Skip to main content
Scientific Reports logoLink to Scientific Reports
. 2016 Dec 5;6:37224. doi: 10.1038/srep37224

Colonic luminal microbiota and bacterial metabolite composition in pregnant Huanjiang mini-pigs: effects of food composition at different times of pregnancy

Xiang-feng Kong 1,2,a,*, Yu-jiao Ji 1,*, Hua-wei Li 1, Qian Zhu 1, F Blachier 3, Mei-mei Geng 1, Wen Chen 1, Yu-long Yin 1,2,b
PMCID: PMC5137017  PMID: 27917879

Abstract

The gut harbours diverse and complex microbiota, which influence body health including nutrient metabolism, immune development, and protection from pathogens. Pregnancy is associated with immune and metabolic changes that might be related to microbiota compositional dynamics. We therefore investigated the colonic luminal bacteria community in Huanjiang mini-pigs fed diets with different nutrient levels from the first to third trimester of pregnancy. The concentrations of intestinal metabolites including short-chain fat acids, NH3-N, indole, skatole, and bioamines were also determined. We found that the colonic bacteria species richness estimators (Chao1 and ACE) decreased with increased gestational age. The dominant phyla identified were Firmicutes and Bacteroidetes; the dominant genera were Lactobacillus, Treponema, Ruminococcus, Clostridium, and Prevotella. In addition, microbiota displayed spatial and temporal heterogeneity in composition, diversity, and species abundance in different colonic segments from the first to third trimester of pregnancy. Furthermore, the bacterial metabolites also changed according to the diet used and the pregnancy stage. These findings suggest that colonic bacteria richness decreased as gestational age increased, and that the higher nutrient level diet increased the production of metabolites related to nitrogen metabolism. However, although the higher nutrient diet was associated with pregnancy syndrome, causal links remain to be determined.


Dietary nutrients during gestation play important roles in the reproductive performance of sows and within-litter variation of their offspring1. It was initially thought that sows in early gestation should receive large nutrient amounts2. However, high nutrient levels during gestation can increase sow fat mass, resulting in farrowing problems, poor dietary intake during lactation, and less reproductive performance in the next cycle3. Notably, sows fed diets with high energy content during early gestation suffer embryo deaths4. Conversely, after mating, nutrition restriction supports progesterone rise and embryonic survival in gilts. Thus, excess or inadequate dietary energy and protein ingestion by pregnant pigs can contribute to poor reproductive performance and impaired physiological functions.

Uterine growth during gestation causes profound gastrointestinal tract modification, giving rise to altered gastric motility and intestinal transit time along with gastrointestinal disturbances5. Recently, Koren et al. demonstrated that in humans, the gut microbiota composition during first trimester pregnancy is very similar to that of a healthy non-pregnant control group but becomes enriched in Proteobacteria and Actinobacteria during the third trimester6. Notably, these latter changes are similar to those detected in inflammatory bowel disease and obesity. In the gut, the bacteria load also increases over the course of gestation7. However, Jost et al. reported little or no modifications of the faecal microbiota composition during early and late pregnancy8. In humans, disorders associated with gut microbiota dysbiosis include obesity9, type 2 diabetes10, and malnutrition11. The mammalian gut, especially the hindgut, is colonised by a large number of microbes with varying microbial composition in the different parts of the gut12. The colon is colonised by up to almost 1012 bacteria per gram digesta13. The microbiota in the gut lumen plays an important role in the digestion and absorption of nutrients, prevention of pathogen colonisation, and maintenance and regulation of normal mucosal immunity14. Recently, the roles of gut microbiome activity and their metabolites in regulating host physiological functions in association with longevity15 and the metabolic changes during pregnancy6 have attracted considerable interest. Intestinal microbiota composition is affected by multiple factors including environmental parameters, dietary composition in terms of quality and quantity, and antibiotics use16. Many dietary components including protein and amino acid-derived bacterial metabolites such as short-chain fatty acids, NH3-N, indole, skatole, and bioamines affect the relationship between gut microbiota and the intestinal mucosa, and this relationship might evolve according to life stage17.

In the present study, we utilised the working hypothesis that sows fed a diet with high nutrient level during the entire gestation period might experience microflora composition and metabolic activity changes in the colon, with possible dysfunctions such as signs of metabolic syndrome. Accordingly, we aimed to document the changing diversity and structure of the colonic lumimal bacteria community from the first to third trimester of gestation in Huanjiang mini-pigs fed relatively higher or lower nutrient level diets formulated to meet the nutrient requirements recommended by the National Research Council (NRC diet) or the Chinese National Feeding Standard for Swine (CNF diet), respectively. Additionally, colonic bacterial metabolites including short-chain fatty acids (SCFAs), NH3-N, indole, skatole, and bioamines were determined.

Results

Diversity of colon bacterial communities

The colon contents were sampled from 32 sows fed the NRC or CNF diets belonging to the three pregnancy stage groups. Paired-end reads generated from the original DNA fragments using Illumina MiSeq next generation sequencing were merged using FLASH, producing 1,328,640 and 1,323,342 row tags from the 32 proximal or distal colon content samples, respectively. After filtering for quality and removing apparent chimeras, 1,291,145 and 1,282,402 effective tags were acquired for analysis, averaging 40,348 and 40,075 sequences in every proximal and distal colon content sample, respectively. The average read length was 253 bp. Operational taxonomic units (OTUs) were defined as a bin of sequence reads sharing ≥97% nucleotide sequence identity. From the effective sequences, we detected 35,540 and 37,358 OTUs averaging 1,110.63 ± 123.57 and 1,167.44 ± 93.52 OTUs in every proximal and distal colon content sample, respectively.

The individual samples were normalised and the OTU table within each sample was rarefied to 31,465 sequence reads based upon the sample rarefaction curves. The normalised sequence reads were then used to calculate the species diversity (Shannon and Simpson indices), richness estimators (Chao1 and ACE), and the coverage (Good’s coverage estimator; Table 1). In the proximal colon content samples, the OTUs decreased significantly according to pregnancy stage and were affected by diet × stage conditions (P < 0.05). Chao1, ACE, and Coverage were decreased (P < 0.05) with later pregnancy stages, but the Simpson and Shannon indices were not affected by diet or stage. In the distal colon content samples, the OTUs displayed a decreasing trend (0.05 ≤ P < 0.10) and Chao1 and ACE were significantly decreased (P < 0.05) with pregnancy stage. Bacterial diversity showed no difference between colonic content samples (P > 0.05).

Table 1. Alpha diversity indices of colon bacterial communities in Huanjiang mini-pigs at different stages of pregnancy.

Items NRC diet
CNF diet
SEM P values
45 d 75 d 110 d 45 d 75 d 110 d Diet Stage D*S
Proximal colon
 Row tags 41039.2 41118.7 42682.7 41276.6 42296.3 40799.0 268.06 0.787 0.602 0.140
 Effective Tags 39920.6 39569.7 41271.3 40541.8 41110.1 39401.7 281.97 0.872 0.983 0.110
 Number of OTUs 1209.8 1002.0 1024.3 1145.9 1161.3 1036.7 21.84 0.377 0.017 0.040
 Chao1 1342.5 1118.6 1146.7 1305.2 1304.0 1142.2 24.66 0.285 0.009 0.055
 ACE 1356.7 1326.6 1165.1 1317.2 1326.6 1165.1 13.59 0.257 0.008 0.037
 Simpson 0.98 0.95 0.94 0.92 0.93 0.92 0.01 0.088 0.642 0.556
 Shannon 7.42 6.56 6.52 6.81 6.75 6.70 0.13 0.768 0.223 0.342
 Coverage 0.993 0.994 0.993 0.993 0.993 0.994 <0.01 0.132 0.010 0.073
Distal colon
 Row tags 42545.2 41068.3 41185.0 41299.1 41226.0 40558.7 289.32 0.368 0.352 0.567
 Effective Tags 41552.6 39506.7 39747.7 40266.6 39892.1 38992.7 296.38 0.384 0.103 0.442
 Number of OTUs 1224.6 1101.7 1101.7 1183.4 1221.3 1101.3 16.53 0.419 0.062 0.063
 Chao1 1372.5 1235.8 1248.0 1332.3 1360.7 1217.4 17.95 0.601 0.038 0.062
 ACE 1392.0 1252.8 1255.3 1348.9 1388.1 1237.4 18.19 0.469 0.031 0.044
 Simpson 0.98 0.98 0.96 0.97 0.97 0.97 <0.01 0.634 0.490 0.632
 Shannon 7.52 7.23 6.86 7.29 7.37 7.34 0.10 0.553 0.593 0.470
 Coverage 0.992 0.993 0.993 0.993 0.992 0.994 <0.01 0.922 0.066 0.076

Changes in the bacterial phylum, genera, and OTUs of colonic content microbiota

Principal coordinate analyses (PCoA) were conducted to determine the phylogenetic relationship between the tested microbial community samples (Supplementary Fig. S1). Using the detected OTUs, we determined whether the data separated into distinct sample clusters. The CNF and NRC diet groups could almost be separated using the proximal colon contents according to pregnancy stage. Pregnancy day 45, 75, and 110 proximal colon content samples were clearly separated in the NRC but not in the CNF diet group. No separation was detected in clusters using the distal colon contents (Supplementary Fig. S1).

At the phylum level, the abundance of 10 phyla in any sample was ≥0.5%; i.e., Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Spirochaetes, Tenericutes, Verrucomicrobia, Fibrobacteres, Cyanobacteria, and Deferribacteres. In the colonic samples, Firmicutes and Bacteroidetes were the two dominant phyla. Firmicutes were decreased in the proximal colon content samples but Bacteroidetes increased from pregnancy days 45 to 110 of sows fed the NRC diet. In comparison, the relative abundance of Firmicutes was higher, but Bacteroidetes was lower in NRC diet-fed than in CNF diet-fed sows (Fig. 1a). In distal colon content samples, the Firmicutes abundance increased whereas Bacteroidetes from pregnancy days 45 to 110 with the NRC diet decreased. The Firmicutes abundance was almost stable from pregnancy days 45 to 110 (Fig. 1b). Overall, the Firmicutes abundance was greater, but Bacteroidetes abundance was lower in the distal compared to the proximal colon content samples.

Figure 1. Taxonomic composition of the colon bacterial communities on the phylum level in Huanjiang mini-pigs at different stages of pregnancy (%).

Figure 1

(a) Luminal content samples of the proximal and (b) distal colon. The taxonomic composition of the colon microbiota among the samples was compared based on the relative abundance (reads of a taxon/total reads in a sample). NRC45, NRC75, and NRC110: data from samples obtained from Huanjiang mini-pigs fed with the NRC diet for 45, 75, and 110 days, respectively. CNF45, CNF75, and CNF110: data from samples obtained from Huanjiang mini-pigs fed with the CNF diet for 45, 75, and 110 days, respectively.

The relative abundance of genera, among the 37 observed, was ≥0.5% in every sample (Fig. 2). In the proximal colon contents, within the Bacteroidetes phylum, the genus Paludibacter and Parabacteroides abundances were higher (P < 0.05) in the CNF vs. the NRC diet group. Within the Firmicutes phylum, the genera Anaerovibrio, Lactobacillus, Mitsuokella, and Roseburia abundances were lower (P < 0.05) in the CNF diet group. The unclassified genera changed significantly with diet (P < 0.05), with higher abundance in the CNF diet group. With increasing pregnancy time, Corynebacterium, Allobaculum, Mitsuokella, Roseburia, Solibacillus, Staphylococcus, Acinetobacter, and the unclassified genus changed significantly (P < 0.05). Within the Actinobacteria and Proteobacteria phyla, genera Corynebacterium and Acinetobacter abundance, respectively, decreased linearly with pregnancy stage (P < 0.05) as did genus Allobaculum abundance within the Firmicutes phylum. Unclassified genus and Firmicutes phylum Mitsuokella abundance peaked on pregnancy day 45 and exhibited its lowest level on pregnancy day 75 (P < 0.05), when Firmicutes Roseburia and Solibacillus abundances were highest (P < 0.05) but Staphylococcus was lowest (P < 0.05). Diet type interacted with the pregnancy stage for the genera of Bifidobacterium, Allobaculum, Megasphaera, Mitsuokella, Roseburia, Solibacillus, and Staphylococcus.

Figure 2. Genus-level taxonomic composition of the bacterial communities of colonic contents in Huanjiang mini-pigs at different pregnant stages (%).

Figure 2

NRC45, NRC75, and NRC110: data from samples obtained from Huanjiang mini-pigs fed with the NRC diet for 45, 75, and 110 days, respectively. CNF45, CNF75, and CNF110: data from samples obtained from Huanjiang mini-pigs fed with the CNF diet for 45, 75, and 110 days, respectively.

In the distal colon contents, within the Actinobacteria phylum, the genus Bifidobacterium abundance was higher (P < 0.05) in CNF vs. NRC diet groups. Within the Firmicutes phylum, the genera Anaerovibrio, Dialister, Roseburia, and Veillonella abundances were lower (P < 0.05) in the CNF diet groups. The effects of pregnancy stage on bacterial composition were as follows: within the Firmicutes phylum, the genera Allobaculum, Dialister, Lactobacillus, Megasphaera, Mitsuokella, Staphylococcus, and [Ruminococcus] abundances changed significantly with pregnancy stage, being lowest (P < 0.05) on pregnancy day 75 for Allobaculum, Megasphaera, and Mitsuokella and on pregnancy day 45 for Dialister (P < 0.05). Lactobacillus and [Ruminococcus] abundances increased whereas Staphylococcus abundance decreased linearly with pregnancy stage (P < 0.05). The unclassified genus abundance was lowest on pregnancy day 75 (P < 0.05) (Fig. 2).

We identified 4,010 OTUs in the colonic contents across the two dietary groups and three pregnancy stages. Consideration was only given to OTUs detected at abundances ≥0.5% in any sample. Overall, we identified 37 OTUs that differed in abundance in the proximal colon samples (Table 2), of which 18 showed differences with diet. Specifically, the abundances of OTU-2 (genus: Lactobacillus), OTU-9 (family: Lachnospiraceae), OTU-36 (g: Treponema), OTU-41 (g: Roseburia), OTU-45 (species: Lactobacillus delbrueckii), OTU-59 (g: Treponema), and OTU-79 (g: Anaerovibrio) were higher (P < 0.05) in NRC vs. CNF diet groups. Conversely, the abundances of OTU-27 (f: Ruminococcaceae), OTU-38 (order: Clostridiales), OTU-46 (g: YRC22), OTU-68 (f: Lachnospiraceae), OTU-82 (g: Parabacteroides), OTU-98 (g: Oscillospira), OTU-105 (o: Bacteroidales), OTU-106 (g: Fibrobacter), OTU-195 (g: [Prevotella]), OTU-1149 (p: Bacteroidetes), and OTU-2187 (f: Sphingomonadaceae) were lower (P < 0.05) in the NRC diet groups. Additionally, 18 OTUs demonstrated pregnancy stage-associated differences: OTU-17 (f: Lachnospiraceae), OTU-20 (g: Oscillospira), OTU-62 (g: Prevotella), OTU-65 (g: Ruminococcus), and OTU-1366 (f: Christensenellaceae) abundances linearly decreased (P < 0.05) as pregnancy processed whereas OTU-36 (g: Treponema), OTU-41 (g: Roseburia), OTU-46 (g: YRC22), OTU-55 (g: Prevotella), OTU-1149 (p: Bacteroidetes), and OTU-3683 (f: Ruminococcaceae) linearly increased (P < 0.05). Furthermore, the abundances of OTU-25 (f: Ruminococcaceae), OTU-28 (f: Streptococcaceae), OTU-33 (g: Ruminococcus), OTU-47 (f: Lachnospiraceae), OTU-85 (f: Lachnospiraceae), and OTU-216 (f: S24-7) were the lowest whereas OTU-68 (f: Lachnospiraceae) was the highest on pregnancy day 75 (P < 0.05).

Table 2. Effects of diet and pregnancy stage on the OTU fractions of the proximal colon bacterial communities in Huanjiang mini-pigs (%).

Items NRC diet
CNF diet
SEM P values
Annotation Phylum
45 d 75 d 110 d 45 d 75 d 110 d Diet Stage D*S
OTU-2 5.31 5.71 4.36 1.50 2.30 1.02 0.58 0.004 0.662 0.981 g: Lactobacillus Firmicutes
OTU-9 3.78 4.17 1.50 1.93 1.28 1.27 0.37 0.028 0.270 0.365 f: Lachnospiraceae Firmicutes
OTU-11 1.65 0.14 0.22 0.30 0.79 0.38 0.18 0.616 0.268 0.037 g: Bifidobacterium Actinobacteria
OTU-16 0.51 0.03 0.07 0.11 0.30 1.00 0.09 0.128 0.261 0.017 g: Sutterella Proteobacteria
OTU-17 1.08 0.60 0.36 0.61 0.53 0.48 0.06 0.237 0.017 0.123 f: Lachnospiraceae Firmicutes
OTU-20 0.68 0.34 0.31 0.44 0.38 0.47 0.04 0.816 0.028 0.061 g: Oscillospira Firmicutes
OTU-25 0.88 0.22 0.36 0.72 0.60 0.73 0.07 0.129 0.019 0.100 f: Ruminococcaceae Firmicutes
OTU-27 0.43 0.35 0.33 0.52 0.46 0.72 0.04 0.013 0.426 0.291 f: Ruminococcaceae Firmicutes
OTU-28 0.16 0.02 0.13 0.12 0.01 0.49 0.04 0.229 0.043 0.194 f: Streptococcaceae Firmicutes
OTU-29 0.57 0.10 0.08 0.06 0.01 0.16 0.05 0.089 0.051 0.042 s: Megasphaera elsdenii Firmicutes
OTU-33 0.88 0.36 0.40 0.62 0.53 0.62 0.06 0.671 0.045 0.131 g: Ruminococcus Firmicutes
OTU-36 0.40 0.77 1.65 0.28 0.34 0.39 0.09 <0.01 0.001 0.003 g: Treponema Spirochaetes
OTU-38 0.39 0.25 0.22 0.43 0.47 0.60 0.05 0.043 0.877 0.430 o: Clostridiales Firmicutes
OTU-41 0.78 2.80 1.18 0.89 0.65 1.10 0.19 0.028 0.030 0.003 g: Roseburia Firmicutes
OTU-44 0.29 0.49 0.21 0.13 0.17 0.50 0.04 0.330 0.175 0.009 f: Lachnospiraceae Firmicutes
OTU-45 0.13 0.25 0.32 0.04 0.05 0.17 0.03 0.005 0.052 0.588 s: Lactobacillus delbrueckii Firmicutes
OTU-46 0.14 0.31 0.44 0.39 0.39 1.72 0.12 0.015 0.015 0.092 g: YRC22 Bacteroidetes
OTU-47 0.44 0.13 0.13 0.15 0.10 0.23 0.03 0.110 0.003 0.005 f: Lachnospiraceae Firmicutes
OTU-55 0.20 0.52 1.04 0.41 0.71 0.82 0.09 0.739 0.041 0.631 g: Prevotella Bacteroidetes
OTU-59 0.12 0.38 0.21 0.13 0.09 0.07 0.03 0.006 0.093 0.021 g: Treponema Spirochaetes
OTU-62 0.57 0.42 0.15 0.27 0.33 0.25 0.06 0.122 0.033 0.053 g: Prevotella Bacteroidetes
OTU-65 0.57 0.17 0.20 0.23 0.25 0.14 0.03 0.065 0.002 0.003 g: Ruminococcus Firmicutes
OTU-68 0.14 0.09 0.05 0.13 0.35 0.13 0.02 0.003 0.007 0.002 f: Lachnospiraceae Firmicutes
OTU-79 0.25 0.32 0.11 0.17 0.13 0.11 0.02 0.048 0.125 0.251 g: Anaerovibrio Firmicutes
OTU-82 0.21 0.31 0.48 0.44 0.49 0.52 0.04 0.046 0.173 0.604 g: Parabacteroides Bacteroidetes
OTU-85 0.29 0.05 0.07 0.08 0.09 0.13 0.02 0.430 0.024 0.007 f: Lachnospiraceae Firmicutes
OTU-98 0.09 0.04 0.06 0.15 0.17 0.28 0.02 0.010 0.597 0.529 g: Oscillospira Firmicutes
OTU-105 0.12 0.04 0.26 0.28 0.49 0.31 0.05 0.028 0.721 0.197 o: Bacteroidales Bacteroidetes
OTU-106 0.12 0.02 0.07 0.22 0.13 0.25 0.03 0.019 0.216 0.867 g: Fibrobacter Fibrobacteres
OTU-195 0.08 0.06 0.12 0.28 0.25 0.12 0.03 0.048 0.761 0.437 g:[Prevotella] Bacteroidetes
OTU-216 0.29 0.02 0.11 0.12 0.08 0.10 0.02 0.324 0.008 0.050 f: S24-7 Bacteroidetes
OTU-805 0.18 0.17 0.65 0.30 0.18 0.19 0.04 0.205 0.104 0.042 g: Treponema Spirochaetes
OTU-1149 0.06 0.22 0.30 0.25 0.34 0.42 0.03 0.027 0.033 0.842 p: Bacteroidetes Bacteroidetes
OTU-1366 0.34 0.05 0.04 0.16 0.13 0.09 0.03 0.772 0.009 0.074 f: Christensenellaceae Firmicutes
OTU-1890 0.21 0.71 0.34 0.22 0.13 0.25 0.06 0.050 0.196 0.037 g: Roseburia Firmicutes
OTU-2187 7.03 13.59 20.99 24.35 20.47 23.47 1.92 0.024 0.407 0.247 f: Sphingomonadaceae Proteobacteria
OTU-3683 0.21 0.21 0.35 0.26 0.23 0.38 0.02 0.320 0.026 0.946 f: Ruminococcaceae Firmicutes

Furthermore, we identified 45 OTUs with differing abundance in the distal colon samples (Table 3), of which 27 exhibited differences according to diet. The abundances of OTU-9 (f: Lachnospiraceae), OTU-14 (s: Clostridium butyricum), OTU-36 (g: Treponema), OTU-75 (f: Lachnospiraceae), OTU-133 (g: Oscillospira), OTU-1967 (g: Prevotella), and OTU-2531 (f: Ruminococcaceae) were higher (P < 0.05) in the NRC vs. CNF diet groups. Conversely, OTU-11 (g: Bifidobacterium), OTU-25 (f: Ruminococcaceae), OTU-28 (f: Streptococcaceae), OTU-34 (g: Prevotella), OTU-38 (o: Clostridiales), OTU-41 (g: Roseburia), OTU-49 (g: Veillonella), OTU-72 (f: Ruminococcaceae), OTU-77 (s: Ruminococcus flavefaciens), OTU-79 (g: Anaerovibrio), OTU-85 (f: Lachnospiraceae), OTU-113 (f: Ruminococcaceae), OTU-120 (class: Clostridia), OTU-164 (g: Staphylococcus), OTU-659 (g: Oscillospira), OTU-1428 (f: Lachnospiraceae), OTU-1890 (g: Roseburia), OTU-2133 (o: Clostridiales), OTU-2768 (o: Clostridiales), and OTU-3219 (f: Christensenellaceae) abundances were lower (P < 0.05) in the NRC diet groups. Of the 17 OTUs showing pregnancy stage differences, OTU-27 (f: Ruminococcaceae), OTU-48 (f: Christensenellaceae), OTU-1428 (f: Lachnospiraceae), OTU-1454 (g: Ruminococcus), OTU-2768 (o: Clostridiales), and OTU-3683 (f: Ruminococcaceae) abundances linearly increased (P < 0.05) with pregnancy progression. However, OTU-31 (s: Lactobacillus reuteri), OTU-62 (g: Prevotella), OTU-89 (g: Prevotella), OTU-1511 (s: Lactobacillus reuteri), OTU-1967 (g: Prevotella), and OTU-2590 (g: Treponema) abundances linearly decreased (P < 0.05). Additionally, OTU-35 (f: [Paraprevotellaceae]), OTU-146 (g: Staphylococcus), OTU-1366 (f: Christensenellaceae), and OTU-1386 (f: Ruminococcaceae) exhibited the highest and OTU-29 (s: Megasphaera elsdenii) the lowest abundances on pregnancy day 75 (P < 0.05).

Table 3. Effects of diet and pregnancy stage on the OTU fractions of the distal colon bacterial communities in Huanjiang mini-pigs (%).

Items NRC diet
CNF diet
SEM P values
Annotation Phylum
45 d 75 d 110 d 45 d 75 d 110 d Diet Stage D*S
OTU-9 2.59 4.11 6.40 4.18 1.50 1.53 0.48 0.039 0.638 0.026 f: Lachnospiraceae p: Firmicutes
OTU-11 0.62 1.81 0.66 0.20 3.11 3.12 0.46 0.030 0.840 0.683 g: Bifidobacterium p: Actinobacteria
OTU-14 0.21 0.37 1.10 0.37 0.27 0.23 0.07 0.046 0.076 0.016 s: Clostridium butyricum p: Firmicutes
OTU-25 0.69 1.34 0.70 0.45 1.17 1.59 0.12 0. 005 0.532 0.924 f: Ruminococcaceae p: Firmicutes
OTU-27 0.44 0.55 0.33 0.34 0.30 0.70 0.04 0.054 0.040 0.121 f: Ruminococcaceae p: Firmicutes
OTU-28 0.07 0.61 0.24 0.03 0.02 1.96 0.18 0.043 0.083 0.190 f: Streptococcaceae p: Firmicutes
OTU-29 0.19 0.09 0.21 0.06 0.03 0.30 0.03 0.785 0.024 0.481 s: Megasphaera elsdenii p: Firmicutes
OTU-31 0.30 0.63 0.29 1.74 0.48 0.73 0.11 0.298 0.001 <0.01 s: Lactobacillus reuteri p: Firmicutes
OTU-34 0.48 0.31 0.25 0.86 0.32 0.19 0.07 0.047 0.077 0.234 g: Prevotella p: Bacteroidetes
OTU-35 0.28 0.29 0.05 0.09 0.16 0.15 0.03 0.313 0.022 0.847 f: [Paraprevotellaceae] p: Bacteroidetes
OTU-36 0.68 0.27 1.28 1.48 0.28 0.34 0.15 0.005 0.368 0.402 g: Treponema p: Spirochaetes
OTU-38 0.34 1.04 0.44 0.33 0.74 1.41 0.13 0.013 0.509 0.685 o: Clostridiales p: Firmicutes
OTU-41 0.87 0.42 0.68 1.68 0.34 0.76 0.13 0.028 0.354 0.073 g: Roseburia p: Firmicutes
OTU-48 0.33 0.56 0.62 0.27 0.37 0.87 0.06 0.115 0.033 0.828 f: Christensenellaceae p: Firmicutes
OTU-49 0.00 0.00 0.95 0.20 0.05 0.08 0.09 0.049 0.075 0.144 g: Veillonella p: Firmicutes
OTU-62 1.03 0.56 0.23 0.38 0.32 0.14 0.10 0.315 0.049 0.597 g: Prevotella p: Bacteroidetes
OTU-72 0.17 0.49 0.14 0.12 0.26 0.44 0.05 0.012 0.358 0.619 f: Ruminococcaceae p: Firmicutes
OTU-74 0.36 0.11 0.24 0.13 0.17 0.31 0.03 0.413 0.147 0.019 g: Bacteroides p: Bacteroidetes
OTU-75 0.35 0.13 0.17 0.28 0.13 0.17 0.02 0.008 0.451 0.135 f: Lachnospiraceae p: Firmicutes
OTU-77 0.08 0.50 0.08 0.06 0.32 0.48 0.06 0.003 0.639 0.756 s: Ruminococcus flavefaciens p: Firmicutes
OTU-79 0.22 0.16 0.16 0.40 0.10 0.14 0.02 0.004 0.138 0.014 g: Anaerovibrio p: Firmicutes
OTU-85 0.16 0.27 0.10 0.08 0.43 0.29 0.04 0.024 0.861 0.420 f: Lachnospiraceae p: Firmicutes
OTU-89 0.61 0.23 0.10 0.07 0.15 0.08 0.06 0.332 0.020 0.138 g: Prevotella p: Bacteroidetes
OTU-111 0.28 0.07 0.09 0.03 0.24 0.15 0.03 0.732 0.758 0.011 g: Oscillospira p: Firmicutes
OTU-113 0.03 0.27 0.05 0.04 0.38 0.23 0.05 0.007 0.766 0.755 f: Ruminococcaceae p: Firmicutes
OTU-120 0.10 0.40 0.10 0.01 0.29 0.32 0.05 0.009 0.615 0.947 c: Clostridia p: Firmicutes
OTU-133 0.28 0.09 0.09 0.21 0.02 0.01 0.03 0.025 0.277 0.746 g: Oscillospira p: Firmicutes
OTU-146 0.28 0.33 0.08 0.05 0.15 0.18 0.03 0.198 0.010 0.882 g: Staphylococcus p: Firmicutes
OTU-164 0.15 0.26 0.20 0.10 0.34 0.24 0.03 0.012 0.929 0.229 c: Clostridia p: Firmicutes
OTU-216 0.34 0.04 0.07 0.03 0.09 0.24 0.03 0.724 0.141 0.011 f: S24-7 p: Bacteroidetes
OTU-659 0.27 0.08 0.07 0.39 0.14 0.05 0.04 0.034 0.068 0.454 g: Oscillospira p: Firmicutes
OTU-1350 0.42 0.37 0.33 0.52 0.38 0.68 0.03 0.437 0.446 0.033 g: Ruminococcus p: Firmicutes
OTU-1366 0.25 0.46 0.09 0.09 0.19 0.21 0.04 0.065 0.020 0.746 f: Christensenellaceae p: Firmicutes
OTU-1386 0.23 0.34 0.13 0.13 0.16 0.15 0.03 0.315 0.047 0.779 f: Ruminococcaceae p: Firmicutes
OTU-1428 0.26 0.26 0.27 0.22 0.25 0.62 0.03 0.009 0.004 0.015 f: Lachnospiraceae p: Firmicutes
OTU-1454 0.14 0.20 0.26 0.21 0.16 0.38 0.02 0.285 0.018 0.189 g: Ruminococcus p: Firmicutes
OTU-1511 0.12 0.32 0.13 0.72 0.19 0.35 0.05 0.671 0.024 <0.01 s: Lactobacillus reuteri p: Firmicutes
OTU-1890 0.19 0.09 0.15 0.27 0.07 0.11 0.02 0.009 0.703 0.262 g: Roseburia p: Firmicutes
OTU-1967 0.47 0.14 0.06 0.25 0.13 0.11 0.03 0.016 0.009 0.026 g: Prevotella p: Bacteroidetes
OTU-2133 0.17 0.25 0.18 0.16 0.20 0.36 0.02 0.039 0.328 0.541 o: Clostridiales p: Firmicutes
OTU-2531 0.53 0.15 0.35 0.24 0.13 0.06 0.05 0.011 0.304 0.428 f: Ruminococcaceae p: Firmicutes
OTU-2590 1.23 1.65 0.33 0.53 0.33 0.26 0.17 0.876 0.009 0.638 g: Treponema p: Spirochaetes
OTU-2768 0.16 0.21 0.21 0.14 0.24 0.48 0.03 0.005 0.036 0.231 o: Clostridiales p: Firmicutes
OTU-3219 0.23 0.68 0.31 0.14 0.42 1.04 0.08 0.003 0.141 0.520 f: Christensenellaceae p: Firmicutes
OTU-3683 0.21 0.21 0.41 0.30 0.22 0.46 0.03 0.829 0.015 0.636 f: Ruminococcaceae p: Firmicutes

Diet, pregnancy stage, and colonic segment correlations with gut microbiota

At the phylum level, Firmicutes (P < 0.05) and Bacteroidetes (P < 0.05) in the proximal colon contents correlated with diet (Supplementary Table S1). Firmicutes was higher in the NRC than in the CNF diet group, whereas Bacteroidetes was the converse. Actinobacteria (P < 0.05) and Elusimicrobia (P < 0.05) in the distal colon contents correlated with diet. Actinobacteria was lower in the NRC diet group, and Elusimicrobia presented the opposite. Consistent with pregnancy stage and gut microbiota correlation, Verrucomicrobia (P < 0.05) and Tenericutes (P < 0.01) decreased with pregnancy stage in the proximal colon contents whereas in the distal colon contents Firmicutes (P < 0.05) increased but Tenericutes (P < 0.05) decreased. Firmicutes (P < 0.01), Actinobacteria (P < 0.05), Verrucomicrobia (P < 0.01), and Lentisphaerae (P < 0.05) were all elevated in the distal vs. the proximal colon contents, whereas Bacteroidetes (P < 0.01) presented the inverse.

At the genus level (Supplementary Table S2), consistent with the correlation between dietary characteristics and gut microbiota composition in the proximal colon contents, Paludibacter and Parabacteroides (p: Bacteroidetes) were lower (P < 0.05) in the NRC vs. the CNF diet group, whereas Lactobacillus (P < 0.01) and Roseburia (P < 0.05) (p: Firmicutes) as well as Escherichia (p: Proteobacteria) (P < 0.05) and Anaeroplasma (p: Tenericutes) (P < 0.05) were higher in the NRC. In the distal colon contents, Bifidobacterium and Corynebacterium (p: Actinobacteria) were lower (P < 0.05) in the NRC vs. the CNF diet group; Dialister (P < 0.05) and Roseburia (P < 0.01) (p: Firmicutes) and Anaeroplasma (p: Tenericutes) (P < 0.01) were higher in the NRC diet group.

Consistent with the correlation between pregnancy stage and the gut microbiota in the proximal colon, Corynebacterium (p: Actinobacteria) (P < 0.05) and Staphylococcus (p: Firmicutes) (P < 0.01) were higher in the NRC vs. the CNF diet group. In the distal colonic luminal content samples, Allobaculum (P < 0.05) and Staphylococcus (P < 0.05) (p: Firmicutes) were higher in the NRC diet group. However, determination regarding [Ruminococcus] (p: Firmicutes) (P < 0.01) yielded opposing results. Consistent with the colonic position and gut microbiota correlation, Bifidobacterium (P < 0.05) and Corynebacterium (P < 0.01) (p: Actinobacteria), Lactobacillus (P < 0.01), Oscillospira (P < 0.01), and Staphylococcus (P < 0.05) (phylum Firmicutes), and Desulfovibrio (P < 0.05) (p: Proteobacteria) were lower in the proximal than in the distal colonic contents. However, Bacteroides (P < 0.01), Paludibacter (P < 0.05), and [Prevotella] (P < 0.01) (p: Bacteroidetes), Coprococcus (P < 0.05) and Roseburia (P < 0.05) (p: Firmicutes), and Campylobacter (P < 0.05) (phylum Proteobacteria) as well as Anaeroplasma (p: Tenericutes) (P < 0.05) exhibited the converse.

Intestinal metabolites: SCFAs

Table 4 shows the SCFA concentrations in the colonic contents of pregnant Huanjiang mini-pigs fed two different nutrient level diets. In the proximal colon, isovalerate and total branched-chain fatty acids (BCFAs) decreased linearly from the first to the third trimester of pregnancy (P < 0.05). Acetate and total SCFAs trended toward decreasing with pregnancy trimester (0.05 ≤ P < 0.10). Acetate, propionate, and total SCFAs displayed significant differences according to diet × stage interaction (P < 0.05), with isobutyrate trending toward change (0.05 ≤ P < 0.10). In the distal colon, isobutyrate displayed significant differences according to diet (P < 0.05). Propionate and total BCFAs trended toward decreasing according to pregnancy stage (0.05 ≤ P < 0.10). Propionate, isobutyrate, total SCFAs, and total BCFAs displayed significant differences (P < 0.05) according to diet × stage interaction, whereas acetate, isopentanoate, and total BCFAs only trended toward change (0.05 ≤ P < 0.10).

Table 4. Concentrations of short-chain fatty acids in the colonic contents of Huanjiang mini-pigs at different stages of pregnancy (mg/g).

Items NRC diet
CNF diet
SEM P values
45 d 75 d 110 d 45 d 75 d 110 d Diet Stage D*S
Proximal colon
 Acetate 7.32 7.58 6.96 9.51 6.94 6.60 0.30 0.484 0.055 0.047
 Propionate 2.48 2.92 2.58 3.14 2.32 1.86 0.13 0.400 0.226 0.035
 Butyrate 0.19 0.15 0.15 0.18 0.15 0.13 0.01 0.501 0.115 0.892
 Isobutyrate 1.22 1.53 1.19 1.40 0.99 0.92 0.07 0.173 0.439 0.073
 Valerate 0.18 0.16 0.15 0.17 0.15 0.15 0.01 0.849 0.509 0.983
 Isovalerate 0.29 0.26 0.19 0.25 0.20 0.20 0.01 0.157 0.013 0.532
 A/P 2.99 2.63 2.71 3.04 3.14 3.53 0.08 0.003 0.399 0.102
 BCFAs 0.37 0.31 0.30 0.35 0.30 0.28 0.01 0.635 0.202 0.986
 SCFAs 11.31 12.29 10.91 14.30 10.46 9.58 0.48 0.951 0.098 0.037
 BCFAs/SCFAs 0.03 0.03 0.03 0.02 0.03 0.03 0.01 0.922 0.861 0.146
 Total short-chain fat acids 11.68 12.60 11.22 14.65 10.76 9.87 0.49 0.938 0.091 0.040
Distal colon
 Acetate 5.63 6.11 5.61 6.78 5.18 4.92 0.21 0.722 0.227 0.062
 Propionate 2.07 2.69 2.05 2.52 1.98 1.64 0.09 0.187 0.075 0.006
 Butyrate 0.20 0.52 0.21 0.28 0.24 0.22 0.05 0.567 0.302 0.224
 Isobutyrate 0.96 1.35 1.15 1.13 0.79 0.82 0.05 0.019 0.781 0.003
 Valerate 0.19 0.27 0.17 0.26 0.25 0.23 0.02 0.264 0.335 0.334
 Isovalerate 0.22 0.29 0.22 0.26 0.22 0.21 0.01 0.545 0.362 0.072
 A/P 2.67 2.35 2.74 2.74 2.62 3.04 0.08 0.210 0.155 0.790
 BCFA 0.39 0.79 0.37 0.55 0.49 0.45 0.05 0.825 0.117 0.077
 SCFA 8.88 10.44 9.02 10.70 8.18 7.59 0.33 0.326 0.224 0.011
 BCFA/SCFA 0.05 0.08 0.04 0.05 0.06 0.06 0.01 0.992 0.213 0.443
 Total short-chain fat acids 9.27 11.23 9.39 11.24 8.67 8.04 0.34 0.294 0.174 0.004

A/P: acetate/propionate; BCFA: branched-chain fatty acids, including isobutyrate and isovalerate; SCFA: straight-chain fatty acids, including acetate, propionate, butyrate, and valerate.

Bioamines

Table 5 summarises the bioamine concentrations in the colonic contents recovered from the pregnant Huanjiang mini-pigs fed two different nutrient level diets. In the mini-pig proximal colon, cadaverine, trytamine, tyramine, and total bioamines differed significantly (P < 0.05) according to diet. 1,7-heptyl diamine trended toward increasing according to pregnancy stage (0.05 ≤ P < 0.10). Although tyramine significantly differed (P < 0.05) according to diet × stage interaction, spermidine and spermine only trended towards change (0.05 ≤ P < 0.10). In the distal colon, eight bioamines and the total bioamines differed significantly according to the diet used (P < 0.05). Among bioamines, 1,7-heptyl diamine, trytamine, tyramine, and total bioamines displayed significant differences (P < 0.05) according to the pregnancy stage, whereas phenylethylamine and spermidine only trended towards changing with pregnancy trimester (0.05 ≤ P < 0.10). Lastly, 1,7-heptyl diamine, putrescine, spermidine, trytamine, tyramine, and total bioamines changed significantly according to diet × stage interactions (P < 0.05).

Table 5. Concentrations of bioamines in the colonic contents of Huanjiang mini-pigs at different stages of pregnancy (μg/g).

Items NRC diet
CNF diet
SEM P values
45 d 75 d 110 d 45 d 75 d 110 d Diet Stage D*S
Proximal colon
 1,7-heptyl diamine 0.75 0.72 1.29 1.01 1.01 1.13 0.06 0.275 0.080 0.337
 Cadaverine 4.07 2.56 2.06 1.05 1.51 1.31 0.29 0.009 0.461 0.167
 Phenylethylamine 3.58 3.07 2.82 2.55 2.25 1.69 0.25 0.101 0.565 0.973
 Putrescine 6.73 9.06 8.68 6.75 7.38 8.23 0.50 0.550 0.375 0.759
 Spermidine 12.96 20.47 21.60 17.22 15.82 17.33 0.93 0.448 0.194 0.087
 Spermine 3.57 7.49 5.21 4.84 4.25 3.85 0.45 0.258 0.218 0.082
 Trytamine 4.49 4.36 5.63 1.18 3.54 1.50 0.47 0.007 0.505 0.288
 Tyramine 2.72 5.00 7.77 3.41 1.73 1.95 0.51 0.007 0.355 0.036
 Total bioamine 38.87 52.74 55.07 40.26 37.35 37.00 2.42 0.029 0.317 0.343
Distal colon
 1,7-heptyl diamine 0.98 0.81 2.56 1.41 0.80 1.03 0.10 0.023 <0.01 <0.01
 Cadaverine 2.35 2.11 1.23 1.02 0.97 1.45 0.18 0.046 0.769 0.265
 Spermidine 18.12 24.19 29.61 17.17 12.17 18.10 1.22 <0.01 0.077 0.025
 Spermine 5.85 6.39 9.56 4.37 2.99 5.08 0.49 0.003 0.117 0.417
 Phenylethyl amine 1.43 2.48 2.25 1.35 1.38 1.68 0.13 0.028 0.080 0.137
 Putrescine 3.63 6.60 7.89 4.40 3.51 4.12 0.41 0.015 0.152 0.031
 Trytamine 1.21 2.25 7.84 1.41 0.68 0.33 0.39 <0.01 0.002 <0.01
 Tyramine 1.59 4.01 8.71 2.09 1.44 1.05 0.44 <0.01 0.009 <0.01
 Total bioamine 35.17 48.83 69.64 33.22 23.95 32.83 2.63 <0.01 0.005 0.002

Indole, skatole, and NH3-N

Table 6 summarises the indole, skatole, and NH3-N concentrations in the pregnant Huanjiang mini-pigs fed two different nutrient level diets. In the proximal colon, NH3-N differed significantly according to diet (P < 0.05), with indole and skatole only trending according to diet × stage interactions (P < 0.05). In the distal colon, indole significantly differed according to the diet used and pregnancy stage (P < 0.05). Lastly, NH3-N differed significantly according to diet × stage interaction (P < 0.05).

Table 6. Concentrations of indole, skatole, and NH3-N in the colonic contents of Huanjiang mini-pigs at different stages of pregnancy.

Items NRC diet
CNF diet
SEM P values
45 d 75 d 110 d 45 d 75 d 110 d Diet Stage D*S
Proximal colon
 NH3-N (mg/g) 20.74 18.86 16.36 13.49 15.54 11.85 1.06 0.005 0.475 0.814
 Indole (μg/g) 9.21 15.42 7.67 11.85 9.53 14.88 0.89 0.436 0.557 0.011
 Skatole (μg/g) 3.89 3.97 2.75 2.83 2.73 5.43 0.27 0.806 0.473 0.014
Distal colon
 NH3-N (mg/g) 11.91 18.38 16.80 15.40 13.61 11.49 0.77 0.156 0.296 0.021
 Indole (μg/g) 7.27 11.20 9.55 9.91 11.19 14.27 0.54 0.016 0.017 0.146
 Skatole (μg/g) 4.98 4.42 3.89 3.88 4.95 4.34 0.26 0.951 0.743 0.382

Discussion

Intestinal metabolites

Complex oligosaccharides and indigestible fibre matter not absorbed in the upper intestinal tract are fermented by the colonic anaerobic microbial community to produce SCFAs18; SCFAs can also be produced from some luminal amino acids19. Acetate, propionate, and butyrate are the major SCFAs produced in the colon. Among these, butyrate is thought to exhibit health benefits by acting as a major energy source for colonic epithelial cells, regulating gene expression in colonocytes, and exhibiting immunomodulatory and anti-inflammatory properties20. BCFAs including isobutyrate and isovalerate are produced during protein fermentation by gut bacteria21. Isobutyrate and isovalerate are produced from L-leucine and L-valine, respectively22. The amount of BCFAs in the faeces and intestinal contents thus constitutes an indicator of protein catabolism in the large intestine19. Decreased BCFA amounts suggest that fewer dietary and/or endogenous proteins escape digestion in the small intestine and enter into the large intestine. In this study, isovalerate and total BCFAs decreased linearly from the first to the third trimester of pregnancy in the proximal colon, and acetate and total SCFAs trended towards decrease. Additionally, propionate in the distal colon decreased linearly with pregnancy progression. Several studies have shown that the SCFA levels in the large intestine influence body weight gain and adiposity23,24. Specifically, obese mice demonstrated significantly increased caecal propionate and butyrate contents vs. their lean counterparts23. The reduced acetate levels would likely result in decreased lipogenesis and the increased propionate would contribute to inhibiting acetate conversion into lipid in the liver and adipose tissue25.

The colonic microbiota catabolises nitrogenous compounds to putrefactive catabolites such as ammonia, biogenic amines, indoles, and phenols19. NH3-N in the colonic lumen increases with an increase in dietary protein intake26 and is produced by the microbiota via urea degradation and amino acid deamination19. In the proximal colon, NH3-N was higher after NRC diet consumption than in the CNF diet groups; notably, excessive NH3-N in the colonic contents has been shown to inhibit colonocyte oxygen consumption27. Leuconostoc, Bacillus, Atopostipes, Bacteroides, and Pseudomonas metabolise tryptophan to indole-3-lactate and then to indole, or further to skatole in animal faeces28. In the distal colon, indole was significantly higher in both the NRC diet groups and according to pregnancy stage, consistent with the findings of Keseler et al., wherein lesser amounts of indoles were produced by the lower vs. the higher crude protein group28. Indole may be beneficial for the colonic epithelium by increasing epithelial cell tight-junction resistance29, whereas skatole has been associated with poor organoleptic characteristics of pig meat30, although its effects on the colonic mucosa remain unknown.

Bioamines comprise organic bases with one or more amine (NH2) group (s), and are formed mainly through the decarboxylation of free amino acids by bacterial decarboxylases31. Putrescine is synthesised from arginine or ornithine, which can occur simultaneously in many bacteria32. Putrescine appears to be strictly necessary for optimal undifferentiated colonic epithelial cell mitosis33. The precursor of cadaverine and tyramine is lysine34, and tyramine is synthesised by the decarboxylation of tyrosine35. Spermine is produced by spermidine, which in turn is produced from histidine, ornithine, arginine, and methionine36.

The bacteria community

The bacterial community of the colon was determined from the first (day 45), second (day 75), and third (day 110) stages of pregnancy. To accurately capture the majority of bacterial OTUs within the colonic contents of Huanjiang mini-pigs, the study was normalised to samples at a depth of 31,465 sequences/sample with good coverage (>99.2%). We selected the V4 variable regions to interrogate the colon content bacterial communities. Alpha diversity analyses were performed using the proximal colonic contents samples across higher and lower nutrient diets and the three pregnancy trimesters. The OTUs decreased significantly according to pregnancy stage, and richness (Chao1 and ACE) changed significantly according to the pregnancy stage in both colonic segments analysed, suggesting that the microbiota diversity is lower at the end than at the beginning of pregnancy. This result is consistent with the results from Collado et al., who detected lower total faecal cell counts in the third than at the first pregnancy trimester7. In addition, Koren et al. also found that gut microbiota diversity and richness decreased with pregnancy6.

Furthermore, Le Chatelier et al. found that non-obese and obese Danish individuals differed in bacterial richness with low richness associated with higher overall levels of body fat37. The sows in our present study also gained considerable weight from the period of remating to late pregnancy. The unweighted UniFrac PCoA reflected the similarities of the bacterial communities within the colon. In contrast, we found that the CNF and NRC diet groups could almost be separated in the proximal colonic samples. However, unlike the samples recovered from the pregnant sows on days 45, 75, and 110 in the NRC diet group, the obtained proximal colonic samples in the CNF diet group could not be separated. Furthermore, our analyses identified no cluster separation in the distal colonic samples. These results demonstrate that the structure or diversity of the bacterial community likely changes to a greater extent in proximal colonic contents and that NRC diet consumption is associated with higher level of changes in the colonic content than is consumption of a CNF diet.

Firmicutes was prominent in the digesta-associated colon microbiota in all samples, for the two diets as well as throughout the three pregnancy trimesters. Firmicutes was composed mainly of the genera Lactobacillus, Clostridium, Oscillospira, Ruminococcus, Turicibacter, Roseburia (≥1%), and the other genera (<1%), which together comprised up to 45.43–74.57% of the total reads in some groups. The dominant taxa belonging to Firmicutes in the digesta-associated with the distal colon likely play an important role in starch and fibre degradation38. Lactobacillus abundance was higher in the proximal colon of pigs receiving the NRC diet vs. the CNF diet. OTU-31 (s: Lactobacillus reuteri) and OTU-1511 (s: L. reuteri) in the distal colon contents were decreased with pregnancy progression. L. reuteri exhibits strain-specific beneficial properties relevant to human health, with human associated lineages distinguished by genes related to bacteriophages, vitamin biosynthesis, antimicrobial production, and immunomodulation, although the strains show evidence of host adaptation39. In addition, Lactobacillus species have been associated with weight changes in humans and animals40. In comparison, the Roseburia component was larger in the whole colon of pigs receiving the NRC diet. Haro et al. reported that Roseburia was modulated by diet and that the degree of Roseburia increase was associated with a protective effect on the development of type 2 diabetes based on a human obese population with different diets41. In our study, the relative abundance of Staphylococcus, which accounted for 0.05–0.29% of the total sequences in the colon, linearly decreased from the first to the third trimester of pregnancy, which is not consistent with the results obtained in pregnant women7.

Bacteroidetes comprised the second dominant phylum in digesta-associated colonic microbiota. This phylum was more abundant in the proximal colonic content in CNF diet-fed animals, and more abundant in the proximal than the distal colon content samples. This phylum was prominent and mainly composed of the genera Prevotella, YRC22, and Parabacteroides. Prevotella comprised 1.83–7.28% of the total sequences in some groups. OTU-62, OTU-89, and OTU-1967 (all g: Prevotella) were linearly decreased with pregnancy. Prevotella has been proposed (together with other genera such as Xylanibacter, Butyrivibrio, and Treponema) to enhance caloric extraction from resistant starches and oligosaccharides, as well as from indigested carbohydrates42. Another study showed that long-term consumption of diets rich in carbohydrates can increase the abundance of Prevotella43. In addition, a study showed that Prevotella decreased in rats fed a high fat diet44.

Firmicutes and Bacteroidetes comprised the dominant colonic phyla, as previously suggested45. Earlier studies showed that obesity and glucose intolerance are associated with an increase in Firmicutes37. Obesity is also associated with a higher Firmicutes/Bacteroidetes ratio46. A high-fat diet can increase the Firmicutes/Bacteroidetes ratio within a small range47. Conversely, a higher saccharolytic diet may preferentially increase Bacteroidetes as compared to Firmicutes48. In our study, Firmicutes abundance was higher in distal colonic contents, whereas Bacteroidetes showed the opposite. Firmicutes was affected by diet and stage, with the NRC diet increasing its abundance in the proximal colon. In addition, Firmicutes was increased at the end of the pregnancy period in the distal colon. This increase was considered to alter the Firmicutes metabolic potential and enhance the body’s capacity to harvest energy from the diet49, a potential advantage during pregnancy to support growth of the foetus and prepare the body for the energetic demands of lactation. Jost et al. revealed that Firmicutes exhibited no detectable change over the perinatal period8. However, Khan et al. found that Firmicutes abundance was profoundly impacted during pregnancy50. Bacteroidetes was affected by diet only in the proximal colon, and the NRC diet decreased its abundance. The ratio of Firmicutes/Bacteroidetes was larger in groups fed the NRC vs. the CNF diet, suggesting that the gut microbiota of sows fed the NRC diet was more similar to obesity models than those fed the CNF diet.

Large abundances of other phyla were identified for Proteobacteria, Actinobacteria, Spirochaetes, and Tenericutes, which are present in the majority of gut-associated phylotypes in mammals51. These phyla are ubiquitous within the mammalian gut suggesting their critical role in the microbial ecology of the mammalian intestine52. Notably, Enterobacteriaceae and E. coli are higher in overweight than in normal-weight women and in women with excessive weight gain during pregnancy53. In the current study, Escherichia (phylum Proteobacteria) were more abundant in the proximal colon of pigs fed with HN NRC diet. As the NRC diet contains higher nutrient level than the CNF diet, the sows fed NRC demonstrated higher body weights. E. coli is sufficient to induce inflammation and glucose and insulin tolerance in germ free mice54, as well as acute diarrhoea in pregnant women55.

Several studies suggested that the gut microbiota change during pregnancy may also induce pregnancy complications such as excessive maternal weight gain56. Notably, our previous study showed that the average back-fat thickness of pregnant Huanjiang mini-pigs increased from days 45 (27.20 mm and 26.90 mm) to 75 (36.60 mm and 28.10 mm) post-service in both NRC and CNF diet groups, respectively57; the live body weight of these sows also increased from days 45 (73.82 kg and 67.52 kg) to 75 (86.14 kg and 75.28 kg) in both groups (Kong et al., unpublished data). These findings suggested that the pregnant sows became obese during pregnancy, especially in the NRC group, and that this was associated with more abundant Escherichia. Actinobacteria was more abundant in the distal colonic contents recovered from animals fed the CNF diet and was in addition more abundant in the distal than in the proximal colon. Among bacteria, Bifidobacterium and Corynebacterium were more abundant in the distal colon from the CNF vs. the NRC groups, consistent with a previous study showing that high-fat feeding is associated with lower Bifidobacteria concentrations9. Notably, the Bifidobacterium group is present in higher numbers in normal-weight than in overweight women7, and in women with lower weight gain over pregnancy7. The present study indicates that consumption of the diet with higher nutrient level coincides with the presence of a colonic microbiota in pregnant sows that resembles the gut bacteria composition observed in obese animals.

Conclusion

Consumption of a higher nutrient level diet increases the colonic luminal content of bacterial metabolites related to nitrogen metabolism, suggesting increased dietary and endogenous protein transfer from the small to the large intestine58 and/or modified composition/metabolic capacities of the intestinal microbiota toward luminal protein/amino acids. The dominate phyla in the colonic luminal contents were, to different extents, Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Spirochaetes, and Tenericutes. The dominate genera in the colonic content of pregnant sows were Lactobacillus, Treponema, Ruminococcus, Clostridium, and Prevotella. The richness of bacteria in the colon decreased from the first to the third trimester of pregnancy. Lastly, consumption of an NRC diet resulted in a colonic microbiota profile of sows resembling the gut bacteria composition observed in human obesity. This might be associated with an increased risk of pregnancy syndrome, although no causal link between these parameters was established in our study, calling for further experimental works.

Materials and Methods

Animals, housing, and treatment

The present study was carried out in accordance with the Chinese guidelines for animal welfare and experimental protocols and approved by the Animal Care and Use Committee of the Institute of Subtropical Agriculture, Chinese Academy of Sciences.

Forty-eight primiparous Huanjiang mini-pigs with a mean body weight (BW) of 43.02 ± 8.87 kg were obtained from a mini-pig farm located in Jixiang town (108°27′40.8″ E and 25°9′50″ N, altitude 578 m), Huanjiang county (Guangxi province, China). These sows were randomly assigned to one of the two dietary groups post service (24 sows per each dietary group and three sows per pen). One group of sows received diet with a higher nutrient level and the other received diet with a lower nutrient level. The higher nutrient level diet was formulated to meet the nutrient requirements recommended by the National Research Council (1998) (NRC diet) and contained 14.50 MJ/kg digestible energy, 13.10% crude protein, and 4.56% crude fibre. The lower nutrient level diet was formulated to meet recommendations of the Chinese National Feeding Standard for Swine (CNF diet), and thus contained 12.20 MJ/kg of digestible energy, 11.00% of crude protein, and 6.86% of crude fibre (Supplementary Table S3). These diets are widely used in commercial crossbred pig farms and commercial Huanjiang mini-pig farms, respectively.

All animals were housed in 2 m × 3 m pens with cement-sclerified flooring. Each pen was equipped with a feeder and a nipple drinker. The room temperature was maintained at 22–28 °C. All pigs had ad libitum access to drinking water and were fed twice daily (08:30 and 16:30) with their diets (about 2.5% of body weight) after service. The feed intake, excreta, and mental state per sow were monitored twice each day throughout the entire experimental period.

Sample collection and preparation

According to the report of Johnston and Trottier (1999)59, the early, middle, and later stages of pregnancy in pigs are from days 1 to 30, from days 30 to 75, and from days 75 to delivery, respectively. Considering that the Huanjiang mini-pigs represent miniature animals and it is difficult to collect the conceptus samples to determine foetus development for subsequent study, we chose 45 days post-service to sample as the early pregnancy stage.

This study was conducted using samples collected from 31 sows offered two experimental diets. On day 45, 75 or 110 of service, the pregnant sows from each treatment group were sacrificed for sample collection at 12 h after the last feeding60. Briefly, the pigs were administered general anaesthesia by intravenous injection of 4% sodium pentobarbital solution (40 mg/kg BW), and sacrificed by exsanguination via the carotid artery61. After colon recovery, the luminal contents were collected from a region 10 cm posterior to the ileocecal valve and a region 10 cm at the end of the colon, respectively, and then stored at −80 °C. Subsequently, the contents of ammonia (considered as the sum of NH3 and NH4+), SCFAs, indole, skatole (3-methylindole), and bioamines were analysed, and the composition of the gut microbiota was determined.

DNA Extraction and PCR Amplification

Total genomic DNA was extracted from the colon content samples using a QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany), according to the manufacturer’s instructions. The quality and quantity of DNA were measured using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE, USA). An absorption ratio (260/280 nm) within 1.8–2.0 was deemed to be of sufficient purity to be used for subsequent analyses. The total DNA of the colon contents was then diluted to 50 ng/μl and used in the preparation of amplicons for high-throughput sequencing of microbial 16S rRNA. Conventional PCR was performed to amplify the V4 regions of the 16S rRNA gene using universal primers 515F (5′-GTG CCA GCM GCC GCG GTA A-3′) and 806R (5′-GGA CTA CHV GGG TWT CTA AT-3′). The reverse primer contained a 6-bp error-correcting barcode unique to each sample. DNA was amplified using the protocol described previously62. DNA samples were sent to a commercial service provider (Novogene, Beijing, China) for amplicon pyrosequencing on an Illumina MiSeq platform according to the manufacturer’s instruction63.

Microbial community analysis

Raw data were screened and assembled by QIIME64 and FLASH63 software packages. The reads flagged as chimeras were removed to form the “effective sequences” collection for each sample. QIIME software package (http://qiime.org/) and UPARSE pipeline (http://drive5.com/uparse) were used for analysis of the sequences and determination of the Operational Taxonomic Units (OTUs) with an identity threshold of 97%. Meanwhile, we picked a representative sequence for each OTU and used the RDP classifier65 to assign taxonomic data to each representative sequence. Taxonomy classifications were assigned against RDP Classifier (Version 2.2, http://sourceforge.net/projects/rdpclassifier/)65 and the Greengenes database (http://greengenes.lbl.gov/cgi-bin/nph-index.cgi)66. Alpha diversity values for colon bacterial communities of pregnant Huanjiang mini-pigs were obtained using abundance-based coverage estimator (ACE), bias-corrected Chao richness estimator, Shannon index, Simpson index, and Good’s coverage, at a sequence depth of 31,460 for all samples. Principal coordinate analysis (PCoA) of microbial communities (jackknifed beta diversity from resampling 100 times at depth of 31,460 sequences) was performed using Bray-Curtis distance.

Metabolites in colon contents

SCFAs including acetate, propionate, butyrate, isobutyrate, valerate, and isovalerate were analysed as described previously67. To ensure the homogenicity of the intestine content sample, the freeze-dried samples were prepared using a Vacuum freeze-dryer (Hrist ALPHA 2-4/LSC, Germany) at −80 °C. Briefly, freeze-dried samples (0.5–0.6 g) were weighed into 10 ml centrifuge tubes and mixed with 8 ml ddH2O, homogenised, and centrifuged in sealed tube at 7,000 g and 4 °C for 10 min. A mixture of the supernatant fluid and 25% metaphosphoric acid solution (0.9 and 0.1 ml, respectively) was centrifuged at 20,000 g and 4 °C for 10 min after standing in a 2 ml sealed tube at 4 °C for over 2 h. The supernatant portion was then filtered through a 0.45-μm polysulfone filter and analysed using Agilent 6890 gas chromatography (Agilent Technologies, Inc, Palo Alto, CA, USA) with a flame ionisation detector and a 1.82 m × 0.2 mm I.D. glass column that was packed with 10% SP-1200/1% H3PO4 on the 80/100 Chromosorb W AW (HP, Inc., Boise, ID, USA). The concentration of NH3-N in the supernatant fluid was measured at 550 nm using a UV-2450 spectrophotometer (Shimadzu, Kyoto, Japan)68. The bioamines including 1,7-heptyl diamine, cadaverine, phenylethylamine, putrescine, trytamine, tyramine, spermidine, and spermine, as well as the indoles and skatoles, were analysed as described previously69.

Statistical analysis

The alpha diversity indices of bacterial communities, the apparent relative abundances at phylum and genus levels, as well as colon metabolites (SCFA, NH3-N, indole, skatole, and bioamines) were analysed in a completely randomised design with the GLM procedure of SAS (SAS Institute, Inc., Cary, NC). The PCoA of overall diversity of microbial communities based on an un-weighted Unifrac metric was performed using Bray-Curtis distance to compare all samples. A study of the relationship among parameters was also carried out using Pearson’s linear correlation coefficient. Phylum and genus at <0.5% relative abundance for both diets were excluded from all analyses. Differences with P values <0.05 were considered as statistically significant while a tendency was considered to exist at 0.05 ≤ P < 0.10.

Additional Information

How to cite this article: Kong, X.-f. et al. Colonic luminal microbiota and bacterial metabolite composition in pregnant Huanjiang mini-pigs: effects of food composition at different times of pregnancy. Sci. Rep. 6, 37224; doi: 10.1038/srep37224 (2016).

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Material

Supplementary Information
srep37224-s1.doc (413.5KB, doc)

Acknowledgments

The present work was jointly supported by grants from the National Nature Science Foundation of China (31270044, 31572421), “Western Light” key program for Talent Cultivation from Chinese Academy of Sciences, and Chinese Academy of Sciences Visiting Professorship for Senior International Scientists (F. Blachier, 2013T2S0014). Key Programs of frontier scientific research of the Chinese Academy of Sciences (QYZDY-SSW-SMC008), National Key R&D Program (2016YFD0501201), Project Supported by Changsha City Science and Technology Program of China (k1508008-21). We would like to thank Editage [http://online.editage.cn/] for English language editing.

Footnotes

The authors declare no competing financial interests.

Author Contributions X.F.K., Y.J.J., and Y.L.Y. performed the experiments. Y.J.J., X.F.K., and F.B. wrote the manuscript text. Y.J.J. performed the statistical analysis. H.W.L. and Q.Z. fed the animals. M.M.G. and W.C. executed the technical assays. All authors reviewed the manuscript.

References

  1. Campos P. H., Silva B. A., Donzele J. L., Oliveira R. F. & Knol E. F. Effects of sow nutrition during gestation on within-litter birth weight variation: a review. Animal 6, 797–806 (2012). [DOI] [PubMed] [Google Scholar]
  2. Virolainen J. V., Tast A., Sorsa A., Love R. J. & Peltoniemi O. A. Changes in feeding level during early pregnancy affect fertility in gilts. Anim Reprod Sci 80, 341–352 (2004). [DOI] [PubMed] [Google Scholar]
  3. Vignola M. Sow feeding management during lactation. In: London Swine Conference. Tools of the Trade 4, 18 (2009). [Google Scholar]
  4. Jindal R., Cosqrove J. R., Aherne F. X. & Foxcroft G. R. Effect of nutrition on embryonal mortality in gilts: assciation with progesterone. J Anim Sci 74, 620–624 (1996). [DOI] [PubMed] [Google Scholar]
  5. Kline L. W. & Karpinski E. Progesterone inhibits gallbladder motility through multiple signaling pathways. Steroids 70, 673–679 (2005). [DOI] [PubMed] [Google Scholar]
  6. Koren O. et al. Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell 150, 470–480 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Collado M. C., Isolauri E., Laitinen K. & Salminen S. Distinct composition of gut microbiota during pregnancy in overweight and normal-weight women. Am J Clin Nutr 88, 894–899 (2008). [DOI] [PubMed] [Google Scholar]
  8. Jost T., Lacroix C., Braegger C. & Chassard C. Stability of the maternal gut microbiota during late pregnancy and early lactation. Curr Microbiol 68, 419–427 (2014). [DOI] [PubMed] [Google Scholar]
  9. Turnbaugh P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–487 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Karlsson F. H. et al. Gut metagenome in european women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013). [DOI] [PubMed] [Google Scholar]
  11. Smith M. I. et al. Gut Microbiomes of Malawian twin pairs discordant for kwashiorkor. Science 339, 548–554 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Leser T. D., Lindecrona R. H., Jensen T. K., Jensen B. B. & Moller K. Changes in bacterial community structure in the colon of pigs fed different experimental diets and after infection with Brachyspira hyodysenteriae. Appl Environ Microbiol 66, 3290–3296 (2000). [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gibson G. R. & Roberfroid M. B. Handbook of prebiotics 1st ed. Boca Raton, FL, USA, CRC Group (2008). [Google Scholar]
  14. Wu X., Ran Z., Kong X. F., Tang Z. R. & Dong X. H. Functions of microbiota in monogastric gastrointestinal tract and new practices in animal production. J Food Agric Environ 8, 695–702 (2010). [Google Scholar]
  15. Lenaerts I., Walker G. A., Van Hoorebeke L., Gems D. & Vanfleteren J. R. Dietary restriction of Caenorhabditis elegans by axenic culture reflects nutritional requirement for constituents provided by metabolically active microbes. J Gerontol a-Biol 63, 242–252 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Penders J. et al. Factors influencing the composition of the intestinal microbiota in early infancy. Pediatrics 118, 511–521 (2006). [DOI] [PubMed] [Google Scholar]
  17. Davila A. M. et al. Intestinal luminal nitrogen metabolism: role of the gut microbiota and consequences for the host. Pharmacol Res 68, 95–107 (2013). [DOI] [PubMed] [Google Scholar]
  18. Christl S. U., Murgatroyd P. R., Gibson G. R. & Cummings J. H. Production, metabolism, and excretion of hydrogen in the large-intestine. Gastroenterology 102, 1269–1277 (1992). [PubMed] [Google Scholar]
  19. Blachier F., Mariotti F., Huneau J. F. & Tome D. Effects of amino acid-derived luminal metabolites on the colonic epithelium and physiopathological consequences. Amino Acids 33, 547–562 (2007). [DOI] [PubMed] [Google Scholar]
  20. Lan A. et al. Mucosal healing in inflammatory bowel diseases: is there a place for nutritional supplementation? Inflamm Bowel Dis 21, 10 (2015). [DOI] [PubMed] [Google Scholar]
  21. Birkett A., Muir J., Phillips J., Jones G. & ODea K. Resistant starch lowers fecal concentrations of ammonia and phenols in humans. Am J Clin Nutr 63, 766–772 (1996). [DOI] [PubMed] [Google Scholar]
  22. Le P. D., Aarnink A. J. A., Ogink N. W. M., Becker P. M. & Verstegen M. W. A. Odour from animal production facilities: its relationship to diet. Nutr Res Rev 18, 3–30 (2005). [DOI] [PubMed] [Google Scholar]
  23. Ridaura V. K. et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Liou A. P. et al. Conserved shifts in the gut microbiota due to gastric bypass reduce host weight and adiposity. Sci Transl Med 5, 178ra41 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hong Y. H. et al. Acetate and propionate short chain fatty acids stimulate adipogenesis via GPCR43. Endocrinology 146, 5092–5099 (2005). [DOI] [PubMed] [Google Scholar]
  26. Mouille B., Robert V. & Blachier F. Adaptative increase of ornithine production and decrease of ammonia metabolism in rat colonocytes after hyperproteic diet ingestion. Am J Physiol-Gastr L 287, G344–G351 (2004). [DOI] [PubMed] [Google Scholar]
  27. Andriamihaja M. et al. Colon luminal content and epithelial cell morphology are markedly modified in rats fed with a high-protein diet. Am J Physiol-Gastr L 299, G1030–G1037 (2010). [DOI] [PubMed] [Google Scholar]
  28. Keseler I. M. et al. EcoCyc: fusing model organism databases with systems biology. Nucleic Acids Res 41, D605–D612 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Venkatesh M. et al. Symbiotic Bacterial metabolites regulate gastrointestinal barrier function via the Xenobiotic Sensor PXR and Toll-like receptor 4. Immunity 41, 296–310 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Cho S., Hwang O. & Park S. Effect of Dietary protein levels on composition of odorous compounds and bacterial ecology in pig manure. Asian-Aust J Anim Sci 28, 1362–1370 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Tuberoso C. I. G., Congiu F., Serreli G. & Mameli S. Determination of dansylated amino acids and biogenic amines in Cannonau and Vermentino wines by HPLC-FLD. Food Chem 175, 29–35 (2015). [DOI] [PubMed] [Google Scholar]
  32. Craciun S. & Balskus E. P. Microbial conversion of choline to trimethylamine requires a glycyl radical enzyme. P Natl Acad Sci USA 109, 21307–21312 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Blachier F., Robert V., Selamnia M., Mayeur C. & Duee P. H. Sodium nitroprusside inhibits proliferation and putrescine synthesis in human colon carcinoma cells. Febs Lett 396, 315–318 (1996). [DOI] [PubMed] [Google Scholar]
  34. Watson N., Dunyak D. S., Rosey E. L., Slonczewski J. L. & Olson E. R. Identification of elements involved in transcriptional regulation of the Escherichia-Coli-Cad operon by external pH. J Bacteriol 174, 530–540 (1992). [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Fernandez de Palencia P. et al. Role of tyramine synthesis by food-borne enterococcus durans in adaptation to the gastrointestinal tract environment. Appl Environ Microbiol 77, 699–702 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Qaisrani S. N., van Krimpen M. M., Kwakkel R. P., Verstegen M. W. A. & Hendriks W. H. Diet structure, butyric acid, and fermentable carbohydrates influence growth performance, gut morphology, and cecal fermentation characteristics in broiler. Poult Sci 94, 2152–2164 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Le Chatelier E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013). [DOI] [PubMed] [Google Scholar]
  38. Kim M., Morrison M. & Yu Z. T. Status of the phylogenetic diversity census of ruminal microbiomes. Fems Microbiol Ecol 76, 49–63 (2011). [DOI] [PubMed] [Google Scholar]
  39. Spinler J. K. et al. From prediction to function using evolutionary genomics: human-specific ecotypes of Lactobacillus reuteri have diverse probiotic function. Genome Biol Evol 6, 1772–1789 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Kong L. C. et al. Gut microbiota after gastric bypass in human obesity: increased richness and associations of bacterial genera with adipose tissue genes. Am J Clin Nutr 98, 16–24 (2013). [DOI] [PubMed] [Google Scholar]
  41. Haro C. et al. Two healthy diets modulate gut microbial community improving insulin sensitivity in a human obese population. J Clin Endocrinol Metab 101, 233–242 (2016). [DOI] [PubMed] [Google Scholar]
  42. Flint H. J., Bayer E. A., Rincon M. T., Lamed R. & White B. A. Polysaccharide utilization by gut bacteria: potential for new insights from genomic analysis. Nat Rev Microbiol 6, 121–131 (2008). [DOI] [PubMed] [Google Scholar]
  43. Wu G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Marques C. et al. High-fat diet-induced obesity rat model: a comparison between Wistar and Sprague-Dawley rat. Adipocyte 5, 11–21 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Pajarillo E. A. B. et al. Characterization of the fecal microbial communities of duroc pigs using 16S rRNA gene pyrosequencing. Asian-Aust J Anim Sci 28, 584–591 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Tsai F. & Coyle W. J. The microbiome and obesity: is obesity linked to our gut flora? Curr Gastroenterol Rep 11, 307–313 (2009). [DOI] [PubMed] [Google Scholar]
  47. Lam Y. Y. et al. Increased gut permeability and microbiota change associate with mesenteric fat inflammation and metabolic dysfunction in diet-induced obese mice. Plos One 7, e34233 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. El Kaoutari A., Armougom F., Gordon J. I., Raoult D. & Henrissat B. The abundance and variety of carbohydrate-active enzymes in the human gut microbiota. Nat Rev Microbiol 11, 497–504 (2013). [DOI] [PubMed] [Google Scholar]
  49. Turnbaugh P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006). [DOI] [PubMed] [Google Scholar]
  50. Khan I. et al. Metagenomic analysis of antibiotic-induced changes in gut microbiota in a pregnant rat model. Front Pharmacol 7, doi: 10.3389/fphar.2016.00104 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Ley R. E. et al. Evolution of mammals and their gut microbes. Science 320, 1647–1651 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Myer P. R., Wells J. E., Smith T. P. L., Kuehn L. A. & Freetly H. C. Microbial community profiles of the colon from steers differing in feed efficiency. Springerplus 4, 454 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Cani P. D. et al. Selective increases of bifidobacteria in gut microflora improve high-fat-diet-induced diabetes in mice through a mechanism associated with endotoxaemia. Diabetologia 50, 2374–2383 (2007). [DOI] [PubMed] [Google Scholar]
  54. Caesar R. et al. Gut-derived lipopolysaccharide augments adipose macrophage accumulation but is not essential for impaired glucose or insulin tolerance in mice. Gut 61, 1701–1707 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Wald A. Constipation, diarrhea, and symptomatic hemorrhoids during pregnancy. Gastroenterol Clin North Am 32, 309–322 (2003). [DOI] [PubMed] [Google Scholar]
  56. Zhang D., Huang Y. & Ye D. Intestinal dysbiosis: an emerging cause of pregnancy complications? Med Hypotheses 84, 223–226 (2015). [DOI] [PubMed] [Google Scholar]
  57. Zhu Q., Ji Y. J., Li H. W., Guo Q. P. & Kong X. F. High- or low-nutrient level diets affects reproductive performance, body composition and plasma biochemical parameters in pregnant huanjiang mini-pigs. Chin J Anim Nutr 28, 1534–1540 (2016). [Google Scholar]
  58. Liu X. X. et al. High-protein diet modifies colonic microbiota and luminal environment but not colonocyte metabolism in the rat model: the increased luminal bulk connection. Am J Physiol-Gastr L 307, G459–G470 (2014). [DOI] [PubMed] [Google Scholar]
  59. Johnston L. & Trottier N. Nutritional methods to improve sow productivity examined. Feedstuffs, October 25, 12–17 (1999). [Google Scholar]
  60. Liu Y. Y. et al. Dietary protein intake affects expression of genes for lipid metabolism in porcine skeletal muscle in a genotype-dependent manner. Brit J Nutr 113, 1069–1077 (2015). [DOI] [PubMed] [Google Scholar]
  61. Kong X. F. et al. Dietary supplementation with Acanthopanax senticosus extract modulates cellular and humoral immunity in weaned piglets. Asian-Aust J Anim Sci 20, 1453–1461 (2007). [Google Scholar]
  62. Qin J. J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–U70 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Magoč T. & Salzberg S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Caporaso J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7, 335–336 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Wang Q., Garrity G. M., Tiedje J. M. & Cole J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73, 5261–5267 (2007). [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. DeSantis T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72, 5069–5072 (2006). [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Zhou X. L. et al. Dietary supplementation with soybean oligosaccharides increases short-chain fatty acids but decreases protein-derived catabolites in the intestinal luminal content of weaned Huanjiang mini-piglets. Nutr Res 34, 780–788 (2014). [DOI] [PubMed] [Google Scholar]
  68. Kong X. F. et al. Dietary supplementation with chitooligosaccharides alters gut microbiota and modifies intestinal luminal metabolites in weaned Huanjiang mini-piglets. Livest Sci 160, 97–101 (2014). [Google Scholar]
  69. Xu L. W. et al. Determination of bioamines in colonic contents of piglets by pre-column derivatization RP-HPLC. Acta Nutrimenta Sinica 36, 78–83 (2014). [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Information
srep37224-s1.doc (413.5KB, doc)

Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

RESOURCES