), and post-weaning periods (22–40 days), and fecal microbiota were assessed by 16S rRNA gene V4 region sequencing using the Illumina Miseq platform. We found age-related biomarker microbes in the fecal microbiota of diarrheic piglets. Specifically, the family Enterobacteriaceae was a biomarker of diarrheic piglets during lactation (cluster A, 7–12 days old), whereas the Bacteroidales family S24–7 group was found to be a biomarker of diarrheic pigs during weaning (cluster B, 20–21 days old). Co-correlation network analysis revealed that the genus Escherichia-Shigella was the core component of diarrheic microbiota, while the genus Prevotellacea UCG-003 was the key bacterium in non-diarrheic microbiota of piglets in Southwest China. Furthermore, changes in bacterial metabolic function between diarrheic piglets and non-diarrheic piglets were estimated by PICRUSt analysis, which revealed that the dominant functions of fecal microbes were membrane transport, carbohydrate metabolism, amino acid metabolism, and energy metabolism. Remarkably, genes related to transporters, DNA repair and recombination proteins, purine metabolism, ribosome, secretion systems, transcription factors, and pyrimidine metabolism were decreased in diarrheic piglets, but no significant biomarkers were found between groups using LEfSe analysis.

Full text

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Identication of the core bacteria
in rectums of diarrheic and non-
diarrheic piglets
Jing Sun1,3,4,5*, Lei Du1,5, XiaoLei Li2,5, Hang Zhong1, Yuchun Ding1,3,4, Zuohua Liu1,3,4 &
Liangpeng Ge1,3,4*
Porcine diarrhea is a global problem that leads to large economic losses of the porcine industry. There
are numerous factors related to piglet diarrhea, and compelling evidence suggests that gut microbiota
is vital to host health. However, the key bacterial dierences between non-diarrheic and diarrheic
piglets are not well understood. In the present study, a total of 85 commercial piglets at three pig farms
in Sichuan Province and Chongqing Municipality, China were investigated. To accomplish this, anal
swab samples were collected from piglets during the lactation (0–19 days old in this study), weaning
(20–21 days old), and post-weaning periods (22–40 days), and fecal microbiota were assessed by 16S
rRNA gene V4 region sequencing using the Illumina Miseq platform. We found age-related biomarker
microbes in the fecal microbiota of diarrheic piglets. Specically, the family Enterobacteriaceae was
a biomarker of diarrheic piglets during lactation (cluster A, 7–12 days old), whereas the Bacteroidales
family S24–7 group was found to be a biomarker of diarrheic pigs during weaning (cluster B, 20–21
days old). Co-correlation network analysis revealed that the genus Escherichia-Shigella was the core
component of diarrheic microbiota, while the genus Prevotellacea UCG-003 was the key bacterium in
non-diarrheic microbiota of piglets in Southwest China. Furthermore, changes in bacterial metabolic
function between diarrheic piglets and non-diarrheic piglets were estimated by PICRUSt analysis,
which revealed that the dominant functions of fecal microbes were membrane transport, carbohydrate
metabolism, amino acid metabolism, and energy metabolism. Remarkably, genes related to
transporters, DNA repair and recombination proteins, purine metabolism, ribosome, secretion systems,
transcription factors, and pyrimidine metabolism were decreased in diarrheic piglets, but no signicant
biomarkers were found between groups using LEfSe analysis.
Diarrhea of neonatal piglets has long been a problem aicting global piglets production. During the last few dec-
ades, reports have described diarrhea in neonatal pigs belonging to various age groups1–3. Porcine diarrhea leads
directly to economic losses because of increased morbidity and mortality, reduced average daily gain (ADG),
and the consumption of extra medication4,5. Intestinal microbes have a profound impact on health and disease
through programming of immune and metabolic pathways6. Diarrhea has various causes, including porcine
parvovirus, porcine kobuvirus, and enterotoxigenic Escherichia coli (ETEC)7–10, all of which have been linked
to imbalances of normal intestinal ora as well as extra-intestinal microecological imbalance11–13. A number of
recent studies have utilized high-throughput sequencing of the 16S rRNA gene to characterize gut microbiota of
diarrheic piglets. Neonatal pigletdiarrheawas associated with increases in the relative abundance of Prevotella
(Bacteroidetes), Sutterella and Campylobacter (Proteobacteria)14. e percentage of Enterococcus (Firmicutes) was
also more abundant in new neonatal porcine diarrhea (NNPD) aectedpiglets13. Genus Veillonella (Firmicutes)
was the dominant bacteria in fecal microbiota in porcine epidemic diarrhea virus (PEDV)-infected piglets
during the suckling transition stage15, while higher Escherichia-Shigella (Proteobacteria) in the feces was in
Enterotoxigenic Escherichia coli-induced diarrhea in piglets16. Although Holman and the colleagues used a
meta-analysis to dene a “core” microbiota in the swine gut17, the key microbial populations related to diarrhea
1Chongqing Academy of Animal Sciences, Chongqing, 402460, China. 2Institute of Animal Genetics and Breeding,
Sichuan Agricultural University, Chengdu, 611130, China. 3Key Laboratory of Pig Industry Sciences, Ministry of
Agriculture, Chongqing, 402460, China. 4Chongqing Key Laboratory of Pig Industry Sciences, Chongqing, 402460,
China. 5These authors contributed equally: Jing Sun, Lei Du and XiaoLei Li. *email: sunjing85026@163.com;
geliangpeng1982@163.com
OPEN
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in piglets being poorly understood. us, we conducted a survey of porcine diarrhea in three medium-scale pig
farms in Southwest China to investigate the eects of diarrhea on fecal microbiota. e cause of diarrhea was
not considered when sampling, and a total of 52 and 33 swab samples were collected from diarrheic piglets and
non-diarrheic piglets, respectively, of the same or similar age in the same hog house for 16S ribosomal RNA gene
V4 region sequencing using the Illumina Miseq platform. We then compared and analyzed bacterial changes
in the composition and function of the feces of piglets that were suering from diarrhea and those that did not
develop diarrhea to identify key dierences in the fecal microbiota of piglets to reveal diarrhea-related bacteria.
Results
Overall information regarding the fecal microbiota of piglets. No signicant dierences in gen-
der or sample location were discerned between diarrheic and non-diarrheic groups (P > 0.05; Table1). Illumina
Miseq sequencing of the V4 region of the bacterial 16S rRNA gene generated 6,868,150 high-quality sequences.
Aer removal of chimeras, ltered high-quality sequences were grouped into 75,943 OTUs based on 97% species
similarity (detail information of OTUs was shown in Supplementary Table1).
Pairwise comparisons between groups were detected and values at P = 0.001, representing the grouping
(D group and ND group), were valid. e four most abundant phyla in the fecal microbiota of diarrheic and
non-diarrheic piglets were Firmicutes, Proteobacteria, Bacteroidetes, and Fusobacteria (Table2). Firmicutes and
Bacteroidetes constituted the top two phyla in the piglet gut microbiota in the ND group, whereas Firmicutes and
Proteobacteria constituted the two predominant phyla in the gut microbiota of diarrheic piglets (D group). A sim-
ilar abundance of Firmicutes was shown in the gut microbiota of piglets in groups D and ND (42.06% vs. 43.09%,
P > 0.05). Diarrheic piglets showed a signicantly lower percentage of Bacteroidetes and a higher percentage of
Proteobacteria than non-diarrheic individuals (P < 0.05). Moreover, the Proteobacteria-Bacteroidetes ratio in the
diarrheic group was 1.96, whereas the ratio in the non-diarrheic group was 0.36 (on average, Table2).
e OTUs were also used to compare the dierences in abundance between D and ND piglets (Table3). e
total abundance of 2 families, 11 genera, and 8 species diered signicantly in the gut microbiota of D and ND
piglets. For example, levels of the genera Bacteroides, Ruminococcaceae, and Prevotella in the fecal microbiota of
diarrheic piglets were signicantly lower than those in non-diarrheic piglets (P < 0.05). Diarrheic piglets also
contained a signicantly higher percentage of several species in the phylum Proteobacteria, including Pasteurella
aerogenes, Enterococcus cecorum, Enterococcus durans, and Escherichia coli (P < 0.05).
Major microbial dierences in dierent stages of piglet diarrhea. e experimental piglets used in
the present study were early-weaned at 21 days of age. To evaluate overall dierences in beta-diversity, we used
principal coordinate analysis (PCoA) to identify discrepancies between groups. As shown in Fig.1A, four distinct
clusters were evident (Clusters A–D). e fecal microbiota of the ND group was distinct from that of group D, and
the fecal microbiota of diarrheic piglets was distinct from the feeding phases. Specically, the gut microbiota of 14
piglets (ranging in age from 7–12 days old) was gathered in cluster A, and these piglets were still in their lactation
Information
Group
P valueDiarrheic (D) Non-diarrheic (ND)
Gender 29 (Female), 23 (Male) 24 (Female), 9 (Male) 0.12
Sampling location 16 (Sichuan), 36 (Chongqing) 15 (Sichuan), 18 (Chongqing) 0.22
Number of samples 52 33
Average age 15 days-old 23 days-old
Clean reads 76,206.58 ± 14,461.35 79,481.82 ± 12,609.14
OTU 879.88 ± 343.35 914.82 ± 368.90
Table 1. Overall microbiological and gene sequencing information regarding stool samples in this study.
Group Firmicutes Proteobacteria Bacteroidetes Fusobacteria
D group 42.06 ± 18.37 32.78 ± 28.21 16.75 ± 17.75 6.31 ± 8.24
ND group 43.09 ± 10.42 11.20 ± 9.69 31.53 ± 8.39 6.64 ± 5.28
P value 0.74 0.00 0.00 0.82
Cluster1
Cluster A 40.24 ± 20.03 56.68 ± 19.54 1.24 ± 0.81 1.38 ± 3.46
Cluster B 52.37 ± 12.93 5.49 ± 2.21 32.44 ± 11.72 7.74 ± 4.85
Cluster C 43.08 ± 11.19 12.27 ± 10.91 30.92 ± 8.50 5.84 ± 5.10
Cluster D 43.12 ± 8.19 7.86 ± 2.08 33.44 ± 8.30 9.12 ± 5.43
Table 2. Percentage of the top four phyla in the gut microbiota of piglets in the diarrheic group (D group) and
the non-diarrheic group (ND group). 1e experimental piglets used in the present study were early-weaned at
21 days of age. To evaluate overall dierences in beta-diversity, we used principal coordinate analysis (PCoA) to
identify discrepancies between groups. As shown in Fig.1A, four distinct clusters were evident (Clusters A–D),
and the relative abundance of the top four phyla in piglet microbiota were calculated.
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period. Cluster B contained the gut microbiota of 23 piglets (ranging in age from 20–21 days old) that were in the
early weaning period. Cluster A was clearly dierentiated from cluster B (Fig.1A). Moreover, 52.17% of samples
in cluster C were from piglets in the post-weaning period (average age = 33 days), and the gut microbiota of the
D and ND piglets were indistinguishable, suggesting that the beta-diversity of their gut microbiota tended to be
more similar across groups with age.
We used LEfSe analysis to identify biomarkers of fecal microbiota of diarrheic piglets (Fig.1B) and found
that the family Enterobacteriaceae was a biomarker of diarrheic piglets in cluster A (7–12 days old), whereas
the Bacteroidales family S24–7 was found to be a biomarker of diarrheic pigs in cluster B (20–21 days old). e
Wilcoxon-rank-sum test was used to identify bacterial genera with signicant dierences in relative abun-
dance in the fecal microbiota diarrheic piglets between clusters A and B. As shown in Fig.1C,D, the genus
Escherichia-Shigella in the family Enterobacteriacae was most abundant in cluster A, whereas the uncultured
genus in the Bacteroidales family S24–7 was the biomarker for cluster B.
Core bacterial genera by co-occurrence network analysis. To identify the potential interactions that
occur in response to diarrhea, co-correlative network analysis of the top 20 taxa was conducted for diarrheic and
non-diarrheic piglets based on Spearman’s correlation coecient (Fig.2). Interestingly, we found that the genus
Escherichia-Shigella was the core node in diarrheic samples, and that it tended to be positively correlated with
aerobes and facultative anaerobes, such as the genera Actinobacillus, Pasteurella, Enterococcus, and Lactobacillus;
however, it was negatively correlated with anaerobes, including the genera Fusobacterium, Eubacterium copros-
tanoligenes group, Prevotella 2, Prevotella 9, Lachnospira, Rumniococcaceae NK4A214 group, Rikenellaceae RC9
gut group, and Alloprevotella (Fig.2A). e genus Prevotellaceae UCG-003 was the core node in non-diarrheic
piglets, and only positive correlations were found between Prevotellaceae UCG-003 and anaerobes and facultative
anaerobes, including the genera Pasteurella, Prevotella, Phascolarctobacterium, Ruminococcaceae UCG-002, and
Rikenellaceae RC9 gut group (Fig.2C). Among these marker genera, diarrheic samples comprised a signicantly
higher percentage of Escherichia-Shigella (22.92% vs.5.73%, P < 0.05), whereas non-diarrheic piglets contained a
higher percentage of Prevotella (4.50% vs. 1.44%, P < 0.05) (Fig.2B,D). e dierent core genera and the transi-
tion from negative correlations in diarrheic samples to positive correlations in non-diarrheic samples appeared to
indicate that there was a correlation between bacterial competition for oxygen and the intestinal health of piglets.
We also found that members of the phylum Proteobacteria were reduced from four genera (Escherichia-Shigella,
Actinobacillus, Pasteurella, and Sutterella) in the diarrheic group to only one genus (Pasteurella) in the
non-diarrheic group, suggesting that an increase in the abundance and diversity of the phylum Proteobacteria
played a pivotal role in piglet diarrhea.
Figure 1. Comparison of fecal microbiota between diarrheic and non-diarrheic piglets. (A) Principal
coordinate analysis (PCoA) shows the fecal microbiota of diarrheic (D) and non-diarrheic (ND) piglets. Red
triangles, ND; green dots, D. (B) Identication of bacterial biomarkers in the fecal microbiota of diarrheic
piglets in cluster A and cluster B using LEfSe analysis, and LDA scores >4.0. Comparison of the top four
bacterial phyla (C) and the top een bacteria genera (D) in the fecal microbiota of diarrheic piglets indierent
stages of development based on the Wilcoxon-rank-sum test are shown in the box plot (Cluster A: 7–12 day-
old piglets; Cluster B: 20–21-day-old piglets). Samples in cluster A are in red, samples in cluster B are in green;
*P < 0.05, **P < 0.01, ***P < 0.001.
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KEGG pathway analysis. To determine if enrichment of gut microbiota was associated with enrichment
of specic metabolic activity associated with piglet diarrhea, the functional contributions of the gut microbiota
were assessed using the PICRUSt tool. We found that KEGG pathways involved in membrane transport, carbo-
hydrate metabolism, amino acid metabolism, and DNA replication and repair were predominant in both groups
(Fig.3A). Overall, 38 pathways related to membrane transport at level 2 were obtained, and membrane transport,
carbohydrate metabolism, amino acid metabolism, and energy metabolism were major KEGG pathways in the
fecal microbiota of piglets in this study (Fig.3B). Interestingly, we also found that membrane transport was the
most abundant pathway in the fecal microbiota of diarrheic piglets during lactation (cluster A) and weaning
(cluster B) based on analysis of the functional contributions of the gut microbiota in cluster A and cluster B. We
used LEfSe analysis to identify biomarkers of the KEGG pathways that diered signicantly between diarrheic
and non-diarrheic microbiota, as well as the number of signicantly discriminative features with an LDA score
>4.0. Similarly, no dierentially abundant features of the KEGG pathways were found in the fecal microbiota of
diarrheic piglets between cluster A and B (LDA score > 4.0). ese ndings clearly indicated that the occurrence
of diarrhea in this study did not aect ecosystem processes of the fecal microbiota.
Moreover, we found that multiple KEGG (level 3) categories were disturbed when piglets had diarrhea. e
gut microbiota of diarrheal piglets were characterized by a reduced representation of proteins involved in metab-
olism of pyrimidine and purine, transporters of the ATP-binding cassette, secretion systems as well as DNA repair
and recombination (Table4).
Discussion
Our study investigated variations in the composition and function of fecal microbiota between diarrheic piglets
and non-diarrheic piglets. Consistent with the results of previous studies, Firmicutes was the dominant phylum
in the piglet gut microbiota18–20, and there were no signicant dierences in relative abundance between groups
(P > 0.05). Proteobacteria constituted the second most common phylum in the gut microbiota of diarrheic piglets,
whereas Bacteroidetes was the second most abundant phylum in the fecal microbiota of non-diarrheic piglets
(Fig.1A and Table2). When compared with non-diarrheic piglets, the abundance of the phylum Proteobacteria
was signicantly higher in samples from diarrheic piglets, while that of the phylum Bacteroidetes decreased sig-
nicantly. Analysis of variations in bacterial genera between groups indicated that the genera Prevotella and
Ruminococcus, which are known to be ubiquitous in the fecal microbiota of piglets17, were signicantly lower
in diarrheic samples (Fig.1B and Table3). Moreover, opportunistic bacteria in the phylum Proteobacteria21,
including Escherichia coli22, Pasteurella aerogenes23, Enterococcus cecorum24,25, and Enterococcus durans24–26, were
signicantly higher in fecal samples from diarrheic piglets.
Taxonomic name1
Average%
P value
Tendency in diarrheic piglets
compared with non-diarrheic
samplesD piglets ND piglets
Family
Clostridiales vadinBB60 group 0.514% 2.220% 0.003 ↓
Erysipelotrichaceae 0.780% 1.802% 0.018 ↓
Genus
Allisonella 0.994% 1.465% 0.033 ↓
Lactobacillus 1.674% 0.393% 0.013 ↑
Bacteroides 0.841% 1.705% 0.000 ↓
Ruminococcaceae NK4A214 group 0.776% 1.807% 0.009 ↓
Ruminococcaceae UCG-002 0.532% 2.193% 0.000 ↓
Ruminiclostridium 9 0.823% 1.726% 0.000 ↓
Anaerotruncus 0.637% 2.026% 0.000 ↓
Eubacterium coprostanoligenes group 0.659% 1.993% 0.007 ↓
Family XIII AD3011 group 0.750% 1.849% 0.000 ↓
Prevotella2 0.789% 1.787% 0.005 ↓
Prevotella9 0.849% 1.692% 0.015 ↓
Species
Lactobacillus salivarius 1.478% 0.708% 0.002 ↑
Lactobacillus vaginalis 1.702% 0.349% 0.001 ↑
Lactobacillus gasseri 0.445% 2.330% 0.020 ↓
Lactobacillus amylovorus 1.588% 0.529% 0.003 ↑
Pasteurella aerogenes 1.667% 0.404% 0.013 ↑
Enterococcus cecorum 1.685% 0.381% 0.010 ↑
Enterococcus durans 1.699% 0.353% 0.019 ↑
Escherichia coli 1.670% 0.399% 0.000 ↑
Table 3. Comparison of the relative abundance of OTUs in the gut microbiota of D and ND piglets. 1OTUs
for which the overall number in each sample was greater than 1000 and the number in half of the samples was
greater than 100 were used to compare dierences in abundances between D and ND piglets.
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In the present study, we ignored the cause of piglet diarrhea, and instead focused on dierences in the compo-
sition of fecal microbiota between groups. Surprisingly, beta-diversity analysis revealed that the fecal microbiota
of diarrheic piglets was also dierentiated by growth phases. Since piglets used in this study were early-weaned at
21 days of age, those aged less than 2 weeks were still in lactation. When combined with LEfSe analysis, the family
Enterobacteriaceae was identied as a biomarker in diarrhetic piglets during lactation (from 7–12 days old in this
study). An increase in Proteobacteria was previously reported as a marker for intestinal microbial community
dysbiosis and a potential diagnostic criterion for disease21. A wide variety of opportunistic pathogens that belong
to Proteobacteria are facultative anaerobes, and changes in the abundance of Proteobacteria might inuence oxy-
gen homeostasis or concentration in the gut27. Enrichment of Proteobacteria, such as Enterobacteriaceae, has also
been observed in response to imbalances in the intestinal community and changes in animal health28,29.
e abundance of Escherichia-Shigella has been reported to decrease sharply as piglets mature from the suck-
ling period to the weaning period19. Several species of Escherichia have been reported to be important to piglet
diarrhea and to have a severe impact on animal intestinal barrier function30,31. Interestingly, in this study, a signi-
cant increase in Escherichia-Shigella that belong to the family Enterobacteriaceae was shown in microbial commu-
nity of diarrheic piglets (Fig.1D), which was assigned as the core node in diarrheic piglets (Fig.2). Prevotellacecea
UCG-003 was identied as a key node in non-diarrheic piglets upon co-correlation network analysis, and dier-
ences in the core genus and the transition from negative correlations in diarrheic samples to positive correlations
in non-diarrheic samples indicate that there is a correlation between bacterial competition for oxygen and the
intestinal health of piglets.
In this study, the average abundance of the Bacteroidales family S24–7 and Escherichia-Shigella in diarrheic
piglets (D group) was 4.94% and 24.50%, whereas their average abundance in non-diarrheic piglets (ND group)
was 7.41% and 5.99%, respectively. is change in fecal microbiota reected the dierent causes of swine diarrhea
Figure 2. Co-correlation network analysis of bacterial genera constructed in diarrheic and non-diarrheic
piglets. Co-correlation networks were deduced from the top 20 genera identied upon16S rRNA sequencing.
Each node represents a genus, the size of each node is proportional to the relative abundance and the color of
the nodes indicates their taxonomic assignment. e width of the lines indicates the correlation magnitude,
while red represents a positive correlation and green a negative correlation. Only lines corresponding to
correlations with a magnitude greater than 0.5 are shown. Co-correlation network of (A) Escherichia-Shigella
genus in the diarrheic group (clustering = 0.82, closeness centrality = 0.86) and (C) Prevotellaceae UCG-003
genus in the non-diarrheic group (clustering = 0.30, closeness centrality = 0.34). Comparison of the relative
abundance of (B) the genus Escherichia-Shigella and (D) the genus Prevotellaceae UCG-003 between diarrheic
and non-diarrheic samples, which were visualized based on the means ± SEM. An independent t test was used
to identify dierences between groups. *P < 0.05; **P < 0.01; ***P < 0.001.
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in dierent stages aer birth. One important reason for piglet diarrhea in lactation in this study was the expan-
sion of swine enteric pathogens (e.g., Escherichia-Shigella). However, when grown, the average abundance of
Escherichia-Shigella in the gut microbiota of diarrheic piglets during weaning was only 1.80% (cluster B, shown
in Fig.1D), suggesting that these enteric pathogens were weakly correlated with diarrhea in weaning pigs in this
study. Abrupt changes in the diet and environment of piglets have been reported as the leading causes of weaning
diarrhea32,33. Interestingly, there was an enormous increase in members of the ber-degrader Bacteroidales family
S24–734,35 when piglets grew up (less than 1.00% in cluster A versus 20.04% in cluster B). However, very little work
regarding Bacteroidales family S24–7 has been conducted to date. In short, it is necessary to conduct ongoing
Figure 3. Comparison of variations in abundance of known KEGG pathways. e functional contributions
of the gut microbiota were assessed using the PICRUSt tool. (A,C) Pathways at level 1 were obtained; (B,D)
Pathways at level 2 were obtained. Diarrheic group (D group); non-diarrheic group (ND group); Cluster A: 7–12
day-old piglets; Cluster B: 20–21-day-old piglets.
Category Level 3 Diarrheic piglets
(D group) Non-diarrheic
piglets (ND group)
Transporters 736755 1199406
General function prediction only 391357 688770
ABC transporters 377209 595634
DNA repair and recombination
proteins 309864 562249
Purine metabolism 255530 443130
Ribosome 236991 481353
Two-component system 202776 279138
Secretion system 201378 242878
Transcription factors 199787 293331
Pyrimidine metabolism 194493 373833
Table 4. e 10 most abundant KEGG pathways (at level 3) in the fecal microbiota of non-diarrheic and
diarrheic piglets based on PICRUSt prediction.
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research regarding its biological function and usage. Nevertheless, the above results suggest that the focus of early
weaning syndrome in piglets should be shied from intestinal pathogens to moderate changes in diet and better
feeding and management.
In the present study, we also found a dysbiosis of intestinal microbiota in diarrheic samples, especially the
higher percentage of several Lactobacillus strains, which was consistent with the results of a previous study36.
e increased abundance of the GABA-producing Lactococcus lactis led to increased expression of IL-17 dur-
ing piglet ETEC infection37. In the present study, several Lactobacillus strains, including Lactobacillus salivar-
ius, Lactobacillus vaginalis, and Lactobacillus amylovorus, were higher in the diarrheic microbiota (Table3).
Lactobacillus salivarius is known for its ability to produce lactic acid. In addition to lactic acids, Lactobacillus sal-
ivarius also produced γ-aminobutyric acid38. Similar to Lactococcus lactis, we believe that this GABA-producing
strain may have increased GABA signaling to actively aect host health and disease states. Future studies should
be conducted to investigate this and explore the mechanisms responsible for the increased abundance of specic
Lactobacillus strain(s) during piglet diarrhea.
e ABC transporters are primary transporters that couple the energy stored in adenosine triphosphate (ATP)
to the movement of molecules across the membrane, which link with multi-drug resistance in both bacteria
and eukaryotes39. A general overview of the DNA damage response pathway in humans indicated that decient
DNA repair could aect genome stability, which could induce tumorigenesis40. In this study, PICRUSt prediction
revealed that the relative abundance of ABC transporters, DNA repair, and recombination proteins were down-
regulated in the fecal microbiota of diarrheic piglets, implying multi-drug resistance and DNA in swine cells
was damaged when diarrhea occurred. However, no dierentially abundant KEGG pathways were found in the
fecal microbiota of diarrheic and non-diarrheic piglets with a LDA score >4.0 (Fig.3). A reliable reason for why
changes in microbial composition did not aect their functional contributions is that the taxa in the microbial
community of diarrheic piglets were functionally redundant41 with the taxa in the community of non-diarrheic
piglets.
Conclusion
We revealed the main variations in the composition of fecal microbiota of diarrheic piglets and non-diarrheic
piglets. Proteobacteria was the second most abundant phylum in intestinal microbiota of diarrheic piglets. We
found that the fecal microbiota of diarrheic piglets was dierentiated by animal growth phases, and the family
Enterobacteriaceae was a biomarker in piglets during lactation, but the Bacteroidales family S24–7 group was a
biomarker in later stages of growth. In addition, Escherichia-Shigella was the core in diarrheic gut microbiota,
whereas Provteollaceace UCG-003 was the core in the fecal microbiota of non-diarrheic piglets.
Materials and Methods
Ethics statement. All animal experiments were conducted pursuant to the Regulations for the
Administration of Aairs Concerning Experimental Animals (Ministry of Science and Technology, Beijing,
China, revised June 2014). All guidelines related to the care of laboratory animals were followed. e institutional
ethics committee of the Chongqing Academy of Animal Sciences (Chongqing, China) reviewed the relevant eth-
ical issues and approved this study (permit number xky-20150113). Only fresh stool samples collected by rectal
swabs were analyzed, and no animals were killed or injured in this study. e preparation of total genomic DNA
was conducted at the Experimental Swine Engineering Center of the Chongqing Academy of Animal Sciences
(CMA No. 162221340234; Rongchang, Chongqing, China).
Sample collection. In the present study, piglets were early-weaned at 21 days of age. We collected a total
of 85 piglet fecal samples during January of 2016. Specically, 31 samples were collected from Shuangjia Farm
(Longchang County, Sichuan Province, China), 41 were obtained from Taoranju Farm (Rongchang District,
Chongqing, China), and 13 were obtained from Pengkang Farm (Yongchuan District, Chongqing, China).
Overall, 52 piglets had diarrhea (diarrhea group or D group), which was characterized by liquid, yellow-green
or taupe feces with a foul smell or stench that stuck around the anus. Thirty-three piglets had no diarrhea
(non-diarrhea group or ND group), as indicated by solid feces with no blood or mucus and no waste attached
around the anal area (non-diarrhea group or ND group).
About 0.5 g of freshly passed stool from the swab samples was transferred into a sterile Eppendorf tube
(Axygen Inc., Union City, CA, USA), aer which 10% glycerol (vol/vol) in sterile pre-reduced saline was added
to each tube. e samples were then homogenized and then immediately frozen at−80 °C until needed for 16S
ribosomal RNA gene sequencing.
Sequencing and Analysis. 16S rRNA gene sequencing. Total genomic DNA was extracted from samples
using the CTAB/SDS method, aer which the 16S rRNA gene of the distinct 16S V4 region was amplied using
specic primers (515F–806 R) with a barcode. e microbial diversity and composition were then determined by
16S rRNA gene sequencing and analysis as previously described6.
LDA eect size (LEfSe). To identify the genomic features of taxa diering in abundance between two or more
biological conditions or classes, the LEfSe (Linear Discriminant Analysis Eect Size) algorithm was used with
the online interface Galaxy (http://huttenhower.sph.harvard.edu/lefse/)42. A size-eect threshold of 4.0 on the
logarithmic LDA score was used for discriminative functional biomarkers.
Co-correlation statistics. According to the calculation method developed by Hartmann et al.43, co-correlation
networks were generated using the python package NetworkX (https://github.com/networkx/networkx) and the
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OTUs as target nodes, with edges (e.g., connecting nodes) representing signicant negative (green) or positive
(red) Spearman’s correlations. We retained OTUs when they had a Spearman’s correlation coecient >0.5.
Predicted functionality of the differently grouped samples. Phylogenetic Investigation of Communities by
Reconstruction of Unobserved States (PICRUSt) (http://galaxy.morganlangille.com/)44 was used to predict the
functional gene content in the fecal microbiota based on taxonomy from the Greengenes reference database
(http://greengenes.lbl.gov/cgi-bin/nph-index.cgi). First, a collection of closed-reference OTUs was obtained from
the ltered reads using QIIME (v 1.7.0, http://qiime.org/scripts/split_libraries_fastq.html)45, and by querying the
data against a reference collection (Greengenes), aer which OTUs were assigned at 97% identity. e resulting
OTUs were then employed for microbial community metagenome prediction with PICRUSt using the online
Galaxy interface (http://huttenhower.sph.harvard.edu/galaxy/). Supervised analysis was conducted using LEfSe
to elicit the microbial functional pathways that were dierentially expressed among samples. PICRUSt was used
to derive relative Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway abundance.
Statistical analysis. Data proportions of sites and gender were regarded as categorical variables and com-
pared by the Chi-square test. Pairwise comparisons between groups were assessed by analysis of similarity
(ANOSIM). Values represent the pairwise test statistic (R) for ANOSIM. e permutation-based level of sig-
nicance was adjusted for multiple comparisons using the Benjamini-Hochberg false discovery rate (FDR) pro-
cedure. A P < 0.05 indicates the dierence between groups is greater than the dierence within the group. e
Wilcoxon-rank-sum test was used to detect the dierent populated bacterial genera between groups. e relative
abundances of bacterial taxa are presented as the means ± SD, and dierences between groups were identied by
the independent-sample t test (for normally distributed data) or the Mann-Whitney U-test (for non-normally
distributed data). A p-value <0.05 was considered statistically signicant, and a p-value <0.01 indicated extreme
signicance. e raw sequences obtained in the present study have been submitted to the NCBI Sequence Read
Archive (accession number SRP134239).
Received: 19 June 2019; Accepted: 26 November 2019;
Published: xx xx xxxx
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Acknowledgements
is work was funded by the National Key R&D Program of China (2017yfd0500501), Performance Incentive Guidance
for Scientic Research Institution of Chongqing Science & Technology Commission (cstc2019jxjl0035),Chongqing
Postdoctoral Research Special Funding Project (Xm2016031), and Chongqing Basic Scientic Research (grant no.
17408).
Author contributions
J.S., L.P.G. and Z.H.L. designed the experiments. J.S., L.D. and H.Z. analyzed the data and draed the manuscript.
X.L.L. and Y.C.D. collected the samples.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/s41598-019-55328-y.
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