Abstract

   Weaned piglets are vulnerable to nutritional, physiological, and
   psychological stressors, leading to abrupt taxonomic and functional
   shifts in the intestinal microbiome. In this study, an integrated
   approach combination of 16S rDNA gene sequencing and the mass
   spectrometry-based metabolomics techniques was used to investigate the
   effects of weaning stress on intestinal microbial composition and its
   metabolic profiles of piglets. Three litters of suckling piglets with
   same parity were chosen. The samples of colonic contents were collected
   from each selected piglets (weaned day, 3 days after weaned) for
   microbial and metabolomics analysis. The results showed that
   Lachnospiraceae, Negativicutes, Selenomonadales, Campylobacterales and
   other 15 species increased after weaning, while Porphyromonadaceace,
   Alloprevotella, Barnesiella and Oscillibacter decreased. Based on the
   function profiles prediction and metabolomic analysis, five key
   metabolic pathways including Phenylalanine metabolism, Citrate cycle
   (TCA cycle), Glycolysis or Gluconeogenesis, Propanoate metabolism,
   Nicotinate and nicotinamide metabolism might be the relevant pathways
   involved in weaning stress-induced gut microbiota dysbiosis. Taken
   together, these results indicated that weaning stress not only changed
   microbial composition and function but altered the microbial metabolic
   profiles in the intestine, which might provide a new insight in
   alleviating weaning stress and facilitating disease prevention during
   the period of weaning in piglets.

Introduction

   Enormous numbers and diverse microbiota population inhabit the
   gastrointestinal tract in human beings and animals, being responsible
   for gut maturation, pathogens resistance and immune modulation. Shaped
   by co-evolution more than millions of years, a symbiotic relationship
   has formed between gut microbiota and host. Gut microbiota play a
   critical role in nutrient metabolism and in return occupy the
   nutrient-rich environment^[34]1. Increasing evidence showed that the
   dysbiosis of gut microbiota was intimately associated with many
   diseases, including inflammatory bowel disease, cardiovascular
   diseases, allergies, diabetes and obesity^[35]2,[36]3. Thus,
   improvement in host health requires a better understanding of
   intestinal environment especially the gut microbiota. Gut microbiota
   remains relatively stable once established. However, many factors such
   as host genetics, environment, diet, immunological pressures and
   antibiotics can cause dramatic changes in the microbial
   community^[37]4. Owing to the similarities to human beings in anatomy
   and nutritional physiology, pigs often served as a biomedical model of
   human^[38]5,[39]6. Investigation of gut microbiota in pigs not only
   benefits to construct a healthy gut of the animal but also provide
   important evidence for human.

   The pig gut microbiota shows dynamic composition and diversity which
   shifts over time and along the whole gastrointestinal tract^[40]7.
   Colonization was started at birth and shaped by consumption of the
   sow’s milk, building a milk-oriented microbiome^[41]8. Therefore, the
   suckling period offers a special window of gut microbiota
   modifications. Weaning is usually conducted at around 3–4 weeks in the
   modern swine industry, and piglets are fed with solid diets instead of
   liquid milk. It is a sudden, complex and highly stressful event in
   pig’s life. Weaning piglets are usually vulnerable to nutritional,
   physiological, and psychological stressors, leading to alterations of
   intestinal morphology, physiological function, and a shift in
   intestinal microbiome (e.g., increased potential pathogens and
   diarrhea)^[42]9–[43]11. And the disruption of gut microbiota is
   regarded as one of the major factors leading to post-weaning
   diarrhea^[44]12. Previous study showed the temporal change of gut
   architecture and function after weaned could be divided two periods: an
   acute change (about 5 days) occurs immediately after weaned and an
   adaptive and maturity phase^[45]13. Thus 1–5days after weaned are
   considered to be a period in which piglets suffered the most severe
   weaning stress.

   Recently, the association among gut microbiota, metabolites and host
   physiology has gained increasing attention. Microbial metabolites are
   indispensable to maintain the majority of the biological effects of gut
   microbiota. The extraction, synthesis and absorption of metabolites
   could be mediated by intestinal commensal bacteria^[46]14. Microbiota
   could exploit ingested dietary components, such as carbohydrates,
   proteins, and lipids to produce metabolites. These metabolites are
   mainly the short-chained fatty acids (SCFAs), as well as branched-chain
   fatty acids. Besides, there are some other metabolites produced by
   microbiota, including choline metabolites, bile acid metabolites,
   indole and phenolic derivatives. These metabolites have been
   appreciated for their beneficial effects on intestinal barrier, immune
   regulation and inflammation^[47]15. Therefore, it is of particular
   significance to probe the metabolic changes associated with
   weaning-perturbed gut microbiota. The present work was designed to
   investigate the effects of weaning stress on gut microbiome and its
   metabolite profiles in piglets via an integrated approach combining 16S
   rDNA gene sequencing and GC-TOF/MS (Gas Chromatography Tandem
   Time-of-Flight Mass Spectrometry) method.

Results

Gut Microbiota Diversity and Structure of Nursing and Weaned Piglets

   An average of 35595 high quality sequences per sample was obtained from
   12 colonic content samples (Supplementary Table [48]S1). The
   rarefaction Curves tended to be flat as shown in Fig. [49]1A,B, which
   demonstrated that almost all the bacterial species were detected in the
   colonic microbiota. Further, these sequences in the colonic contents of
   nursing and weaned piglets were assigned to 1,157 and 1,584 OTUs
   (operational taxonomic units) based on a 97% sequence similarity
   (Supplementary Table [50]S2), respectively. Venn diagram reflected the
   number of OTUs that were common in groups as well as within the groups.
   This analysis showed there were 137 and 85 unique OTUs in weaned
   piglets and nursing piglets, respectively (Fig. [51]1C). The indexes of
   Chao1, observed species, PD-whole-tree, Shannon and Simpson were
   calculated to estimate alpha diversity. Shannon and Simpson indices,
   which reflect the species diversity, showed no significant difference
   in two groups. While, there were significant decrease in richness
   indices (Chao 1 and observed species) in weaned piglets compared to
   nursing piglets (Table [52]1). Unweifhted Unifrac Anosim and unweifhted
   Unifrac cluster tree based on UPGMA were used to analyze beta diversity
   (that is, diversity between individuals). Figure [53]1E showed a strong
   difference in the community composition of gut microbiota between two
   groups.

Figure 1.

   [54]Figure 1
   [55]Open in a new tab

   The overall structure of gut microbiota in nursing and weaned groups.
   (A), (B): Bacterial rarefaction curves based on Chao 1 and observed
   species index were used to assess the depth of coverage for each
   sample. (C) Venn diagram for bacterial OTUs compositions in two groups.
   (D) Similarity Analysis (Anosim Analysis) based on Unifrac algorithm.
   (E) Multiple sample similarity phylogenetic tree was built by
   Unweighted pair group method with arithmetic mean. Each sample was
   distinguished by different colors of lines. Blue and red represent
   nursing group and weaned group, respectively.

Table 1.

   Richness and diversity of indices of colonic microbiota in nursing and
   weaned groups.
    Items     Richness indices          Diversity indices
           Chao 1 Observed species PD_whole_tree shannon simpson
   nursing 306.86 255.33           22.2          5.24    0.94
   weaned  223.06 185.33           15.33         4.76    0.91
   p value 0.03   0.03             8.66E^−03     0.24    0.24
   [56]Open in a new tab

   The coverage percentage, richness estimators (Chao1, Observed species),
   and diversity indices (PD_whole_tree, Shannon and Simpson) were
   calculated using the QIIME program. PD_whole_tree: (phylogenetic
   distance) whole tree.

   Four predominant phyla (Bacteroidetes, Firmicutes, Proteobacteria,
   Fusobacteria), which consisted of over 1% of total sequences on
   average, were found in colon as shown in Fig. [57]2A. There was no
   difference detected in dominant bacterial phyla between two groups
   (Supplementary Table [58]S3). At family level, Prevotellaceae was
   predominant, followed by Ruminococcaceae, Lachnospiraceae,
   Porphyromonadaceae, and Bacteroidaceae (Fig. [59]2B and Supplementary
   Table [60]S4). At genus level, Precotella, Bacteroides, Lactobacillus
   and Alloprevotella were the predominant genera (Fig. [61]2C and
   Supplementary Table [62]S5).

Figure 2.

   [63]Figure 2
   [64]Open in a new tab

   Gut microbial community structure in nursing and weaned groups. (A)
   Microbial community bar plot by phylum. (B) Microbial community bar
   plot by family. (C) Microbial community bar plot by genus. Each bar
   represents the average relative abundance of each bacterial taxon
   within a group. The top 20 abundant taxa are shown.

Differences in Microbiome between Nursing and Weaned Piglets

   Figure [65]3 showed the microbiota with significant differences between
   nursing and weaned piglets. Lachnospiraceae, Negativicutes,
   Selenomonadales, Campylobacterales and other 15 species increased in
   weaned piglets, while Porphyromonadaceace, Alloprevotella, Barnesiella
   and Oscillibacter decreased.

Figure 3.

   [66]Figure 3
   [67]Open in a new tab

   Cladogram. Bacterial taxa significantly differentiated between Nursing
   and Weaned group identified by linear discriminant analysis coupled
   with effect size (LEfSe) using the default parameters.

Predicted Function of Gut Microbiota in Nursing and Weaned Piglets

   PICRUSt analysis was performed to predict the potential functions of
   gut microbiota. In comparison two groups, nursing piglets had higher
   enrichment of the pathways in Lipid biosynthesis proteins, Energy
   metabolism, Transcription machinery, Nicotinate and nicotinamide
   metabolism, Carbon fixation pathways in prokaryote, Propanoate
   metabolism, naphthalene degradation, Streptomycin biosynthesis and
   peroxisome. While other pathways such as Photosythesis proteins,
   Thiamine metabolism, Sulfur relay system were overrepresented in weaned
   piglets (Fig. [68]4).

Figure 4.

   [69]Figure 4
   [70]Open in a new tab

   Predicted microbial function in the colon lumen of nursing and weaned
   groups. The third level of KEGG pathway was showed in the extended
   error bar. Blue and red represent nursing group and weaned group,
   respectively.

Metabolic Differences of Gut Microbiota between Nursing and Weaned Piglets

   Untargeted metabolomics analysis was performed to evaluate the
   metabolic differences of gut microbiota in two groups. An OPLS-DA
   method was performed to better understand the different metabolic
   patterns. Figure [71]5A showed significantly separated clusters between
   two groups. The values for R^2X and Q^2 and the results of permutation
   tests indicated that the samples were of reasonable quality
   (Fig. [72]5B). In total, relative levels of 433 biomarker metabolites
   differed significantly between nursing and weaned piglets. There was a
   clear distribution in metabolite between two groups (Supplementary
   Fig. [73]1). Figure [74]6 showed the variety of metabolic profiles in
   gut microbiome, with 239 increased and 194 decreased molecular features
   based on a VIP > 1 in 95% jack-knifed confidence intervals,
   respectively. And a total of 83 biomarker metabolites were filtered
   according to the standard with similarity >600, VIP > 1 and p < 0.05.
   These metabolites, including lipids, amino acids, carbohydrates,
   benzene, organic acids, amines and others, involved in multiple
   biochemical processes in the colon (Table [75]2).

Figure 5.

   [76]Figure 5
   [77]Open in a new tab

   PCA 3D OPLS-DA score plots and corresponding validation plots of
   OPLS-DA derived from the GC-TOF/MS metabolite profiles in the colon
   lumen of nursing and weaned piglets. (A) OPLS-DA score plots showed
   significantly separated clusters in nursing and weaned group. (B)
   Permutation test of OPLS-DA model of nursing and weaned group. Blue and
   red represent nursing group and weaned group, respectively.

Figure 6.

   Figure 6
   [78]Open in a new tab

   Volcano plot of nursing and weaned group. Each point represents a kind
   of metabolite, the scatter color represents the final screening result.
   Significant up-regulated metabolites are indicated in red,
   significantly down-regulated metabolites are indicated in blue, and
   non-significant differences in metabolites are grey.

Table 2.

   Identified metabolites for discriminating between nursing and weaned
   groups based on the untargeted metabolomics study.
   Metabolic Similarity RT^a (min) MEAN -Weaned MEAN -Nursing VIP t-test^b
   Lipids
   heptadecanoic acid 828 20.337 5.57818E-11 0.000117 1.09225 *
   Sphingosine 904 23.217 0.000373 0.004645 1.50233 ***
   phytosphingosine 872 24.471 0.000206 4.89435E-11 1.31316 **
   alpha-Tocopherol 861 28.163 8.80993E-05 0.000391779 1.50347 ***
   Cholesterol 899 28.352 0.002760734 4.89435E-11 1.31090 **
   Cholestan-3beta-ol 778 28.426 0.000120339 0.001103108 1.53157 ***
   Coprostan-3-one 767 28.529 5.57818E-11 8.6931E-05 1.54168 ***
   Stigmasterol 722 29.197 0.000277268 4.89435E-11 1.31269 **
   L-Allothreonine 929 12.201 0.000465693 0.008418029 1.53677 ***
   2-Methylglutaric Acid 866 12.596 5.57818E-11 0.000126571 1.54227 ***
   Capric Acid 742 13.163 5.57818E-11 1.7529E-05 1.54125 ***
   lauric acid 708 15.517 0.001545153 0.002149401 1.11408 **
   Myristic acid 850 17.694 0.000486967 0.040605731 1.54161 ***
   pentadecanoic acid 759 18.671 0.002088078 0.017616468 1.41982 **
   palmitoleic acid 935 19.428 0.001754687 0.020718886 1.44486 ***
   stearic acid 893 21.495 0.040239752 4.89435E-11 1.30941 **
   Arachidic acid 934 23.100 0.004225461 0.051756593 1.52418 ***
   Behenic acid 951 24.606 0.002671156 0.033936219 1.51484 ***
   Lignoceric acid 937 26.038 0.001859809 0.062046719 1.53010 ***
   Cerotinic acid 902 27.387 0.000111121 0.001223183 1.50911 ***
   Carbohydrates
   Lyxose 828 15.414 0.001186306 9.8097E-05 1.51917 ***
   xylose 953 15.589 0.031706877 0.002424436 1.51325 ***
   6-deoxy-D-glucose 807 16.220 0.000764127 0.000445482 1.34719 **
   fructose 900 17.856 0.000335583 0.000993779 1.49148 ***
   Sedoheptulose 628 18.461 0.000268555 4.89435E-11 1.09879 *
   D-galacturonic acid 864 18.579 0.000413546 4.89435E-11 1.31565 **
   lactose 708 25.021 0.00031404 1.14153E-05 1.01325 ***
   trehalose 839 25.328 0.00033928 4.89435E-11 1.54237 ***
   maltose 775 25.296 0.000274583 0.000162243 1.03989 *
   Organic acids
   Pyruvic acid 921 7.430 6.32753E-05 0.000116271 1.25376 **
   glycolic acid 930 7.829 0.007297916 0.000592752 1.45508 **
   3-Hydroxypropionic acid 843 8.826 0.00325326 8.7607E-05 1.39673 **
   glutaric acid 708 12.458 0.001529942 0.000342019 1.28988 **
   oxoproline 950 13.968 0.04241735 0.082504831 1.38890 ***
   Amino acids
   valine 810 8.041 0.001631261 0.000197131 1.46254 **
   Maleimide 927 8.098 0.00013127 9.90848E-05 1.04632 **
   sarcosine 733 8.732 5.97859E-05 0.000118821 1.08313 *
   serine 907 10.480 5.57818E-11 0.000131568 1.54214 ***
   Isoleucine 841 10.923 0.000249148 8.0456E-06 1.35077 **
   proline 960 11.050 0.002575346 0.012998171 1.50201 ***
   glycine 957 11.163 0.002625406 0.016532029 1.47505 ***
   beta-Alanine 945 12.773 0.001489759 9.94769E-05 1.43201 **
   aspartic acid 956 13.927 0.00684775 0.015713239 1.25782 ***
   4-aminobutyric acid 844 14.067 0.007020642 0.000608468 1.51846 ***
   D-alanyl-D-alanine 755 14.881 0.000863753 0.000171924 1.33635 **
   asparagine 865 15.681 5.57818E-11 0.000313104 1.30777 **
   alpha-Aminoadipic acid 806 16.208 0.000124128 4.9774E-06 1.35393 ***
   glutamine 692 16.834 5.57818E-11 0.00042769 1.30790 **
   ornithine 940 17.323 0.001909938 0.00740552 1.48481 ***
   citrulline 806 17.389 0.000271639 0.000529457 1.19390 *
   histidine 833 18.369 5.57818E-11 0.00052798 1.09632 *
   tyrosine 933 18.574 0.004176509 0.014777744 1.50781 ***
   Benzene
   m-Cresol 681 8.987 2.3733E-05 0.000171394 1.44746 ***
   Catechol 627 11.302 5.97445E-06 4.89435E-11 1.31768 **
   phenylethylamine 627 11.302 5.97445E-06 4.89435E-11 1.31768 **
   Phenyllactate 787 14.703 0.002295753 3.14665E-05 1.24267 **
   gentisic acid 611 16.852 3.101E-05 4.89435E-11 1.54228 ***
   tyramine 846 18.329 0.014177717 4.89435E-11 1.30991 **
   noradrenaline 705 20.732 8.42385E-05 4.89435E-11 1.54199 **
   Amines
   Ethanolamine 882 10.600 0.001545522 0.00042396 1.35263 *
   1,3-diaminopropane 928 15.449 0.001651697 0.000436322 1.25857 **
   putrescine 920 16.513 0.163908181 0.02821584 1.50530 **
   spermine 799 25.154 4.66359E-05 4.89435E-11 1.31451 **
   Organoheterocyclic compounds
   2-Furoic Acid 604 8.695 2.60681E-05 4.89435E-11 1.54240 **
   5-aminovaleric acid lactam 843 8.834 0.00394493 0.000152673 1.51907 **
   nicotinic acid 976 11.021 0.000714674 0.001438858 1.34719 ***
   Orotic acid 635 16.509 5.57818E-11 7.6404E-05 1.08298 *
   Others
   N-Methyl-DL-alanine 906 9.238 0.009567796 0.000546294 1.13615 **
   Bis(2-hydroxypropyl)amine 622 13.296 5.57818E-11 3.53733E-05 1.28973 **
   Digitoxose 727 15.012 5.57818E-11 5.96126E-05 1.09183 **
   Cyclohexane-1,2-dione 793 15.685 2.64145E-05 4.89435E-11 1.11298 *
   fucose 958 16.319 0.005013265 0.010654332 1.46744 ***
   Methyl-beta-D-galactopyranoside 650 17.678 0.000220863 0.001169638
   1.46330 ***
   fructose 840 17.954 0.000134698 0.000284574 1.51000 ***
   Methyl Palmitoleate 740 18.258 4.88292E-05 0.000690577 1.16641 ***
   8-Aminocaprylic acid 734 18.511 0.002636825 4.89435E-11 1.54253 ***
   Gly-Pro 867 18.984 8.3268E-05 0.000492377 1.51841 ***
   xanthine 913 19.307 0.000738454 4.89435E-11 1.54224 **
   N-Acetyl-D-galactosamine 920 19.958 0.000980973 0.001963837 1.39968 ***
   5-Methoxytryptamine 649 23.263 0.000471539 0.000281012 1.07002 **
   DL-dihydrosphingosine 909 23.535 0.000341501 0.00271986 1.46487 ***
   adenosine 640 24.531 3.20881E-05 6.79893E-05 1.32913 ***
   Monoolein 662 25.420 2.73317E-05 7.12348E-06 1.48583 ***
   [79]Open in a new tab

   ^aRentention time.

   ^bStudent t-test of the relative levels of compounds detected in weaned
   and nursing piglets(n = 6). *p < 0.05, **p < 0.01 and ***p < 0.001,
   respectively.

Key metabolic pathways analysis

   The commercial databases including KEGG
   ([80]http://www.genome.jp/kegg/) and MetaboAnalyst
   ([81]http://www.metaboanalyst.ca/) was applied to search for the
   pathways of metabolites. The result showed that beta-Alanine
   metabolism, Arginine and proline metabolism, Alanine, aspartate and
   glutamate metabolism, Nitrogen metabolism and other pathways were
   altered (Table [82]3). Among them, five metabolic pathways including
   Phenylalanine metabolism, Citrate cycle (TCA cycle), Glycolysis or
   gluconeogenesis, Propanoate metabolism and Nicotinate and nicotinamide
   metabolism were also found in the functional prediction of gut
   microbiota as shown in Fig. [83]4.

Table 3.

   Pathway analysis for nursing group and weaned group.
   Pathway Total^a Hits^b Raw p^c -ln(p)^d Holm adjust^e FDR^f Impact^g
   Beta-Alanine metabolism 17 6 0.0012 6.7056 0.0992 0.0992 0.6667
   Arginine and proline metabolism 44 9 0.0055 5.2020 0.4404 0.1825 0.3289
   Alanine, aspartate and glutamate metabolism 23 6 0.0068 4.9967 0.5340
   0.1825 0.4633
   Nitrogen metabolism 9 3 0.0277 3.5847 1 0.5618 0
   Valine, leucine and isoleucine biosynthesis 11 3 0.0486 3.0246 1 0.6558
   0.6667
   D-Glutamine and D-glutamate metabolism 5 2 0.0520 2.9559 1 0.6558 0
   Aminoacyl-tRNA biosynthesis 64 9 0.0567 2.8704 1 0.6558 0
   Glycine, serine and threonine metabolism 32 5 0.0991 2.3117 1 0.9605
   0.3528
   Pantothenate and CoA biosynthesis 15 3 0.1067 2.2375 1 0.9605 0.0408
   Phenylalanine metabolism 9 2 0.1526 1.8796 1 1 0.2222
   Butanoate metabolism 20 3 0.2024 1.5974 1 1 0.0290
   Ubiquinone and other terpenoid-quinone biosynthesis 3 1 0.2173 1.5265 1
   1 0
   Tyrosine metabolism 42 5 0.2280 1.4783 1 1 0.0532
   Histidine metabolism 14 2 0.3012 1.1999 1 1 0.2662
   Pyrimidine metabolism 37 4 0.3286 1.1129 1 1 0.0634
   Pentose and glucuronate interconversions 15 2 0.3311 1.1052 1 1 0
   Glutathione metabolism 26 3 0.3336 1.0978 1 1 0.0057
   Cyanoamino acid metabolism 6 1 0.3877 0.9475 1 1 0
   Biosynthesis of unsaturated fatty acids 42 4 0.4210 0.8651 1 1 0
   Pentose phosphate pathway 19 2 0.4461 0.8072 1 1 0.1209
   Propanoate metabolism 20 2 0.4732 0.7483 1 1 0
   Citrate cycle (TCA cycle) 20 2 0.4732 0.7483 1 1 0.1398
   Sphingolipid metabolism 21 2 0.4995 0.6942 1 1 0.0526
   Methane metabolism 9 1 0.5213 0.6515 1 1 0
   Vitamin B6 metabolism 9 1 0.5213 0.6515 1 1 0
   Valine, leucine and isoleucine degradation 38 3 0.5838 0.5383 1 1 0
   Riboflavin metabolism 11 1 0.5938 0.5212 1 1 0
   Steroid hormone biosynthesis 67 5 0.6158 0.4849 1 1 0.0541
   Nicotinate and nicotinamide metabolism 13 1 0.6555 0.4223 1 1 0
   Primary bile acid biosynthesis 46 3 0.7143 0.3365 1 1 0.0667
   Purine metabolism 68 4 0.7967 0.2274 1 1 0.0269
   Lysine degradation 20 1 0.8068 0.2147 1 1 0
   Pyruvate metabolism 22 1 0.8363 0.1788 1 1 0.1875
   Starch and sucrose metabolism 23 1 0.8493 0.1633 1 1 0
   Porphyrin and chlorophyll metabolism 25 1 0.8724 0.1365 1 1 0
   Galactose metabolism 26 1 0.8826 0.1250 1 1 0.0161
   Glycolysis or Gluconeogenesis 26 1 0.8826 0.1250 1 1 0.0989
   Cysteine and methionine metabolism 28 1 0.9006 0.1047 1 1 0.0210
   Glycerophospholipid metabolism 29 1 0.9085 0.0960 1 1 0
   Steroid biosynthesis 35 1 0.9446 0.0570 1 1 0.0539
   Amino sugar and nucleotide sugar metabolism 37 1 0.9532 0.0480 1 1 0
   Fatty acid metabolism 39 1 0.9604 0.0404 1 1 0
   [84]Open in a new tab

   ^aThe number of metabolites in the pathway.

   ^bThe number of differential metabolites that hit the pathway.

   ^cThe P value of Metabolic pathway enrichment analysis.

   ^dThe P value of Multiple Hypothesis Test Corrected by Holm-Bonferroni
   Method.

   ^eThe P values of Multiple Hypothesis Tests for Corrected via the false
   discovery rate (FDR) Method.

   ^fThe Impact value of metabolic pathway topology analysis.

Discussion

   Weaning is a sudden, complex and highly stressful event in piglets’
   life, accumulating evidence indicated that piglets have an abrupt
   taxonomic and functional shift in the intestinal microbiota after
   weaned^[85]8,[86]12. However, the effects of weaning stress on
   microbial metabolites in piglets are still an unsolved issue. In this
   study, piglets at weaned day (nursing group) and 3 days after weaned
   (weaned group) were chose to investigate the effects of weaning stress
   on the composition of gut microbiota and its metabolites in the colon.
   The results showed that the diversity of gut microbiota in weaned
   piglets was similar with nursing piglets, while the richness decreased.
   These results were analogous with previous study, which found a
   continuously decreased alpha diversity until 11 days after
   weaned^[87]16. However, other studies presented opposite phenomenon.
   Those showed a continuous increase in alpha diversity of gut microbiota
   during weaning transition^[88]17,[89]18. The reason might be that our
   studies focused on the gut microbiota during the early period after
   weaning, whereas their studies investigated the gut microbiota during
   the period from weaning to adulthood in pigs. It seems that alpha
   diversity of gut bacterial community undergoes decreases first during
   the early period after weaning, and then increases from weaning to
   adulthood in pigs on the whole.

   In line with previous studies, Bacteroidetes and Firmicutes were the
   two most dominant phyla in the intestine of piglets, regardless of
   weaning^[90]16,[91]19. Proteobacteria and Fusobacteria were the
   followed abundant phylum. There was no difference in bacterial dominant
   phyla in the colon between nursing and weaned piglets. And Candidatus
   Saccharibacteria, Planctomycetes, Verrucomicrobia and Lentisphaerae
   even cannot be detected in the gut of weaned piglets, suggesting that
   these bacterial species may not adapt to the intestinal tract
   environment after weaning. What’s more, the results showed that several
   bacterial species had a remarkable difference between nursing and
   weaned piglets. For example, Alloprevotella and Oscillospira were
   decreased, while Campylobacterales, Campylobacteraceae and
   Campylobacter were increased in weaned piglets. Alloprevotella mainly
   produce succinate and acetate, which could improve the gut barrier and
   exhibit anti-inflammatory function^[92]20. Oscillospira species are
   butyrate producers and can use host glycans as a source of
   energy^[93]21. Study found that the presence of Oscillospira is reduced
   in diseases that involve inflammation^[94]22. Campylobacterales was one
   of opportunistic pathogens, causing a life-threatening gastrointestinal
   disease^[95]23. And Campylobacteraceae and Campylobacter were also
   increased after weaning. The increase of this bacterial species in
   weaned piglets may be one of the major reasons of post-weaning
   diarrhea. These results indicated that unhealthy alterations in the
   composition of gut microbiota triggered by weaning stress.

   Gut microbiota participate in many functional metabolic pathways
   through which nutrient digestion and absorption, lipid metabolism, and
   hormone biosynthes are influenced^[96]24. To investigate how the
   functional capacity of the gut microbiota changed after weaning in
   piglets, Functional metagenomics prediction approach was used to
   analyze the KEGG pathways compositions in microbial populations. The
   results showed that many metabolic pathways such as lipid biosynthesis
   proteins, energy metabolism and phenylalanine metabolism were changed
   during the period of weaning. Gut microbiota could also directly alter
   its metabolic capacity through microbial products, affecting intestinal
   function. Butyrate, a kind of metabolite, produced by gut microbiome
   could regulate energy metabolism and autophagy in the mammalian
   colon^[97]25. So GC-TOF/MS method was applied in the current study to
   quantitatively measure small molecular metabolites in biological
   samples and identify relevant Pathways. Based on PICRUSt and
   metabolomics analysis, we found that five metabolic pathways including
   Phenylalanine metabolism, Citrate cycle (TCA cycle), Glycolysis or
   Gluconeogenesis, Propanoate metabolism and Nicotinate and nicotinamide
   metabolism were the key relevant metabolic pathways involved in weaning
   stress-induced gut microbiota dysbiosis.

   Previous study indicated that alterations in the microbiota composition
   elicited by nutrition are associated with neurotransmitter
   concentrations^[98]26. Nutrition can influence hormonal,
   neurotransmitter, and signalling pathways, which in turn modulate
   several brain processes impacting both appetite and mood^[99]27.
   Phenylethylamine, as a neuromodulator or neurotransmitter, participates
   in the phenylalanine metabolic pathway and plays an important role in
   mood regulation^[100]28. In the present study, the phenylethylamine
   level decreased in weaned piglets, which indicated piglets had a bad
   mood caused by stress. And a low level of phenylethylamine could not
   modulate the mood well, which may cause a decreased feed intake and
   weight gain of piglets. In addition, changed neurotransmitter
   noradrenaline and 4-aminobutyric acid level were also found in the
   present work. Gut microbiota could influence the immune system via
   neurotransmitters, and proinflammatory cytokines released through
   microbiota-induced immune response can in turn have a direct influence
   on the secretion of neurotransmitters such as norepinephrine^[101]29.
   Compelling studies have linked weaning to an upregulation of expression
   of inflammatory cytokines in the intestine of piglets^[102]10,[103]30.
   Thus the regulatory mechanism and potential role of
   “microbiota-brain-gut” axis that play during weaning period in piglets
   are warranted to further investigation.

   During a short period after weaning, small intestine length, microvilli
   height, crypt depth, and barrier function of piglets were changed.
   What’s more, digestive enzyme activity and absorptive capacity of small
   intestine were reduced, resulting in a low absorption capacity of amino
   acids and carbohydrates^[104]31. In the present study, decreased
   essential amino acids such as valine and some carbohydrate in weaned
   piglets supported this view. TCA cycle is responsible for the oxidative
   degradation of sugars, fats, and amino acids and could link different
   types of nutrients together. Pyruvic acid, as an important intermediate
   involved in the Citrate cycle (TCA cycle), was decreased in the weaned
   piglets. The reduction in energy metabolism may be part of the gut
   adaptation process. Microorganisms may engage energy for adaptation
   mechanisms to ensure necessary physiological functions^[105]32.

   Notably, pathways related to short chain fatty acid (SCFA) metabolism
   such as propanoate metabolism were enrichment in piglets. Functional
   metagenomics prediction analysis using PICRUSt revealed that genes
   involved in Propanoate metabolism were more abundant in nursing
   piglets. And the bacteria Oscillospira, known to be as a butyrate
   producer, decreased in the gut microbiota of weaning piglets, which
   might indicate the decreased SCFA production after weaning. SCFAs are
   major anions in the gut and could be absorbed rapidly by colonic
   epithelial cells. Growing evidence proved that these small molecules
   could protect the host against colonic diseases, improve the gut
   barrier function and exhibit anti-inflammatory effects^[106]33,[107]34.
   In addition, SCFA-mediated beneficial metabolic effects on health can
   be mediated by induction of intestinal gluconeogenesis^[108]35. A
   recent study showed that SCFAs could also improve the feed efficiency
   of swine^[109]36. Thus the reduction in Propanoate metabolism of weaned
   piglets in the current study might be related to a low growth
   performance and unhealthy gut environment after weaning.

   Administration of niacin (nicotinic acid and nicotinamide) has been
   shown to beneficially effect the host-microbiome interaction in a mouse
   model^[110]37. Nicotinamide adenine dinucleotide (NAD+) is the central
   cofactor of metabolism, mediating ATP generation, reactive oxygen
   species (ROS) detoxification, biosynthetic processes, DNA repair, and
   nutritionally sensitive gene regulation^[111]38. In the present study,
   decreased nicotinic acid was found in weaning piglets, which indicated
   an adverse influence on piglets trigged by stress.

Conclusion

   In conclusion, our current work demonstrated that weaning stress not
   only disturbed the gut microbiota but also altered the metabolome
   profiles in the colon. After weaning, some benefit bacterial species
   such as Alloprevotella and Oscillibacter decreased in weaned piglets,
   while some opportunistic pathogens such as Campylobacterales,
   Campylobacteraceae and Campylobacter increased. In addition, based on
   PICRUSt and metabolomics analysis, five key metabolic pathways
   including Phenylalanine metabolism, Citrate cycle (TCA cycle),
   Glycolysis or Gluconeogenesis, Propanoate metabolism, Nicotinate and
   nicotinamide metabolism were identified as the relevant pathways
   associate with weaning stress-induced gut microbiota dysbiosis. These
   results may provide a new insight in alleviating weaning stress of
   piglets and facilitating disease prevention during weaning by
   manipulating host-microbiome metabolites interactions. And future study
   might focus on the potential functions of neurotransmitters in gut
   microbiome, neurogenesis, behavior and immune of piglets suffering
   weaning stress.

Materials and Methods

Animals and Sample Collection

   All methods in this study were performed in accordance with the Guide
   for the Care and Use of Laboratory Animals prepared by the
   Institutional Animal Care and Use Committee of Zhejiang University, and
   animals used in this experiment were approved by the principles of the
   Zhejiang University Animal Care and Use Committee (NO. 2012-0178).
   Three litters of suckling piglets (21d, Duroc × Landrace × Yorkshire)
   with same parity were chosen from a commercial pig farm located in
   Ningbo city, Zhejiang province, China. Two barrows from each litter
   were randomly selected and humanely euthanized. The samples of colonic
   content were collected and transferred into sterile conical tubes. Then
   remaining piglets in the same trial were separated from sows and
   transferred into the same pen. These weaned piglets were fed a
   corn/soybean- based diet and had free access to water and feed. All
   nutrients reached or exceeded NRC (2012) recommendations for piglets.
   Three days after weaned, two barrows from each litter were selected and
   humanely euthanized. The samples of colonic content were collected into
   sterile conical tubes. All collected colonic samples were immediately
   frozen in liquid nitrogen and stored at −80 °C.

DNA extraction and 16S rDNA gene amplicon sequencing

   Total Microbial genomic DNA extraction was performed from colonic lumen
   using E.Z.N.A.® Stool DNA Kits (Omega Bio-tek, Norcross, US) according
   to the instructions of the manufacturer. The method of microbial
   community analysis was performed according to previous study^[112]39.
   Bacterial univers alprimers (341 F: ACTCCTACGGGAGGCAGCAG, 806 R:
   GGACTACHVGGGTWTCTAAT) were used for the amplification of the V3–V4
   region of the bacterial 16S rDNA gene and subsequent pyrosequencing of
   the PCR products. Amplicons were confirmed by electrophoresis on a 2%
   agarose gel, purified with the AxyPrep DNA kit (AXYGEN, Tewksbury, MA,
   US), quantified by Qubit 2.0 Fluorometer (Thermo Fisher Scientific,
   Waltham, US) to pool into even concentration. Amplicon libraries were
   sequenced on Illumina Miseq PE250 platform (Illumina, San Diego, US).

Sequencing data analysis

   The paired-end reads were obtained 2 × 250 bp using MiSeq platform. In
   order to obtain high quality sequences, tags with length of <220 nt,
   average quality score of <20, and tags containing >3 ambiguous bases
   were removed by PANDAseq. Data was de-chimeric and clustered using
   Usearch software, a standard clustering of 97% similarity was applied
   to obtain OTU (Operational Taxonomic Units). And the taxonomy-based
   analysis to the OTUs was performed by RDP algorithm using GreenGene
   database ([113]http://greengenes.lbl.gov). QIIME was used for analyzing
   alpha diversity, which included calculation of the observed species,
   Chao 1, Shannon, and Simpson indices. And principal coordinate analysis
   (PCoA), Anosim Analysis, and unweighted pair group method with
   arithmetic mean (UPGMA) were performed using QIIME for beta-diversity
   analysis. Linear discriminant analysis coupled with effect size (LEfSe)
   was applied to identify the bacterial taxa differentially represented
   between groups at genus or higher taxonomy levels^[114]40.

Metagenome prediction based on 16S rDNA gene data

   Phylogenetic Investigation of Communities by Reconstruction of
   Unobserved States (PICRUSt), which makes up for the shortage of 16S
   rDNA gene studies, was used for predicting the gene family abundances
   of bacterial populations based on the precalculated GreenGenes (v13.5)
   database. In brief, the analysis process is divided into two steps:
   gene content inference and metagenome inference. The software STAMP was
   conducted to detect the differentially abundant Kyoto Encyclopedia of
   Genes and Genomes (KEGG) pathways in two groups with false discovery
   rate correction.

   The rank sum test was used to identify species with significant
   differences between groups. Two-sided Welch’s t-test and
   Benjamini-Hochberg FDR correction were performed in two-group
   functional prediction analysis. P < 0.05 was considered statistically
   significant. And the prediction accuracy of PICRUSt was evaluated by
   the Nearest Sequenced Taxon Index (NSTI), with lower value indicating a
   higher accuracy of prediction.

Sample preparation for GC-TOF/MS analysis

   Briefly, approximately 1.0 g colonic contents were mixed with 3 ml 4 °C
   H[2]O, followed by centrifugation at 3,200 × g for 15 minutes. 50 μl of
   the supernatant was taken and added with 200 μl of methanol, which
   contains 12.5 μg/ml of [13C2] myristic-acid. The mixture was maintained
   at 4°C for 1 h and subsequently centrifuged at 20,000 g for 10 minutes.
   Then the supernatant fraction was transferred to a glass vial and dried
   in a vacuum concentrator. And added methoxyamine salt reagent
   (methoxyamine hydrochloride, dissolved in pyridine 20 mg/mL), incubated
   in an oven at 80 °C for 30 min, then derivatized using N, O Bis
   (trimethylsilyl) trifluoroacetamide (BSTFA), incubated for 1 hour at
   70 °C. The final mixture was strongly vortexed for 1 min and then
   subjected to detection.

GC-TOF/MS analysis

   The derivatized samples were analyzed using an Agilent 7890 GC system
   equipped with a Pegasus 4D TOFMS (LECO, St. Joseph, MI) with a DB-5MS
   capillary column (30 m × 250 μm inner diameter, 0.25 μm film thickness
   coated with 95% dimethylpolysiloxane cross-linked with 5% diphenyl
   under the following conditions: initial temperature was kept at 80 °C
   for 0.2 min, increased to 180 °C at a rate of 10 °C/min, to 240 °C at a
   rate of 5 °C/min, and further to 290 °C at a rate of 20 °C/min; the
   column was then maintained for 11 min. One μL of sample solution was
   injected with helium as the carrier gas at a flow rate of 1 mL/minute.
   The temperatures of transfer line, and ion source were 245 °C, and
   220 °C, respectively. The MS data were acquired with a mass-to-charge
   ratio (m/z) range of 20−600 in a full-scan mode.

GC-TOF/MS data acquisition and processing

   The raw peaks extraction, data baselines filtering and calibration,
   peak alignment, deconvolution analysis, peak identification, and
   integration of the peak area were operated using the Chroma TOF4.3X
   software (LECO) and LECO-Fiehn Rtx5 database. The peak identification
   was tested using the RI (retention time index) method. The missing
   values of the original data were filled by half of the minimum value
   via a numerical simulation method. Noise removal was performed based on
   an interquartile range to filter data, then, data were normalized by
   area normalization methods. The SIMCA14.1 software package (Umetrics,
   Umea, Sweden) was used for multivariate variable pattern recognition
   analysis: principal component analysis (PCA), partial least squares
   discriminant analysis (PLS-DA), and orthogonal partial least-squares
   discriminant analysis (OPLS-DA). PCA was used to show the internal
   structure of the data and display the similarity and difference.
   OPLS-DA was applied to obtain a higher level of group separation and
   better explain the variables. To evaluate the predictive ability and
   fitting level of the model, the parameters R^2Y and Q^2 were applied.
   The metabolites responsible for differentiating two groups were
   filtered with the following requirements: variable importance in the
   projection (VIP) >1 and P-values of 0.05 (threshold) with 95%
   Hotelling’s T-squared ellipse.

   Data was analyzed by SPSS 20.0 using Student’s t-test to profile
   metabolite differences between two groups. The commercial databases
   including KEGG ([115]http://www.genome.jp/kegg/) and MetaboAnalyst
   ([116]http://www.metaboanalyst.ca/) was utilized to search for the
   pathways of metabolites. We can further screen pathways and find the
   key pathways with the highest correlation with metabolites through
   comprehensive analysis of the pathways where the differential
   metabolites are located (including enrichment analysis and topological
   analysis).

Electronic supplementary material

   [117]Supplementary Material^ (332.6KB, docx)

Author Contributions

   X.H. conceived the experiment(s), Y.L., S.C. and S.G. conducted the
   experiment(s), Y.L., X.J. and X.M. analysed the results. All authors
   reviewed the manuscript.

Data Availability

   All data generated or analysed during this study are included in this
   article and its Supplementary Information files.

Competing Interests

   The authors declare no competing interests.

Footnotes

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

Electronic supplementary material

   Supplementary information accompanies this paper at
   10.1038/s41598-018-33649-8.

References